Firming Elixirs On-the-Go is rated 4.8 out of 5 by 16.
DHC CoQ10 Mini Set helps firm and plump for younger-looking skin. Includes convenient mini sizes of calming CoQ10 Lotion toner, lightweight CoQ10 Milk moisturizer, age-defying Olive Soap and silky, rich Q10 Cream intensive moisturizer.
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Rated 3 out of 5 by betsyross from meh I see no firming or plumping from this set used am & pm as directed. Glad I only got small sizes and did not invest in full sizes.
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Rated 5 out of 5 by Anonymous from One of the best skin care sets for skin! I just LOVE the COQ10 Mini Set! It goes along way too. It lasted me over a month using day & night 7 days a week. My skin was really slack but within about a week I visibly noticed a difference & so did my family and friends. When asked what I use of course I told them. I recommend DHC to everyone. Even strangers when they ask what I use. I always carry a catalog in car when going out so I have DHC's phone# and give the web site out also. I have gotten friends that order it and love sharing my samples with strangers to get them into DHC. Of course I have to keep some for me. I love the samples too! I had a nurse leave my doctors office an she was asking for advise me for advise on her skin care concerns, so I gave her an old catalog and marked the products that I already used an new would work for her. She could not wait to start DHC's products!
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text:
Primary sources for the Catonsville Nine action, trial, and participant profiles include interviews and materials provided by Baltimore activists and support group members Dean Pappas , Bill O'Connor, Herman Heyn, and David Eberhardt, as well as by Daniel Berrigan and filmmaker Lynn Sachs. Also helpful were Baltimore Sun and New York Times accounts, as well as documents from the Daniel and Philip Berrigan collection at Cornell University.
Anderson, Terry H. The Movement and the Sixties: Protest in America from Greensboro to Wounded Knee. New York : Oxford University Press, 1995.
Berrigan, Daniel. The Trial of the Catonsville Nine. Boston: Beacon
Press, 1970.
Byrne, Richard Jr. "Revolution 9," Baltimore City Paper 29 January 1993.
DeBenedetti, Charles with Charles Chatfield, An American Ordeal: The Antiwar Movement of the Vietnam Era. Syracuse, NY: Syracuse University Press, 1990.
Edmonds, Anthony O. The War in Vietnam. Westport , CT: Greenwood Press, 1998.
Fitzgerald , Frances. Fire in the Lake: The Vietnamese and the Americans in Vietnam. Boston : Little, Brown, 1972.
Garfinkle, Adam. Telltale Hearts: The Origins and Impact of the Vietnam Antiwar Movement. New York : St. Martin 's, 1995.
Gioglio, Gerald R. Days of Decision: An Oral History of Conscientious Objectors in the Military During the Vietnam War. Trenton : Broken Rifle Press, 1989.
Gitlin, Todd. The Whole World is Watching: Mass Media in the Making and Unmaking of the New Left. Berkeley : University of California Press, 1980.
Gray, Francine du Plessex. "Profiles: Acts of Witness." New Yorker 14 March, 1970.
Jeffreys-Jones, Rhodri. Peace Now! American Society and the Ending of the Vietnam War. New Haven : Yale Univ. Press. 1999.
Herring, George C. America's Longest War: The United States and Vietnam , 1950-1975. New York : Wiley, 1979.
Levy, David W. The Debate over Vietnam. Baltimore : The Johns Hopkins Univ. Press, 1991.
Meconis, Charles. With Clumsy Grace: The American Catholic Left, 1961-1975. New York : The Seabury Press, 1979.
Mollin, Marian. "Communities of Resistance: Women and the Catholic Left of the Late 1960's." Oral History 2004 Summer/Fall 31:2. 29-51.
McNeal, Patricia. Harder than War: Catholic Peacemaking in Twentieth-Century America. Brunswick NJ : Rutgers Univ. Press, 1992.
O'Gorman, Angie, ed., The Universe Bends Toward Justice: A Reader on Christian Nonviolence in the U.S. Philadelphia : New Society Publishers, 1990.
Sitkoff, Harvard. The Struggle for Black Equality: 1954-1980. New York: McGraw-Hill Ryerson Ltd., 1984.
Polnar, Murray Polnar and Jim O'Grady. Disarmed and Dangerous: The Radical Lives and Time of Daniel and Philip Berrigan. New York : Basic Books, 1997.

redpajama_set_name: RedPajamaCommonCrawl


Tea is thought to lower cholesterol by inhibiting the absorption of cholesterol from your large intestine. When it comes to lowering your cholesterol, there are two types of tea that are the most favored; green tea, which was found to help lower LDL and total serum cholesterol, and Chinese Pu-erh tea, which is renown for its ability to lower cholesterol. Most sources recommend drinking a five cups per day for the minimum results, but studies have shown that a higher consumption of tea each day will provide the largest reduction in cholesterol.
Research has shown that the combination of L-Theanine (a natural occurring amino acid) and caffeine found in tea, improves memory and reaction time as well as increases concentration and focus. White tea is thought to provide the highest level of L-Theanine for a sharp mind, focus and concentration.
Seasonal allergies can cause an array of uncomfortable symptoms, from itchy eyes to continuous sneezing. Although some allergies may require prescription medications, consuming certain teas can ease the symptoms of milder cases. Certain teas, like stinging nettle, contains powerful antioxidants, known as EGCG) that have been shown to lessen or eliminate some symptoms associated pollen allergies, such as congestion, sneezing and itchy eyes. Green tea contains quercetin (a natural flavonol) that has been shown to initiate a histamine response and theophylline, which relaxes the muscles that support your bronchial tubes, thus potentially reducing the severity of your asthma.
Research has shown that drinking tea can reduce the risk of cavities, bad breath and periodontal disease. Yes, it is true that tea can stain your teeth, but if you drink lighter teas, such as green tea, there is a less risk of staining. Drinking black tea, which contains polyphenols, reduces the formation of plaque and it kills the cavity-causing bacteria. Green tea also destroys the viruses and bacteria that may cause periodontal disease and it slows the growth of bacteria that often leads to bad breath.
Acne, which is one of the most common types of skin problems, occurs when the pores in your skin get clogged. Although drinking tea cannot cure acne, certain teas may help to reduce the amount of breakouts. Herbal teas, such as chamomile and red rooibos, contain natural anti-inflammatory benefits, which can help to reduce acne as well as dry skin. Spearmint tea is also beneficial for reducing inflammatory rosacea and preventing the growth of bacteria that causes acne.
Free radicals damage healthy cells in your body, which leads to skin aging, wrinkles, cancer, neurological degeneration and heart disease. Green tea contains a powerful antioxidant known as polyphenols and it has a high oxygen radical absorbency, which means it can destroy free radicals, so it helps protect your skin and promotes longevity.
Green tea contains polyphenols and flavenoids, which help boost your immune system, making your body stronger at fighting against infections. Properties found in black tea, alkylamine antigens, also help give your immune system a boost and it also contains tannins, which help fight viruses, such as the stomach flu, influenza and other common viruses.
Tea, especially green tea, can help reduce and possibly prevent the risk of rheumatoid arthritis. It contains active compounds that block harmful enzymes from destroying cartilage and it helps to lower the levels of inflammatory and inflammation reactions, which is connected to both rheumatoid arthritis and osteoarthritis. The inflammation associated with arthritis can cause water retention and puffy skin, but drinking a few cups of tea each day will help reduce water weight gain.
Both green tea and black tea contain L-theanine (an amino acid) that has been shown to help reduce stress and anxiety, so you can relax and concentrate better. Black tea has also been shown to reduce the levels of cortisol (stress hormone) when it is consumed in moderate amounts and on a regular basis. Chamomile tea is also excellent for reducing anxiety and promoting a restful sleep. The longer tea is brewed, the greater the amount of L-theanine will be in each cup.
If you want a healthier heart, studies have shown that drinking green tea lowers the inflammation levels, which may provide protection against cardiovascular disease. Green tea contains antioxidants known as catechins that not only help prevent against stroke and heart disease by lowering cholesterol levels, but even after a heart attack, it prevents further cell deaths and speeds up heart cell recovery. Green tea also helps to keep your blood pressure down by repressing angiotension (a peptide hormone), which may lead to high blood pressure.
Green tea, white tea and yerba mate tea all have various properties that can help with weight loss. Green tea contains a polyphenol antioxidant known as EGCG that helps to burn fat, naturally boosts your metabolism and stops the movement of glucose in fat cells. White tea also contains the polphenol antioxidant which prevents new fat cells from forming and yerba mate tea, contains plant compounds that slow digestion, so you feel full longer and eat less.
Teas made from ginger, chamomile and/or peppermint are known for their stomach-calming properties. Research has shown that ginger contains chemicals that work in your stomach and intestines to relieve nausea as well as calming the brain and nervous system. Peppermint has been used for ages to settle an upset stomach, but studies have also shown that peppermint tea may also help relieve the symptoms of irritable bowel syndrome (IBS), promote healthy digestion and alleviate gas. Chamomile tea is beneficial form relieving heartburn, flatulence, digestion and the muscle spasms that often accompany IBS.
Drinking tea may help to relieve the pain associated with tension headaches and migraines. Ginger tea may also help reduce the vomiting and nausea that often accompanies a migraine. Chamomile tea has a variety of compounds that can relax you and ease the pain. Feverfew tea contains compounds that provide a sedative and anti-inflammatory effect, which can help reduce the symptoms associated with both tension headaches and migraines. Tip: feverfew tea is slightly bitter, but adding a bit of cream and sugar to it will improve the taste.
If you have trouble sleeping at night, chances are there is a tea that can help. Valerian root tea is excellent for insomnia. It contains natural compounds that work like a sedative, but it is safe than over-the-counter sleep aids, but it is important to talk with your doctor before using valerian root tea, because with prolonged use it may lead to dependency and it may interfere with various prescription medications. Chamomile is also an excellent tea for inducing sleep. It also helps to reduce anxiety, so you can have a restful sleep.
Research has shown that drinking green tea may prevent sharp increases in blood sugar levels and balances lipid and glucose metabolism rates. Green, black and white tea contain polyphenols and polysaccharides that may help to lower your blood sugar level and adding a touch of cinnamon to your tea will increase the effects of the tea.
Teas, including black tea, white tea, green tea and red Rooibos tea contain various cancer-fighting compounds. Both black and green tea may help counteract the negative effects of smoking and may even reduce the risk of lung cancer. These teas contain powerful antioxidants that may help protect against a variety of cancers, including skin cancer, stomach cancer, colon cancer, pancreas cancer, and ovarian cancer. Keep in mind that tea is not a miracle cure, however, studies have shown the antioxidant properties found in tea, such as catechins and polyphenol, are extremely beneficial at preventing the cell damage that is linked to cancer. White tea that is uncured and unfermented has been shown to have the most potent anticancer properties when compared to processed teas.


Every once in a while, you take one for the Hometeam. Recently, Now This visited with me at home and at work to hear my story about working at AT&T as a Black woman. What they didn't capture were my words on extraordinary mentor and sponsorship that I found at the company, and how I wield my black girl magic everyday. But 60 seconds on an IG video doesn't always capture everything. What they did capture was good though. Even Joni makes a cameo.


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When the soldiers came back from the Vietnam war, they were jeered and scoffed at. People threw things at them, screamed at them, scorned and shunned them. It was not a matter of pride to be a veteran—it was a badge of shame. Some of these young men had given their lives so the people who were jeering at them could have peace.


text:
Anahi guaraniticus är en skalbaggsart som beskrevs av Martinez 1958. Anahi guaraniticus ingår i släktet Anahi och familjen Melolonthidae. Inga underarter finns listade i Catalogue of Life.

Källor

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redpajama_set_name: RedPajamaWikipedia


Online brand community refers to a specialized, non-geographically bound, online community, based on social communications and relationships among brand's consumers. Consciousness of kind, rituals and traditions, and moral responsibility are the three markers of brand community. The present study tested the effects of online brand community by modeling the three community markers as formative indicators of online brand community. The effects of online brand community on four value creation practices, social networking, impression management, community engagement, and brand use, and the mediating effects of community loyalty were tested. Participants (N=370) of this study were members of online brand communities. A survey-based empirical study was conducted using Amazon Mechanical Turk. Data were analyzed through structural equation modeling. Test results showed that,among the three brand community markers,consciousness of kind and rituals and traditions are formative indicators of online brand community while moral responsibility is not. The effects of online brand community on all four value creation practices were revealed.
Test results showed that online brand community has the strongest effects on social networking, moderate effects on community engagement and brand use, and the weakest effects on impression management practices. Community loyalty was found to fully mediate the relationship between online brand community and value creation practices. Implications for theory, practice, and future research are discussed.
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Trends come and go, but the words "simple" and "clean" continue to be uttered by designers. These concepts surpass trends and that's because design, unlike fine art, is all about communication. If the message is unclear, the most beautiful of designs is a failure.
And that's where people get into trouble. They're very aware of the need to communicate. And there's a lot they want to say. But with each line that gets added to copy, with each element that is placed onto a logo, and with each burst that gets wrenched into an ad, the message is muddied. The desire to communicate many things is understandable. There's nothing wrong with that. But it's not realistic. The reality is that the more messages you add to a design, the less likely the viewer will invest time into comprehending it.
And that's the crux of the whole "less is more" concept. Let's be honest. When someone takes the time to read ten bullet points included in a cluttered and busy magazine ad, they will understand more of that company's message than if they had read a single compelling and clear title. But when was the last time you read all the copy in a copy-heavy ad? Unless it's a for a product you're already interested in, never! But you will take the time to read a one-sentence message that is presented in a compelling, clean way.
The other aspect of this is retention. Again, the person who reads all ten bullet points gains a lot of information. But one month later, ask that person to tell you everything they learned from the ad. Would they remember more than the person who read a single message within the context of a memorable design? Would they even remember the ad at all?
A clean and simple message will be noticed and digested more times than one that is busy and cluttered.
More people will retain your message long-term when you don't overwhelm them.
And so you find a balance. I'm not saying there needs to be a single message, although many times that's exactly what you should shoot for. It depends on what you're creating. Is it a logo? Go for a single message. Is it a full-page ad? You have space for multiple messages. However you still need to decide what the single most important message of the bunch is, and then make sure that message cannot be missed.
Consider someone who lectures for one hour. If they drone on with point after point, few of those points will be retained long term. If, however, the lecturer begins by announcing his main points and keeps returning to those points, backing them up throughout his time in front of the microphone, he will have a good chance of successful communication.
The converse is not necessarily true. We can't assume you'll be heard more if you say less. To a degree this is true—if the speaker/communicator already has a good reputation. If you represent a respected brand, your focused communication will be noticed even without any visual fanfare. When I'm in a group setting with a quiet person I admire, I listen very closely when that person does speak.
But even when there's no reputation to speak of, if the message is told in an impactful way, it will be noticed and retained. Impact does not require large type or bold type. This is where creativity comes in using the various elements of design. But no matter which elements you utilize, the goal of creating an impactful design is very likely to be met when the end product is both simple and clean.


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VCSA SSO and vCAC: Expecting but not delivering.
November 5, 2014 by Kalen·Comments Off on VCSA SSO and vCAC: Expecting but not delivering.
This problem pissed me off for a while last night when I was trying to rebuld my lab after being on the road for a while.
I almost always deploy a VCSA and configure it with defaults on the wizard and then customize the IP, Hostname, Time, etc later. This finally came back to bite me and it was only with vCAC.
I know to regenerate the certificates after setting the IP and hostname but I didn't realize that there are entries on the VCSA that don't update.
When trying to join the SSO instance on the appliance from the vCAC appliance I was greeted with the following extremely generic message.
Welp….I started going through the vCAC logs but I wasn't able to find anything due to it not being really configured.
That appears to be the original IP and not the 192.168.1.220 that I have in DNS. After verifying that the IP was correct and that forward and reverse lookups were working…I had to start looking at the VCSA itself.
And when you cat out these files you find the old entries.
You need to stop vpxd and idmd because it will just keep re-writing this file over and over which I didn't notice for a good 2 minutes.
Looks like the updates took place!
Huzzah! I can't wait for something else to break.


text:
Q: rewrite .htaccess https all but one folder I am looking for a way to add https to all my website but one folder and all its files
the folder is sitename.com/shop
when i put on htpps for all folder the shop don't work right
is there a way i can do this in .htaccess i am using
open-eshop.com product i try with the full https for the entire site  but it don't seem to work with https
Thank you
i did this but still didn't work
RewriteEngine On
RewriteCond %{HTTPS} !on
RewriteCond %{REQUEST_URI} ^(login|/support|/community/)
RewriteRule ^(.*)$ https://mydomain.com/$1 [R,L]

A: If you have access to apache files (usually httpd.conf, but some times virtualhost settings by domains can be in separate files). In this case you can allow using .htaccess for ssl (AllowOverride All), or what is better - add rules directly into virtualhost settings.
Based on what you told,  i think you already have some rules for redirect index.php in you apache configs.

redpajama_set_name: RedPajamaStackExchange


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text:
Q: Why is the conditional subschema (JSON Schema Draft7) validation not working? Ref: https://json-schema.org/understanding-json-schema/reference/conditionals.html
- Schema snippet
"properties": {
    "country": {
      "default": "United States of America",
      "enum": [
        "United States of America",
        "Canada"
      ]
    },
    "address": {
      "properties": {
        "AddressLine1": {
          "type": "string"
        },
        "if": {
          "properties": {
            "country": {
              "const": "United States of America"
            }
          }
        },
        "then": {
          "postal_code": {
            "type": "integer"
          }
        },
        "else": {
          "postal_code": {
            "type": "string"
          }
        }
      },
      "required": [
        "AddressLine1",
        "postal_code"
      ]

Sample Data 1 - Expected Pass
{
"address": {
"AddressLine1": "Address Line 1 in US",
"postal_code": 11779
}
}
Sample Data 2  - Expected Fail
{
"address": {
"AddressLine1": "Address Line 1 in Canada",
"postal_code": "RA90 188"
}
}
I expected the 2nd sample to fail but it passes without any issues.  I am using PowerShell Test-JSON for validation

A: You forgot to wrap postal_code in "properties": { ... } in your then and else clauses.


text:
Q: Eclipse Lightning Bundle Files Open in Same Tab The fact that I can't find others with this issue makes me think I'm missing something simple.
In Eclipse when I have a lightning component bundle (component, controller.js, helper.js, renderer.js) and open one of these files is opens in a new tab within Eclipse.

Then when I go to open the Controller.js (or any other bundle) file, it opens in the exact same tab...not in a new tab.

Any thoughts as to how to remedy this?
Thanks
Jason

A: This is how the Lightning support is intended to work in Eclipse. If you look at the bottom of the editor, you'll see a set of tabs for the various files that make up your component, and you can switch between those files at any time by choosing a new tab. This is similar to how the XML editor works in Eclipse, where you can change between a "source" view and an "xml editor" view.


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text:
Large scale bias and stochasticity of halos and dark matter
Uroš Seljak 1 & Michael S. Warren 2
1 Department of Physics, Jadwin Hall
Princeton University, Princeton, NJ 08544
2 Theoretical Astrophysics (T-6), Mail Stop B227, Los Alamos National Laboratory, Los Alamos, NM 87545
On large scales galaxies and their halos are usually assumed to trace the dark matter with a constant bias and dark matter is assumed to trace the linear density field. We test these assumption using several large N-body simulations with 3843−10243 particles and box sizes of 96−1152h−1Mpc, which can both resolve the small galactic size halos and sample the large scale fluctuations. We explore the average halo bias relation as a function of halo mass and show that existing fitting formulae overestimate the halo bias by up to 20% in the regime just below the nonlinear mass. We propose a new expression that fits our simulations well. We find that the halo bias is nearly constant, b∼0.65−0.7, for masses below one tenth of the nonlinear mass. We explore next the relation between the initial and final dark matter in individual Fourier modes and show that there are significant fluctuations in their ratio, ranging from 10% rms at k∼0.03h/Mpc to 50% rms at k∼0.1h/Mpc. We argue that these large fluctuations are caused by perturbative effects beyond the linear theory, which are dominated by long wavelength modes with large random fluctuations. Similar or larger fluctuations exist between halos and dark matter and between halos of different mass. These fluctuations must be included in attempts to determine the relative bias of two populations from their maps, which would otherwise be immune to sampling variance.
Determination of the power spectrum of mass fluctuations and its redshift evolution is one of the main goals of modern observational cosmology. Its accurate measurement would allow us to test some of the most fundamental questions in cosmology today, such as the shape of primordial power spectrum and its relation to fundamental theories of structure formation, the mass of neutrino and the nature of dark energy.
In general there are two approaches to the measurement of the matter power spectrum. One is to measure galaxies, either in redshift space or in angular position (perhaps supplemented by photometric redshift information) and to assume they trace the dark matter. This assumption is believed to be valid on large scales, where the so called linear bias model assumes that the galaxy density field is proportional to the matter density field times a free parameter called bias. While power spectrum measurements of galaxies with modern surveys such as SDSS (Tegmark et al., 2003) or 2dF (Percival et al., 2002) have enormous statistical power, they can only determine the shape of the matter power spectrum and not its amplitude because of the bias uncertainties. This limits their use in the study of the growth factor evolution, important for investigations of dark energy models. In addition, on small scales information from galaxy clustering is limited by the uncertainties in the relation between the galaxies and the dark matter, which make the bias scale dependent. For this reason the small scale information is usually discarded.
The situation with galaxies would not be as dimm if we could determine the bias. Here we explore a method to determine galaxy bias based on determination of clustering amplitude of faint galaxies. These are likely to occupy low mass halos which, as we show in this paper, have a well determined large scale bias that is nearly independent of halo mass. While some fraction of these galaxies are satellites in larger halos, this can be quantified and corrected for (see 2004astro.ph..6594S for a first application of this method to the real data). In addition, there exist populations, such as IRAS galaxies, for which this fraction may be small. In these cases measuring the large scale power spectrum amplitude for these galaxies determines the matter power spectrum amplitude as well.
The main problem with using faint galaxies as tracers of large scale structure is that in a typical flux limited survey faint galaxies occupy a small nearby volume, so the sampling variance errors for power spectrum on large scales are large. However, we can still determine their bias relative to a population of brighter galaxies. If there is no stochasticity between the two populations then a direct comparison of the maps gives an accurate determination of the relative bias with no sampling variance. We can then use the power spectrum determination of the brighter population, with smaller sampling variance errors because of larger volume covered, to determine the power spectrum of the fainter population and of the matter itself. The limiting source of noise is the stochasticity between these fields, which we explore in this paper.
Another approach to determine matter fluctuations is to use weak lensing induced correlations between background galaxy ellipticities. These are sensitive to the dark matter fluctuations directly and as such this approach holds the promise to improve upon the limitations of the galaxy clustering methods. Its main limitation is that it traces the dark matter in angular projection and has large sampling variance errors on large scales. This limits the statistical power of the weak lensing surveys. On small scales the nonlinear corrections, noise, intrinsic correlations and other systematic contaminations become significant, all of which may complicate the modelling.
A possible approach to achieve the best of both worlds is to combine the weak lensing and galaxy clustering surveys: one can use the weak lensing to determine the galaxy bias and then use the 3-d galaxy clustering information to improve on the statistical errors. One way to do this is to use galaxy-dark matter cross-correlation analysis from weak lensing and combine it with the galaxy auto-correlation analysis. If galaxies are tracing perfectly the dark matter then it suffices to have a few well measured modes in both fields to determine the galaxy bias. This has been proposed as a way to get around the sampling variance in weak lensing surveys (Pen, 2004) . In the absence of stochasticity it gives the bias (and so the dark matter power spectrum) without the usual sampling variance errors, assuming the analysis is done on the same patch of the sky and with the correct radial weighting of the galaxies to match that of the dark matter.
In both of these cases the underlying assumption is that there is no stochasticity between these fields on large scales. While there have been analytic attempts to address this assumption (Matsubara, 1999) , it has not been tested well with simulations in the past due to the lack of sufficient dynamic range (but see 2002MNRAS.333..730C for a related study). One must resolve the halos small enough to be suitable as galaxy hosts (with typical masses at or below 1012h−1M\sun). At the same time, the simulations must be large enough so that many long wavelength modes are sampled to determine the statistics of interest. We achieve this by using a set of new simulations with a larger dynamical range. The number of particles in these simulations, 108−109, and their box size, 100-1000h−1Mpc, allow a much better exploration of the halo bias and stochasticity on scales larger than available before.
In addition to exploring the relation between halos and matter we can also investigate the relation between the initial and final matter distribution. Weak lensing measures the nonlinear matter field, while for the study of linear growth factor one would like to know the relation between galaxies and linear matter field instead. We explore the relation between the final and initial dark matter density field on large scales, where this relation is believed to be perfect. This case is amenable to perturbation theory analysis and as such allows one to interpret and verify the numerical simulation results.
Finally, we revisit the question of halo bias as a function of halo mass with the new simulations. This has been addressed by previous generation of simulations using 2563 particles and box sizes of order (100-140)h−1Mpc (Jing, 1998; Sheth & Tormen, 1999; Sheth et al., 2001) . This, as noted by the authors themselves, is barely adequate for this purpose because of large shot noise at high halo masses and insufficient number of large scale modes where linear evolution is valid. The goal of these papers was to provide expressions which fit over a range of power law simulations and were not specifically optimized for realistic ΛCDM models. They provide expressions that fit the simulations available at the time to a reasonable accuracy, but which can be systematically wrong by as much as 10-20%. To put things into a current context, the statistical error on the amplitude of galaxy clustering from SDSS using k<0.2h/Mpc modes is 1% (Tegmark et al., 2003) , so a perfect bias determination, for example using the faint galaxies as described above, would allow us to reach this accuracy on the matter power spectrum. In this era 20% accuracy no longer suffices and the goal of the present paper is to provide more accurate expressions for halo bias as a function of mass and cosmological model.
2 Simulations
The N-body code we use in this paper is the Hashed Oct-tree code (HOT), a parallel, tree based, code (Warren & Salmon, 1993) . This code was compared to a variety of other simulation codes in Frenk et al. (1999) , and further validation studies will be presented elsewhere. For this paper we performed several simulations with this code. The smallest was a 96h−1Mpc box size, 5123 particle run (HOT1). This simulation has a particle mass of 5.5×108h−1M\sun and is useful for probing the halo bias at the low mass end, below 1011h−1M\sun. It suffers from the small box size which makes the investigations on linear scales difficult and makes the shot noise fluctuations for higher mass halos (with lower halo numbers) very large. Next up in size is a simulation with 288h−1Mpc box and 7683 particles (HOT2). This is the main simulation that we use in this paper, as it has an optimal combination of box size and particle mass for our purposes. It samples the Fourier modes down to k∼0.02h/Mpc and has many modes at k∼0.1h/Mpc, where the power spectrum is still close to linear. This is also the typical scale probed by the current surveys such as SDSS and 2dF. The particle mass for this simulation is 4.4×109h−1M\sun and can resolve halos down to a few times 1011h−1M\sun, which is sufficient for typical galaxies in a flux limited sample. Finally, for determination of the halo bias at the high mass end we use a simulation with 1152h−1Mpc box size and 7683 particles (HOT3). This simulation has large enough box to sample long wavelength modes well, but its particle mass of 2.8×1011h−1M\sun does not allow us to resolve galactic size halos and we limit its use to group and cluster size halos.
The tree-code accuracy was controlled using the absolute error criterion described in Salmon & Warren (1994) , which ranged from 10−5Mtot/R20 per interaction at the start of the each simulation, to 10−3Mtot/R20 at the end. Plummer smoothing was used, with softening lengths of 7, 20 and 95 comoving kpc for models 1, 2 and 3 respectively. The number of timesteps for model 1 was 1475, 736 for model 2, and 725 for model 3. Model 1 started at a redshift of 50, model 2 at 44, and model 3 at 27. All particle masses were identical, with the initial particle displacements imposed on a cubical lattice.
All of these simulations are normalized to σ8=0.9 and have Ωm=0.3, Ωb=0.04 and Hubble parameter h=0.7. They use realistic transfer functions from CMBFAST (Seljak & Zaldarriaga, 1996) . σ8=0.9 corresponds to δζ=4.624×10−5 normalization in CMBFAST. In the following all the results are for HOT2 whenever not explicitly specified otherwise.
For the purpose of studying bias as a function of halo mass we also ran a suite of simulations varying one parameter at a time. The box size for these simulations is 192h−1Mpc with 5123 particles. Their force and mass resolution is the same as for HOT2. We ran the basic simulation with the same parameters as for HOT1-3, as well as Ωm=0.2, σ8=0.8, n=0.9, dn/dlnk=−0.04 and h=0.6, 6 simulations in total. We used the same seed in random generator for all of these cases to minimize the sampling errors. To investigate the bias at low mass end we supplemented these simulations with another run with 5123 particles in 96h−1Mpc.
Finally, to investigate the bias at the high mass end we used additional simulations with 3843 particles in 768h−1Mpc box, with standard parameters and σ8=0.90, σ8=0.775 and σ8=1.046. We also used another simulation with 700h−1Mpc and 5123 for which σ8=0.767, Ωm=0.27 and h=0.71. While this paper was undergoing the refereeing process we finished another very large simulation with 768h−1Mpc box and 10243 particles, again with the standard parameters. We use this simulation to verify the results obtained with other simulations, finding a good agreement among them.
3 Stochasticity of dark matter
We begin by exploring the relation between the final dark matter density field and the initial density field, rescaled to z=0 using the linear growth factor. We Fourier transform both fields and denote individual modes with δi(k) and δf(k) (we treat real and imaginary components as separate modes). Figure 1 shows the ratio b(k)=δf(k)/δi(k) for k<0.1h/Mpc. This is the scale at which one often assumes linear theory to be valid. We see that there are significant fluctuations between the initial and final field, suggesting that there are large corrections to the linear evolution even for k<0.1h/Mpc.
Figure 1: Ratio of final to initial density perturbations as a function of wavemode amplitude k. There is a large scatter between the two quantities even on large scales, where linear theory is usually assumed to be valid.
We can define the ratio of the power spectra as
⟨b2(k)⟩≡Pf(k)Pi(k)=⟨δ2f(k)⟩⟨δ2i(k)⟩, (1)
where ⟨⟩ denotes ensemble average over different realizations of the universe. We can define relative rms fluctuations in b as
(σbb)2=⟨(δf−⟨b2⟩1/2δi)2⟩⟨δ2f⟩. (2)
This is related to the cross-correlation coefficient r, defined as
r(k)=⟨δi(k)δf(k)⟩√⟨δi(k)δi(k)⟩⟨δf(k)δf(k)⟩,. (3)
The two are related via
σbb=√2(1−r). (4)
Figure 2 shows σb/b as a function of wavemode k, where the average has been done over a large number of wavemodes so that r converges (at very low k this condition is not satisfied and r is biased high, which underestimates the rms fluctuations). We see that σb/b changes from 10% at k∼0.02h/Mpc to 40% at 0.1h/Mpc, above which it rapidly increases and the two fields become incoherent. Figure 2 also shows the ratio of nonlinear to linear power spectrum at z=0, ⟨b2(k)⟩. For k>0.15h/Mpc the final power spectrum rapidly grows with k and exceeds the linear power spectrum, while for k<0.15h/Mpc the final spectrum is slightly anti-biased on large scales, ie Pf(k)<Pi(k). We note that this effect is larger when small boxes are used. We discuss this further below.
Figure 2: Relative rms fluctuation σb/b=[2(1−r)]1/2 between the final and the initial density field for HOT3 (solid). Also shown is the power spectrum ratio between the two fields (dashed). Results for HOT2 are similar, but show a somewhat larger supression of nonlinear versus linear power spectrum for k<0.1h/Mpc.
The main result arising from figures 1-2 is that the fluctuations between the linear and nonlinear fields are large on large scales, despite the fact that the nonlinear power spectrum is very close to the linear one. This is not so evident from the cross-correlation coefficient r itself, which can be close to 1 and still lead to large rms fluctuations: even for r=0.995 the rms fluctuations between initial and final field are 10% for any given mode.
One can get some understanding of these results by using second order perturbation theory results (see Bernardeau et al., 2002, for a recent review) . To compute the power spectrum to second order one must derive the density field to 3rd order, δ=δ1+δ2+δ3. Second order contributions to the power spectrum arise both from δ2δ2 and δ1δ3 terms. The δ2δ2 is the mode-mode coupling term, while the δ1δ3 is the nonlinear growth evolution term. These terms have different behaviour in various limits and have differing signs in the contribution: while δ2δ2 is strictly positive, δ1δ3 has a negative component. For k<0.15h/Mpc the negative contribution wins and the second order correction to the power spectrum is negative. At its peak around k∼0.1h/Mpc the correction is 5% and slowly decreases towards k→0. While the correction is small on average, this is a result of a cancellation between positive and negative contributions. The dominant perturbative corrections come from the mode-mode couplings at wavelengths close to the wavelength of the mode itself: for k=0.1/Mpc the dominant contribution to the positive component is from the modes around k∼0.05/Mpc, which contribute around a third of the total correction or around 5% of the final power spectrum (Jain & Bertschinger, 1994) . These are long wavelength modes and in any finite volume there will be large statistical fluctuations in their power relative to the true power. This leads to significant fluctuations in the second order corrections depending on the actual realization of the mode amplitudes. Thus the final amplitudes of individual modes fluctuate significantly relative to their initial values because of perturbative large scale effects.
A similar effect is observed in the average nonlinear power spectrum, which is suppressed relative to linear one. The suppression of nonlinear power for k<0.15h/Mpc relative to linear one is dominated by long wavelength mode-mode coupling, so to get to a percent precision in simulations one needs very large simulation boxes. We find that the difference between HOT2 (320h−1Mpc) and HOT3 (1152h−1Mpc) is 5% in power at k=0.1h−1Mpc, with larger HOT3 simulation being closer to the linear power spectrum than HOT2. Thus while these mode-mode induced fluctuations are small compared to sampling variance for individual modes, they are not small when one averages over many modes and may dominate the accuracy of amplitude determination on large scales.
4 Stochasticity of halos
Galaxies are believed to form inside dark matter halos, which are virialized structures of high density. They can be labelled by their virial mass. Observations suggest that about 80% of the galaxies in a typical flux limited survey form at the centers of halos with masses ranging between 1011h−1M\sun to 1013h−1M\sun, while the remaining 20% of galaxies are non-central and occupy groups and clusters (Guzik & Seljak, 2002; 2004astro.ph..6594S) . The exact radial distribution of galaxies inside halos and the form of the halo mass probability distribution is the subject of a lot of current observational and theoretical effort. Here we will use centers of dark matter halos as a proxy for galaxy positions. This will not give the correct correlation properties on small scales, where correlations between central and noncentral galaxies within the halos are important, but should be valid on large scales, where halos can be thought of as pointlike. We will show the results for a range of halo masses, which can be roughly thought as corresponding to galaxies with different luminosities since there is a tight relation between the halo mass and luminosity (McKay et al., 2001; Guzik & Seljak, 2002; 2004astro.ph..6594S) . Alternatively, the different samples can be thought of as varying the flux limit of a survey, since going to fainter limits increases the number density of galaxies and thus reduces the shot noise and the same effect is achieved by going to fainter galaxies.
Dark matter halos are identified from the simulations using the standard friends of friends algorithm with a linking length of 0.2. The resulting mass functions agree well with the fitting formulae in the literature (Sheth & Tormen, 1999; Jenkins et al., 2001) . We order them by mass and use subsamples separated roughly by a factor of 2 in mass. As in previous section we can define the halo fluctuation δh(k) and bias b(k)=δh(k)/δm(k), as well as the cross-correlation coefficient between the two fields (equation 3). Figure 3 shows the relative rms fluctuations σb/b as a function of scale for several halo masses, relative to both the initial and the final density field. We show the case with and without the subtraction of shot noise contribution to the halo power spectrum (the dark matter power spectrum does not require shot noise subtraction because of large number of dark matter particles). The lines without shot noise subtraction are always above the ones with subtraction and are the relevant ones if one is interested in the stochasticity between the halos and dark matter. The lower lines for which the shot noise has been subtracted show the remaining stochasticity which is not due to the shot noise. Because of the shot noise subtraction the cross-correlation coefficient can exceed 1, in which case we do not show the result.
Figure 3: Relative rms fluctuation σb/b=√2(1−r) between the halo density field and the initial (solid) or final (dashed) matter density field. Lower curves have been obtained by applying the shot noise subtraction from the halo power spectrum. Average masses are 4.5×1011h−1M\sun (a), 1012h−1M\sun (b), 2×1012h−1M\sun (c) and 1013h−1M\sun (d). The corresponding halo densities are 7×10−3h3/Mpc3, 2.7×10−3h3/Mpc3, 1.5×10−3h3/Mpc3 and 3.5×10−4h3/Mpc3.
From figure 3 we see that the halos are even less well correlated to the initial density field than the dark matter is. The stochasticity begins at the level of 20% at low k, increasing to 50% at k∼0.1h/Mpc. The shot noise contribution to stochasticity is small for halos with high spatial density (low mass halos), but increases significantly for halos with low spatial density, as expected. There is no obvious difference in the shot noise subtracted values, suggesting that the shot noise simply adds an additional component of stochasticity on top of that induced by nonlinearities in the relation between the halos and the initial density field.
Correlation coefficient between the halos and the final density field is also shown in figure 3 (dashed lines). Compared to the halo-initial field correlations the stochasticity is similar on the largest scales, but there is a better agreement between the halo and the final dark matter field on smaller scales (k>0.1h/Mpc). The cross-correlation coefficient r would likely be even larger on small scales if we had modelled the galaxy distributions within the halos more realistically, since this would lead to an enhancement of correlations on small scales, similar to that seen in the dark matter. Results from GIF simulations and analytic results using halo models suggest that the cross-correlation coefficient can remain close to unity up to a fairly high k∼1h/Mpc (Seljak, 2000) , but this may not be generic and depends on the details of how galaxies are populated within the halos, which are quite uncertain. Observational evidence suggests that there is some stochasticity on 1Mpc scale, with r∼0.5 (Hoekstra et al., 2002) . If r<1 it would complicate the interpretation of the results based on the comparison between galaxy-galaxy correlations and galaxy-dark matter correlations, such as those from the galaxy-galaxy lensing analysis (Sheldon et al., 2003) . Here we are more concerned with the correlations on large scales, k<0.1h/Mpc, where the details of galaxy distribution within halos are not important and where direct observations are not yet available. The results suggest that the fluctuations between halos and initial or final matter field are never below 10-20%.
The dark matter distribution cannot be directly observed, so results shown in figure 3 are not directly applicable to any observational test. The closest example to a direct observation of the dark matter is through the weak lensing effect. Here the light from distant galactic sources is being distorted by the mass distribution along the line of sight. By averaging over the image distortions we can reconstruct the 2-d shear and convergence maps. These are given by the line of sight projection of the matter density, weighted by a radial function that is very broad. Correlations at a given angular scale receive dominant contributions from a transverse distance at half the distance to the source, but significant contributions are also coming from much smaller transverse separations produced by the mass distribution closer to the observer.
It has been suggested by Pen (2004) that if one cross-correlates the properly radially weighted galaxy field with the weak lensing maps then one determines the bias of galaxies exactly if the two are perfectly correlated. Under these assumptions one can use the galaxy clustering information to determine the amplitude of dark matter fluctuations with higher accuracy than from the weak lensing itself, because the galaxy clustering can be done in 3-d (if redshifts are measured) and so one has more independent modes to reduce the sampling variance compared to the 2-d analysis. For this method to work the correlation between the projected matter density and galaxy field must be close to perfect.
To address this assumption one must correlate 2-d projections of final dark matter and galaxies. While properly projected weak lensing 2-d maps have been constructed from N-body simulations (Jain et al., 2000; White & Hu, 2000) , we take a simplifying approach here and cross-correlate the 2-d projections of the simulations along each of the 3 axes. The resulting rms scatter as a function of projected wavevector k is found to be significantly larger than in 3-d case, a consequence of the projection effects, which cause shorter wavelength modes to contribute to longer wavelength modes in projection. For 1012h−1M\sun halos, which corresponds roughly to L∗ galaxies, we find that the rms scatter is 20% at k∼0.03h/Mpc and 40% at k∼0.1h/Mpc. This is reduced by a factor of 2 if 1011h−1M\sun galaxies are used instead. This last example is shown in figure 4. In reality the stochasticity is likely to be larger for the lensing case, since projections at a fixed angle (rather than at a fixed transverse separation as done here) receive contributions from nearby small scale structures, for which the stochasticity between the galaxies and the dark matter will be much larger.
Figure 4: Ratio of 2-d projected halo density perturbations (M=1011h−1M\sun) to the initial density field as a function of wavemode amplitude k. The projections are along each of the three axes (288h−1Mpc for HOT2 simulation used here). The scatter is larger than the corresponding 3-d case in figure 3.
We can estimate the effect of this scatter on the amplitude determination from the weak lensing cross-correlation analysis. If the lensing kernel peaks at z∼0.3−0.4 then k∼0.1h/Mpc corresponds to l∼100. In a 200 square degree survey such as the upcoming CFHT Legacy Survey (Van Waerbeke & Mellier, 2003) we will have about 5 independent modes at l∼30 and 50 at l∼100. This means that for galaxies in 1012h−1M\sun halos the overall linear amplitude will have an error of 20%/√5∼ 9% at l∼30 and 6% at l∼100, arising just from this effect (the power spectrum amplitude error will be twice as large). Additional errors of comparable magnitude will arise from the lensing noise and projection effects. Such a poor determination of the growth factor as a function of redshift is unlikely to improve our current constraints on the dark energy significantly. This source of error was not included in the previous analysis (Pen, 2004) and is much larger than the prognosticed errors without it. This complicates the prospects of this method for studies of dark energy through the growth factor evolution. The errors can be reduced with a larger survey area: for a survey covering 25% of full sky the errors on the power spectrum amplitude may approach 1% because more modes are being sampled and because the largest modes have the smallest amount of stochasticity. It remains to be seen whether this is ever competitive with a straight weak lensing analysis on smaller scales.
Figure 5: Ratio of halo density perturbations δh1/δh2 as a function of wavemode amplitude k. The halos are of mass 1011h−1M\sun (h1) and 1012h−1M\sun (h2).
As discussed in the introduction another method to determine the bias is to combine the clustering analysis of faint galaxies, for which we know the theoretical bias, with the luminous galaxies, for which we can measure the clustering on large scales with a small statistical error. To determine the relative bias between the populations we can simply compare the smoothed maps. In the absence of stochasticity between the two galaxy populations one could determine the amplitude of mass fluctuations directly. Suppose that we want to determine the clustering amplitude of faint galaxies, which are in low mass halos (around 1011h−1M\sun for galaxies 2-3 magnitudes below L∗) and L∗ galaxies, which typically occupy 1012h−1M\sun. Figure 5 shows the relative rms fluctuations in ratios of Fourier mode amplitudes between halos of mass 1011h−1M\sun and 1012h−1M\sun. We see again from figure 5 that the scatter is large. Both shot noise and stochasticity due to nonlinearities limit this method. The rms fluctuations between 1011h−1M\sun and 1012h−1M\sun halos are 8% at k∼0.1h/Mpc and 23% at k∼0.2h/Mpc. This is somewhat smaller than between the halos and the matter. Moreover, galaxies in redshift surveys provide 3-d information, so there are more large scale modes to reduce the scatter. Nevertheless, any attempt to determine the linear bias using the cross-correlations must include this source of stochasticity in the analysis.
5 Halo bias as a function of mass
One of the important questions that can be addressed with these simulations is the relation between halo and dark matter power spectrum as a function of halo mass. This relation has been theoretically predicted from the spherical collapse model (Cole & Kaiser, 1989; Mo & White, 1996) and from the ellipsoidal collapse model (Sheth et al., 2001) , which suggest that the halo bias is related to a derivative of the halo mass function. The relation has also been extracted from the numerical simulations, with a good quantitative agreement between the theoretical predictions and the simulations over a range of different cosmological models (Jing, 1998; Sheth & Tormen, 1999) . Since these comparisons were done for a wide range of initial power spectra they were not specifically designed for realistic ΛCDM models and the predictions and simulations could differ by up to 20%. Moreover, the simulations used in previous work were based on 2563 particle simulations and did not have sufficient dynamic range to sample the long wavelength modes and resolve small halos at the same time. For the more massive halos shot noise is large, so the bias estimate is noisy. For halos close to the resolution threshold (typically of the order of 50-100 particles) some fraction of the halos may be missed by the halo finder, leading to biased results in the bias determination as the low mass end.
The simulations used in this paper are a significant improvement over the previous generation. They contain 8-64 times more particles and cover a wide range of masses. We use HOT1 simulation for halos in the mass range (5×1010−3×1011)M\sun, HOT2 for halos in the mass range (3×1011−1014)M\sun and HOT3 for halos in the mass range (1013−1015)M\sun (the latter two are also checked with 768h−1Mpc box 10243 particle simulation).
Figure 6 shows the ratio of the (shot noise corrected) halo power spectrum to the linear mass power spectrum as a function of a wavevector k. One can see that the assumption of constant bias is reasonable for k<0.1h/Mpc and even beyond, so a linear bias can be defined as an appropriate average over these modes. The exception are the most massive halos in HOT3 with b>1.5, for which the power spectrum is suppressed already at k∼0.1h/Mpc due to the fact that the FOF halos do not overlap and so cannot be closer than two times the virial radius. Here we use all of the modes with k<0.1h/Mpc, except for the smallest 96h−1Mpc simulation where we use k<0.15h/Mpc. We note that there is a good agreement between the simulations in the overlap mass range, but the larger simulation has smaller statistical errors. The smallest simulation (HOT1) has very few modes in the linear regime and the fluctuations in the ratio caused by perturbative effects beyond linear theory are large, so the bias determination from this simulation is somewhat less reliable. On the other hand, all of the low mass halos in this simulation have almost the same bias and at the upper end of the mass range there is a good agreement in bias with halos of the same mass from HOT2.
Figure 6: Ratio of halo to linear density field power spectrum as a function of wavevector k for halos of varying mass. At the bottom are the halos from HOT1 simulation, next up are those from HOT2 and at the top are the HOT3 halos.
Figure 7: Bias as a function of mass in units of the nonlinear mass. Points are from 96h−1Mpc 5123 (HOT1, green), 144h−1Mpc 5123 (cyan), 192h−1Mpc 5123 (red), 288h−1Mpc 7683 (HOT2, blue), 1152h−1Mpc 7683 (HOT3, magenta) and 768h−1Mpc 10243 (black) simulations. Note that in several cases the points from two simulations overlap exactly. Upper (dashed blue) line is theoretical prediction from Sheth and Tormen (1999). Lower (solid black) line is the expression from equation 5.
For simplification with theoretical comparisons we will scale all the masses relative to the nonlinear mass Mnl, defined as the mass within a sphere for which the rms fluctuation amplitude of the linear field is 1.68. While the theoretical predictions for the bias depend on the cosmological model, most of that dependence is accounted for if the mass is expressed in terms of the nonlinear mass. For HOT1-3 simulations with σ8=0.9 and Ωm=0.3 at z=0 the nonlinear mass defined with 1.68 overdensity is 8.73×1012h−1M\sun. Figure 7 shows the bias determinations as a function of halo mass from the simulations used in this paper. The dashed line is the theoretical prediction from Sheth & Tormen (1999) (the fitting formula given in Jing (1998) is very similar; while these fitting formulae are not very accurate we find a good agreement between the simulation results in these papers and our simulations). We see that these theoretical predictions overestimate the bias below Mnl and are a good fit above Mnl. The largest discrepancy is below Mnl, where the relative error can be up to 20%. The various simulations are in a reasonable agreement among themselves and the scatter between the points at the same mass is mostly due to the shot noise and small volume over which one is averaging. There may be some systematical error due to the fact that the nonlinear mass computed from the theoretical power spectrum can differ from the value obtained if one uses the actual realization. We find this can lead up to a 10% effect on nonlinear mass and would cause a horizontal shift by this amount. This is of almost no consequence for masses below Mnl, where bias is only weakly dependent on the mass, but may lead to a larger error at the high mass end.
We find that the unbiased galaxies with b=1 are at M=1.5Mnl and the bias is rapidly changing above 0.1Mnl, while below this it is essentially constant with the value around 0.68. In all simulations we see bias increasing at the lowest masses (figure 8), which is a numerical artifact. For example, such an increase is seen in HOT2 at the low mass end and is not confirmed in HOT1, where the mass resolution improves by a factor of 8 (figure 8). Moreover, this increase at low mass end changes into a decrease if we remove unbound particles from the halos. To be safe we only present results where the difference between the two cases is less than 0.01. Note that in HOT1 we find b∼0.65 at the low mass end. Even in the region of overlap with HOT2 the bias in HOT1 is systematically lower by 0.03. This is likely to be due to the sampling variance in HOT1, as can be seen from figure 6, which shows considerable fluctuations as a function of wavevector for this simulation. With a 144h−1Mpc box 5123 particle simulation we again find that b∼0.65−0.68 at the low mass end and that there is indeed significant scatter due to small box size (figure 7). For this reason the empirical fit given below goes above HOT1 at the low mass end. It is not entirely clear that this is the correct procedure, as HOT2 could have been already affected by the resolution, but the fact that both unbound and bound halos give the same result argues against this.
While there is some uncertainty in the bias value at the low mass end, all simulations agree very well around nonlinear mass. In addition to HOT2 simulation we also have another 5123 simulation with 192h−1Mpc box simulation and a 768h−1Mpc box with 10243 particles simulation that both sample well this regime. At the high mass end uncertainty increases again because of a small number of high mass halos. In addition to HOT3 and 768h−1Mpc box with 10243 particles simulation we use another 768h−1Mpc simulation with 3843 particles.
The solid curve in figure 7 is an empirical expression that fits all simulations. Over the range between 10−3<M/Mnl<102 it is given by
b0(x=M/Mnl)=0.53+0.39x0.45+0.1340x+1+5×10−4x1.5. (5)
This expression should be accurate to about 3% or better for this model, as suggested from the scatter in figure 7.
In figure 8 we show the bias as a function of mass for several simulations for which we varied one parameter at a time, roughly spanning the range of interest from cosmological constraints today. We see that there is very little difference in the theoretical predictions for halo bias as a function of M/Mnl, suggesting that instead of deriving full expressions, which depend on all cosmological parameters, one can simply use a single relation with mass in units of nonlinear mass, as in equation 5. The deviations from this relation are qualitatively consistent with the predictions given by Sheth & Tormen (1999) . We can generalize the results from equation 5 by linearizing the bias relation in terms of cosmological parameters,
b(x) = b0(x)+log10(x)[0.4(Ωm−0.3+ns−1) (6)
+ 0.3(σ8−0.9+h−0.7)+0.8αs].
This correction should be reasonable for 1>x>0.1, while below that the correction appears to saturate at −0.4[Ωm−0.3+ns−1]−0.3[σ8−0.9+h−0.7]−0.8αs. For massive halos with M>Mnl (x>1) the differences among models in the bias predictions from Sheth & Tormen (1999) become larger, but this is difficult to observe in these simulations, where the number of such halos is small and the bias measurements have large shot noise. In this regime the analytic predictions may be more accurate than equation 5: we do not see much evidence against the analytic expressions from our comparisons (figure 7) and analytic expressions can be more easily generalized to more general cosmological models. Note however that the simulations used in this paper improve upon the previous generation simulations over this regime as well.
Figure 8: Bias as a function of mass in units of the nonlinear mass for several cosmological models. We varied one parameter at a time relative to the fiducial concordance model, roughly covering the range of interest. This figure shows that the bias predictions depend predominantly on the nonlinear mass, while other cosmological parameters play only a minor, but not entirely negligible, role.
6 Conclusions
In this paper we have addressed the relations between the matter density field, halos and initial density field, focusing on large scales where these are often assumed to be proportional to each other. We focus on two issues. First, what is the scatter between these fields around the average relation? This is expressed here in terms of relative scatter between the mode amplitudes, which is related to the stochasticity parameter r, defined as the cross-correlation coefficient between the two fields. While the two are related we emphasize that even small deviations of r from unity may lead to large relative fluctuations between the two fields. These are of interest whenever one is trying to relate the fields to one another to determine their relative amplitudes. One example is the bias determination using the cross-correlation between the weak lensing signal (tracing the matter density) and the galaxies. Another example is the relative bias determination between two different galaxy populations, which we propose here as an alternative method to determine the galaxy bias, because galaxies in low mass halos have a bias of b∼0.7 independent of their mass. In all cases we find the scatter between the fields in individual modes is significant and one cannot assume the fields are simply proportional one to another. This scatter, coupled with a small number of modes on large scales, makes it difficult to accurately determine the bias (or relative bias) and needs to be included in the predictions of how accurately can one determine the matter power spectrum with these methods.
The second goal of this paper was to revisit the halo bias as a function of halo mass. This relation is a fundamental ingredient of any halo model (see Cooray & Sheth, 2002, for a recent review) and plays an important role if one is trying to model galaxy clustering by connecting it to the underlying halos. The previous generation of simulations (Jing, 1998; Sheth & Tormen, 1999) had a limited dynamical range and the predictions were not tuned specifically for ΛCDM models. As a result the existing expressions overestimate the bias by as much as 20% in the range below the nonlinear mass, which is likely to be the mass range for halos that host most of the galaxies. We propose a new expression that fits the simulations better. We argue that this expression should be fairly accurate for other cosmological models of interest as well, as long as the mass is expressed in units of nonlinear mass. We give corrections for small deviations from this model. The overall accuracy on bias-halo mass relation is at the level of 0.03 or better (for b<1), which should help with the bias determination from the current generation of observations.
We thank P. McDonald, N. Padmanabhan and L. Teodoro for help and useful comments, S. Habib for encouraging this collaboration and Zheng Zheng and David Weinberg for comments on the manuscript. US is supported by Packard Foundation, Sloan Foundation, NASA NAG5-1993 and NSF CAREER-0132953. Simulations by MSW were performed on the Space Simulator Beowulf Cluster and ASCI Q at Los Alamos. Work by MSW was performed under the auspices of the U.S. Dept. of Energy, and supported by its contract #W-7405-ENG-36 to Los Alamos National Laboratory.
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This outfit is also known as my "it's too damn hot to function" outfit. I wore this easy ensemble while out running errands all over the city yesterday. It's been over 100 degrees for too many days in a row and I'm over it. I've never been one to wish summer away, but wow, am I ready for fall. Plus, summer is my least favorite time of year when it comes to clothes. Bring on boots, coats and sweaters please!
Simple and very cute, laidback outfit!
Love this casual look! Especially the pants.


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text:
Deadline set for Project Backpack
Nick Nolan
Donations in the form of school supplies are now being accepted at various locations for the 2018 Chippewa County Project Backpack distribution ahead of the upcoming school year.
Donors originally had until Friday to bring supplies to a drop off, but will now have a Monday deadline to bring pencils, scissors, glues and more to designated areas. Book bags will then be packed full of supplies the Aug. 9 at the Salvation Army's soup kitchen starting at 2 p.m. Public drop off sites are marked with yellow school buses that serve as donation areas.
Some of the items being looked for include crayons, fat pencils, binders, highlighters and notebooks.
The Salvation Army, Michigan Department of Health and Human Services and United Way of the Eastern Upper Peninsula ran program will take place Aug. 10 at the Big Bear Arena from 11 a.m. until 1 p.m.
"Back to school supplies can be expensive," said United Way of the EUP CEO Raulaniesa Aranda. "This was an easy way to help families out in the area, and it's been beneficial to so many."
Project Backpack is open to students from pre-kindergarten through the eighth grade. There are no income guidelines. It's not uncommon for more than 500 backpacks to be distributed throughout the community at these events.
"We had to do some emergency filling on the spot last year," said Aranda. "We gave out 583 backpacks."
Last year saw a venue change to the Big Bear. Aranda said the arena works well because it has plenty of space and helps ward off both rainy and hot weather.
Project Backpack sees roughly 2,000 people per year attend including parents and siblings. Aranda said it's a mini-carnival type atmosphere with bouncy houses and resources on hand.
"It's well over 10-years-old. It started small scale with the Department of Health and Human Services helping foster families," recounted Aranda. "The Salvation Army got involved a number of years ago with helping disperse backpacks. When United Way of the EUP got involved, we increased to the whole county."
Donation sites with yellow buses include Soo Motors, the Sault Tribe Administration Building, Central Savings Bank, Century21 Advantage Plus, the United Way of the EUP, the Salvation Army office, the Department of Health and Human Services and Soo Co-Op Credit Union.
Paper backpacks are for sale at businesses that will allow buyers to give a donation and have the store purchase school supplies on their behalf. They're available at the Salvation Army Thrift Store and Check and Cash.
Kids' blunt scissors
Washable crayons
Washable markers


text:
Eublemma minutata es una especie de Lepidoptera de la familia  Noctuidae. Se distribuye por Gran Bretaña, Bélgica y norte de Alemania centro y este de Dinamarca, sur de Escandinavia hasta Estonia y San Petersburgo. Hacia el sur se puede encontrar en el norte de España, Córcega, norte de Italia, el mar Adriático costa de Croacia, Albania y Líbano.

Tiene una envergadura de 13-15 mm. Los adultos vuelan desde julio a agosto.

Las larvas se alimentan de Helichrysum arenarium.

Sinonimia
 Thalpochares paula
 Porphyrinia noctualis

Enlaces externos

 lepiforum.de
 funet.fi
 Fauna Europaea

minutata


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text:
Ascogaster nigricansa är en stekelart som beskrevs av Chen och Ji 2003. Ascogaster nigricansa ingår i släktet Ascogaster och familjen bracksteklar. Inga underarter finns listade.

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