text:
Efteling Discussion Thread
P. 74: Park expansion approval and rethemed rides announced!
By Pepper,
September 14, 2005 in Theme Parks, Roller Coasters, & Donkeys!
And I really think that's the whole problem... The most coaster enthusiast start when they are young, they don't have a car (what Marwin mentioned), they don't have a large amount of cash (They don't get credit cards thank god) and so on. That's perhaps where these reactions come from. The older enthusiasts won't have that problem.
Indeed that is what I trying to say. Older people its less or no problem if they are still interested in doing these kind of things. However they are less over-excited.
Last Reply Jul 3
Nrthwnd 74 posts
Illegal_Alien 88 posts
RCT3Bross 149 posts
JooordiW 74 posts
CenturyFlyer
How much time and money did they throw at this joke (turn on subtitles)? 😂 I find it interesting that the Efteling YouTube channel has nearly 200k subscribers!
RCT3Bross
Source: Looopings.nl There is news about the expansion. The Dutch High Court has given the final verdict today and approved the plans, this means the Efteling is now officially allowed to expand
Efteling Vs. Disney for "Best Sinbad Song" contest. I'm here for it. It's great to see they're keeping Vogel Rok as a centerpiece attraction. That coaster is so much more fun than many people w
andybarnes84
Talking of preshows how many on here have ridden Galaxy Express 999 in Shinagawa, Tokyo? Wow! Seriously the most tedious and random preshows (yes there were several I think) for such a short ride. It was just two of us and one very enthusiastic staff member literally reciting War and Peace, I wonder why it closed down?
I don't really mind standing through the preshow for RNR at Disney Studios in FL as at least it's a little relevant, but the Paris one I REALLY can't stand, you can tell that even Aerosmith couldn't be with doing it, it's not good when the people getting paid to make the preshows can't stand preshows.
Preshow rooms are the root of all evil and should be destroyed with fire
Anyways, it's a lovely looking coaster with a great deal of thought going into it, but again it's dressing up a mediocre layout at the end of the day, was hoping for a little more but oh well, this is Europe after all, the continent where we prioritise the unspoilt romantic view over ancient forest from a romantic cottage balcony rather than a decent giga coaster that'll ACTUALLY kick your
julioot
About the pre show(s).
The eftling has stated earlier that the ususage of (dutch) language will be minimal. This is because they are trying to make the park and the attractions more appealing to international guest. So I'm expecting a sort of "de vliegende hollander 2.0 queue". With some effects waiting and we know there will be at least one animatronic. I'm not expecting a RNRC type pre show.
The ride itself is a little dissapointing to me for the lac of thrill and the overal shortness of the track. Bit I'm totally inlove with the lifthill. Is this B&M's firt "custom" lifthill/support design?
turbolaaf
No it's not the first. An example is Alpengeist at Busch Gardens Europe.
http://americacoasters.com/Photos/thumbs/lrg-998-img_3362.jpg
Size is to big....
These have been themed to a ski-lift.
Right, totally forgot about that one.
Matclou
Despite being a Efteling fan and knowing the park since my childhood, i understand, why Robb and others are not that enthusiastic about Baron. In my opinion the outside theming is great, like all here said, but the track itself is pretty normal for an European Dive Coaster. A bit better maybe like Krake, because it has one more inversion, but in the end normal.
I think, that the goal the Baron has to achieve is to design an overall experience. If the station building with its preshow will constitute an unity with the ride itself, then the Baron will have success. All, what comes before the ride, should create a certain atmosphere, guiding to the ride.
Efteling showed in De Vliegende Hollander, that they can do something like this. Of course there are many voices saying the coaster part is too short and boring, but they're also many, who are over this – considering the atmosphere they saw at the darkride part and the queue.
Much of the enthusiasm is maybe based on the expectation, Efteling could create something similar to De vliegende Hollander. BUT: Some of the facts we know are militating against this – there will be an animatronic, which is connected to traditional ways of story telling, and there is no darkride part. So the worry is justified: what happens if Efteling didn't get this queue part right: In this way it would be "just" a B&M-Coaster with a normal track and a very nice looking station building (and maybe an a bit annoying queue). That is nothing to be enthusiastic about, but nethertheless a good addition to park, because it's the first B&M in the park with normal technical figures for the European market with a nice setting. That's why I understand, that Robb isn't enthusiastic.
But if Efteling would be able to create something similar to De Vliegende Hollander, it's maybe another case - and that's why I understand that some people are enthusistic, though they shouldn't spam Robb like that.
An addition to "European travelling culture": Eurpeans normally go on holiday for a couple of days or weeks, and they also take long distances for this, this is no problem and common. But the idea, to spend a couple of days at a theme park hotel or doing long voyages to theme parks is rather something with american origin. European theme parks are traditional targets for day trips (within or near two hours), allthough something changes and the European theme park hotels get bigger and bigger. But it's not nearly as good developed as (maybe?!) in the US. This narrow view may also contribute to the different assessements (also in evaluating the technical figures).
But this makes ZERO sense to me! You don't need a car or cash to look at something online and make an evaluation of "This looks like the best roller coaster in the world", "This looks like it could be fun", "This looks like the theming will be great at the ride will be crap", "this looks stupid", "Can you pass me the mustard?", "I like turtles."
You do notice that other people, who are probably no more or less traveled than you are, no more or less older/younger than you are are NOT making stupid comments like this, right???
Despite being a Efteling fan and knowing the park since my childhood, i understand, why Robb and others are not that enthusiastic about Baron.
WHAT. THE. ACTUAL. FUCKK?!?!?! How is me saying things like:
"The ride will actually stand on it's own just fine. It will look beautiful and ride great. No additional over-hyping is needed."
"Yes, I'm looking forward to the ride. The theming is impressive and I'm sure the ride will be a lot of fun"
being looked at negatively?!?!? What the hell European wackos!?!?!? Honestly, on one hand it's great to read these idiotic comments because it's comforting to see that "the dumb" has affected everywhere and it's not just America!
Seriously, many of you are making arguments for the sake of making arguments and NONE of it is grounded in any sort of reality at all. Please just stop. Like I said before, you are looking like fools. And I repeat:
Edited February 23, 2015 by robbalvey
(you can delete this, if you want)
Edited February 23, 2015 by Matclou
Seriously, many of you are making arguments for the sake of making arguments and NONE of it is grounded in any sort of reality at all. Please just stop. Like I said before, you are looking like fools.
Hey sorry, I didn't want to say, that you just look negative on the Baron and I also didn't understand it like that. I understand the word enthusiastic as something very high positve, as something like over-hyping, which you critisize - if this is not the case, i should better say" "... are not freaking out about Baron"...
But in no part of my post, I say, that you're looking too negative on the Baron, in opposite; read this:
"In this way it would be "just" a B&M-Coaster with a normal track and a very nice looking station building (and maybe an a bit annoying queue). That is nothing to be enthusiastic about, but nethertheless a good addition to park, because it's the first B&M in the park with normal technical figures for the European market with a nice setting. That's why I understand, that Robb isn't enthusiastic."
I mean you are saying that you like the ride, but you also critisize other for making a (too) big deal about it. So you like it, but you're not really enthusiastic, which doesn't mean that you look negative at it. I think I displayed your thoughts correctlly
But this makes ZERO sense to me! You don't need a car or cash to look at something on-line and make an evaluation of "This looks like the best roller coaster in the world", "This looks like it could be fun", "This looks like the theming will be great at the ride will be crap", "this looks stupid", "Can you pass me the mustard?", "I like turtles."
You do realize that most people that discuss rollercoasters do like to ride them right? You do realize that most people do compare their rides with formal experiences right? Sure, some people give their opinion on a ride without any experiences about a ride they can compare. But the gross of those will visited other parks to enjoy their hobby. That's the whole reason we're on this website isn't it?
If everyone was only yelling about rides without riding them, parks wouldn't have to build them anyway.
You do notice that other people, who are probably no more or less travelled than you are, no more or less older/younger than you are are NOT making stupid comments like this, right???
With reactions like this.... ah never mind. Avoiding a permanent ban in this one. Let's pretend that I'm only making stupid comments...
vacoaster09
Loving this thread!
With reactions like this.... ah never mind. Avoiding a permanent ban in this one.
Too late. You should have quit while you were ahead, but thankfully, the dumb always end up weeding themselves out!
I really don't exactly know why my post justified XX facepalms xD There are reasons to be enthusiastic and hyped about a the project of a theme park and there are reasons not to be enthusiastic and hyped about the project of a theme park (without automatically looking negative on it). I just wanted to say, that both sides may be justified and may coexist - different circumstances / different preferecnes, different views...
And dear turbolaaf, going and posting disparaging things about TPR on your Facebook page doesn't actually make you look any better. If anything, you have only justified our reactions to your stupid posts and our actions in banning your account.
I will never understand why so many Europeans feel the need to turn things into an "America vs Europe" argument. It's dumb. And it just proves once again that you are not someone we would want on our forums.
Good riddance.
Location: Williamsburg, VA
^I guess we're at the stage now where saying "hey, that new ride looks like fun, so I'll probably like it" isn't enough. Everything must be unequivocal love and devotion.
Hoo, boy.
I get a little sad reading this thread the last few day's. Not enough to quit going here yet.
The reason is: European (Dutch) fans defending Baron from something that hasn't been said. People should read more or deeper, Robb said he looks forward to the ride, what is it that you want him to say? I mean he was very positive about the ride, is that not good enough?
People should stop defending the over-hypers that spam him about the ride. Like I said before: "We getting a good solid addition but this isn't a ride we should shove into people's noses." Baron could very well be a fun ride but not the next big thing.
I personally think the ride will be better then Krake because the ride is bigger, Yet is that what will make it a better ride? No, I keep in mind that the length could also work on the ride's negative side, Krake is a fun ride that packs more then I thought it would and the short ride makes it sharp and sweet. Baron being lower but having more elements could very well mean the ride doesn't has the same "power" like Krake. However, I will only know that this summer.
Seregon
Honestly, I don´t understand this whole discussion. I mean people, this coaster isn´t really finished yet.
And this "European vs. American" thing is just stupid as Robb said before. Don´t we have a lot of other "Problems" in the World?
DJeXeL
Location: So Cal
Some of the members on this forum like to complain about rides that aren't completed yet. Hell, we like to complain about a ride based on CONCEPTUAL ART!!
In all seriousness, I think Baron 1898 looks like you're run-of-the-mill B&M dive coaster however it's station and lift are decently themed. While I will admit that Baron is not "groundbreaking," it will be fun I'm sure. Hope to make it to Europe in the next couple years. Efteling is on my "List of things to do."
And what's up with that strange Google Translate translation?
Will i be looking forward to riding Baron 1898 this summer? Yes, ofcourse! It's a mayor new ride that will be great for many parks around the world. Will it be favorites material? Proberly not, but it looks fun to ride and watch over the coming years!
Lot's of rides in Europe got hyped these past years for me, i'm with Robb on that part. Oh no! I'm European how can i say that? Well, because i've ridden most of them by now and think that they didn't live up to the ground breaking rides some gave them to be on Dutch forums. Am i still looking forward to riding other hype coasters in Europe? Helix is the one that comes to mind, and yes i'm still looking forward to that one! A good friend bashed that coaster even before TPR got there last year, and we have many of the same rides on our high spots of favorites! FAVORITES! Not top 10,20,30... I can't really list my favorites, they are just to different.
But back to Baron. Will i be there on opening day? Maybe, maybe not. Don't know if i will make the trip that day, but it will proberly be fast after opening!
DaveDowning
This whole discussion is exactly the reason while I don´t post on German Coaster Boards. It´s always the same. The ride will be the best in the world, it will have some special element that nobody has, it will be better than everything anywhere in Germany, but in the end it´s just a standard coaster.
I don´t know why people feel this need for comparing. For me personal, if I like a ride, it doesn´t matter if people from the US love or hate it. I just plainly don´t care. I mean, I can understand a certain amount of "loving" your home park, but this need to compare and judge what is better is just pointless (and lame!).
When it comes to comparing European coasters to American ones, I don´t understand it at all. When I visited florida I loved all the B&Ms I´ve ridden, but I would never go and compare, lets say Montu, to Black Mamba. And even if I did, it wouldn´t matter to me at all which one was better. I loved Montu, I loved Black Mamba, but they are just so different that a comparison does not make any sense. For me, the biggest difference is that I can ride Black Mamba when I want, because it´s close to where I live. But that doesn´t make it a better coaster. I hate all this hyping of new coasters. Kärnan is another example of that. In the german scene, this is already "THE BEST RIDE EVAAR" even though we don´t even know the Layout.
My thought on this: Maybe size DOES matter after all. I mean, America has huge rides, Europe...well not so much...
Anyway, I look forward to Baron as I love well themed coasters. Nothing more, nothing less. Will I ever compare it to Sheikra? NO WAY !
The reason is: European (Dutch) fans defending Baron from something that hasn't been said. People should read more or deeper, Robb said he looks forward to the ride, what is it that you want him to say? I mean he was very positive about the ride, is that not good enough? People should stop defending the over-hypers that spam him about the ride.
This may be my first post here, but I have been reading and enjoying this forum for years now. I was planning on posting something else, but wanted to weigh in on the recent discussion.
I think nobody here meant to defend the over-hypers, nor to attack Robb on his opinion about Baron 1898. Some people have tried to explain why there is a certain level of excitement for Baron, especially in the northwest part of Europe. Maybe some of their arguments got lost in translation, but I with all due respect: I think some valid points were made. This will be the first B&M coaster in the Benelux (Belgium, the Netherlands, Luxemburg) and there has been speculation for years over which park would build it (Efteling? Walibi Holland? Walibi Belgium? Toverland?). So even a run-of-the-mill B&M coaster is still quite special to a lot of people over here, short and low as it may be. Add the theming, which is at the level we have come to expect from Efteling, a decent layout (the first without the curve before the drop) and this looks like a very nice addition.
Is that a reason for over-hyping? No. Is it a reason to spam Robb's social media accounts? No. But I have to agree with Heth that I haven't read any post here unreasonably "jumping down Robb's throat". Maybe that happened on social media, but not here on the forum. I have seen people trying to explain respectfully why there is some excitement for this coaster. Maybe that excitement is not justified, but trying to explain its origin is something different than defending over-hyping IMHO.
This is so mad. Some kiddo's who are exited about the first B&M in their backyard like to hear CoasterGod Robb's opinion about this beauty. That's all. The part I do not understand is why Robb you have to be so rude about it. What's the big deal? And why nobody read these so called overhyped questions, but you? You like to exaggerating things to make your point?
Then, Marwin is exactly right when he tried to explain you very gentle about our ways and our culture. That you are too stubborn to look at things from another perspective is another thing. Please don't try to teach us who we are from your American chair. Thank you.
^^ WHY ARE WE STILL DISCUSSING THIS STUPIDSHIT???
Seriously, what the hell is wrong with all you European fanboys??? Fuuuuuuck just shut the hell up already!!!!


After her relationship abruptly ends, a woman isolates herself in a new apartment in an attempt to rebuild her life. Her sister recommends a popular self-help guide that focuses on organizing, tidying, and simplifying life, written by an artist-turned-newsletter-writer-turned-influencer. As she falls deeper into the world of the book, the woman finally feels in control of her life for the first time in years - but nothing can stop her as she attempts to obtain and maintain An Aspirational Space.


The quality of processes improves due to the removal of the sources of nonconformity.
Productivity rises due to a better functioning of processes.
Costs decrease due to a better use of resources.
Competitiveness increases due to an increased efficiency in the company.
Quality Manager works in a preview mode, which means that every analysis is shown online and the user chooses whether to print hardcopy. It can also work in batch mode, where a large database of several variables can be selected at once, and then a number of different charts can be previewed and printed, so that the user avoids having to run each analysis separately.
In the MM4XL floating toolbar, click on the button shown to the left and the tool opens.
Alternatively, select Quality Manager from the main MM4XL drop down menu. The first window that opens is shown below. It enables you to select the range of data to be analyzed. If the data range is selected before opening the tool, the input field in the window automatically shows the range selection. If you click the Next button without making a range selection on the sheet, Quality Manager works in reduced mode, and only some charting options are available. The checkbox Use automatic update is useful when working with large data series, such as hundreds of items, which can keep Quality Manager busy for long a time in order to show the results. When this checkbox is unchecked you have to click the button Recalculate in window 2 to update results. Click Next to move to window 2.
Quality Manager allows you to select several variables at once in the input range. Then, in the second window described in the next paragraph, you can select one variable at a time and draw the appropriate control chart. To select multiple variables from the picture below, for instance, position the mouse in cell A1, click and hold down the left mouse button, then drag to cell H35 and release the mouse button.
The second window is the place where most of the work is done. Depending on the selected chart type, this window can change appearance and look slightly different from the sample below. Most options, however, work similarly for most charts. A detailed explanation concerning use and interpretation of each chart type available in Quality Manager can be found in the sections of this help file dealing with the different chart types. The sample below provides an explanation of the elements of the window.
When the analysis is done and you wish to print the results on the sheet, click the Next button to display the third and last window, shown below. We strongly suggest that you try the options in the Learning Center. That is where you will find help with the tool you are using and, more generally, with the whole MM4XL software. The Learning Center also provides a number of links useful to marketers.
By default the tool places the results for the selected chart type in a New sheet. Select the option Output range and select a place in the sheet where you want to print results if you do not want to add a new worksheet (Excel sheet) to the workbook (Excel file). The labels describe the functioning of most features in the window above. Note that the option Close this dialog when done is unchecked by default. Check it if you want to close Quality Manager after a report has been printed; otherwise click on the Back button to return to the previous window and run a new analysis. When unchecked, the option Display lines prints charts in output that exhibit item points not connected with a line, as shown in the chart below, where the green diamonds show the simulated data and the blue squares refer to the user input data.


For your stay Alcatel-Lucent Enterprise hold rooms at Monte-Carlo Bay Hotel.
Reminder: the 2 nights are booked only for customers travelling from outside Europe.
ALE will pay for the hotel accommodation costs for the duration of the event: nights of 16 or 17 or 18 weather you are travelling from Europe or not.
During the event, we will organize personal sessions (30 minutes) with our ALE Executives on site.
We required your attention with the fill in of your flight details.
I accept Events Terms and Conditions * Please read them carefully.
Yes, keep me informed by email about Alcatel-Lucent Enterprise solutions, news and promotions. Alcatel-Lucent Enterprise will never provide your email address to third parties and each communication includes an unsubscribe option.
This is a notification under the Alcatel-Lucent Enterprise brand sent to you via e-mail. Materials from this notification cannot be reproduced, altered, or further distributed without prior written consent of ALE International or ALE USA Inc.
You are receiving this notification because you are a valued ALE partner.
You can unsubscribe from this type of communication, or from all our digital communications.


text:
Former IU star among 7 players drafted by Indiana Fever Monday night
The Fever drafted players who have been part of the last three NCAA basketball championship teams.
Baylor's NaLyssa Smith, left, poses for a photo with commissioner Cathy Engelbert after being selected by the Indiana Fever as the second overall pick in the WNBA basketball draft, Monday, April 11, 2022, in New York. (AP Photo/Adam Hunger)
Author: WTHR.com staff
Published: 11:15 PM EDT April 11, 2022
Updated: 12:12 AM EDT April 12, 2022
INDIANAPOLIS — The Indiana Fever got a big jump on rebuilding their roster with seven picks in Monday's WNBA draft in New York.
Indiana had seven picks in the three-round draft, including four in the first 10 selections. They used two of those picks to take Baylor teammates NaLyssa Smith, who was the second pick of the draft, and Queen Egbo, who was picked at 10.
The Fever also picked fourth and sixth in the first round, selecting Louisville's Emily Engstler and Lexie Hull of Stanford.
"We have four top picks, so there is the opportunity for this team to grow tremendously," Smith said.
Interim general manager Lin Dunn said last week the Fever were "going in a different direction," having already changed their coaching staff and revamping the roster, including trading center Teaira McCowan to Dallas for the picks they turned into Engstler and Hull Monday night.
Indiana also released veteran Jantel Lavender. None of the team's first-round picks from the last three seasons remain on the roster.
"When you are last, there needs to be change," Dunn said.
INDY WADDUUPP!!
— #1 (@NaLyssaSmith) April 12, 2022
Smith, the 2021 national player of the year, averaged 15.5 points and 8.3 rebounds in four years at Baylor, scoring over 2,000 points in her college career. She averaged more than a block per game her senior season.
Her teammate, Egbo, blocked 183 shots in her career with the Bears, averaging nearly two blocks per game her last two seasons.
MAN! What an opportunity. Thank you @IndianaFever, y'all not gone regret this one! Super excited 😭😭❤❤❤❤
— Queen (@QueenEgbo_) April 12, 2022
Engstler, who transferred to Louisville from Syracuse, led the Cardinals to the Final Four this past season, where they lost to eventual national champion South Carolina. She averaged 11.9 points, 9.4 rebounds and 2.7 steals this season for Louisville.
Hull, a 6-foot, 1-inch guard, won a national championship with Stanford in 2021 and earned All-Pac-12 honors three times in her four-year college basketball career. She averaged 12.5 points, 2 assists and 5.1 rebounds per game in her senior season.
In the second round, Indiana nabbed the star of this year's national championship game, Destanni Henderson. She scored 26 points to lead the Gamecocks to their second national title.
Her 12.8 points per game in the NCAA Tournament helped her to All-Tournament honors both at the Final Four and the Greensboro Regional. Henderson's effort all season – 11.5 points per game on 40.3 percent shooting and 3.9 assists per game – put her on the national stage where she picked up All-America honors for the first time in her career, making the USBWA third team and honorable mention status from AP and WBCA.
The pick was met with enthusiasm from Fever legend Tamika Catchings.
Oooooooo shooot!!!! We got HENNY!!!! Welcome to the @IndianaFever @dh3nny 🙌🏽👊🏾💪🏽 #Fever4Life
— Tamika Catchings (@Catchin24) April 12, 2022
In Smith, Egbo, Hull and Henderson, the Fever drafted players who were part of the last three national championships.
Indiana went big again in the third round, selecting center Ameshya Williams-Holliday from Jackson State, then kept Indiana University star Ali Patberg close to home with their second third-round pick.
Indiana guard Ali Patberg (14) looks to shoot over Ohio State guard Taylor Mikesell, right, in the second half of an NCAA college basketball game at the Big Ten Conference tournament in Indianapolis, Saturday, March 5, 2022. (AP Photo/Michael Conroy)
Patberg, a Columbus North High School graduate, started her career at Notre Dame before transferring to IU. She averaged 14.4 points and 4.4 assists per game in four seasons with the Hoosiers, ranking ninth in career points and third in assists.
The Fever have had just one winning season since Dunn led them to the WNBA title in 2012, that coming in 2015, when they returned to the WNBA Finals, losing in five games to Minnesota. They haven't made the playoffs since 2016.
They finished last season with a 6-26 record, the worst in the league.
Dream move up in WNBA draft, now poised to make No. 1 pick
Indiana Fever trade McCowan to Wings, now has 4 1st-round WNBA picks
Tamika Catchings leaving Fever front office

redpajama_set_name: RedPajamaCommonCrawl


text:
\section{Introduction}

The Large Sky Area Multi-object Fiber Spectroscopic Telescope (LAMOST) survey \citep{czc+12, dnl+12, lzz+12, zzc+12} has thus far obtained medium-resolution ($R \sim 2000$) spectra for over 3~million stars, on its way to a goal of acquiring some 6-8~million stellar spectra over a planned 5-year survey. Such a vast survey provides an invaluable resource for studies of Milky Way stellar dynamics. LAMOST data have been used to explore kinematical asymmetries in the nearby disk \citep{cdn+13}, nearby stellar moving groups (\citealt{zzc+14,xia15}), high-velocity \citep{zcl+14} and hyper-velocity stars \citep{zcb+14}, and stellar kinematics in the solar neighborhood \citep{tlc+15}. More distant halo structure is also accessible via red giant branch (RGB) stars observed by LAMOST. However, RGB stars can be difficult to identify among the much more numerous foreground dwarfs. \citet{ldc+14} developed a method to select K-type giants from their LAMOST spectra, and a technique to identify M giants (which are not processed in the main LAMOST stellar parameters pipeline) has been developed by \citet{zll+15}; see also the study of the Sagittarius tidal stream M giants by Li et al., {\it in preparation}). All of these studies require an estimate of the distances to the stars involved in order to place them within the structure of the Galaxy. To use the LAMOST data to understand the dynamics of the Milky Way, it is thus vitally important to devise a method for determining stellar distances.

One of the main outputs of a survey such as LAMOST is the spectroscopically derived line of sight (radial) velocity (RV) for each star. In order to exploit this velocity information as a probe of Galactic dynamics, one must know the three-dimensional position of each star within the Galaxy. The position on the sky is, of course, well known, but estimating the third dimension of each star's position (its distance) is non-trivial. Furthermore, even if reliable proper motions are known for each star, combining these with the RVs to derive a three-dimensional space motion for each star requires knowing its distance. A survey such as LAMOST inevitably contains numerous nearby (mostly disk) dwarfs, with a smaller fraction of intrinsically bright RGB stars (e.g., K giants; see \citealt{ldc+14}) that can be used to study more distant structures in the halo of the Milky Way. Thus, in order to fully exploit LAMOST data for studies of Galactic structure, we require not only reliable estimates of stellar parameters, but a robust estimate of the distance to each star, based on available photometry and information gleaned from its spectrum.

For each star whose spectrum has sufficient signal-to-noise (S/N), the LAMOST pipeline (see, e.g., \citealt{wll+11, lzz+12, wld+14}) derives effective temperature ($T_{\rm eff}$), surface gravity ($\log{g}$), and metallicity ([Fe/H]). When these stellar parameters are known for a single star, it is typical to compare the measured parameters to theoretical isochrones, and use the best theoretical match to estimate the absolute magnitude of the star in question. This is fairly straightforward for a single star, but to robustly determine distances by this technique for large numbers of stars in an automated way presents a challenge.

Recently, a variety of techniques have been presented for deriving distances to large numbers of stars with stellar parameters resulting from spectroscopic surveys such as RAdial Velocity Experiment (RAVE; \citealt{szs+06,kgs+13}), SDSS/SEGUE (\citealt{yrn+09}), and SDSS/APOGEE.
These include a $\chi^2$ minimization routine that compares RAVE stellar parameters to a grid of isochrones \citep{bsh+10}, and a modification of this technique \citep{zmb+10} to account for the stellar luminosity function of the isochrones. Bayesian methods that include models of Galactic stellar populations as priors (e.g., \citealt{bbs+11,bbk+14}) have also been applied to RAVE data. Distances presented by the SEGUE Stellar Parameters Pipeline (SSPP; \citealt{lbs+08}) are based on empirical fits from \citet{bcy+00} to globular cluster fiducial sequences, and require separate calibrations for stars in different evolutionary stages. An alternate method that has been applied to SEGUE halo K-giants uses a Bayesian approach to account for the luminosity function and metallicity distribution \citep{xmr+14}. Distances for stars observed by APOGEE have thus far been limited to well-characterized stars such as red-clump stars \citep{bnr+14} and red giants in the Kepler field that have asteroseismic surface gravities \citep{rgm+14}.

Our method of deriving distances from LAMOST spectroscopic parameters was chosen to avoid introducing assumptions about stellar populations and their distribution in the Galaxy. We simply want the best empirical estimate of the distance, so that we can {\it use} this to explore the distribution of stellar populations in the Milky Way. The distribution of observed stellar parameters will be biased by the method used to select targets (e.g., the color and magnitude selection criteria), the intrinsic properties (including the stellar parameters and luminosity function) of the Galactic sub-populations sampled in each observed region, and perhaps even the observing conditions under which each spectrum was obtained. One benefit of LAMOST is that each star is observed as part of a ``plate'' on which $\sim3000$ stars are simultaneously targeted. Each plate covers a narrow magnitude range and has a simple target selection function (see, e.g., \citealt{cln+12,lyh+14}), making it possible for us to use the observations themselves to account for both the selection function and the stellar luminosity function in our distance estimates. In practice, the selection function has not remained the same throughout the LAMOST survey, making it impossible to back out the probability of observing a star with given properties explicitly. We account for the effects of target selection by using the empirically measured distribution of stellar parameters on each plate as a prior for the likelihood of finding a star of a given surface gravity at a given color and magnitude. In this way, we are explicitly including the observed $\log{g}$ for all stars on a plate to derive an estimate of our expectations along each line of sight, thus removing selection biases and the effects of differently sampled Milky Way populations along each line of sight.

This paper is outlined as follows. Section~\ref{sec:dist_method} discusses the techniques we have developed for deriving distances to stars with LAMOST stellar parameters. In Section~\ref{sec:verification} we verify the effectiveness of our technique using several catalogs from the literature, as well as simulated data sets. We follow with a brief illustration of the results from applying our algorithms to the entire LAMOST data set of $\sim1.8$~million stellar spectra in Section~\ref{sec:lamost_dist}. Finally, we conclude with some remarks about the utility of this method for Galactic structure science with LAMOST.

\section{Distance determination methods}\label{sec:dist_method}

We derive distances to stars by comparing measured stellar parameters to a grid of synthetic isochrones. Initially, a simple $\chi^2$ method is tested. Though the results from this algorithm seem reasonable, it is difficult to derive reasonable uncertainties. We thus turn to a Bayesian technique, which also has the advantage of allowing the priors to be easily adjusted in the future as desired.

\subsection{Adopted Isochrones}\label{sec:iso}

We began by creating a grid of isochrones from the Dartmouth Stellar Evolution Database \citep{dcj+08}. This particular set of isochrones was chosen in part because it more accurately reproduces the lower main sequence in SDSS colors than other systems \citep{fc12, fjc14}, and in part simply for the convenience with which one can generate a custom grid of isochrones in the Dartmouth system.\footnote{Unlike most available isochrone systems, the Dartmouth isochrones include atomic diffusion (see \citealt{dcj+08} and references therein), and are thus more reliable for age estimates. While measuring stellar ages is not a current concern of ours, we may wish to use the grid for this purpose in the future.} We tested our programs with Padova isochrones \citep{gbb+02}, and found that the differences in derived distances are less than a few percent, with most of the discrepancies at the cooler end of the main sequence. Our adopted grid contains isochrones ranging from $-2.5 <$ [Fe/H] $< +0.5$ in 0.1~dex increments, and 1-15 Gyr in linearly spaced 1~Gyr increments.
All isochrones were generated with $[\alpha$/Fe] = 0.0.
Isochrone grids were generated for the SDSS $ugriz$ and the $UBVRIJHK_sK_p$ photometric bands, in order to allow for the use of a variety of input magnitudes to derive distances. We removed low-mass stars ($M < 0.4 M_\odot$) and all evolutionary stages other than main sequence, subgiant, and RGB. Other evolutionary stages (e.g., horizontal branch) are not well classified at present by LAMOST spectra, and are also not well represented in the isochrones, so we chose to excise them and keep only ``normal,'' well-behaved stars.

Before using the grid of isochrones for derivation of distances, it was interpolated onto a regularly spaced distribution in absolute magnitude. Because we need an absolute magnitude that corresponds to each metallicity, age, surface gravity, and temperature in the grid, we begin by creating a dummy array of absolute magnitudes spanning the relevant range ($8 > M_{\rm K_S} > -8$ for 2MASS colors/magnitudes, and $12 > M_r > -4$ for SDSS), in increments of 0.02 magnitudes. For each combination of the 15 age steps and 31 steps in [Fe/H] making up our grid, we then use a cubic spline interpolation to map the effective temperature ($T_{\rm eff}$) and surface gravity ($\log{g}$) behavior as a function of absolute magnitude. In this way, we create a grid with identical values of absolute magnitude for each age/metallicity combination, which then simply reflects the $T_{\rm eff}$ and $\log{g}$ of a theoretical star at that age/metallicity that would have each value of absolute magnitude in the array. In other words, at each absolute magnitude, we create arrays with all combinations of age, [Fe/H], temperature, and surface gravity that are predicted by the isochrone grid.

\subsection{Chi-squared Technique}

The goal is to take the measured stellar parameters $T_{\rm eff}$, $\log{g}$, and [Fe/H], along with known photometric magnitudes and colors, and derive a distance to each star. We employ near-infrared 2MASS \citep{scs+06} magnitudes and colors from here onward, but these can be replaced with magnitudes from any other system (e.g., SDSS) for which the Dartmouth isochrones have been calculated. We chose 2MASS because $\sim97\%$ of the objects in the LAMOST catalog have matches in 2MASS, thus providing a uniform input catalog (note that the majority of the objects that do not have 2MASS counterparts are at the faintest magnitudes reached by LAMOST). The use of 2MASS also simplifies comparisons to other catalogs that may not overlap the SDSS footprint or magnitude range.

Assuming we have measured input parameters (``observables'') $T_{\rm eff}$, $\log{g}$, [Fe/H], and $(J-K_S)$ (or any other input color), and associated errors $\sigma_{T_{\rm eff}}, \sigma_{\rm{log}(g)}, \sigma_{\rm [Fe/H]}$, and $\sigma_{(J-K_S)}$, we can define a $\chi^2$ statistic:

\begin{equation}
\chi^2 = \sum\limits_{i=1}^n \frac{(O_i - O_{i, \rm{mod}})^2}{\sigma_{O_i}^2},
\end{equation}

\noindent where the $O_i$ are the observables ($n=4$ in this example) with associated errors $\sigma_{O_i}$, and $O_{i, \rm{mod}}$ are the isochrone model parameters corresponding to each observable. To determine the best value for our data, we find the model point at which $\chi^2$ is a minimum. The distance modulus then simply consists of the difference between the model star's absolute magnitude at this best-fit point and the input (measured) magnitude.

To account for errors on the observables, while also deriving an uncertainty on our derived distance, we resample $N$ times (we adopted $N$=100 per star after confirming that this relatively small number of samples produces nearly identical results as $N=1000$ per star, while keeping computation times reasonable) from within Gaussian-distributed errors on each of the parameters, then minimize $\chi^2$ for each sample. The mean and standard deviation of the probability distribution function (PDF) for the absolute $K_S$ magnitude ($M_K$) from this Monte Carlo process are measured for each star. We then combine these with the observed $K_S$ magnitude to derive the distance and its error. At this point, the uncertainties on many of the distances were found to be unrealistically high (often greater than 100\% for stars from LAMOST); this is likely because the errors on the stellar parameters quoted in the LAMOST catalog are overestimated (as found by \citealt{lbc+15} via comparison to SDSS spectra of stars in common between the surveys). Although the grid spacing of 0.1~dex in [Fe/H] is half the smallest metallicity uncertainty we have considered ($\sigma_{\rm [Fe/H]} = 0.2$), it may also be that finer grid spacing (in both age and metallicity) would reduce the scatter in Monte Carlo-resampled distance estimates. Furthermore, though we tested resampling $N=100$ and 1000 times and found little effect on the derived distances and their errors, it is also possible that even larger samples are required to fully reproduce the correct PDF of $M_K$ for each star; this was not explored further because of the computationally prohibitive cost of resampling 10,000 or more times per star.

\subsection{Bayesian Technique}

In order to obtain a posterior PDF for the distance rather than a simple distance estimate with associated error bar, we adopt a Bayesian method. This more readily allows for statistical studies of Galactic populations that require the full PDF. We choose to keep the priors in our method extremely simple, unlike methods (e.g., \citealt{bbs+11,bbk+14}) that consider priors based on models of Milky Way stellar populations. Because we intend to use the derived distances to study the kinematics and density distributions of Milky Way stellar populations and their metallicity distribution functions, we wish to avoid priors that assume any uncertain properties of these populations. It is simple to incorporate more complex priors into our algorithm in the future, should we wish to do so.

Consider a vector of observed stellar parameters ${\bf O} = (T_{\rm eff}, \log{g}, {\rm [Fe/H]}) \equiv (T, G, Z)$ with measurement errors ${\bf \sigma_O}$. Assume that these can be mapped via stellar models onto a vector of intrinsic properties ${\bf X} =$ (age, mass, metallicity)$ \equiv (A, M_0, Z_0)$ that together determine the evolution of each star.\footnote{The Dartmouth isochrones are given explicitly in [Fe/H] rather than $Z$. However, the values of $Z$ and $Y$ (the helium fraction; calculated as $Y = 0.245 + 1.54 Z$) are given in the header of each isochrone, with details about the initial compositions of the models given in \citet{dcj+08}. Thus, if desired, one can use the standard definition of [Fe/H]$=\log{(Z/X)/(Z/X)_\odot}$, with standard solar values from \citet{gs98} of $(Z/X)_\odot = 0.0267$ (initial) and $(Z/X)_\odot = 0.0229$ at the current solar age, to find $Z$.}
These intrinsic properties combined with the stellar models give the absolute magnitude distribution $M_{\rm abs} (A, M_0, Z_0)$. The mapping from observables ${\bf O}$ to $M_{\rm abs}$ represents a convolution of the intrinsic luminosity function and the ways in which the selection function has sampled this luminosity function. We will denote the selection function as $S$.  Stellar models relate $A$ to ${\bf O}$; we do not include any explicit dependence of model parameters on $A$ because we do not know anything about the ages of observed stars in advance (i.e., we take a uniform prior $p (A) = 1$).
Given a set of stellar models, observed stellar parameters, and the selection function, a PDF for absolute magnitude can be derived. The full PDf is:

\begin{equation}
p(M_{\rm abs}, {\bf O}, {\bf \sigma_O}, S) = p(M_{\rm abs} | {\bf O}, {\bf \sigma_O}, S) p({\bf O}, {\bf \sigma_O}, S)
\end{equation}

\noindent This can be rewritten as:

\begin{equation}
\begin{aligned}
p(M_{\rm abs}, {\bf O}, {\bf \sigma_O}, S) &= p(M_{\rm abs} | {\bf O}, {\bf \sigma_O}, S) p({\bf O}, {\bf \sigma_O}, S) \\
& = p({\bf O} | M_{\rm abs}, {\bf \sigma_O}) p({\bf \sigma_O} | M_{\rm abs}) p(M_{\rm abs} | S) p(S),
\end{aligned}
\end{equation}

\noindent where in the first term on the right-hand side, we have removed the dependence on $S$, because once the star has been observed, the likelihood $p({\bf O}| M_{\rm abs}, {\bf \sigma_O})$ no longer depends on the selection function. Rearranging, we obtain:

\begin{equation}
p(M_{\rm abs} | {\bf O}, {\bf \sigma_O}, S) = \frac{  p({\bf O} | M_{\rm abs}, {\bf \sigma_O}) p({\bf \sigma_O} | M_{\rm abs}) p(M_{\rm abs} | S) p(S) }{p({\bf O}, {\bf \sigma_O}, S)}
\end{equation}

We assume that the measured errors ${\bf \sigma_O}$ are independent of $M_{\rm abs}$. This may not strictly be true -- the errors may indirectly depend on intrinsic properties of the star (e.g., low-metallicity stars may have larger $\sigma_Z$, or giants near the RGB tip may have higher $\sigma_G$), which in turn affect the $M_{\rm abs}$. However, this should be a minimal effect for our purposes, so we take $p({\bf \sigma_O} | M_{\rm abs})$ to be independent of $M_{\rm abs}$ (i.e., this term becomes $p({\bf \sigma_O})$). We also neglect the denominator, $p({\bf O}, {\bf \sigma_O}, S)$, which contributes only a normalization factor.
The term $p(M_{\rm abs} | S)$ is the absolute magnitude distribution given the selection function. If there is no selection function, this term would be the luminosity function. This leaves us with a final expression for the posterior PDF of $M_{\rm abs}$:

\begin{equation}
p(M_{\rm abs} | {\bf O}, {\bf \sigma_O}, S) \propto p({\bf O} | M_{\rm abs}, {\bf \sigma_O}) p(M_{\rm abs} | S) p({\bf \sigma_O}) p(S)
\label{pdf_eqn_final}
\end{equation}

We take the likelihood $p({\bf O} | M_{\rm abs}, {\bf \sigma_O})$ to be Gaussian in each of $T, G$, and $Z$, i.e.,

\begin{equation}
p({\bf O} | M_{\rm abs}, {\bf \sigma_O}) \propto \prod_{i=1}^{3}{ \exp { [ -(O_i-O_{\rm mod})^2 / 2 \sigma_{O,i}^2 ]}},
\end{equation}

\noindent where $i=1-3$ to indicate the inclusion of $T, G,$ and $Z$ in the product, and $O_{\rm mod}$ indicates the corresponding parameters for the model isochrone grid (and thus implicitly $M_{\rm abs}$). In practice, this is accomplished by calculating this Gaussian residual for the input star relative to every point in the model isochrone grid. Because every point on the grid with its parameters $O_{\rm mod}$ has an associated $M_{\rm abs}$, the likelihood given by this product of Gaussians can be mapped to a likelihood distribution in $M_{\rm abs}$.

\begin{figure}[!t]
\begin{center}
\includegraphics[width=0.975\columnwidth]{sample_likelihood_plots15_85.eps}
\caption{Examples of the posterior PDF values from the Bayesian distance code. Each panel shows the likelihood distribution derived from the stellar parameters for a randomly chosen star from the Besan\c{c}on \citep{rrd+03} model catalog. Red squares represent the 15\%, 50\%, and 85\% values in the cumulative PDF, which we take as the best estimate (50\%) and error bars for the absolute magnitude. Dashed blue lines in each panel show the ``true'' absolute magnitude given by the Besan\c{c}on model.  }
\label{samp_likelihood}
\end{center}
\end{figure}

The term $p(M_{\rm abs} | S)$ in Equation~\ref{pdf_eqn_final} encodes the relative numbers of stars as a function of $M_{\rm abs}$, given the selection function $S$ near each star's line of sight; i.e., how likely a star of a certain $M_{\rm abs}$ is to have made it into the sample given the catalog of observed stars.
This term is necessary because the entire luminosity function is not sampled by a given region of color-magnitude space. To account for this selection effect (which depends on color and magnitude, but more importantly on position in the sky), we derive an empirical correction based on the stars actually observed in a given LAMOST plate. To do so, we select stars from the same LAMOST plate that are within 0.25 magnitudes in color and magnitude (e.g., $K_{S,0}$ and $(J-K_S)_0$, or whatever color-magnitude system is being used to derive distances) of the star of interest. For each nearby star, we generate a Gaussian centered at its measured $\log{g}$ with width equal to its associated error, $\sigma_{\log{g}}$, and then normalize its sum to unity to create a PDF. We create a generalized histogram by summing these PDFs for all of the color-magnitude selected stars in the plate, then normalize the resulting $\log{g}$ distribution to yield the probability of finding a star at a given $\log{g}$ value in the vicinity (in both position and color-magnitude) of the star of interest.
This $\log{g}$ distribution is then mapped onto each input isochrone via interpolation of the $\log{g}-M_{\rm abs}$ relation of the isochrone itself. The resulting histogram in absolute magnitude is normalized to provide the probability of finding stars of a given $M_{\rm abs}$ based on the measured $\log{g}$ distribution. We incorporate this probability distribution as $p(M_{\rm abs} | S)$ in Equation~\ref{pdf_eqn_final} to properly account for the underlying luminosity function along each line of sight, and the selection effects of the survey (which corrects for both the fraction of stars that were selected as a function of color and magnitude as well as the volume of the Galaxy sampled by the selected stars).

\begin{figure*}[!t]
\includegraphics[width=0.45\textwidth]{methods_compare_distresid_besancon_sep2014.eps}
\includegraphics[width=0.46\textwidth, trim=0.0in 0.5in 0.0in 0.0in]{methods_distcompare_besanconb60_sep2014.eps}
\caption{Comparison of distances derived by our two algorithms for Besan\c{c}on model galaxy stars in a simulated field at $(l, b) = (180^\circ, 60^\circ)$. The left panel shows the residuals from comparison of the Besan\c{c}on model distances, $d_{\rm Besancon}$, to distances derived from the model stellar parameters using our code, $d_{\rm LAMOST}$. The solid black line is the output from the $\chi^2$ algorithm, and the dashed gray histogram is from the Bayesian code. The results from the Bayesian method have slightly larger scatter than those from the $\chi^2$ algorithm, but little systematic offset is seen in either method. The right panel directly compares the input (Besan\c{c}on) distances to those recovered by the code ($d_{\rm LAMOST}$). Open symbols are $\chi^2$ distances, and filled symbols are results from the Bayesian code. The agreement is good out to $d \sim 15$~kpc. Beyond this distance, our algorithms systematically overestimate the distances by $\sim20\%$ for the majority of stars. We believe this discrepancy is due to our use of isochrones with solar $[\alpha/$Fe]; because metal-poor halo giants are typically alpha-enhanced relative to Milky Way disk stars, a more appropriate alpha-enhanced isochrone grid may remedy this deficiency for halo stars (see Section~4.2 and Figure~\ref{alpha_enhanced_dist} for further exploration of this effect).
}
\label{compare_methods}
\end{figure*}

In the absence of a selection function, $p(M_{\rm abs} | S)$ could be represented by the theoretical luminosity function of the isochrones. For general usage, we include this feature in the code for instances where the parameters of nearby stars are not known.
In the Dartmouth isochrones, the density of points along each isochrone as given encodes equal steps in ``equivalent evolutionary phase'' \citep{bbc+90}. Assuming that this roughly mimics the luminosity function, we calculate the normalized density of points as a function of absolute magnitude for each isochrone in histogram bins of 0.2 magnitudes, and use this as the starting estimate for $p (M_{\rm abs} | S)$ when there are not sufficient nearby, simultaneously observed stars to use for the selection function correction. In tests of the effect of the selection
function correction using 20,000 stars, we found that the fractional change in distance between measurements with/without this correction was less than 10\% for 93\% of the stars, and less than 20\% for 97\% of the stars. However, a small number of stars (~1.4\%; mostly red giants) had
their distances change by more than 30\% between these two methods. As expected, then, the correction is important for the much rarer RGB stars than for the ubiquitous nearby dwarfs in the LAMOST database.

The algorithm to evaluate each of the three terms on the right side of Equation~\ref{pdf_eqn_final} produces an array of points corresponding to all of the theoretical stars in the isochrone grid. Each of these points has an associated $M_{\rm abs}$ and a likelihood value. Thus Equation~\ref{pdf_eqn_final} also produces an array of posterior PDFs for a large grid of absolute magnitudes. We sum the PDF for each $M_{\rm abs}$ value to produce a marginalized PDF in $M_{\rm abs}$ for the input star. This is normalized to produce the final PDF $p(M_{\rm abs} | {\bf O}, {\bf \sigma_O}, S)$; some examples are shown in Figure~\ref{samp_likelihood}. We take the median (i.e., 50th percentile) of this PDF as the best estimate for $M_{\rm abs}$, with uncertainties derived using the $M_{\rm abs}$ corresponding to the 15th and 85th percentile values from the cumulative PDF. We also retain the full PDFs so that they can be used instead of single estimates of distances and their errors.

Figure~\ref{compare_methods} shows a comparison of the results from running the two versions ($\chi^2$ and Bayesian) of the code on stars from a mock galaxy field generated by the Besan\c{c}on \citep{rrd+03} model.\footnote{Throughout this work, we use the notation $d_{\rm LAMOST}$ to refer to distances derived by the algorithm discussed in this paper. Note that this does not mean that these distances are derived using LAMOST data, but rather that our LAMOST {\it distance code} is being applied.}  Uncertainties on the stellar parameters for all stars in the mock catalog were set to $\sigma_{\rm Teff} = 100$~K, $\sigma_{\rm log~g} = 0.3$~dex, and $\sigma_{\rm [Fe/H]} = 0.2$~dex. The left panel compares the residuals (in the sense $(d_{\rm LAMOST} - d_{\rm Besancon})/d_{\rm Besancon}$) of measured distances to the input (model) distances. Both methods recover the input distances fairly well, with an asymmetric tail to high (overestimated) residuals. The right panel of Figure~\ref{compare_methods} compares these distance measurements directly. It is clear from this panel that the large positive residuals are mostly for distant, metal-poor giants, whose distances are overestimated by $\sim20\%$. Having now verified that the $\chi^2$ and Bayesian methods produce similar results, we henceforth use only the Bayesian method to obtain the full distance PDF.

\begin{figure}[!t]
\begin{center}
\includegraphics[width=0.9\columnwidth]{distresid_hipparcos_lfmod.eps}
\caption{Results of running the distance code on 1199 stars from the \citet{gcg+03,gcg+06} catalogs. The histogram shows fractional residuals from comparison of the Hipparcos parallax distances, $d_{Hipparcos}$, to distances derived from the Gray et al. stellar parameters, $d_{\rm LAMOST}$. For reference, the dashed vertical line is one-to-one agreement. Our distances show no systematic offset with respect to the parallax distances, with $\sim17\%$ scatter.}
\label{hiptest}
\end{center}
\end{figure}

\begin{figure*}[!t]
\begin{center}
\includegraphics[width=0.4\textwidth]{distresid_rave_breddels_aug2014.eps}
\includegraphics[width=0.4\textwidth]{distresid_rave_zwitter_sep2014.eps}

\caption{Left panel: results of running the distance code on $\sim16,000$ stars from RAVE in the \citet{bsh+10} catalogs. As in Figure~\ref{hiptest}, the panels represent the fractional residuals relative to the catalog values from the Bayesian method. Our distances are systematically shifted by $\sim26\%$ relative to those of \citet{bsh+10}, with $\sim23\%$ scatter.
Right panel: as in the left panel, but for the RAVE sample from \citet{zmb+10}. Our distances are systematically smaller by
$\sim12\%$, with $\sim16\%$ spread about this value. There are decidedly non-Gaussian tails in these residuals on toward large relative underestimates.
}
\label{ravetest}
\end{center}
\end{figure*}

\begin{figure*}[!t]
\begin{center}

\includegraphics[width=0.4\textwidth]{distresid_besancon_b30_lfmod.eps}
\includegraphics[width=0.4\textwidth]{distresid_besancon_b60_lfmod.eps}

\caption{Results from running the distance code on two realizations of the Besan\c{c}on Galaxy model. The left panel is for a field at $l=180\arcdeg, b=30\arcdeg$, and the right panel shows a field at $l=180\arcdeg, b=60\arcdeg$. }
\label{disttest}
\end{center}
\end{figure*}

\section{Verification of the methods}\label{sec:verification}

To test the code, we need a sample of stars with known spectroscopic parameters and distances. For this, we use the \citet{gcg+03,gcg+06} measurements of stellar parameters for Hipparcos stars within 40~pc of the Sun as part of the NStars program. These nearby stars have good-quality parallaxes ($\sigma_\pi/\pi~\lesssim~0.05$), and are all found in the 2MASS catalog. Uncertainties on the stellar parameters for individual stars are not provided in the catalog; we choose to set them to $\sigma_{\rm Teff} = 100$~K, $\sigma_{\rm log~g} = 0.3$ dex, and $\sigma_{\rm [Fe/H]} = 0.2$ dex. Furthermore, we cannot rely on the method used for LAMOST data, where we selected stars from the same observed plate to derive the selection and luminosity functions. For this and all subsequent tests of our code, we obtain the underlying luminosity distribution using only the color-magnitude selection from the algorithm outlined in Section~2.3, which we apply to all stars in the test catalog without regard to position on the sky. Figure~\ref{hiptest} shows the results of running our distance code using the Gray et al. stellar parameters plus 2MASS magnitudes and colors for stars with temperature in the range $3500$~K$ < T_{\rm eff} < 9000$~K. This temperature cut reduces the sample from a total of 1525 stars to 1199 used for Figure~\ref{hiptest}. The histogram compares the known distances, $d_{\rm Hipparcos}$ (from the trigonometric parallaxes) to the derived distances, $d_{\rm LAMOST}$, expressed as a residual: $(d_{\rm LAMOST} - d_{\rm Hipparcos})/d_{\rm Hipparcos}$.
The residuals are centered on zero (i.e., no systematic offset is present), with a scatter of $\sim17\%$ (median absolute deviation of the data; we use this instead of fitting a Gaussian because the residuals are obviously asymmetric and non-Gaussian). Because of their location in the Solar neighborhood, the majority of these stars are metal-rich main-sequence stars (1151 are dwarfs with $\log{g}<3.5$, and 48 are giants). Thus, while these stars are a useful test, they do not explore the variety of stellar populations we expect to find in a survey such as LAMOST.

To examine our algorithm's behavior on a more heterogeneous data set, we test our code on two samples of RAVE DR2 stars. The first sample is the RAVE-6D catalog: \url{http://www.astro.rug.nl/~rave/}, which is from \citet{bsh+10}. We use the RAVE stellar parameters from this table as inputs to the distance code. Uncertainties on the stellar parameters were set to $\sigma_{\rm Teff} = 100$~K, $\sigma_{\rm log~g} = 0.3$~dex, and $\sigma_{\rm [Fe/H]} = 0.2$~dex to approximate the typical errors in RAVE.  The left panel of Figure~\ref{ravetest} shows residuals from a comparison of our results to those from \citet{bsh+10}. There is a systematic shift of $\sim26\%$ between our distances and those of \citet{bsh+10}, with scatter of $\sim23\%$.
On close examination, there is no obvious correlation of the distance residuals with any of the input stellar parameters (e.g., $\log{g}$, [Fe/H]). Thus the systematic offset between our distance scale and that of \citet{bsh+10} may be due to differences in the isochrones used in the fitting. \citet{bsh+10} used Yale-Yonsei isochrones in a grid with 40 logarithmically spaced ages between 0.01 and 15~Gyr. The differences between the Dartmouth and Yale-Yonsei isochrones, and the heavy emphasis on very young ages in the \citet{bsh+10} grid, seem to cause systematic shifts. Reassuringly, when we applied our $\chi^2$ code, which defines $\chi^2$ in the same way as \citet{bsh+10}, to these data, the scatter about the mean difference is small (but the systematic offset remains).

The second set of RAVE data on which we tested the code is the catalog of \citet{zmb+10}. These authors improved upon the method of \citet{bsh+10}
 by using a linearly spaced grid of ages, deriving distances separately using Yale-Yonsei, Padova, and Dartmouth isochrones, and by weighting stages of stellar evolution to account for the relative numbers of stars of different masses. We ran our code on the parameters of roughly 16,000 stars ($\sim5800$ giants and $\sim9900$ dwarfs) from \citet{zmb+10} and compared directly to their Dartmouth results. The comparison is shown in the right panel of Figure~\ref{ravetest}, with residuals calculated in the same way as for the Breddels et al. sample.
Again, these residuals show a systematic shift such that our estimates are lower than the RAVE distances. The systematic difference between our result and the RAVE distances is smaller than for the comparison to \citet{bsh+10}, as expected since the grid spacing in age is similar and we are using the same isochrone sets (Dartmouth).  Our Bayesian method produces decidedly non-Gaussian residuals, with a median offset of $\sim12\%$ and scatter of $\sim16\%$.

As a final test of the accuracy of our derived distances, we generate catalogs using the Besan\c{c}on model of the Milky Way \citep{rrd+03} for two fields of view: $(l, b) = (180^\circ, 30^\circ)$ and $(l, b) = (180^\circ, 60^\circ)$. The $b=60^\circ$ catalog contains 5614 stars (299 giants and 5315 dwarfs) with $3500$~K$ < T_{\rm eff} < 10000$~K; the $b=30^\circ$ field has 469 giants and 13384 dwarfs in the same temperature range. We again assign uncertainties to the stellar parameters of $\sigma_{\rm Teff} = 100$~K, $\sigma_{\rm log~g} = 0.3$~dex, and $\sigma_{\rm [Fe/H]} = 0.2$~dex. Figure~\ref{disttest} shows the results of running our algorithm with the stellar parameters from the Besan\c{c}on model as inputs. We recover the distances well,
with a mildly bimodal distribution and a slight tail extending to $\sim50\%$ overestimate of distances. These residuals have roughly zero median offset, but it is clear that a large fraction of stars have distances underestimated by $\sim5\%$. The positive ``bump'' in these residuals appears to consist mostly of the youngest nearby stars and old, metal-poor halo giants in the Besan\c{c}on model catalog.

\section{Application to LAMOST data}\label{sec:lamost_dist}

Having verified the effectiveness of our distance code on catalog and simulated data, we now apply the code to the existing LAMOST data. As of this date, the LAMOST catalog (internal data releases 1 and 2) consists of $\sim1.8$~million stars with stellar parameters (out of $\sim3.6$~million that have been observed; stars with low S/N, cool M-type stars, and hot OBA stars do not have parameters from the LAMOST pipeline). In this section, we show some simple ``sanity checks'' to verify that the code is producing reasonable results, and to provide an idea of the scope of the LAMOST data set.

\begin{figure}[!t]
\begin{center}
\includegraphics[width=0.9\columnwidth]{tefferr_disterr.eps} \\
\includegraphics[width=0.9\columnwidth]{loggerr_disterr.eps} \\
\includegraphics[width=0.9\columnwidth]{feherr_disterr.eps}
\caption{Fractional errors on the derived distances for LAMOST stars compared to the uncertainties on the stellar parameters $T_{\rm eff}$, $\log{g}$, and [Fe/H] (from top to bottom). The grayscale encodes the number of stars in each bin on a logarithmic scale between 10 and 20,000. The distance errors are only weakly correlated with $T_{\rm eff}$ or [Fe/H] uncertainties. By far the strongest dependence is seen in the center panel, which shows a roughly linear correlation between surface gravity uncertainty and the error in the derived distance.
}
\label{param_dist_err}
\end{center}
\end{figure}

\subsection{LAMOST Stellar Parameters}

The LAMOST parameters for stars in the range $3500$~K$ < T_{\rm eff} < 8000$~K and with S/N$ > 5$ in $g$ and $r$ bands have median uncertainties of  $\sim160$~K, $\sim0.5$~dex, and $\sim0.3$~dex, in $T_{\rm eff}$, $\log{g}$, and [Fe/H], respectively. We note that the [Fe/H] uncertainties in the second full year of survey operations (2013 September -- 2014 June) are significantly smaller (median $0.18$~dex) than the earlier periods; the $T_{\rm eff}$ and $\log{g}$ errors are similar in earlier and later data. It is unclear whether this is due to changes in the LAMOST data reduction pipeline, or improved data quality as the survey progresses.

The parameter that most strongly affects the derived distance errors is surface gravity. This can be seen in Figure~\ref{param_dist_err}, which compares the errors on $T_{\rm eff}$, $\log{g}$, and [Fe/H] with the error in derived distances based on those parameters. There is a slight correlation of $\sigma_{\rm Teff}$ and $\sigma_{\rm d}$, but little dependence of distance errors on uncertainty in [Fe/H]. The middle panel, showing $\sigma_{\rm log~g}$ vs. $\sigma_{\rm d}$, exhibits a roughly linear correlation between the surface gravity uncertainty and the errors on the derived distances. For an uncertainty of $\sim0.5$~dex in $\log{g}$, Figure~\ref{param_dist_err} suggests that we can expect a $\sim25-35\%$ distance error. It is thus vital that surface gravities from LAMOST spectra are determined as precisely as possible. \citet{lfw+14} recently published a method to improve $\log{g}$ estimates for giant stars in the Kepler field that have also been observed with LAMOST. Based on corrections from comparison to asteroseismic $\log{g}$ measurements from Kepler, Liu~et~al. obtain uncertainties in $\log{g}$ from LAMOST spectra of $\sim0.1$~dex, which yields distance estimates with better than 10\% precision. Indeed, at a given temperature, $\Delta M_{\rm abs} \sim 2.5 \Delta (\log{g}),$\footnote{Because $M_{\rm abs}\propto -2.5 \log{L}$, $L \propto R^2$, and $g \propto R^{-2}$. Thus $M_{\rm abs} \propto 2.5 \log{g}$.} so if the uncertainty of $\log{g}$ is improved by 0.1 dex, the uncertainty in absolute magnitude improves by 0.25 mag, and the accuracy of the distance estimate improves by $\sim$12\%.

\begin{figure}[!t]
\begin{center}
\includegraphics[width=0.95\columnwidth]{distdiff_alphaenhanced_solaralpha_hist.eps}
\caption{Difference between distances to halo giants ($\left|Z\right| > 3$~kpc, $-2.4 < $[Fe/H]$ < -1.0$, with S/N$ > 10$ in $g$ and $r$-band) measured with solar-scaled isochrones ($[\alpha$/Fe] = 0.0; labeled ``solar $\alpha$'') and an $\alpha$-enhanced ($[\alpha$/Fe] = 0.4) grid of isochrones. On average, the $\alpha$-enhanced isochrones find distances $\sim13\%$ smaller (derived via the dashed-line Gaussian fit shown above) than those from the solar-scaled grid.
}
\label{alpha_enhanced_dist}
\end{center}
\end{figure}

\subsection{Effect of $\alpha$-element Abundances on Distances to Metal-poor Halo Giants}

As noted in Section~2, our algorithm tends to overestimate the distances to metal-poor halo giants in synthetic catalogs from the Besan\c{c}on model. It is well established that the metal-poor stellar populations of the Milky Way halo are typically enhanced in $\alpha$-elements relative to disk populations (e.g., \citealt{vis+04}), with metal-poor ([Fe/H]$ < -1.0$) halo stars typically having $[\alpha$/Fe]$\approx0.4$. We now return to a subset of stars for which we have LAMOST stellar parameters, and examine the effect of replacing the solar-scaled isochrones with $\alpha$-enhanced versions in our distance code. To do so, we generate a new isochrone grid with $[\alpha$/Fe] = +0.4, [Fe/H]$ < -1.0$, and the same steps in age as the original isochrone set. We run our distance algorithm on a set of stellar parameters from 239,446 LAMOST spectra (comprised of recent, third-year LAMOST spectra) with the $\alpha$-enhanced isochrones. From the resulting distance catalog, we then select out only likely metal-poor halo stars with S/N$ > 10$ in $g, r$-bands, $-2.4 < $[Fe/H]$ < -1.0$, $\log{g} < 3.5$, and at least 3~kpc from the Galactic plane. This produces a sample of 542 likely halo stars. Figure~\ref{alpha_enhanced_dist} compares the distance from the $\alpha$-enhanced grid to the distance from the original isochrone grid, in the sense $(d_{\rm \alpha-enhanced} - d_{\rm solar~\alpha})/d_{\rm solar~\alpha}$. We find that the $[\alpha$/Fe] = +0.4 grid produces distances on average 13\% nearer than those from the $[\alpha$/Fe] = 0.0 grid. This likely explains the $\sim20\%$ systematic overestimation of distances for halo stars from the Besan\c{c}on catalogs. Because halo populations in the Besan\c{c}on model were oxygen-enhanced relative to disk populations \citep{rrd+03}, our assumption of solar $\alpha$-abundances likely biases the derived distances. Adopting a more appropriate $\alpha$-enhanced isochrone grid for metal-poor halo stars would remedy this situation. Indeed, one would ideally incorporate a measured $[\alpha$/Fe] from the LAMOST spectrum itself into the distance estimation for each star; we will include this in future upgrades to the distance code as abundance estimates become available for LAMOST stars.

\begin{figure}[!t]
\begin{center}
\includegraphics[width=0.8\columnwidth]{ngrp_hist.eps}
\caption{Number of repeat observations of the 214,514 unique stars with multiple spectra in the LAMOST database. The majority of these objects have fewer than 4 observations, but some have as many as 14 separate spectra.
}
\label{lamost_ngrps}
\end{center}
\end{figure}

\subsection{Internal LAMOST Checks on Repeat Observations}

Of the $\sim1.8$~million stellar spectra in the LAMOST catalog, $\sim550,000$ of them ($\sim30\%$) are stars with repeat observations. There are 214,514 unique stars that have been observed multiple times and have sufficient quality spectra at each epoch to derive stellar parameters. An individual star may have as many as 14 observations, but most have 2-4 observations; the distribution of the number of repeat measurements is shown in Figure~\ref{lamost_ngrps}. Figure~\ref{lamostrepeats} shows the standard deviation of our distance measurements for stars with multiple observations. This is expressed as a fractional deviation of the mean measured distance, $\sigma_{\rm d}/$d$_{\rm mean}$, and plotted as a function of the minimum signal to noise of the measurements being compared. One would expect that the scatter in derived distances would increase if one (or more) of the spectra has low S/N. This is precisely what is seen in Figure~\ref{lamostrepeats} -- the scatter is $\sim5\%$ for spectra with minimum S/N$>20$, and begins to rise for S/N below 20. However, even when the minimum S/N is as low as 2.5, the typical scatter in distances in only $\sim20\%$. This verifies that (a) our code produces repeatable results when applied to multiple observations of the same star, and (b) the LAMOST pipeline provides consistent estimates of stellar parameters from these multiple observations.

\begin{figure}[!t]
\begin{center}
\includegraphics[width=1.0\columnwidth]{minsnrg_distdiff_trendonly.eps}
\caption{Standard deviation $\sigma_d$ of the distance estimate for the 214,514 stars with multiple measurements in LAMOST. This is expressed as a fractional deviation of the distance, $\sigma_d / d_{\rm mean}$, as a function of the minimum signal to noise of the spectra included in the derivation of $\sigma_d$ for each star. We calculated mean scatter (filled diamonds) and its standard deviation (error bars) for these results in bins of 2.5 in S/N. The scatter from repeat measurements is typically $\sim5\%$ for high $S/N$ stars (min. S/N$ \gtrsim 20$), then increases to $\sim20\%$ at the low S/N end. This suggests that even for fairly poor quality spectra, our distance derivations (and thus the stellar parameters on which they are based) are robustly repeatable.
}
\label{lamostrepeats}
\end{center}
\end{figure}

\begin{figure}[!t]
\begin{center}
\includegraphics[width=1.0\columnwidth]{feh_vs_z_lamosttest_b_gt_60_contour.eps}
\caption{Metallicity from the LAMOST pipeline versus height above the plane for a sample of 189,106 stars at $b > 60^\circ$ that have S/N$_g>10$. The $Z$ coordinate is based on distances derived by our code using LAMOST stellar parameters. Contours contain (2, 5, 10, 25, 50, 100, 200, 300, 400, 500, 750, 1000, 1500, 2500, 4000) stars. As expected for disk stars, the mean metallicity falls from near solar just above the Galactic plane to $\langle{\rm [Fe/H]}\rangle  \approx -0.6$ near $Z \sim 1$~kpc. This peak metallicity, which is typical of the Galactic thick disk, persists as far out as we probe, with a long tail to lower metallicity. }
\label{feh_vs_z_hilat}
\end{center}
\end{figure}

\subsection{Results from LAMOST Data}

After running our distance code on the entire catalog of LAMOST stellar parameters, we perform some checks to verify that the results make sense, and to explore the utility of our distances for Galactic structure studies. Using our distances, we calculate Galactocentric Cartesian coordinates (assuming the Sun is at $R_0 = 8$~kpc, with $(X, Y, Z)_{\rm Sun} = (-8, 0, 0)$~kpc). The first test is to see whether the metallicity distribution as a function of height above the Galactic plane near the north Galactic cap is as expected. We select stars at $b > 60^\circ$, keeping only those with S/N$ > 10$ in the SDSS $g$-band. This yields 189,106 stars. This sample should roughly probe the Galactic metallicity gradient with height; one expects that on average the metallicity should be nearly solar close to the plane, and decrease with height as the thin disk transitions into the lower-metallicity thick disk. Indeed, this is exactly what is seen in a contour plot of these data in Figure~\ref{feh_vs_z_hilat}. The peak metallicity decreases from slightly subsolar at $Z \sim 0.3$~kpc to [Fe/H]$ \sim -0.6$ at $Z \sim 1$~kpc. Above this, the peak metallicity remains roughly the same, with a long tail to low metallicities representing predominantly local halo stars.

Though giant stars in the Galactic halo represent a tiny fraction of the stars observed by LAMOST, we also hope to use them to explore structure (and substructure) in the halo. We thus wish to check whether our distances can be used to isolate a relatively pure sample of Milky Way halo giants. To test this, we
select stars with Galactocentric radii $R_{\rm GC} > 20$~kpc that are also at heights $\left | Z_{\rm GC} \right | > 5$~kpc above/below the plane. Such a sample of stars should be predominantly halo stars. We check this by plotting a metallicity histogram (dashed line in Figure~\ref{hiz_feh}) for the 1528 stars selected in this way. These stars peak at a metallicity around [Fe/H]~$\sim -1.5$, as expected for inner-halo stars, with very few metal-rich stars. In contrast, a sample selected to be inside $R_{\rm GC} < 20$~kpc and near the disk ($\left | Z_{\rm GC} \right | < 2.5$~kpc; solid line in Figure~\ref{hiz_feh}) contains mostly metal-rich stars with disk-like [Fe/H].

Note that neither of these sanity checks showing metallicity distributions for different Galactic populations (Figures~\ref{feh_vs_z_hilat} and \ref{hiz_feh}) represents the true Galactic metallicity distribution for these populations. To derive the intrinsic distribution would require correcting the selection effects present in LAMOST data. These figures are simply meant to illustrate that stellar samples selected using our derived distances have properties similar to what one might expect based on our knowledge of metallicity distributions of Milky Way components.

\begin{figure}[!t]
\includegraphics[width=1.0\columnwidth]{rgc_gt20_feh_hist_lamosttest.eps}
\caption{Normalized metallicity distribution of 1,473,135 stars at $R_{\rm GC} < 20$~kpc and $\left|Z\right| > 2.5$~kpc (solid histogram). The 1,705 stars at $\left|Z\right| > 5$~kpc and $R_{\rm GC} > 20$~kpc are represented by the dashed line. The latter should be mostly halo stars, and peaks at [Fe/H]$ \sim -1.5$ as expected for the Galactic halo, with few metal-rich ([Fe/H]$~>~-1.0$) stars. The $\left|Z\right| < 2.5$~kpc sample contains mostly metal-rich stars, as expected for predominantly disk populations. The abrupt cutoff at [Fe/H]$ = -2.5$ is due to the lower limit of metallicities produced by the LAMOST pipeline rather than a real effect. \citet{lbc+15} adapted the Sloan SSPP for more general usage; use of this pipeline on LAMOST spectra avoids the artificial cutoff at [Fe/H]$ = -2.5$. }
\label{hiz_feh}
\end{figure}

Finally, we search the LAMOST database for open cluster member stars. We begin with the compilation of known Galactic open clusters available at \url{http://www.astro.iag.usp.br/ocdb/} \citep{dam+02}. For each cluster in this list, we initially selected all stars from LAMOST within the published cluster diameter that also have LAMOST RV within 20 km~s$^{-1}$ of the published value (note that we only used clusters with known RVs for this exercise). After this initial cut, we examined histograms of velocities, distances, and metallicities for each of the clusters with more than 15 candidates. For clusters with obvious distance and velocity peaks, we manually select stars within $\sim10$~km~s$^{-1}$ of the peak value, and fit a Gaussian to the distance distribution of these cluster candidates. Clusters with obvious signatures were NGC~1039, NGC~1662, NGC~2168, NGC~2281, NGC~2548, ASCC~26, and NGC~1647. The number of candidates selected ranged from 19 to 102 stars, with the nearest clusters having the most candidates. Figure~\ref{lamost_ocs} compares our measured distances (from the Gaussian fits) for these seven clusters, $d_{\rm LAMOST}$, to those from \citet{dam+02}, $d_{\rm lit}$. Error bars on these points represent the Gaussian $\sigma$ of the stars included. The dashed line corresponds to one-to-one agreement between our measurements and literature values. All but one of the seven clusters' distances is consistent with values from the literature. Thus, we have confirmed the effectiveness of our distance estimations. This simple exercise highlights the potential of LAMOST to amass a sample of open cluster stars with homogeneously measured metallicities, velocities, and distances that can be used to probe the Galactic disk in exquisite detail.

\section{Conclusions}

We present a method to derive distances to stars with measured stellar parameters ($T_{\rm eff}$, $\log {g}$, and [Fe/H]). This was developed with particular interest in deriving distances to the many millions of stars that will be observed by the LAMOST survey, in order to enable studies of Galactic structure with this vast data set. The code is based on a Bayesian method that evaluates the posterior PDF in absolute magnitude for a given star, estimated via comparison to a grid of theoretical isochrones. The PDF incorporates information about $T_{\rm eff}$, $\log {g}$, and [Fe/H], along with their uncertainties. To account for selection effects, we take advantage of the fact that each LAMOST plate typically observes $\sim3000$ stars simultaneously. The observed distribution of $\log{g}$ for stars within 0.25 magnitudes in color-magnitude space is mapped onto theoretical isochrones to derive a proxy ``luminosity function'' expected for stars in that region of sky at the given color and magnitude. This accounts simultaneously for the selection function through which stars were chosen for LAMOST observation, and the variation in stellar populations with Galactic line of sight (and distance).
A flat age prior is implemented, since we have no information about the ages of individual stars. This could, in principle, be modified to account for the relatively well-known age-metallicity relation in the Milky Way, but we choose to leave it flat so as not to bias studies of Galactic stellar populations based on LAMOST data. Likewise, we do not impose any priors related to Milky Way stellar populations; since we wish to study Galactic structure, we prefer to avoid introducing assumptions into our distance calculations.

\begin{figure}[!t]
\begin{center}
\includegraphics[width=1.0\columnwidth]{oc_dist_litcompare.eps}
\caption{Comparison of our derived distances ($d_{\rm LAMOST}$) to those from the literature \citep[$d_{\rm lit}$]{dam+02} for seven open clusters found in LAMOST. Points represent the central value of the best-fitting Gaussian for each cluster's distance distribution, and error bars show the Gaussian $\sigma$. The dashed line represents one-to-one agreement. All but one of the clusters agrees very closely with the known distance.
}
\label{lamost_ocs}
\end{center}
\end{figure}

We test our code by measuring distances to samples of stars from Hipparcos and RAVE that have known stellar parameters. Our distances agree with the parallax distances from Hipparcos, with roughly 17\% scatter in the residuals. We find a 12\% systematic shift between our distances and those measured by \citet{zmb+10} from the same RAVE sample, with only 16\% scatter, but with a large tail toward underestimate of the distance. We also test our code on simulated data along two lines of sight from the Besan\c{c}on model, and find that we recover the model distances with no net offset. There is, however, an apparent $\sim20\%$ overestimate of distances to distant, metal-poor halo stars by our code. The source of this systematic shift is unclear, but it may be due to the fact that we have used isochrones with solar $\alpha$-element abundances. Stars in the Milky Way halo are, on average, old and $\alpha$-enhanced relative to the Solar neighborhood (see, e.g., \citealt{vis+04}). Because the RGB of an isochrone is shifted slightly to fainter absolute magnitudes when $[\alpha$/Fe] is increased (at fixed [Fe/H] and age), the use of Solar-scaled isochrones will tend to bias results for $\alpha$-rich stars toward overestimation of distances. We confirmed this impression by re-running the distance derivation for a subset of metal-poor halo giants, using an $\alpha$-enhanced isochrone grid ($[\alpha$/Fe]$=+0.4$). Indeed, this exercise shifted the distance estimates for these stars by an average of 13\% closer than the estimates based on the Solar-alpha grid. Thus we suggest that adopting a more appropriate $\alpha$-enhanced isochrone set for metal-poor halo stars would remedy the overestimation of distances to these stars, and that ultimately the measured [$\alpha$/Fe] should be incorporated into the distance estimation process.

Finally, we present some results based on LAMOST data. A sample of $\sim189,000$ stars near the north Galactic cap shows expected trends in [Fe/H] with height ($Z$) above the Galactic plane. We also show that a sample selected to be distant halo stars based on our derived distances consists solely of metal-poor stars with a distribution peaked around [Fe/H]$ \sim -1.5$, as expected for the Galactic inner halo. Some studies of kinematics of nearby stars based on LAMOST data and with distances from this code have already appeared in the literature \citep{tlc+15,xia15}, and we anticipate that distances derived by this method will prove useful for numerous upcoming studies of Galactic structure. Furthermore, as the LAMOST data reduction pipelines continue to improve, we anticipate that uncertainties on stellar parameters derived from the spectra will become smaller, which will in turn improve our estimates of the distances. Eventually, parallaxes that will come from the $Gaia$ mission \citep{perryman2001} will likely supersede these distance measurements for the majority of the stars observed by LAMOST, as part of a vast sample of direct distance measurements throughout the Galaxy.

\acknowledgements

We thank the anonymous referee for careful and thoughtful comments. This work was supported by the U.S. National Science Foundation under grants AST 09-37523 and AST 14-09421. C.~L. also acknowledges the Strategic Priority Research Program ``The Emergence of Cosmological Structures'' of the Chinese Academy of Sciences, grant No. XDB09000000, the National Key Basic Research Program of China, grants No. 2014CB845700, and the National Science Foundation of China, grants No. 11373032 and 11333003. T.~C.~B. acknowledges partial support from grant PHY 08-22648: Physics Frontiers Center/Joint Institute for Nuclear Astrophysics (JINA), and PHY 14-30152; Physics Frontier Center/JINA Center for the Evolution of the Elements (JINA-CEE), awarded by the U.S. National Science
Foundation. W.~Y. appreciates support from the National Science Foundation of China, grant No. 11403056. Guoshoujing Telescope (the Large Sky Area Multi-Object Fiber Spectroscopic Telescope LAMOST) is a National Major Scientific Project built by the Chinese Academy of Sciences. Funding for the project has been provided by the National Development and Reform Commission. LAMOST is operated and managed by the National Astronomical Observatories, Chinese Academy of Sciences. This publication makes use of data products from the Two Micron All Sky Survey, which is a joint project of the University of Massachusetts and the Infrared Processing and Analysis Center/California Institute of Technology, funded by the National Aeronautics and Space Administration and the National Science Foundation.

\bibliographystyle{apj}

redpajama_set_name: RedPajamaArXiv


The Lander Timp backpack is a comfortable, weather-resistant backpack with a sleek, minimalistic, uncomplicated design. It has a separate padded laptop sleeve, four well-placed grab handles for multiple hand-carrying options and well-placed shoulder straps. However cyclists may notice the lack of stability when riding over rough pavement or bumps.
The Lander Timp is a lightweight pack that provides good overall comfort even while carrying heavier loads on distances shorter than ten miles.
While the Lander Timp does not offer a hip belt, its non-slip shoulder straps are approximately 2.5-inches in width and are moderately adjustable. Despite the minimal padding our tester found them to be comfortable when carrying moderate loads. The straps are wide enough to prevent any chafing or contact with her neck.
The back panel is a minimally-padded mesh that will help mitigate back sweat in warmer muggier months. However because our tester lives in in Minneapolis and tested this pack in December, she was unable to experience true back sweat.
The Lander Timp is moderately-stable pack when riding a commuter bike and carrying a laptop. Our tester found that the adjustable sternum strap and padded, non-slip shoulder straps offer just enough adjustability to prevent the pack from moving around on her back on smooth pavement. However the pack does have the ability to move when riding on rough pavement or over bumps.
The Lander Timp provides twenty-five liters of cargo capacity. An easy-access padded exterior pocket, which is secured by a waterproof zipper, holds up to a seventeen-inch laptop. A smaller pocket on the pack's exterior allows the user to stash items that need to access quickly, like a wallet or ID badge. Inside the main compartment is a dedicated sleeve that can hold documents or an iPad. Two additional small organizational sleeves for storing items like a phone and an e-Reader.
Made of a weather-resistant TPU-coated rip-stop material, the Lander Timp is a durable and tough pack. The daisy-chain webbing loops are heavy-duty and strong. The zippers are weather-resistant and coated and are very easy to grasp, even when wearing gloves.
The Lander Timp can hold a hydration bladder and is designed with a hydration tube port and an elastic tube holder down right shoulder strap.
Sturdy grab-handles on all four sides of the Lander Timp allow the cyclist the option of carrying like a briefcase. Though the grab handles are non-padded, our tester did not find that they dug into her hand when carrying heavier loads briefcase-style.
Additionally, there are six daisy chain loops on the Lander Timp's exterior to allow the cyclist the option of carrying additional items. Our tester used this for securely carrying her rain jacket when the pack was too full.


1) I've always liked cities. Wauwatosa, a close suburb of Milwaukee, Wisconsin is my lovely hometown and on one of the few days I *gasp* dared to skip out of high school, I took the bus to downtown Milwaukee. For excitement. And then there was always the ultimate thrill of going 90 miles south to Chicago! I couldn't wait for the first glimpse of that skyline. I still feel that way.
2) I've paid attention to buildings all my life but didn't know anything about architecture. As a child in 1960s Milwaukee, I remember being fascinated by (what I now know as) Modern architecture: the War Memorial at the lakefront, the County Zoo buildings, a few Catholic churches, the glassy Marine Bank downtown, Milwaukee's Public Museum. In the early 80s, I lived for a few years in Burlington, WI, a town filled with grand Victorian houses set on spacious, hilly lots. I walked my baby son and dog right past those beauties, admiring, but not knowing a darn thing about them.
3) Architecture finally, seriously, got my attention in the 1990s. I woke up to the fact that I really, really liked looking at and learning about great buildings. And I simply liked being near them.
4) Two developments at my parish church, St. Sebastian's in Milwaukee, gave me two great opportunities. The person who gave a "behind the scenes" tour of the church left and the person who handled the church archives could no longer handle them. I volunteered to do both. I began to give a historical-architecture tour of the richly designed 1920s church for adults and the school kids – while also explaining some of the mysteries involved. And I dug into the archives and created a slide show history talk that I presented often to many different groups. I am so honored that my work was useful in the recent 100th parish anniversary celebration.
5) Having gotten a taste of being a tour guide, I wanted more! I completed training to be a Historic Milwaukee tour guide and eventually led three different downtown walking tours. I had moved downtown, too, and was now a member of the Cathedral of St. John, which, happily, provided many regular tours of its sanctuary. I was soon one of their busiest tour guides, especially once the cathedral opened after a major (and a little controversial) remodeling.
6) Up to this point, being a tour guide was a volunteer labor of love. But I went on a choir tour to Europe (we sang at the Vatican, in Venice, in Vienna…) and it rocked my world. The musical experience was unforgettable, but crucially: I saw all of these European tour guides making a living. I was determined: I was going to do the same.
7) I completed my BA in Communication at Alverno College-Milwaukee and it was on to graduate school to pursue my goal: being a European tour guide for the PBS personality I so admired: Rick Steves. I decided to study Art History (European, of course) and Italian – and to take a Rick Steves tour. Things were going along fine: I was specializing in Bernini and studied in Italy for a semester and took that Rick Steves tour – of Florence and Siena – and the tour guide even let me lead a little bit of it! Toward the end of grad school, I was indeed hired by Rick Steves and worked one season in Europe as an assistant tour guide.
8) But a couple of things changed my course. I had been working at Ashland, The Henry Clay Estate in Lexington, KY and was absolutely devoted to it. Further, as a teaching assistant in grad school, I happened to catch another professor begin her class after ours… and I stopped and listened, transfixed. This phenomenal professor was Wallis Miller and the class was 20th Century Architecture. I immediately made arrangements to get into her class – and to make sure Architecture would still fit under the Art History program I was already deeply into.
9) I left Bernini and European tour guiding behind and instead focused on the history of Ashland as a public-private place (and fascinating architectural hybrid), took on museum studies and architectural history courses with Wallis Miller and was incredibly fortunate to have her as my mentor and thesis director. She challenged, encouraged, and inspired me. I am writing a book based on my thesis: Ashland, The Henry Clay Estate as House Museum: Private Home and Public Destination.
10) Through my thesis work and work at Ashland, I discovered my life's calling. When friends suggested I go on to get a PhD in Public History, I realized that I was already doing this work of interpreting history (and architecture!) for the public. Call it Public History, call it Architectural History – it's what I love and it's what I do.
* I am a docent at the Glessner House.
* I am a docent at Shoreline Sightseeing.
* I'm also a member of the Society of Architectural Historians and on the board of the Chicago Tour-guide Professionals Association.
* I maintain a blog about Ashland, The Henry Clay Estate: History of a House Museum.


Today on The Wired To Hunt Podcast we're sharing updates on the 2014 rut and then are joined by Jared Sheffler of Whitetail Adrenaline to discuss extreme rut hunting tactics. Enjoy!
– The fully detailed, first hand account of the crazy conclusion to my Ohio season and how I filled my first tag of the season!
– For more great rut hunting advice and insight, check out the Rules of the Rut 2.0. Included in this package are two information packed eBooks and a 3-part podcast series with nearly three hours of expert interviews. The contributors to these books and podcasts are some of the most accomplished whitetail hunters in the country and you'll likely recognize their names from Field & Stream, Outdoor Life, North American Whitetail, Deer & Deer Hunting and a number of acclaimed books.


Last week I listened to Tony Hsieh the CEO of the American on-line retailer, Zappos talking about happiness at work. Hsieh believes that great companies have a strong culture and a vision with a higher purpose than just the pursuit of profits. For Zappos that higher purpose was about creating happiness. By focusing on a happy workplace with happy personnel, he's convinced that Zappos achieves "by-products" such as productivity, employee retention, a strong customer service, and profits.
The core values really do focus on employee happiness and wellbeing – they concentrate on employees enjoying work and having fun, being adventurous & creative, passionate & determined, open & honest, embracing change & driving opportunities for personal & professional growth, and being friendly & a positive influence on others.
The core values are embedded in everything they do – they influence recruitment (they only hire people who fit well with the company culture and not just because they have a great CV) and they're written in to job descriptions (anyone found not to be in tune with them is managed out).
And if you're interested, our core values boil down to: accepting work we enjoy, we find absorbing and which allows us to apply our strengths; striving for excellence; keeping on learning; and working in harmony with others.


Aldea's Napa and Fairfield offices are closed through Friday 10/13 due to the fires and poor air quality in the area. Currently, Aldea's school-based therapists, alcohol and drug counselors, and therapists from our clinic have been have been redeployed to support mental health needs in our community. It is our plan to reopen all Napa and Solano county offices on Monday 10/16, assuming conditions improve.
Thanks to Napa Valley Community Foundation & Napa Valley Community Organizations Active in Disaster (COAD), Aldea is one of the local agencies trained in disaster response, and is coordinating with other agencies and Napa County to help plan crisis counseling, housing, children exposed to trauma assistance, and among other needs. Napa County Mental Health has requested Aldea's support in the shelters through Sunday and we are already supporting our community members in need.
Staff are in contact with their supervisors, and as of now, everyone on the Aldea team is still safe and no one has lost their home. We are grateful for this news and hope that you and your family members are safe as well.
We will be posting regular updates on our website and Facebook page. If you need to contact us, please message us via Facebook, as that is the easiest way that we can respond at this time. We know that our community will come together to support one another during this unprecedented disaster and come back stronger than ever. Thank you for being a part of the Aldea "little village."
American Canyon High School(3000 Newell Dr.) in American Canyon.
Boys & Girls Clubs of Napa Valley is providing childcare for children 6-18 regardless of membership status at its Napa (1515 Pueblo Ave.) and American Canyon (100 Benton Wy.) locations. Click here for more information.
V.O.I.C.E.S./LGBTQ Connection (780 Lincoln St.) and Innovations Community Center (3281 Solano Ave.) in Napa are open Thursday 10/12 from 9:00-3:00 for coffee and community. For more information, click here.
If you are able, please support our ongoing relief efforts in our community. Click here to make a donation to Aldea.