text:
Beyond Diversity to Justice
Beyond Diversity :
The Struggle for Justice and Solidarity
"Sit down and read. Educate yourself for the coming conflict."
- Mary "Mother" Jones
We need to understand our society, both the positives and the negatives, in order to work for more democracy. In 1903 W.E.B. Dubois stated, " For the problem of the Twentieth Century is the problem of the color-line." And now, at the start of the Twenty First Century our nation and our movements continue to struggle with racism. We believe that the danger to our democracy is not race-- it is racism; the oppression of a group of people based upon their perceived race. Racism is both a belief system and the domination of a people based upon these beliefs.
Racism is a great and dangerous falsehood: i.e., that one "race" is inherently superior in the biological sense and, conversely, that other races are inherently "inferior." Racism grew out of an effort to justify such self-serving economic thrusts as genocide against Native Americans, the enslavement of Africans, the seizure of much of Mexico by the United States, and much more -- and has continued as a means through which oppressors exploit labor and maintain their political power. The mythology of racism is deeply rooted and extremely tenacious.
Racial prejudice is a negative attitude toward a person or group of people. Racial discrimination encompasses the actions taken to further oppress the target -- the victim -- group. In our time, racism has remained a central characteristic of modern capitalism and contributes to the extreme inequalities in our world.
Although racial definitions, and ethnic definitions are often vague and imprecise, racism continues to divide our communities and our movements. For a racist: defining race is easy. A race is "them", those people, the other, the not you. Any group which the racist hates or fears. Often it is a group that shares certain characteristics with the racist. The racist seldom speaks of the great myriad of basic human similarities, but instead the differences are emphasized to a high degree as to draw distinction and justify intolerance and oppression.
Institutional racism is the use of power and authority of a dominant group, working through the social structures of society, to enforce prejudices and to prevent the subjugated group from gaining access to public services such as good schools, effective health care, decent housing, and equal opportunity, particularly in meaningful employment.
The philosophers have only interpreted the world, in various ways; the point
is to change it. Karl Marx
DSA presents itself as a democratic, socialist, feminist, and anti-racist organization. Since its founding in 1982, the Anti Racism Commission of DSA has defined itself as pursuing an anti-racism agenda. This agenda includes participating in the dialogue and intellectual efforts of DSA to advocate for a pluralist, anti-racist society and to advance the democratic socialist project within this society.
Racism has produced a tortuous history in the U.S. intellectual community even among socialists and progressives. Socialism has often had a troubled relationship with anti racism struggles. Marxism has contributed significantly to the understanding of social and economic relationships, but has proven inadequate to explain the origin, development and institutional role of racism in our society. ( See Toward a Socialist's Theory of Racism, Cornel West)
Since our 1983 publication of Third World Socialist, we have defined the anti racism project beyond the white-black paradigm to include a multi-polar understanding of the role of race and the connected role of class oppression in the US society. While the issue of African American slavery and oppression is central and important, to only focus on Black-White issues is to ignore the objective reality of U.S. race relations.
In 1985 the organization adopted this language in its priorities document, " In our society there are few problems more urgent than the effects of racism and sexism. While rooted in the history of slavery, invasions, and the early development of the Americas, racism has remained a central characteristic of modern capitalism. Racism and sexism are brutal and oppressive system of institutions and ideologies which we must be committed to resisting as a fundamental part of the struggle for new economic relationships."
In 2001, DSA adopted a new political priorities document which stated that, "The politics of DSA will be guided by a demand for social and economic justice for all - - now! In pursuit of justice, multi racial and anti-racism politics shall become a priority in our work. Agendas in our organizations should consistently include the issues of communities of color. This calls for an immediate re-orientation of our practice toward multi racial coalition building. We will consistently look for opportunities to work with activists in communities of color."
As Democratic Socialists we propose to create a multi racial class-based movement for social change. Only a movement with such an anti-racist, pro-justice viewpoint at its core has a possibility of success in the U.S. To build a multi racial movement, we need to have a conversation about race, oppression, class, language, and culture.
Generally people are willing to consider race and class oppression when they see the specific effects of these oppressions on their own lives. We recognize class as a relationship between two or more groups of people who have specific roles and power relationships in the economic system. Business owners and employees are the most obvious examples. The racial stratification of our nation has promoted and extended the class domination. Racial oppression is such an integral part of economic and political domination that the struggle against racism is necessary for all working class progress.
Our movement cannot exclude the white working class from the dialogue about race. And, working class unity is necessary part of any realistic strategy for positive structural change.
Some in the white working class remain influenced by the racist ideology still dominant in the institutions of our society. While rejecting white supremacist ideology, we also need to explore the oppression that these working class whites experience.
First, we choose to briefly deconstruct whiteness. Many have been influenced by the currently popular arguments of identity and white privilege. We acknowledge that white is a socially constructed category with both positive AND negative connotations.
Identities are constructed by the individual defining and interpreting their own experiences and their interactions with others. A person becomes Chicano, or Latino, or White, or Asian American. They learn and accept this identity. And, their identity is potentially constantly changing.
We propose that the same mechanisms that bestow privilege upon those who self-identify as "white" also rob many of the other associations that people of color are forced to use (for positive and negative ends). Being classified as white prevents one from choosing another identity ( e.g., Irish, or progressive) or a combination of identities that more accurately and usefully describes oneself. THIS INCLUDES CLASS IDENTITY. We recognize that many people accept and respond to multiple identities.
Studies of identity have led to a focus on White Privilege. This privilege is substantive, damaging, and we resist it in our work. The white privilege argument, while a useful scholarly endeavor, often does not help much within class based popular movements because , in part, the critique of white privilege has often been misguided and misappropriated to cloud class. Yes, a Black man in Manhattan may encounter a problem getting a cab, even from a Black cab driver. And so would a white person who is obviously homeless. Yes, a Black or Latina college professor may encounter prejudice while shopping in a boutique clothing store- as would a poor white, or working class white woman who entered the store in her work clothes or even the Salvadoran sister who sewed the clothes.
This argument about white privilege, in part because it derived from studies in psychology, is complex and often distorts the issues of class. Most writings, most intellectuals, and certainly the popular media refuse to talk about economic class. There are reasons for this. A black factory worker and a white factory worker have a lot more in common with each other than either has with the CEO, no matter what the color of the CEO. To be accurate, we should really discuss white /upper class privilege, Please read Barbara Ehrenreich's book, Nickeled and Dimed.: On (Not) Getting By in America. (2001) The subjects in this book are not privileged. They are oppressed. While they do not have major privileges, they do live and work within an economy organized based upon White Supremacy. Some have argued that this delivers privileges, because they are white. But, does not the organization of White Supremacy keep in power the very class which is oppressing working people?
Beyond Diversity: Some allies , good folks, from a liberal persuasion avoid recognition class issues and class oppression by arguing for diversity. They argue that we need to develop a more representative group of teachers, faculty, etc. As socialists, we find diversity to be a weak, timid, corporate, commodified goal. Instead, we seek justice!
Let us suggest what we regard as a more useful response.
Some people learned their anti-racism by going to diversity workshops. Workshops are valuable. Some in a younger generation have learned anti-racism by not learning racism in the first place. This group has learned anti-racism first hand from their advantage of growing up in a less racially stratified society than that of their parents. While frequently not experiencing racism directly, these young people do live and work in a society organized on the basis of White Supremacy. We must recognize this progress as well as the changes in realities of working class life in the United States as important variables in building the anti-racist movement.
Still others learn anti racism by working with social movements. We encourage all to work against racism within popular movements. Within the many struggles for justice we learn anti-racism and unlearn class bias on a practical level.
It helps us to move beyond the white privilege argument by recognizing the difference between guilt and responsibility. The white privilege argument is usually about guilt. (not a very useful emotion). We prefer to deal with responsibility. ie. what are we going to do differently? Focusing on white privilege usually does not build movements, while focusing on anti racism and class solidarity will.
Rather than assign guilt we find it more useful to work in solidarity with people of color in opposition to real oppression and in favor of real change. The important task is to do something about oppression and White Supremacy. We recognize that we are each either sustaining the current social order-including its racial injustice and white/upper class privilege, or we are engaged in resisting this social order. We choose to resist. The goal of the Anti Racism Commission of DSA is to help build a movement for social justice. We are not satisfied with calls for diversity, instead we demand Justice NOW!
WE MUST BUILD MOVEMENTS.
Power concedes nothing without demand. It never did and it never will. Frederick Douglass.
Our goal is to build a movement to change the nation. To do this, we need unity- - unity with justice.
Socialists point out that one of our difficulties in seeing class in our society is that we look at poverty (class) and we see race. We racialize class. As a consequence we seldom accurately see race or poverty or class oppression.
Lets talk about the working class. If we are going to do class politics, we need to look at our own attitudes toward both race and class. Class refers to the economic and social differences between groups in our society. (poor, working class, middle and upper class). As socialists we recognize the working class as a potential agent for change. The U.S. has a multi racial working class. Workers of color make up the majority of workers in many cities and industries. And whites of the working class make up at least 40% of the potential voters in the nation.
Working class politics is most frequently expressed in the organizing efforts of labor unions. U. S. unions have historically been divided over racism. Some have perpetuated racism and discrimination and social inequality and others fight against racism. Left organizations have a similar history. Overcoming the divisions of race is central to our struggle in building a progressive working class majority. Are we going to support racism, and divisions, or are we going to build a unified social movement to change this society?
At present the working class all over the world is losing ground. If you are interested in doing something enduringly effective about this -- working to reverse this trend and moving forward -- then please join us.
Our experience tells us that many in the working class are ready for an alternative. They are looking for something beyond the choice between Republicans and Democrats. We believe that that alternative is socialism and a socialist analysis. African Americans, Latino, Native Americans, Asians, and Anglos in the working class are ready for socialism because they think it is wrong for some people to live in mansions while others live in public housing that looks like a war zone. We think it is wrong for some people to live in luxury while others cannot adequately feed their children. We do not accept a social order in which one in five children live in poverty and the U.S. has one of the highest infant death rates in the modern world. We do not accept a social order in which one group has quality schools in safe neighborhoods, and the working poor have inadequate and unsafe schools. We do not accept a world economic system where over 1 billion are without safe water, and 149 million children are malnourished.
Only a vigorously committed multi racial movement with multi racial leadership -- and the vision of full socio-economic equality -- can organize the necessary power and carry it through to the full development of the genuinely democratic, egalitarian society. That is the kind of movement that will be strong, enduring, vital -- and successful. We invite you to join us.
Chief Oren Lyons [Onondaga] presenting the statement of the Grand Council of
the Iroquois to the United States Government, 1973:
"We have not asked you to give up your religions for ours. We have not
asked you to give up your ways of life for ours. We have not asked you to
give up your government for ours. We have not asked you to give up your
territories. Why can you not accord us with the same respect? For your
children learn from watching their elders, and if you want your children to
do what is right, then it is up to you to set the example. That is all we
have to say at this moment. Oneh."
A working paper of the Anti Racism Commission of Democratic Socialists of America.
Suggestions welcome
Posted by Duane Campbell at 11:59 AM 4 comments:
Los Angeles times vrs. UFW
Major newspapers plicked up the Los Angeles Times series attacking the United Farmworkers Union.
We hope that readers also consider the response from the UFW.
http://www.ufw.org/
In memory of Br. Ed Dunn
Anti immigrant forces
an interesting essay on who makes up the main anti immigrant groups:
http://www.prospect.org/web/page.ww?section=root&name=ViewPrint&articleId=10485
The New Nativism
The alarming overlap between white nationalists and mainstream
anti-immigrant forces.
By Leonard Zeskind


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What Happened At A Glance – The First Round Of The Men's 2013 NCAA Tournament
College Soccer News November 22, 2013 Articles
November 21, 2013 – As expected there were a few upsets in the first round of play in the NCAA Tourney and a lot of contests required penalty kicks to determine who advanced but for the most part the games went as expected. Sixteen teams survived and earned the right to advance to the second round of play on November 24 while sixteen teams saw their season come to an end.
UCLA Bracket
Elon 1 – Clemson 1 – Elon advances 4-1 in penalty kicks to face UCLA. Both teams had opportunities to score but were unable to convert in the first half. The action picked up in the second stanza with junior forward Jason Waterman scoring from eight yards out for Elon with an assist from Nathan Diehl and Thales Moreno scoring for Clemson with an assist from back Jack Metcalf. An enthusiastic crowd of 3,348 was on hand at Rudd Field.
Connecticut 2 – Quinnipiac 1 – Senior forward Mamadou Diouf got into the scoring column for the first time this year when he headed in what proved to be the game-winning goal for the Huskies with ten minutes remaining in regulation after receiving a pass from freshman back Jakob Nerwinski. Nicholas Zuniga scored UConn's first goal in the 50th minute of play. Simon Hinde netted his tenth goal of the season for Quinnipiac in the 72nd minute to knot the score at 1-1 when he placed a shot in the upper right corner of the goal. Connecticut improves to 12-2-6 and advances to face UMBC. A determined Quinnipiac team ends an historic season with a 9-5-7 record.
Akron 3 – Indiana 2 – Akron jumped out to a 3-0 lead behind two first half goals from senior forward Reinaldo Brenes and a single score in the second half from senior midfielder Aodhan Quinn. Indiana made a game out of it with second half goals from midfielder Dylan Lax and defender Jacob Bushue. Jake Fenlason had seven saves in goal for the Zips. Akron improves to 17-3-1 and advances to face number nine seeded Marquette. Defending National Champion Indiana concludes their season earlier than most thought they would at the beginning of the season with a 8-12-2 record.
St. John's 2 – Delaware 1 – Gabriel Camara scored in the 17th minute of play to give St. John's the early advantage. Freshman midfielder Guillermo Delgado netted his fifteenth goal of the season for Delaware in the 88th minute of regulation out of a corner kick to send the contest into overtime. Jordan Rouse scored the game-winning goal for the Red Storm with a little over a minute remaining in the second overtime period. St. John's improves to 11-6-2 and keeps their season alive. They now travel to Charlottesville to square off with the Virginia Cavaliers. The Red Storm lost to Virginia 2-0 in an early regular season contest played on September 2.
Notre Dame Bracket
Wisconsin 1 – Milwaukee 0 – Defense was the name of the game in this one. Chris Prince's goal from the top of the six yard box just thirteen minutes into the match was the only score of the evening. Senior Max Jentsch had four saves in goal in route to notching the shutout for the Badgers. Wisconsin improves to 14-4-2 and advances to face number three seeded Notre Dame. Milwaukee ends a banner season with a 15-3-2 record.
Navy 3 – VCU 0 – This one looked like it might go down to the wire until the Mids broke it wide open in the second half behind a goal from senior midfielder Martin Sanchez in the 68th minute followed by goals from junior midfielder Geoff Fries in the 71st and 76th minutes of play. Senior Gavin Snyder had four saves in goal for Navy to record his fourth consecutive shutout and tenth for the year to date. Navy is now 16-3-2 and travels to Winston-Salem to challenge Wake Forest. VCU ends their season with an 11-8-2 record.
Louisville 0 – Denver 0 – Survive and advance was applicable to this one as defense ruled with Louisville prevailing 3-2 in penalty kicks after neither team was able to score during 110 minutes of play. The Cardinals improved to 11-4-4 and earned the right to face Michigan State on Sunday, November 24. Junior Joachim Ball had two saves in goal during regulation and extra time for Louisville and made three stops during the penalty kick shootout. Denver ended an impressive first season with a 13-3-4 record.
Old Dominion 5 – Drexel 1 – The Monarchs dominated this one scoring three unanswered goals in the first sixteen minutes of play. Senior forward Tim Hopkinson scored twice and junior forward Ivan Militar contributed three assists to power ODU past Drexel. The Monarchs are now 11-5-1 and advance to face Georgetown.
California Bracket
Providence 1 – Penn 1 – This one required penalty kicks to determine who advanced. Providence prevailed 3-1 in the shootout. Penn took the early lead when freshman forward Alec Neumann scored in the ninth minute of play with an assist from fellow freshman midfielder Matt Poplawski. Providence tied it at 1-1 on a goal in the 23rd minute of play from midfielder Wilder Arboleda with a helper from forward Mac Steeves. Sophomore Max Polkinhorne had nine saves in goal for Penn while Keasel Broome had five save between the pipes for the Friars. Providence hopes to keep a magical season alive as they now travel to College Park to tangle with number five seeded Maryland. Ivy League champion Penn concludes their season with an 8-8-2 overall record.
North Carolina 1 – South Florida 0 – The crowd at Fetzer Field in Chapel Hill had just begun to settle into their seats when Omar Holmes netted what proved to be the game-winning goal for the Tar Heels in just the fourth minute of play when he one-touched a cross into the back of the net that was served in out of a corner kick by teammate junior Cooper Vandermass-Peeler. North Carolina now travels cross country to face U.C. Irvine out of the Big West Conference on Sunday, November 24 in second round action. Brendan Moore had two saves in goal for UNC. A determined South Florida team finishes the 2013 campaign with an 8-4-9 record.
Coastal Carolina 2 – ETSU 0 – Sergio Camargo scored his ninth goal of the season in the 16th minute of play and Jakub Stourac added his sixth goal of the season in the 31st minute of play to give the Chanticleers the edge they needed to advance past ETSU. Senior Devin Cook had four saves in goal to record his 11th shutout of the 2013 campaign. Coastal Carolina ups their record to 18-4-0 and earns the right for a rematch with a Charlotte team out of CUSA that they were defeated by 2-1 at home back on September 6. A good ETSU side ends the season with a 10-6-4 record.
Bradley 3 – Northwestern 2 – This one featured plenty of scoring. Scott Davis opened the scoring for Bradley in the sixth minute of play with a double assist from Wojciech Wojcik and Grant Bell. Senior forward Wojcik, the MVC Player of the Year, scored twice for Bradley including the game-winning header from five yards out in the 96th minute. Nikko Boxall and Grant Wilson were the goal scorers for Northwestern. This is the same result that the two achieved when they faced each other on October 2 in a regular season matchup. Bradley improves to 14-6-2 and advances to face number four seeded California on Sunday, November 24. Northwestern ends an up and down season with a 10-8-3 record.
Washington Bracket
George Mason 2 – William and Mary 2 – George Mason earned the right to advance by prevailing 4-2 in a penalty kick shootout. The momentum changed several times in this one. Timi Mulgrew converted a rebound in the 33rd minute to give George Mason the early advantage. William and Mary responded two minutes later with a Roshan Patel goal out of a corner kick. Wes Sever scored in the 71st minute with an assist from Zach Herstek to put George Mason back on top. Jackson Eskay drove a blast into the back of the net from twenty-two yards out in the 78th minute for William and Mary to knot the score at 2-2. George Mason out of the Atlantic 10 conference survives and now hits the road traveling to Albuquerque to tangle with a good New Mexico side in round two. William and Mary out of the Colonial Athletic Association brings to a close very interesting season which included several huge wins with an 11-5-2 record.
Penn State 1 – Saint Francis Brooklyn 0 – Eli Dennis scored the lone goal of the match from eight yards out in the 36th minute of play with an assist from midfielder Drew Klingenberg. Andrew Wolverton had four saves in goal for the Nittany Lions to record his seventh shutout of the season to date. Tom Giovatto's Saint Francis Terriers finish a stellar season with a 12-6-1 record. Senior forward Gabriel Bagot completed a successful tenure at Saint Francis with a total of twenty-five career goals. Penn State out of the Big Ten Conference now take their game to the west coast where they tangle with number ten seeded UC Santa Barbara out of the Big West Conference in the second round of the NCAA Tourney.
Stanford 1 – Loyola Marymount 1 – Stanford came out on top 3-2 in penalty kicks. Stanford dominated the first half of play but could not find the back of the net. Adrien Perez scored his eleventh goal of the season in the 82nd minute of play to give LMU what appeared to be the edge they needed to secure the victory. However with seventeen ticks remaining in regulation Stanford's Zach Batteer sent a blast into the back of the net from eight yards out off a throw-in with a double assist from Jimmy Callinan and goalkeeper Drew Hutchins to knot it at 1-1 and send the match into extra time. Paul Blanchette was outstanding in goal for LMU with eight saves on the evening. Hutchins was the man of the match for Stanford assisting on the lone Cardinal goal, converting a penalty kick in the shootout, and making several key stops including the deciding one in the shootout. Stanford now advances to face Cal State Northridge.
Seattle 2 – Creighton 1 – In what was perhaps the biggest surprise of first round play, Seattle came from behind to upset Creighton behind two goals from forward Miguel Gonzalez. Gonzalez netted the game-winning goal in the 72nd minute when he fired a bullet into the back of the net from the top of the box. Gonzalez has now scored fifteen goals season to date. Sonny Makh gave Creighton the early lead when he scored from five yards out in the 15th minute of the match. Seattle improves to 11-8-4 and will face number two seeded Washington on Sunday, November 24. After advancing to the Final Four in 2012 and 2013, Creighton ends the 2013 season with a 9-9-2 overall record.
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Syracuse Wins A Thrilling, Back And Forth Battle With A 2-2 (7-6 PKs) Win Over Indiana

redpajama_set_name: RedPajamaCommonCrawl


Have you ever heard the expression "the journey of a thousand miles begins with a single step"? The quote by Chinese philosopher Laozi is recited often, especially in situations where someone needs encouragement in order to reach their goals or to overcome a challenging circumstance. The quote couldn't be more true, though, and it applies not only to areas of your personal life, such as signing up to run a marathon or eliminating some bad habit, but for sales and business professionals, goal setting and taking steps to reach those goals is just as paramount.
Sales professionals have constant goals placed over them – some that are given to them and that have serious consequences if not met, and others that sales professionals create for themselves. But setting goals and then achieving those goals, let alone exceeding them, are two entirely different things. Almost everyone is good at setting big, audacious goals. In fact, just a few weeks ago you likely set resolutions for 2016. You'll work out more, spend more time with family, eat a more balanced diet, and read more often. But how long did that last? According to a recent Harvard Business Review article, it depends on your grit. Yes, your grit.
Sales professionals who are always looking for ways to get better consistently stand out from the crowd. They are the ones asking for feedback, participating in role plays, shadowing team members who are excelling in a particular area, and asking to be mentored. Sales professionals that want to be challenged and aren't afraid to look foolish in front of their peers in the name of improvement are already preparing to excel past their goals.
Each and every day is an opportunity for sales professionals to set goals. Rather than be daunted by the massive goal coming up at quarter end or year end, think about what you want to accomplish just today or this week that will take you towards that goal.
For sales professionals, this could mean consistently having three prospect conversations a day, or securing at least two VP-level meetings each week, rather than putting off the cold calls until end of week and building up dread and fear that you won't hit your target. Break down the big goals into smaller, more attainable steps and controllable behaviors that can be managed and executed every day. Then be sure to track progress and performance so you know the areas where you need extra focus and attention.
Isn't that so true for your personal life, let alone professional life? If it weren't for your mortgage being due, would you ever pay it on time? Generally, but it wouldn't be consistent. In fact, there's an entire theory about this called Parkinson's Law: "Work expands so as to fill the time available for its completion". According to this law, you'll take as much time to do a simple task as you're given.
In practice for sales professionals, try giving yourself a set amount of time to accomplish a certain set of activities. But don't just keep it to yourself, share it with your peers or manager for even more accountability. If you don't reach your goal, it won't just affect you, but others will also know. For those with grit, this accountability will drive you to achieve even more – and likely in a much shorter time period.
Setting stretch goals is a common practice for sales teams – especially those that excel and work towards something great as a team, and individually. But there are also such things as impossible or unrealistic goals that can do more damage than good when not met. Don't set out to quadruple the effort or results that you achieved last quarter if you were already working hard and achieving your goals. Instead, make incremental improvements and stretch yourself to do better in specific areas, rather than setting an overarching goal that seems impossible. This can be deflating – especially halfway through the quarter when the results aren't coming in. What about practicing your prospecting approach? Set a goal to double the amount of prospecting activity each week, then monitor your results and continue improving.
You've heard the expression "think positively" a thousand times. Or how about "the glass is half full"? Thinking positively is a skill and takes time to develop, but doing so each day on the sales floor is crucial. In a recent TED Talk, Kelly McGonigal says, "It's so uncomfortable to be in that place of self-doubt or self-criticism and guilt." Anticipating that you're likely to get off track or fall behind will prepare you to push ahead. It's all about maintaining that growth mindset and grit to push forward." There's that word again: grit. Maintaining grit and keeping your head up, even amidst the many pitfalls and unexpected twists and turns in sales is crucial for success and reaching goals.
Sales professionals who believe in their goals, stay positive, take small steps, measure improvements and look for ways to continually get better will be successful.


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text:
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Packing for Safari: What You Need and What You Don't
Packing for safari flummoxes a lot of travelers because it's a weird hybrid of exotic, outdoor adventure and very luxurious accommodations. There are a lot of safari packing lists out there, that's not what this is.
Elevate Destinations, Contributor
Eco-luxury travel that gives back to wildlife and places
Apr 22, 2013, 03:50 PM EDT | Updated Dec 6, 2017
Congratulations! You're going on the trip of a lifetime! Whether you've selected your safari in Kenya, South Africa, Tanzania or some other exotic location, at this point you've been dreaming of your itinerary for months. But now it's getting closer and you actually have to pack something. Hopefully you're not like me with your packing habits. Without fail I get out my suitcase to pack one hour before a domestic flight and two hours before an international one.
Packing for safari flummoxes a lot of travelers because it's a weird hybrid of exotic, outdoor adventure and very luxurious accommodations. There are a lot of safari packing lists out there, that's not what this is. This is a quick run down of commonly packed safari items and an evaluation of whether or not they're actually at all useful.
Don't need (if you're a man): Dinner Jacket
Even the nicest safari camps and lodges are still casual for dinner. You will be comfortable in khakis and a button down at even the highest end accommodations. After a long, dusty day of game drives, the main emphasis is on showering and feeling clean and comfortable.
Need (if you're a woman): Below the knee skirts
In many safari countries, wearing shorts above the knee is very disrespectful and you will stand out like a sore thumb. Having two or three long, lightweight skirts in your bag is the best thing you can do. There is a misconception that you need to be wearing hiking boots and pants (see below) to go on a game drive. Not true! Unless you're going on a bush walk, you will be viewing animals from the comfort of a Land Cruiser, so skirts and sandals are completely fine. They are also extremely versatile; I wore mine to dinner, to visit a local school, on game drives, and in transit.
(Check out this nifty one I found on etsy with a subtle pocket for cash.)
Debatable: Convertible Khaki Pants
Let me sum this question up this way, if your very first time ever setting foot into an EMS is to buy these pants, then you don't need these pants. Was it nice having a light-weight, non-skirt option with me? Absolutely. Did I ever once convert them into shorts? Nope. If you're someone who enjoys hiking, camping, etc, and will use these again in this lifetime, then go for it. Otherwise, they're really not necessary.
Don't Need: Pith Helmet
Don't be that guy in camp. Just don't.
Need: A hat
A hat is an essential part of your packing list. The sunrays are very strong and you'll be spending a lot of time in an open top vehicle. They're also remarkably handy for swatting tsetse flies if you're in an area where they're prevalent. Sun hats are great, but really a baseball cap will do just fine as well.
Don't Need: Mosquito Netting
I seriously saw this on several popular packing sites on the world-wide web, so apparently it needs to be addressed. There are a few basic things that any half-way decent lodge or camp should be able to provide you with. Mosquito netting is one. Protection from lions is another. If they're missing the first, I would be very seriously concerned about the second.
Debatable: Hiking Boots
If you are planning on including bush walks on your safari, then hiking boots are great to have to protect your feet and ankles from brush and critters. If you'll only be doing game drives however, you don't need them at all. They're quite cumbersome to pack, so definitely think before including them.
Need: Headlamp
Your room or tent is bound to include a flashlight or two, and there are typically escorts around camp at night, but I still find a headlamp to be incredibly useful to have around. Hands-free lighting comes in handy for all sorts of things such as reading, rifling through a suitcase before dawn, or doing a critter check from the comfort of your bed.
Great if you have room: Pack for a Purpose
Use leftover space in your luggage to bring supplies for needy children in the area you're visiting. Many lodges and camps have teamed up with this great organization, just search for where you're going and they'll give you a list of things they need. Examples include deflated soccer balls, colored pencils, and band-aids. When you arrive just pass the donations off to the hotel staff who will take care of everything. It's an easy way to give back to the local community.
For more information about travel that gives back, visit Elevate Destinations.
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It may be that you want measures to be taken quickly in a Member State other than where your main case is pending without waiting for a final judgment to be given.
It could be that you have started an action in the courts, but proceedings are slow and you are feeling rather put off. You fear that your debtor will take advantage of the long-drawn-out procedures and the various redress facilities to escape his/her creditors before judgment is actually given. For example, s/he might be tempted to organise her/his own insolvency or to transfer assets. If so, it is in your interests to apply to the court for interim measures.
The court may order interim or precautionary measures against the debtor's assets. The purpose of all these measures is to anticipate the final judgment on the merits for a certain period so as to ensure that it will be possible to enforce it.
However there are quite substantial differences in the conditions for ordering these measures in the Member States.


text:
\section{Introduction}
\label{sec:intro}
In the standard  model (SM), the decay $\ensuremath{B^0}\xspace \ensuremath{\rightarrow}\xspace \gamma  \gamma$ occurs through a
flavor-changing  neutral current  (FCNC) transition  involving  electroweak loop
diagrams,  as illustrated  in  Figure~\ref{fig:diagrams}.  The  decays $\ensuremath{B^0}\xspace  \ensuremath{\rightarrow}\xspace
\gamma \gamma$ and $B_s \ensuremath{\rightarrow}\xspace \gamma  \gamma$ are closely related, with the $b \ensuremath{\rightarrow}\xspace
d \gamma  \gamma$ transition being  suppressed with respect  to $b \ensuremath{\rightarrow}\xspace  s \gamma
\gamma$ by Cabbibo-Kobayashi-Maskawa  (CKM) factors ($|V_{td}|^2/|V_{ts}|^2 \sim
0.04$).   Hadron  dynamics  introduces  uncertainties  into  the  prediction  of
branching fractions  for these  decays and  may modify the  ratio away  from the
CKM-implied value.  While $\ensuremath{B^0}\xspace \ensuremath{\rightarrow}\xspace \gamma  \gamma$ is expected to have a smaller
branching fraction than  $B_s \ensuremath{\rightarrow}\xspace \gamma \gamma$, a search  for the latter faces
the experimental challenge of obtaining  a large sample of $B_s$ mesons, whereas
large samples of \ensuremath{B^0}\xspace mesons  are readily available from $B$ Factory\ experiments running
on the $\Upsilon(4S)$ resonance.

\begin{figure}

  \includegraphics[width=\linewidth,clip=true]{bgg_Feynman1}

  \caption
  {
    \label{fig:diagrams}
    Examples  of lowest  order SM  Feynman diagrams  for \ensuremath{\ensuremath{B}\xspace^{0} \ensuremath{\rightarrow}\xspace \gamma \gamma}.   The  symbol $q$
    represents a  $u$, $c$, or  $t$ quark.  In  some new physics  scenarios, the
    $W$-boson may be replaced by a charged Higgs particle.
  }

\end{figure}

A  leading order  calculation  for the  branching  fraction of  \ensuremath{\ensuremath{B}\xspace^{0} \ensuremath{\rightarrow}\xspace \gamma \gamma}\ yields  an
estimate  of   $3.1^{+6.4}_{-1.6}  \times  10^{-8}$~\cite{BB}.    This  mode  is
sensitive to  new physics  that could  lead to an  enhancement of  the branching
fraction due  to possible contributions  of non-SM heavy particles  occurring in
the  loop of  the  leading-order  Feynman diagrams.   Such  enhancements to  the
branching fraction for \ensuremath{\ensuremath{B}\xspace^{0} \ensuremath{\rightarrow}\xspace \gamma \gamma}\ are less constrained than those for $B_s \ensuremath{\rightarrow}\xspace \gamma
\gamma$ due  to the fact that the  $b \ensuremath{\rightarrow}\xspace s \gamma$  transition, responsible for
$B_s \ensuremath{\rightarrow}\xspace  \gamma \gamma$, is known much  more accurately than $b  \ensuremath{\rightarrow}\xspace d \gamma$.
For example, some  new physics scenarios involving an  extended Higgs sector may
considerably   enhance  the  branching   fractions  with   respect  to   the  SM
expectation~\cite{Aliev-Iltan}.          Supersymmetry        with        broken
$R$-parity~\cite{Gemintern-etal}   also  provides   scenarios  where   order  of
magnitude enhancements  are possible.  In  addition, since the  two-photon final
state can be either $\ensuremath{C\!P}\xspace$-even  or $\ensuremath{C\!P}\xspace$-odd, studies of $\ensuremath{C\!P}\xspace$-violating effects
may ultimately be possible.

The best previous upper limit on the branching fraction at 90\% confidence level
(CL) is ${\cal B}(\ensuremath{B^0}\xspace \ensuremath{\rightarrow}\xspace \gamma \gamma)  < 6.2 \times 10^{-7}$ set by the Belle
experiment~\cite{Belle-Villa} using  a dataset recorded at  the \Y4S resonance
with an integrated luminosity of $104  \, \ensuremath{\mbox{\,fb}^{-1}}\xspace$.  For the related process $B_s
\ensuremath{\rightarrow}\xspace \gamma \gamma$, Belle has set a  an upper limit on the branching fraction of
$8.7 \times 10^{-6}$ (90\% CL) based on $23.6 \, {\rm fb^{-1}}$ of data taken on
the $\Upsilon(5S)$ resonance~\cite{Belle-Wicht}.

We report herein a new search for  the decay \ensuremath{\ensuremath{B}\xspace^{0} \ensuremath{\rightarrow}\xspace \gamma \gamma}\ which uses a data sample with
integrated luminosity  of $426\,  \ensuremath{\mbox{\,fb}^{-1}}\xspace$ taken at  the \Y4S  resonance.  This
corresponds to the entire \mbox{\sl B\hspace{-0.4em} {\small\sl A}\hspace{-0.37em} \sl B\hspace{-0.4em} {\small\sl A\hspace{-0.02em}R}}\ \Y4S dataset and contains 226 million \ensuremath{\Bz {\kern -0.16em \Bzb}}\xspace
pairs.  The analysis  does not distinguish between \ensuremath{B^0}\xspace  and \ensuremath{\Bbar^0}\xspace, and throughout
this article, charge conjugation is implied for all reactions.

The analysis proceeds through several  steps.  The full dataset is first reduced
to  a manageable  size by  selecting events  based on  loose  kinematic criteria
consistent with the \ensuremath{\ensuremath{B}\xspace^{0} \ensuremath{\rightarrow}\xspace \gamma \gamma}\ hypothesis. Studies are then performed to determine an
optimal set of event selection criteria to maximize the efficiency for detecting
\ensuremath{\ensuremath{B}\xspace^{0} \ensuremath{\rightarrow}\xspace \gamma \gamma}\ events  while effectively rejecting  background events.  This  analysis is
performed  ``blind''  in  the  sense  that  the  event  selection  criteria  are
determined without  considering the on-resonance  data within a  specific signal
region, as defined below.  We use an unbinned extended maximum likelihood fit to
extract  the  signal  yield  from  the  remaining  events.   Finally,  since  no
statistically  significant \ensuremath{\ensuremath{B}\xspace^{0} \ensuremath{\rightarrow}\xspace \gamma \gamma}\  signal  is  observed, an  upper  limit on  the
branching  fraction is calculated  based on  the likelihood  function determined
from the fit.

Section~II  of  this article  describes  the  \mbox{\sl B\hspace{-0.4em} {\small\sl A}\hspace{-0.37em} \sl B\hspace{-0.4em} {\small\sl A\hspace{-0.02em}R}}\  detector and  the  \Y4S
dataset.  Section~III outlines the  optimization of the event selection criteria
and discusses backgrounds due to  exclusive \ensuremath{B}\xspace decays.  Section~IV describes the
fit methodology and Section~V discusses the sources of systematic uncertainties.
Finally,  Section~VI reports  the  resulting branching  fraction  and the  upper
limit.

\section{The \mbox{\sl B\hspace{-0.4em} {\small\sl A}\hspace{-0.37em} \sl B\hspace{-0.4em} {\small\sl A\hspace{-0.02em}R}}\ Detector and Dataset}
\label{sec:detector}
The  \mbox{\sl B\hspace{-0.4em} {\small\sl A}\hspace{-0.37em} \sl B\hspace{-0.4em} {\small\sl A\hspace{-0.02em}R}}\ detector  recorded data  from the  PEP-II $B$ Factory\  located at  the SLAC
National  Accelerator  Laboratory.   In  PEP-II, head-on  collisions  of  9.0~GeV
electrons  and  3.1~GeV  positrons  provide  a  center-of-mass  (CM)  energy  of
10.58~GeV  that lies  at the  peak of  the \Y4S  resonance.  The  \Y4S meson
decays almost  exclusively to \BB pairs.   The subsequent $B$  meson decays were
observed  in  the   \mbox{\sl B\hspace{-0.4em} {\small\sl A}\hspace{-0.37em} \sl B\hspace{-0.4em} {\small\sl A\hspace{-0.02em}R}}\  detector,  which  has  been   described  in  detail
elsewhere~\cite{BABARNIM}.  Briefly, a superconducting solenoid produces a 1.5~T
magnetic  field approximately parallel  to the  colliding electron  and positron
beams.  Most detector subsystems are inside the volume of the solenoid.  Charged
particle tracking is accomplished by  a combination of a five layer double-sided
silicon  strip  vertex  detector  and  a  40 layer  drift  chamber.   The  track
reconstruction algorithm accepts tracks  with a transverse momentum greater than
$50\, \ensuremath{{\mathrm{\,Me\kern -0.1em V\!/}c}}\xspace$.   Identification  of  charged  particles  is  accomplished  using  a
ring-imaging Cherenkov  detector augmented with energy loss  measurements in the
tracking  detectors.  Photons  are detected  in the  electromagnetic calorimeter
(EMC), which  consists of 6580~CsI(Tl) crystals, oriented  in a quasi-projective
geometry with  respect to the interaction  point.  Outside of  the solenoid, the
steel flux return is instrumented with a combination of resistive-plate chambers
and limited streamer tubes to  provide detection of muons and long-lived neutral
hadrons.

The  EMC is  the most  important detector  subsystem for  the \ensuremath{\ensuremath{B}\xspace^{0} \ensuremath{\rightarrow}\xspace \gamma \gamma}\  search.  It
provides  polar angle  coverage in  the  laboratory frame  from $15.8^\circ$  to
$141.8^\circ$,  and  full azimuthal  coverage,  corresponding  to a  solid-angle
coverage of 90\% in the $\Upsilon(4S)$ CM frame.
When  a photon  interacts  with the  EMC  it creates  an electromagnetic  shower
depositing  its  energy into  many  contiguous  crystals,  typically 10  to  30,
hereafter called a ``cluster''.  If no track in the event points to the cluster,
it is designated as a photon candidate.
Individual crystals are  read out by a pair of  silicon PIN photodiodes attached
to the  rear face of the  crystal.  Amplified signals are  sampled and digitized
with a  period of 270~ns, providing a  continuous data stream to  which gain and
pedestal corrections are applied.  When  a first level trigger is recorded, data
samples in  a time window of $\pm1  \, \mu$s are selected,  producing a waveform
which is  analyzed by  a feature  extraction algorithm running  in real  time in
readout modules.  For events passing a higher level trigger, any EMC signal with
energy  above  a  0.5~\ensuremath{\mathrm{\,Me\kern -0.1em V}}\xspace  threshold   has  its  deposited  energy  and  timing
information recorded for offline analysis.

The energy resolution of the EMC is  parameterized as the sum of two terms added
in quadrature, given by~\cite{SLAC-EMC}:
\begin{equation*}\label{eq:eRes}
  \frac{\sigma_E}{E} = {2.30 \% \over \sqrt[4]{E\, (\ensuremath{\mathrm{\,Ge\kern -0.1em V}}\xspace)}} \oplus 1.35\%,
\end{equation*}
while the angular resolution in the polar angle $\theta$ and the azimuthal angle
$\phi$ is given by:
\begin{equation*}\label{eq:angRes}
 \sigma_\theta = \sigma_\phi = {4.16 \over \sqrt{E\, (\ensuremath{\mathrm{\,Ge\kern -0.1em V}}\xspace)}} \, {\rm mrad}.
\end{equation*}

In addition to  the on-resonance data, a data sample of  $44$ \ensuremath{\mbox{\,fb}^{-1}}\xspace taken about
40~\ensuremath{\mathrm{\,Me\kern -0.1em V}}\xspace below the  \Y4S peak is recorded and used to  validate the Monte Carlo
(MC) simulation of continuum processes, \ensuremath{e^+e^-}\xspace\ensuremath{\rightarrow}\xspace\ensuremath{q\overline q}\xspace, where $q = u,d,s,\, {\rm
or}\ c)$ and  \ensuremath{e^+e^-}\xspace\ensuremath{\rightarrow}\xspace\ensuremath{\tau^+\tau^-}\xspace.  MC simulated events are  produced using the {\tt
EVTGEN}~\cite{evtGen} package to  model the physics of \ensuremath{B}\xspace  meson decays and {\tt
JETSET}~\cite{jetset}   to  model   quark  fragmentation.    The   {\tt  GEANT}4
toolkit~\cite{geant4} is used  to simulate the interaction of  these events with
the detector model.   These tools are designed to take  into account the varying
detector and beam conditions encountered during data-taking.  The MC events were
analyzed with  the same reconstruction  algorithms, event selection  and fitting
procedures as  data.  MC samples  of $B \kern 0.18em\overline{\kern -0.18em B}{}\xspace$  events correspond to  about four
times  the  integrated  luminosity of  the  data.   Those  for  $e^+ e^-  \ensuremath{\rightarrow}\xspace  c
\overline{c}$ events  correspond to twice  the data luminosity, while  those for
$e^+ e^- \ensuremath{\rightarrow}\xspace q  \overline{q}$ (where $q = u, \, d, {\rm or}  \, s$) and $e^+ e^-
\ensuremath{\rightarrow}\xspace \tau^+ \tau^-$ correspond to  approximately the same luminosity as data.  In
addition, special  MC data sets  are created in  which large samples of  rare \ensuremath{B}\xspace
meson decays  are generated  for the purpose  of investigating the  signal \ensuremath{\ensuremath{B}\xspace^{0} \ensuremath{\rightarrow}\xspace \gamma \gamma}\
decay as well as possible backgrounds due to other \ensuremath{B}\xspace decays.

\section{Event Selection And Backgrounds}
\label{sec:evtReco}

\subsection{Event Selection}
\label{sec:evtSel}
The full \Y4S  on-resonance dataset is first reduced  by selecting events that
contain at least two photons with energies of $1.15 \leq E^*_{\gamma} \leq 3.50$
\ensuremath{\mathrm{\,Ge\kern -0.1em V}}\xspace,  where the  asterisk indicates  a  quantity in  the \Y4S  CM frame.   We
consider all  combinations of two photons  whose energies lie in  this range and
add their four-momentum to create \ensuremath{B}\xspace meson candidates.  Hereafter, these photons
are  referred  to  as  \ensuremath{B}\xspace  candidate photons.   The  distribution  of  correctly
reconstructed \ensuremath{B}\xspace candidates will peak in two nearly uncorrelated variables, \mbox{$m_{\rm ES}$}\xspace
and \mbox{$\Delta E$}\xspace.  The beam energy substituted mass is defined as $\mbox{$m_{\rm ES}$}\xspace \equiv \sqrt{
E^{*2}_{\rm  beam} -  \vec{p}^{*2}_{\rm B}}/c^2$  and the  energy  difference is
$\mbox{$\Delta E$}\xspace \equiv  E^*_{\rm B}  - E^*_{\rm beam}$,  where $E^*_{\rm beam}$  is the
beam energy,  and $\vec{p}^*_{\rm B}$  and $E^*_{\rm B}$ are  the three-momentum
and  energy of  the  \ensuremath{B}\xspace candidate,  respectively.   For \ensuremath{\ensuremath{B}\xspace^{0} \ensuremath{\rightarrow}\xspace \gamma \gamma}\  events, the  \mbox{$m_{\rm ES}$}\xspace
distribution will peak at the \ensuremath{B}\xspace meson mass, $5.279$ \ensuremath{{\mathrm{\,Ge\kern -0.1em V\!/}c^2}}\xspace~\cite{PDG2010}.  The
MC predicts  a full-width at half  maximum (FWHM) of $6.5  \ensuremath{{\mathrm{\,Me\kern -0.1em V\!/}c^2}}\xspace$.  The \mbox{$\Delta E$}\xspace
distribution is asymmetric  and will peak near zero with a  tail to the negative
\mbox{$\Delta E$}\xspace side  due to photon  energy loss outside  the active volume of  the EMC.
The FWHM  for \mbox{$\Delta E$}\xspace predicted  by the  MC is about  $150 \ensuremath{\mathrm{\,Me\kern -0.1em V}}\xspace$.  We  select an
event for further analysis if it  contains exactly one \ensuremath{B}\xspace candidate with $\mbox{$m_{\rm ES}$}\xspace >
5.1$ \ensuremath{{\mathrm{\,Ge\kern -0.1em V\!/}c^2}}\xspace and $-0.50  \leq \mbox{$\Delta E$}\xspace \leq 0.50$ \ensuremath{\mathrm{\,Ge\kern -0.1em V}}\xspace.  We find  that in \ensuremath{\ensuremath{B}\xspace^{0} \ensuremath{\rightarrow}\xspace \gamma \gamma}\ MC
only $0.06\%$ of events have more than  one \ensuremath{B}\xspace candidate and are removed by this
selection.    Events  from   continuum   processes,  $\ensuremath{e^+e^-}\xspace   \ensuremath{\rightarrow}\xspace  \ensuremath{q\overline q}\xspace$   and
\ensuremath{e^+e^-}\xspace\ensuremath{\rightarrow}\xspace\ensuremath{\tau^+\tau^-}\xspace, are suppressed  by requiring the ratio of  the second to zeroth
Fox-Wolfram moments~\cite{fox},  $R_2$, to be  less than $0.90$.  This  ratio is
calculated from the  momenta of all charged and neutral  particles in the event.
To suppress  backgrounds from \ensuremath{e^+e^-}\xspace\ensuremath{\rightarrow}\xspace\ensuremath{\tau^+\tau^-}\xspace  events, which tend to  have lower
multiplicity  compared to  \ensuremath{\ensuremath{B}\xspace^{0} \ensuremath{\rightarrow}\xspace \gamma \gamma}\  events, the  number  of reconstructed  charged
tracks in the event is required to be greater than two.

We define a signal region in  the \mbox{$m_{\rm ES}$}\xspace-\mbox{$\Delta E$}\xspace plane by fitting each variable in
\ensuremath{\ensuremath{B}\xspace^{0} \ensuremath{\rightarrow}\xspace \gamma \gamma}\ MC  and selecting a range around  the peak of the  distribution.  The \mbox{$m_{\rm ES}$}\xspace
distribution is fit  with a Crystal Ball (CB) shape~\cite{Gaiser}  and we take a
$\pm 3\, \sigma$ region around the CB peak corresponding to $5.27 \leq \mbox{$m_{\rm ES}$}\xspace \leq
5.29$ \ensuremath{{\mathrm{\,Ge\kern -0.1em V\!/}c^2}}\xspace.   The \mbox{$\Delta E$}\xspace distribution  is parameterized using  a double-sided
modified Gaussian with tail parameters given by
\begin{equation}\label{eq:cruijff}
  f(\mbox{$\Delta E$}\xspace) \propto \exp
  \left(
    \frac{-(\mbox{$\Delta E$}\xspace - \mu)^2}
	 {2\sigma^2_{\rm L,R} + \alpha_{\rm L,R}(\mbox{$\Delta E$}\xspace - \mu)^2}
  \right),
\end{equation}
where $\mu$ is the peak of  the \mbox{$\Delta E$}\xspace distribution, $\sigma_{\rm L,R}$ are the
distribution widths, and $\alpha_{\rm L,R}$  are tail parameters on the left and
right side of the peak, respectively.  We again take a $\pm 3\, \sigma$ interval
around  the peak corresponding  to $-0.30  \leq \mbox{$\Delta E$}\xspace  \leq 0.13$  \ensuremath{\mathrm{\,Ge\kern -0.1em V}}\xspace.  This
region is blinded  in the on-resonance data until the  maximum likelihood fit is
performed.  With this  definition of the signal region,  an \mbox{$m_{\rm ES}$}\xspace sideband region
is defined as $5.20 < \mbox{$m_{\rm ES}$}\xspace < 5.27$ \ensuremath{{\mathrm{\,Ge\kern -0.1em V\!/}c^2}}\xspace, and lower and upper \mbox{$\Delta E$}\xspace sidebands
are defined as $-0.50 \leq \mbox{$\Delta E$}\xspace <  -0.30$ \ensuremath{\mathrm{\,Ge\kern -0.1em V}}\xspace and $0.13 < \mbox{$\Delta E$}\xspace \leq 0.50$
\ensuremath{\mathrm{\,Ge\kern -0.1em V}}\xspace, respectively.

We  select photon  candidates  according  to criteria  chosen  to remove  poorly
reconstructed photons.  The  energy of the cluster must be  spread over at least
10  crystals with all  crystals in  the cluster  having active  electronics with
correct calibrations.  The shape of the  cluster must be consistent with that of
a photon  in the  defined energy  range; we therefore  require clusters  to have
lateral moments~\cite{LAT} in  the range $0.15 \leq f_{\rm  lat} \leq 0.50$.  To
ensure the shower  is fully contained within the EMC  volume, only photons whose
polar  angle is in  the range  $22.9^{\circ} <  p_{\theta} <  137.5^{\circ}$ are
selected.  The photons  are kept for further analysis if  they are isolated from
all other clusters in the event by at least 25 cm.

If an \ensuremath{e^+e^-}\xspace collision that results  in a trigger is accompanied by another \ensuremath{e^+e^-}\xspace
collision  nearby  in time,  EMC  signals  from  the out-of-time  collision  may
populate  the event  of  interest.   Due to  the  large Bhabha-scattering  cross
section, these ``pile-up'' events  often involve high-energy electrons which may
produce  EMC  clusters.  Since  the  tracking  detectors  are sensitive  over  a
narrower time window, electron-induced tracks may not point to the EMC clusters,
causing  them to  be treated  as  photon candidates.   If their  energy is  also
measured incorrectly, artificial \ensuremath{\ensuremath{B}\xspace^{0} \ensuremath{\rightarrow}\xspace \gamma \gamma}\  candidates may result.  This scenario is
effectively  rejected  by requiring  the  total event  energy  to  be less  than
$15.0$~\ensuremath{\mathrm{\,Ge\kern -0.1em V}}\xspace and  the cluster time of  each \ensuremath{B}\xspace candidate photon  to be consistent
with the trigger event time.

The dominant  source of backgrounds are  photons produced from  high energy \ensuremath{\pi^0}\xspace
and  $\eta$ decays in  continuum events.   These events  are suppressed  using a
likelihood ratio  rejection technique.  Each  \ensuremath{B}\xspace candidate photon  is separately
combined with all other photons in  the event and the invariant mass, $m_{\gamma
\gamma'}$, and energy of the  other photon, $E_{\gamma'}$, are used to calculate
a likelihood ratio given by
\begin{equation}\label{eq:lrRatio}
  L_{i} = \frac{P_{i}(m_{\gamma\gamma'},E_{\gamma'})}
              {P_{\rm sig}(m_{\gamma\gamma'},E_{\gamma'}) +
	       P_{i}(m_{\gamma\gamma'},E_{\gamma'})}.
\end{equation}
In  this equation,  $i$ is  a label  for \ensuremath{\pi^0}\xspace  or $\eta$,  and $P$  represents a
two-dimensional  probability  density function  (PDF).   For  each \ensuremath{B}\xspace  candidate
photon  the pairing  that gives  the largest  value of  the likelihood  ratio is
assigned.  The signal PDF, $P_{\rm  sig}$, is constructed using simulated \ensuremath{\Bz {\kern -0.16em \Bzb}}\xspace
events containing a \ensuremath{\ensuremath{B}\xspace^{0} \ensuremath{\rightarrow}\xspace \gamma \gamma}\ decay where all \ensuremath{B}\xspace candidate photon pairings are used.
The PDF  for a \ensuremath{\pi^0}\xspace or $\eta$,  $P_i$, is constructed from  simulated $\ensuremath{e^+e^-}\xspace \ensuremath{\rightarrow}\xspace
\ensuremath{q\overline q}\xspace$ and  \ensuremath{e^+e^-}\xspace\ensuremath{\rightarrow}\xspace\ensuremath{\tau^+\tau^-}\xspace events.   The \ensuremath{B}\xspace candidate  photon in this  case is
required to  be produced  from a \ensuremath{\pi^0}\xspace  or $\eta$  decay, while the  other photon
daughter is required to be reconstructed  in the calorimeter.  The energy of the
other photon and the  invariant mass of the pair are then  used to construct the
\ensuremath{\pi^0}\xspace and $\eta$ PDFs.  A likelihood  ratio near 1.0 (0.0) is consistent with the
\ensuremath{B}\xspace  candidate   photon  originating  from   a  \ensuremath{\pi^0}\xspace  or  $\eta$   (signal  $B$).
Figure~\ref{fig:LR}  shows the \ensuremath{L_{\ensuremath{\pi^0}\xspace}}\  and \ensuremath{L_{\eta}}\  distributions for  \ensuremath{\ensuremath{B}\xspace^{0} \ensuremath{\rightarrow}\xspace \gamma \gamma}\ MC
events and for \ensuremath{B}\xspace candidate photons  from \ensuremath{\pi^0}\xspace and $\eta$ decays in MC continuum
background events.
\begin{figure}

  \includegraphics[width=\linewidth,clip=true]{pi0LR_altArrow}
  \includegraphics[width=\linewidth,clip=true]{etaLR_altArrow}

  \caption
  {
    \label{fig:LR}
    (a)  \ensuremath{\pi^0}\xspace likelihood  ratio for  \ensuremath{B}\xspace  candidate photons  in simulated  signal
    events (open  histogram) and  simulated continuum background  events (shaded
    histogram) where the photon is required to originate from a \ensuremath{\pi^0}\xspace decay.  (b)
    $\eta$ likelihood ratio for \ensuremath{B}\xspace  candidate photons in simulated signal events
    (open   histogram)  and  simulated   continuum  background   events  (shaded
    histogram)  that are  required to  originate from  an $\eta$  decay.  Events
    where both  \ensuremath{B}\xspace candidate photon likelihood  ratios are less  than $0.84$ are
    selected, as denoted by the arrows.
  }

\end{figure}

For high energy \ensuremath{\pi^0}\xspace decays with $E_{\ensuremath{\pi^0}\xspace} \gtrsim 2$ \ensuremath{\mathrm{\,Ge\kern -0.1em V}}\xspace, the daughter photons
may not  be separated enough  in the EMC  to be resolved individually.   In this
case the photon clusters  are said to be ``merged''.  A merged  \ensuremath{\pi^0}\xspace can mimic a
\ensuremath{B}\xspace candidate photon because the cluster  will have the full energy of the parent
\ensuremath{\pi^0}\xspace and will have no associated track.  At a given energy, the second moment of
the energy distribution around the center of the cluster will be different for a
photon and a merged \ensuremath{\pi^0}\xspace.  This allows for the construction of a quantity called
the  merged \ensuremath{\pi^0}\xspace  consistency  based on  the  energy and  second  moment of  the
cluster.  This variable compares the two inputs against known distributions from
photons as  well as  merged $\ensuremath{\pi^0}\xspace$  decays to estimate  the likelihood  that the
cluster originates from  either source.  The \ensuremath{\pi^0}\xspace energy  range that contributes
\ensuremath{B}\xspace candidate photons  in this analysis begins at about  2~\ensuremath{\mathrm{\,Ge\kern -0.1em V}}\xspace and extends above
6~\ensuremath{\mathrm{\,Ge\kern -0.1em V}}\xspace.  To reduce this source of merged \ensuremath{\pi^0}\xspace background, we select \ensuremath{B}\xspace candidate
photons whose merged \ensuremath{\pi^0}\xspace consistency is compatible with that of a photon.

In  the  \Y4S  CM frame,  the  \ensuremath{B}\xspace  mesons  are  produced  nearly at  rest  and
subsequently decay isotropically, whereas events produced in continuum processes
are  typically collimated in  jets along  the \ensuremath{q\overline q}\xspace  axis.  This  difference in
event shape  is exploited to  further separate signal from  continuum background
events using a neural network  (NN) multivariate classifier.  The NN utilizes 19
input variables that characterize  event level features whose distributions show
separation power between signal and continuum background events.
The inputs  include the  minimum distance between  each \ensuremath{B}\xspace candidate  photon EMC
cluster and  all other  EMC clusters  in the event,  the polar  angle of  the \ensuremath{B}\xspace
candidate  momentum  in   the  lab  frame,  the  number   of  neutral  particles
reconstructed, the  number of tracks  in the event,  the missing energy  and the
total  transverse momentum.  We  also include  quantities that  characterize the
rest-of-the-event  (ROE), calculated  using all  reconstructed particles  in the
event except  one or both \ensuremath{B}\xspace candidate  photons.  These are: the  polar angle of
the event  thrust axis when  either \ensuremath{B}\xspace candidate  photon is removed;  the first,
second, and third angular moments of the event when the \ensuremath{B}\xspace candidate photon with
the larger  energy in the  lab frame is  removed; and the second  angular moment
with respect to the thrust axis of  the event when both \ensuremath{B}\xspace candidate photons are
removed.   Additionally, $R_2$, the  total sphericity,  and the  sphericity with
both  \ensuremath{B}\xspace  candidate photons  removed  are used;  all  energies  and momenta  are
calculated in the CM frame.
We train and validate the NN using independent samples of \ensuremath{\ensuremath{B}\xspace^{0} \ensuremath{\rightarrow}\xspace \gamma \gamma}\ and continuum MC
events.   The training  samples are  constructed  by first  applying the  photon
quality,  event pile-up,  and  merged \ensuremath{\pi^0}\xspace  selections  to the  MC events.   The
surviving events  are randomly  divided into  one set for  training and  one for
validation.  Each set contains 45,200 events  where half are \ensuremath{\ensuremath{B}\xspace^{0} \ensuremath{\rightarrow}\xspace \gamma \gamma}\ MC events and
the other half  are continuum MC events whose  composition of \ensuremath{e^+e^-}\xspace\ensuremath{\rightarrow}\xspace\ensuremath{q\overline q}\xspace and
\ensuremath{e^+e^-}\xspace\ensuremath{\rightarrow}\xspace\ensuremath{\tau^+\tau^-}\xspace events is scaled to  the luminosity of the on-resonance data for
each component.  The parameters of the NN are tuned to achieve the highest level
of  background  rejection  while  avoiding  overtraining  the  classifier.   The
validation sample is then used to  verify its performance.  The NN response is a
value     between    0.0     (background-like)     and    1.0     (signal-like).
Figure~\ref{fig:NNoutput} shows  the NN response for the  validation samples for
\ensuremath{\ensuremath{B}\xspace^{0} \ensuremath{\rightarrow}\xspace \gamma \gamma}\ and continuum background MC events.
\begin{figure}

  \includegraphics[width=\linewidth,clip=true]{NNoutput_altArrow}

 \caption
 {
   \label{fig:NNoutput}
   Output of  the neural network on  the validation samples  of simulated signal
   (open histogram)  and background (shaded histogram)  events.  Selected events
   have a NN response greater than 0.54 as denoted by the arrow.
 }

\end{figure}

The selection  criteria for \ensuremath{L_{\ensuremath{\pi^0}\xspace}}, \ensuremath{L_{\eta}},  the NN response, and  the number of
tracks are optimized using \ensuremath{\ensuremath{B}\xspace^{0} \ensuremath{\rightarrow}\xspace \gamma \gamma}\  MC events and on-resonance sideband data.  The
number of background events is estimated by extrapolating the sideband data into
the signal  region.  The  optimization proceeds by  iterating over the  space of
each    variable   individually    until   we    find   a    maximum    of   the
figure-of-merit~\cite{punzi} given by the  expression $\varepsilon_{\rm sig} / (
3/2 + \sqrt{B} )$.  In  this equation, $\varepsilon_{\rm sig}$ is the efficiency
of the event selection derived from \ensuremath{\ensuremath{B}\xspace^{0} \ensuremath{\rightarrow}\xspace \gamma \gamma}\ MC and $B$ is the number of background
events.  The  iterative process continues  to cycle through all  variables until
the selection  values converge.  We find  the optimum values to  be: $\ensuremath{L_{\ensuremath{\pi^0}\xspace}} <\,
0.84$, $\ensuremath{L_{\eta}}  <\, 0.84$, NN  response greater than  or equal to 0.54,  and the
number of  tracks to be  greater than two.   The optimum selection  criteria are
found  to have  an overall  efficiency  of $26.7\%$  on a  collection of  $1.96$
million  simulated \ensuremath{\ensuremath{B}\xspace^{0} \ensuremath{\rightarrow}\xspace \gamma \gamma}\ events,  while rejecting  about $99.9\%$  of background
events.

\subsection{Exclusive \boldmath{$B$} Decay Backgrounds}
\label{sec:Bkg}
Backgrounds from \ensuremath{B}\xspace  decays that may peak in the \mbox{$m_{\rm ES}$}\xspace  and \mbox{$\Delta E$}\xspace signal region
are studied with large samples of  simulated events.  Twelve \ensuremath{B}\xspace decay modes were
identified  as  potential background  sources,  and  exclusive  MC samples  were
generated for  each mode.  The optimized  event selection is applied  to each of
these  samples  and  the  estimated  number of  background  events  expected  in
on-resonance    data    are     determined    from    the    latest    branching
fractions~\cite{PDG2010}.   After  scaling the  yields  of  these  modes to  the
luminosity  of the on-resonance  data, it  is estimated  that they  contribute a
total of $1.18 \pm 0.22$ background events to the signal region.  This number is
comparable  to  the expected  number  of signal  events  predicted  from the  SM
branching  fraction  ($\sim  4$  events).   The  modes  expected  to  contribute
significantly are $\ensuremath{B^0}\xspace \ensuremath{\rightarrow}\xspace \ensuremath{\pi^0}\xspace \ensuremath{\pi^0}\xspace$, $\ensuremath{B^0}\xspace \ensuremath{\rightarrow}\xspace \ensuremath{\pi^0}\xspace \eta$, $\ensuremath{B^0}\xspace \ensuremath{\rightarrow}\xspace \eta \eta$,
and $\ensuremath{B^0}\xspace \ensuremath{\rightarrow}\xspace \omega \gamma$.  The  \mbox{$m_{\rm ES}$}\xspace distributions of these modes peak at the
same value  as true  signal events,  while the \mbox{$\Delta E$}\xspace  distributions peak  at a
value less  than zero.   This difference in  shape of the  \mbox{$\Delta E$}\xspace distributions
between \ensuremath{\ensuremath{B}\xspace^{0} \ensuremath{\rightarrow}\xspace \gamma \gamma}\ decays and these  ``peaking'' background \ensuremath{B}\xspace decays is exploited by
adding a component that describes them to the maximum likelihood function.

\section{Maximum Likelihood Fit}
\label{sec:MLFit}
The signal yield is extracted  using a two-dimensional unbinned extended maximum
likelihood (ML)  fit in the  region $\mbox{$m_{\rm ES}$}\xspace >  5.2$ \ensuremath{{\mathrm{\,Ge\kern -0.1em V\!/}c^2}}\xspace and $-0.5  \leq \mbox{$\Delta E$}\xspace
\leq 0.5$ \ensuremath{\mathrm{\,Ge\kern -0.1em V}}\xspace.  The likelihood function for a sample of $N$ events with signal,
continuum, and peaking \BB background components is given by
\begin{equation}\label{eq:likelihoodFunc}
  {\cal L} = \exp \left( - \sum_{i=1}^{3} n_i \right)
                  \left[
		    \prod_{j=1}^{N}
		      \left(
		        \sum_{i=1}^{3} n_i {\cal P}_i( \vec{x}_j; \vec{\alpha}_i)
		      \right)
		  \right],
\end{equation}
where $i$ in this  equation is an index for the three  components in the fit and
$n_i$ is  the event  yield for  each.  Since the  correlations between  \mbox{$m_{\rm ES}$}\xspace and
\mbox{$\Delta E$}\xspace are found to be small,  the signal and continuum background PDFs, ${\cal
P}_i$, are each defined as a  product of one-dimensional PDFs in the observables
$x_{j}   \in   \{\mbox{$m_{\rm ES}$}\xspace,   \mbox{$\Delta E$}\xspace\}$,   with  parameters   $\vec{\alpha}_i$.    A
two-dimensional histogram PDF is used for the peaking background component.

The signal PDF  shapes for \mbox{$m_{\rm ES}$}\xspace and \mbox{$\Delta E$}\xspace are  determined from simulated \ensuremath{\ensuremath{B}\xspace^{0} \ensuremath{\rightarrow}\xspace \gamma \gamma}\
events.    The  \mbox{$m_{\rm ES}$}\xspace   distribution   is  parameterized   by   a  Crystal   Ball
function~\cite{Gaiser}, and the \mbox{$\Delta E$}\xspace shape is parameterized by a double-sided
modified Gaussian with tail parameters given by Equation~\eqref{eq:cruijff}.  In
the ML  fit, the signal  PDF parameters are  fixed to the  MC-determined values.
All  fixed  signal  parameters  are  later varied  to  evaluate  the  systematic
uncertainty that this choice of parameterization has on the signal yield.

The  continuum  background  \mbox{$m_{\rm ES}$}\xspace  distribution  is  parameterized  by  an  ARGUS
shape~\cite{ARGUS},  while the \mbox{$\Delta E$}\xspace  distribution is  fit with  a first-order
polynomial.  The endpoint of the ARGUS  function is fixed to the kinematic limit
for \ensuremath{B}\xspace decays ($5.29$ \ensuremath{{\mathrm{\,Ge\kern -0.1em V\!/}c^2}}\xspace), while  all other parameters are allowed to float.
The  PDF for  the  peaking  background component  is  parameterized using  large
samples  of simulated  exclusive  \ensuremath{B}\xspace decays  in  the form  of a  two-dimensional
histogram PDF in  \mbox{$m_{\rm ES}$}\xspace and \mbox{$\Delta E$}\xspace.  Both the shape and  yield of this component
are fixed in the ML fit.  The yield is fixed to $3.13 \pm 0.54$ events, which is
the predicted number in the fit region determined from the exclusive MC studies.
The fixed  peaking background PDF shape  and yield are later  varied to evaluate
the systematic uncertainty on the signal yield.

The  fit is  validated on  an ensemble  of prototype  datasets whose  signal and
background content and shape are as  expected in the on-resonance data.  For the
signal  content,  one-half,  one,  and  two  times  the  expected  SM  branching
fractions~\cite{BB} are used  to populate the prototype datasets.   Two types of
datasets are  constructed: one where both  the signal and  background events are
generated by randomly  sampling from their respective PDFs,  and the other where
the background events are generated from a random sampling of the background PDF
while the signal events are embedded directly from the simulated signal dataset.
The results of the validation show that the fit returns the correct signal yield
and does not introduce a bias.

The  on-resonance \Y4S  data contains  1679 events  after the  optimized event
selection criteria  are applied.  We  perform the ML  fit to extract  the signal
yield  and  find  $N_{\rm  sig}  = 21^{+13}_{-12}$  events  corresponding  to  a
statistical  significance of $1.9\,  \sigma$.  The  significance is  computed as
$\sqrt{2 \cdot \Delta  {\rm ln} {\cal L}}$, where $\Delta {\rm  ln} {\cal L}$ is
the difference in  the log-likelihood between the best  fit to on-resonance data
and a fit  where the signal yield is fixed  to zero.  Figure~\ref{fig:fit} shows
projections of the PDF components from the ML fit.  For the \mbox{$m_{\rm ES}$}\xspace projection, the
range of  \mbox{$\Delta E$}\xspace has been  restricted to $-0.30  \leq \mbox{$\Delta E$}\xspace \leq  0.13$ \ensuremath{\mathrm{\,Ge\kern -0.1em V}}\xspace.
For the  \mbox{$\Delta E$}\xspace projection, the  range of \mbox{$m_{\rm ES}$}\xspace  is restricted to $\mbox{$m_{\rm ES}$}\xspace  > 5.27$
\ensuremath{{\mathrm{\,Ge\kern -0.1em V\!/}c^2}}\xspace.
\begin{figure}

  \includegraphics[width=\linewidth,clip=true]{FullFit_mes_cuts}
  \includegraphics[width=\linewidth,clip=true]{FullFit_deltaE_cuts}

  \caption
  {
    \label{fig:fit}
    Projections of the ML fit onto  \mbox{$m_{\rm ES}$}\xspace and \mbox{$\Delta E$}\xspace.  (a) The projection of the
    \mbox{$m_{\rm ES}$}\xspace component when the range of  \mbox{$\Delta E$}\xspace has been restricted to $-0.30 \leq
    \mbox{$\Delta E$}\xspace \leq  0.13$ \ensuremath{\mathrm{\,Ge\kern -0.1em V}}\xspace.  (b)  The \mbox{$\Delta E$}\xspace projection  of the fit  when the
    range  of \mbox{$m_{\rm ES}$}\xspace  has been  restricted to  $\mbox{$m_{\rm ES}$}\xspace >  5.27$ \ensuremath{{\mathrm{\,Ge\kern -0.1em V\!/}c^2}}\xspace.   The points
    represent the on-resonance data.  The  solid curve represents the total PDF,
    the dashed curve is the continuum background component, the dot-dashed curve
    is the signal component, and the long-dashed curve is the peaking background
    component.  With an  expected yield of approximately one  event, the peaking
    background component is nearly indistinguishable from the $x$-axis.
  }

\end{figure}

\section{Systematic Uncertainties}
\label{sec:Systematics}
Systematic  errors that  affect the  calculation of  the branching  fraction are
investigated and  include uncertainties  on the number  of \ensuremath{\Bz {\kern -0.16em \Bzb}}\xspace events  in the
dataset,  signal efficiency,  and the  signal yield  from the  fit.  Differences
between data and MC can lead to  an error on the derived signal efficiency.  The
identified  sources  that  can  lead  to this  error  include  uncertainties  in
tracking,  track multiplicity,  photon reconstruction,  the \ensuremath{L_{\ensuremath{\pi^0}\xspace}}\  and \ensuremath{L_{\eta}}\
requirements, and  the NN selection.  The  uncertainties in the  modeling of the
signal and \BB  background shapes in the maximum likelihood  fit can also affect
the uncertainty on the signal yield.

The systematic  error associated with  counting the number  of \BB pairs  in the
dataset is $1.1\%$.   The number of \ensuremath{\Bz {\kern -0.16em \Bzb}}\xspace pairs is  obtained by multiplying the
\Y4S\ensuremath{\rightarrow}\xspace\ensuremath{\Bz {\kern -0.16em \Bzb}}\xspace    branching   fraction,    equal   to    $(48.4    \pm   0.6)\,
\%$~\cite{PDG2010}, with the total number  of \BB pairs, from which a systematic
error of  $1.7\, \%$  is assigned.   A study of  the track  finding inefficiency
results  in an  assignment  of a  $0.2  \, \%$  systematic  uncertainty for  the
selection of events  with at least three reconstructed  tracks.  The uncertainty
in the signal efficiency due to  the requirement of at least three reconstructed
charged  tracks  is  estimated  to  be  3.4\%,  including  components  for  both
generator-level  simulation  errors   and  detector-associated  data  versus  MC
differences.  The efficiencies in data  and MC for detecting high-energy photons
which pass the  selection criteria were compared using a sample  of $e^+ e^- \ensuremath{\rightarrow}\xspace
\mu^+ \mu^- \gamma$ events with the  photon energy in the CM frame restricted to
be consistent  with the energy of  \ensuremath{\ensuremath{B}\xspace^{0} \ensuremath{\rightarrow}\xspace \gamma \gamma}\ photons.  No  significant difference is
observed.  An uncertainty of 2\% per  photon is assigned to account for possible
data  versus MC differences  due to  the required  minimum distance  between the
candidate-photon cluster and all other  clusters, based on a study that embedded
high-energy photons  in both  data and MC  events.  We combine  this uncertainty
linearly  for both  photons in  \ensuremath{\ensuremath{B}\xspace^{0} \ensuremath{\rightarrow}\xspace \gamma \gamma}\ and  assign an  overall  photon efficiency
systematic of 4\%.  The cluster time selection is compared in data and MC and we
assign  a  systematic  uncertainty  of  $0.7\%$  for  each  \ensuremath{B}\xspace  candidate.   The
systematic uncertainty due to the \ensuremath{\pi^0}\xspace and $\eta$ likelihood ratios is estimated
to be 1.0\% for each, based on a study that embedded signal-like photons in data
and MC events  in which one $B$  meson was fully reconstructed in  the decay $\ensuremath{B}\xspace
\ensuremath{\rightarrow}\xspace D \pi$.  The signal efficiencies  for the \ensuremath{L_{\ensuremath{\pi^0}\xspace}}\ and \ensuremath{L_{\eta}}\ selections for
data and MC  are calculated by pairing the embedded  signal-like photon with all
other photons  in these  events that are  not associated with  the reconstructed
$B$.  The  systematic uncertainty due  to the NN  is estimated by  comparing the
efficiencies  of data  to  MC  in signal-like  events.   Signal-like events  are
selected by applying all event  selection criteria, but reversing either the NN,
the \ensuremath{L_{\ensuremath{\pi^0}\xspace}}\ or  the \ensuremath{L_{\eta}}\ selection for one of the  \ensuremath{B}\xspace candidate photons.  The
efficiencies are then  calculated from events in the fit  region with the signal
region  excluded.   For all  selection  reversal  scenarios,  the ratio  of  the
efficiencies is found to be consistent  with unity and has a typical statistical
error of $3.0\%$, which is taken as the systematic uncertainty.

The systematic  uncertainty on the signal yield  due to the choice  of fit model
has four components.   The first is due  to the fixed signal shape  for the \mbox{$m_{\rm ES}$}\xspace
and \mbox{$\Delta E$}\xspace PDFs.  To estimate the systematic uncertainty related to this choice
of  parameterizations, the  fixed parameters  are varied  within their  $\pm 1\,
\sigma$ errors, the on-resonance dataset is  refit, and the change in the signal
yield is  calculated for  each parameter.  The  total systematic  uncertainty is
taken to  be the sum in  quadrature of all variations  and is found  to be $0.6$
events.  A comparison between photon  response in $\ensuremath{e^+e^-}\xspace \ensuremath{\rightarrow}\xspace \mu^+ \mu^- \gamma$
events in data and  MC, using photons in the energy range  relevant to the decay
\ensuremath{\ensuremath{B}\xspace^{0} \ensuremath{\rightarrow}\xspace \gamma \gamma}, shows  that the size  of the variation  in the signal shape  parameters is
sufficient to take  into account any systematic effects  from parameterizing the
signal PDF shapes from MC.
The second component is due to the parameterization choice for the signal shape.
While the  PDF used to fit the  \mbox{$m_{\rm ES}$}\xspace distribution replicates the  shape in \ensuremath{\ensuremath{B}\xspace^{0} \ensuremath{\rightarrow}\xspace \gamma \gamma}\
MC, there is a slight disagreement  between the \mbox{$\Delta E$}\xspace distribution in \ensuremath{\ensuremath{B}\xspace^{0} \ensuremath{\rightarrow}\xspace \gamma \gamma}\ MC
and  that   of  the  double-sided   modified  Gaussian  used  to   describe  it,
Eq.~\eqref{eq:cruijff}.  To test how large of an effect this difference may have
on the signal  yield, the \mbox{$\Delta E$}\xspace distribution is  parameterized using a Crystal
Ball shape~\cite{Gaiser} that  provides a larger discrepancy from  the MC shape.
Ensembles  of  simulated  experiments  are  performed  wherein  each  experiment
consists of an independent dataset  fit first using the \mbox{$\Delta E$}\xspace parameterization
described   in   Section~\ref{sec:MLFit}   and   then  using   the   alternative
parameterization described  here.  The signal  yields for each fit  are compared
and the average difference is found to  be $0.2$ events which is assigned as the
systematic uncertainty.
The third  component is due  to the choice  of the \mbox{$\Delta E$}\xspace  continuum background
shape.  Repeating  the fit to data  using a second-order  polynomial for \mbox{$\Delta E$}\xspace
results in an increase of $1.9$ events  in the signal yield, which we take to be
the systematic error for this component.
The fourth  component is due  to the choice  of shape and normalization  for the
peaking background  PDF, both of which  are fixed in  the ML fit.  The  shape is
fixed from the \mbox{$m_{\rm ES}$}\xspace and \mbox{$\Delta E$}\xspace distributions of simulated exclusive $B$ decays,
while the yield is fixed to  the expected number of events in on-resonance data.
To estimate  the systematic  uncertainty on the  signal yield, both  the peaking
background yield and  shape are varied.  The uncertainties on  the yields of the
individual  peaking  background  modes  are  added  linearly  to  determine  the
uncertainty on  the total yield  of the peaking  component in the ML  fit.  This
results in  a range  for the peaking  yield between  2.02 and 4.24  events.  The
shape of the peaking PDF is varied by replacing it solely with the shape derived
from $B^{0} \ensuremath{\rightarrow}\xspace \pi^{0} \pi^{0}$ MC.  The \mbox{$\Delta E$}\xspace distribution of this mode most
closely resembles that from \ensuremath{\ensuremath{B}\xspace^{0} \ensuremath{\rightarrow}\xspace \gamma \gamma}\ MC.  Another ensemble of simulated experiments
were  performed where the  differences in  the signal  yield were  compared when
using these  set of extreme variations  in the peaking component  and those from
the ML fit to data.  We take the maximal change in the signal yield and assign a
conservative systematic  uncertainty of $0.5$  events.  The four  components are
added in  quadrature to give  a systematic uncertainty  for the ML fit  of $2.1$
events corresponding to $9.9\%$.

These results are added in quadrature  to give a total systematic uncertainty on
the  signal yield  of $2.6$  events,  corresponding to  $12.1\%$.  The  separate
contributions    to    the   systematic    uncertainty    are   summarized    in
Table~\ref{tab:systErr}.
\begin{table}

  \caption
  {
    \label{tab:systErr}
    Summary of the systematic uncertainties expressed as a percent of the signal
    yield.
  }

  \vspace{0.25cm}

  \begin{tabular}{lr}
    \hline

    Source                   &  Uncertainty on $N_{\rm sig}$ (\%) \\
    \hline

    \ensuremath{\Bz {\kern -0.16em \Bzb}}\xspace counting          &  1.7 \\
    Tracking efficiency      &  0.2 \\
    Track multiplicity       &  3.4 \\
    Photon efficiency        &  4.0 \\
    Cluster time             &  0.7 \\
    \ensuremath{L_{\ensuremath{\pi^0}\xspace}}\ and \ensuremath{L_{\eta}}       &  2.8 \\
    Neural network           &  3.0 \\
    Fit uncertainty          &  9.9 \\
    \hline
    Sum in quadrature        & 12.1 \\
    \hline
  \end{tabular}

\end{table}

\section{Results}
\label{sec:Results}
The branching fraction is calculated from the measured signal yield using
\begin{equation}\label{eq:bf}
  {\cal B}(\ensuremath{B^0}\xspace  \ensuremath{\rightarrow}\xspace \gamma \gamma)
               = \frac{N_{\rm sig}}{ \varepsilon_{\rm sig} \cdot 2 \cdot
		       N_{\ensuremath{\Bz {\kern -0.16em \Bzb}}\xspace}},
\end{equation}
where  $N_{\rm  sig}$ is  the  signal yield  from  the  maximum likelihood  fit,
$\varepsilon_{\rm  sig}$  is the  signal  selection  efficiency determined  from
simulated \ensuremath{\ensuremath{B}\xspace^{0} \ensuremath{\rightarrow}\xspace \gamma \gamma}\ events, and $N_{\ensuremath{\Bz {\kern -0.16em \Bzb}}\xspace}$ is the number of neutral \ensuremath{B}\xspace meson pairs
in the on-resonance dataset. We calculate the branching fraction to be
\begin{equation}\label{eq:bf_result}
  {\cal B}(\ensuremath{B^0}\xspace \ensuremath{\rightarrow}\xspace \gamma \gamma ) = ( 1.7 \pm 1.1 \pm 0.2 ) \times 10^{-7},
\end{equation}
with  a statistical significance  of $1.9\,  \sigma$, where  the first  error is
statistical and the second is systematic.

The upper limit  at the 90\% CL is obtained by  integrating the likelihood curve
resulting from the ML fit from zero to the value of $N_{\rm sig}$ which contains
$90\%$ of the  area under the curve.  To  incorporate the systematic uncertainty
into the  determination of  the upper limit,  the likelihood curve  is convolved
with a Gaussian  shape whose width is equal to  the total systematic uncertainty
of $2.6$ events.  This yields a value of $N_{\rm sig} = 39$ events corresponding
to an upper limit of
\begin{equation}\label{eq:ul_result}
  {\cal B}(\ensuremath{B^0}\xspace \ensuremath{\rightarrow}\xspace \gamma \gamma) < \ensuremath{3.3\times10^{-7}}\ \ \ {\rm (90\%\ CL)}.
\end{equation}
Figure~\ref{fig:conv} shows the likelihood  curve from the fit after convolution
with the Gaussian shape.  The shaded  region corresponds to the 90\% integral of
the curve.
\begin{figure}

  \includegraphics[width=\linewidth,clip=true]{likelihoodConv_pub_shade}

  \caption
  {
    \label{fig:conv}
    Likelihood  curve from  the maximum  likelihood fit,  as a  function  of the
    signal yield, after  convolution with a Gaussian shape  whose width is equal
    to  the  total systematic  error.   The  shaded  region corresponds  to  the
    integral of the curve up to  90\% of its total area.  The $y$-axis, $L/L_0$,
    is the  ratio of the  likelihood function for  a given $N_{\rm sig}$  to the
    maximum likelihood.
  }

\end{figure}

This limit  is nearly a  factor of  two below the  best previous upper  limit of
${\cal B}(\ensuremath{\ensuremath{B}\xspace^{0} \ensuremath{\rightarrow}\xspace \gamma \gamma})  < 6.2 \times  10^{-7}$ set by Belle~\cite{Belle-Villa},  and is
consistent  with  the SM  branching  fraction.   This  limit may  allow  tighter
constraints on models that incorporate physics beyond the SM.

\section{Acknowledgements}
\input{pubboard/acknowledgements}

redpajama_set_name: RedPajamaArXiv


Whats your top 5 Accolades?
Cool I'll post mine later. Can you edit in what they are for ligy?
Had a class night lastnight with pepe, dridge, johny and ligy and my top one for the night was........ "most kill cams watched"
yeah there was something wrong with the servers i think,,J5 coming top? i think not!!
I must say he has improved. Played j5!
Subject: Re: Whats your top 5 Accolades?


text:
Report: 2015 Tour de France to feature highest ever finish
Peter Cossins 2014-09-02T15:10:00Z
Radio report suggests 2800-metre Bonette-Restefond could host stage finale
Vincenzo Nibali (Astana) on his way to winning stage 18 atop Hautacam (Image credit: Tim de Waele/TDWSport.com)
The yellow jersey group nears the top of the Bonette which looks like the edge of the world. (Image credit: Bettini Photo)
Vincenzo Nibali in yellow and protected by his Astana teammates during stage 14. (Image credit: Tim de Waele/TDWSport.com)
With little more than a month to go before the presentation of the 2015 Tour de France route, there are already plenty of rumours circulating about potential stages. Among the most surprising is a report from the D!Ci radio station in the southern Alps, which suggests that next year's Tour will spend three days in that region and could feature a summit finish on the Col de la Bonette-Restefond.
Liège may host 2015 Tour de France stage
2015 Tour Down Under route revealed
Vegni hopes Nibali will ride 2015 Giro d'Italia
2015 Vuelta a Espana to start in Malaga
At 2802 metres, the remote Bonette-Restefond is highest pass the Tour de France has ever tackled and would naturally be the highest summit finish in the race's history, eclipsing the 2645-metre finish on the Galibier in 2011.
According to D!Ci, the Tour, which will start in the Dutch city of Utrecht on 4 July, will reach the southern Alps on 20 July. That day's stage looks set to start in Digne-les-Bains – which has been confirmed by Digne's town hall in a separate report – and could finish on the Bonette-Restefond. The Tour would then spend a rest day in the region on 21 July, before heading northwards on 22 July, with L'Alpe d'Huez the probable destination, although the stage could head west to finish atop Mont Ventoux.
D!Ci says that hotels in the region have already been block booked on those dates, but suggests some issues remain to be resolved, primarily the race's passage through the spectacular Mercantour national park. The park authorities are reported to be opposed to the Tour going through the park, which last featured on the race in 2008.
That year South Africa's John-Lee Augustyn famously went off the edge of the Bonette-Restefond after leading over the pass and Cyril Dessel subsequently won the stage into Jausiers.
The Tour organisers are said to have a Plan B in the shape of a summit finish at the resort of Pra-Loup, where Bernard Thévenet ousted Eddy Merckx from the yellow jersey before going on to win the 1975 race. Next year will be the 40th anniversary of that historic moment.
Italy's Vincenzo Nibali (Astana) won this year's Tour de France and is expected to defend his victory in 2015.


Orsi's Italian Bakery & Pizzeria in Omaha, NE offers delicious pizza and Italian Cuisine. They are a long-running Italian throwback also offering specialty foodstuffs & baked goods in casual digs. Check them out for delightful delicatessen items, including meats, cheeses, peppers and olives. Orsi's has been in business for over 100 years, so you know it has to be good.


James Reese Net Worth & Bio/Wiki 2018: Facts Which You Must To Know!
James Reese was born in 1898 in Buena Vista, Georgia, USA as William James Reese. He was an actor, known for The Young Don't Cry (1957), Winterset (1959) and Suspense (1949). He died on February 17, 1960 in New York City, New York, USA.


The Mauser 86SR is an upgraded Mauser SP66. It features a new bolt, and a different stock which is ventilated to help dissipate heat from the barrel. The 86SR also features a detachable box magazine, a useful feature for both Law Enforcement (Change from high penetration to low penetration rounds quickly) and military (Rapid reloads). The rifle is a little longer, and a little less weight then the SP66. But the accuracy is no less then the SP66!


The light, feathery substance that we use to wipe off sweat, blow our noses or just clean up a surface in the kitchen or use in the toilet has been given a few names such as toilet paper, facial tissues, paper towels, industrial wipes, table napkins, diapers etc. Within each type there are varieties of tissues based on colour, textures, decorations, patterns, perfumed or moistened. There are a few types of tissues that are being used and the following guideline will inform you the characteristics of each type.
This category of tissue is the most commonly used in households. Many households feel that they might not be able to survive without toilet tissues. The lowest grade of paper can be found in this variety of light feathery substance. These can be used not only in toilets but also to remove make up, for nose care and to wipe up spills on any surfaces. Toilet tissues can be colourful, be perfumed, patterned and even medicated. The most important features of these papers are fibre quality, fiberness and robustness.
This is the second most purchased category of tissue by consumers. Not only is this used in households, but you will find it stacked up in an toilet paper Australia at hotels, airports, hospitals and restaurants too. The strength of this paper tissue solely depends on the quality of the fibre, the chemicals and the use. The weight of this napkin ranges from 20g to 24g.
This is the softest tissue that is used by consumers for cleaning faces. It is thin, absorbent, soft and can be disposed easily. They are also known as handkerchiefs and are manufactured and distributed in cardboard boxes. Facial handkerchiefs can be used to reduce the spread of infection when an individual is having a flu or another disease.
These napkins will be found mainly on dining tables at hotels, restaurants and other food outlets. Bulk napkins can be bought for commercial purposes and will be provided in different colours, textures and patterns.
The thinnest and translucent tissue paper you find in the market is usually used for wrapping gifts made of ceramics and glass. You can even use wrapping tissue to wrap up clothes, shoes and other gifts.
Knowing the exact type of tissue that should be used for a particular purpose will not only help save cost, but it will look more effective when in use. This will be very important in the service industry and should be part of the training to all staff.