I just found out that Molly Ivins died. I have been a huge fan of her books and columns, and enjoyed the perspective she brought to the worst administration in American history.

Ivins was known for her incisive wit and her strong liberal views, which stood out all the more given her Texas roots. If you want to understand our president’s ignominious background, the range of his business failures and bailouts, and his callous record as governor of Texas, you need do little more than read her wonderful book Shrub : The Short but Happy Political Life of George W. Bush.

In an epoch when good journalism is hard to find and much of our most honest news comes from humorous sources, Ivins was a national treasure and will be sorely missed.

I love science, because the universe has very little tolerance for wishful thinking. You can believe whatever kind of nonsense you like about how the world works, but eventually the data will come along and slap you upside the head. Sadly, not everyone lets the sting of reality affect their prejudices, but that’s another story.

Here’s a fact: among chess grandmasters, there are a lot more men than women. Chess is great, because it’s pretty much a meritocracy, not an old-boys network (colorful parables notwithstanding). There is a simple old-fashioned sexist explanation for this phenomenon, which is that women just aren’t as good at chess as men are. Back in the veldt, you see, when the men were celebrating a successful hunt by playing chess with sticks in the dirt, the women were busy washing the dishes, so there was no evolutionary pressure for them to develop those skills. These days, however, there is a more sophisticated new-fangled sexist explanation for these kinds of discrepancies, which invokes bell curves. It’s not, so the story goes, that the average woman isn’t just as good as the average man, it’s just that their standard deviations are different, so there is underrepresentation at the high end. This hypothesis suffers under the weight of making all sorts of predictions that aren’t true, but it’s kind of scientific-sounding, so it’s gained a measure of popularity in certain circles.

So now someone has looked in detail at the situation in chess. Jake Young at Pure Pedantry points to a study by Chabris and Glickman, “Sex Differences in Intellectual Performance: Analysis of a Large Cohort of Competitive Chess Players.” I noticed the link at Marginal Revolution, and I agree with Tyler Cowen about the most striking findings:

They found no greater variance in men than women. It had been suggested that since science selects for individuals at the upper tail of the distribution, a higher variance in men than women might explain their greater representation. However, the researchers found that — with respect to chess — if anything in most age groups women had a higher variance then men. Upper tail effects do not explain the differences in the numbers of grandmasters…

And:

If you look at the participation rate of women and relate that to performance, you find that in cases where the participation rate of women and men is equal the disparity in ability vanishes. Basically, this means that in zip codes where there are equal numbers of men and women players there is no great disparity between male and female ability — and certainly not a disparity in ability large enough to explain the difference in the numbers of grandmasters.

How about that? It’s not any differences in innate ability, it’s just that women are “choosing” not to play competitive chess. Choosing is put in scare quotes because there’s obviously going to be a great deal of influence from parents encouraging/discouraging their kids at a very young age, but whatever. It’s a shame if young girls who would have been enthusiastic about chess are pushed away by social pressures of one form or another, but for most people chess is not a central part of their lives.

It’s a much bigger deal when women (or whomever) are enthusiastic about choosing something as a career, and are pushed away by an impressive battery of systematic biases. Which is what is clearly going on in science, especially in physics. If girls are given just as much encouragement and opportunity as boys are, and nevertheless choose to become truck drivers or gourmet chefs rather than scientists, that’s fine with me — the goal has never been equal representation of the genders, it’s equal chances for everyone to do what they find interesting. But we have a long way to go before we get there.

Some years ago I heard a fascinating talk by the magician and pseudoscience debunker James Randi, in which he spoke of many interesting things ranging from ESP and UFOs to medical quackery. One thing he talked about was a strange phenomenon involving, of all things, the number 17.

In the advanced undergrad lab course I am teaching this term, my students and I today were talking about random numbers. At one point, the memory of Randi’s talk bubbled up in my head and I said “Hey, did you know that if you ask people to choose a random number between 1 and 20, inclusive, and record their answers, there is a big excess at 17?” Ordinarily, if you ask 100 people to do this, you would think that would get about 5 responses for any given number like 6 or 12 or 15. Of course it will vary statisticaly, but there should be no real preponderance of any particular number. Right?

My students thought this 17 stuff was nonsense, and saw an opportunity to see if I was just teasing them. So, with about 30 people to attack in the class, they started recording real data, asking first the students that had arrived, and then students who filtered in for the next 20 minutes or so.

The result? Well, to my own great surprise the number of people who answered 17 was an early favorite: three out of the first 12 or so! And then a string of four people ALL answered “17″ as they were asked the second they came through the door! We were actually shocked by the prepomderance of 17’s. The students have the data sheet, and I think are continuing to ask their other freinds and roommates this evening.

We of course tried to explain this really odd phenomenon, but so far have no good theories. Clearly 17 is a number that’s not to far away from the maximum allowed, 20, and it’s one that you don’t often encounter like the small integers, or 10, 12, 15, 16, etc. So perhaps it seems more “random” to people. But still it’s quite weird. I wonder if it would still work if asked for a number between 1 and 100, for example.

I know there are a lot of readers out there who might find this intriguing and want to do their own experiment. I am willing to try to compile the data that folks send me via email - just send a message with one line per integer, and one frequency:

1 5
2 4
3 8
4 3
.
.
.

et cetera, so I can put them into a program and make a combined histogram which I will show here in a week or so if I get data. Make sure you don’t tip off the group you are in as to the nature of the test, and don’t give your victims more than a second or two to think about it.

Have fun! (Okay, it’s a bit nerdy…)

For the first time in years, Hillary Clinton says something honest and funny that made me smile. Is it surprising that the media don’t know how to react, and run around in rapidly shrinking hermeneutic circles in attempt to make sense of this phenomenon? No, it is not surprising.

It’s a joke, people! A pretty good joke, actually. Good for her.

I’ve not yet actually posted about what I’m working on. I was saving up for a nice juicy post, oozing information and insight from every line. However, I’m compelled to post sooner than planned because my primary research tool may have just gone belly-up.

So, what I had been working on was an enormous Hubble Space Telescope (HST) project to map millions of stars in nearby galaxies. As Steinn nicely outlined, HST has become primarily an instrument for imaging. It has exquisite spatial resolution, which allows one to distinguish between photons emitted from closely adjacent locations on the sky. From the surface of the Earth, with the turbulent atmosphere in between, telescopes would see a blurry mush of stuff (not to get too technical on you). However, when viewed with HST, the blurry mush would resolve into individual features, much like you’d experience if you cleaned off a layer of Vaseline from your glasses. Now, this ability to zoom in on tiny astronomical features is useful for both imaging and spectroscopy, but, like an episode of the Sopranos, HST’s spectrographs have kept getting bumped off, leaving imaging (i.e. “taking pretty pictures”) as HSTs primary capability.

So what kind of science can you do with “taking pretty pictures”? One thing you can do is to measure the color and brightnesses of individual stars. If you change the age, mass, or metal content of the star, its internal structure changes as well, leading to measurable changes in its color and luminosity. As a result, when you make a plot of color vs brightness (where redder is to the left, and brighter is upwards), you see revealing patterns that tell you a tremendous amount about what kind of stars there are, how old the stars are, and what the stars are made of:

You can see how the stars are not distributed randomly across the plot, and instead tend to cluster in well-defined sequences. It turns out that stars with different ages, masses, and metal contents fall into different sequences, so with data like those above, we can try to piece together the history of the galaxy on a star-by-star basis — stellar archeology if you will.

The project I was running was designed to extract this information for all the galaxies in a many cubic megaparsec volume of the local Universe. We were gathering hundreds of images like:

which, when zoomed in, look like this:

See all those individual stars, with different colors and brightnesses? Neat, huh? Anyways, the project would have measured the properties of more than ten million stars. I’d been planning this for a couple of years, and it had been underway since September. We have about half of the data in hand, but now the camera we were using, the Advanced Camera for Surveys (ACS), is probably down for the count. What this leaves me with is a half-finished data set, and an uncertain future for me, my students, and my postdoc who moved his pregnant wife across the country to work on this. There’s plenty to do with what we have in hand, but it’s not going to be exactly what we’d planned. I have no regrets, because one of my principle motivations for designing this project was that we’d be embarrassed not to have these data if Hubble fell into the ocean. It’s better that we have some of it than none of it.

Still, it’s not the best birthday present I’ve ever received.

Sometimes rare and wonderful things happen. This weekend, for example, it was sunny in Seattle.

Which allows me to share pictures of one of my all-time favorite Cool-Physics-Around-the-Home tricks:

Anyone who has blinds in their home or office has probably seen something like the pictures above — sunlight streaming through cracks in the blinds, producing a row of spots on the opposite wall. It would be natural to assume that those nice round spots are due to the nice round holes that were poked through your miniblinds at the factory. However, you would be wrong. Those spots are actually pinhole camera images of the Sun!

You observe the same effect, albeit more subtly, when sun comes through tree branches. The network of leaves and branches creates many small holes, each of which produces its own pinhole image of the Sun. These images tend to overlap, making the circles less obvious than with blinds, but you can still see the faint imprints of the circles in places. Sometimes, however, the Sun is not actually round. At sunset when it’s low on the horizon, parts of the Sun can be blocked by trees and buildings. In that case, the pinhole camera images are not round either. I took the picture above shortly before sunset, when the lower half of the Sun was blocked by a neighboring building. You should be able to see that the pinhole images have turned into half circles. (The half-circles are upside down, since a pinhole camera inverts the image.) The effect can get even weirder than the picture above. For example, there’s a large bridge due west from my office window, and sometimes the Sun sets directly behind the bridge. When the Sun is partially eclipsed by the bridge deck, I can even see pinhole images of upside-down trucks driving across the bridge when my blinds are down.

How cool is that!??!! (Ok, I admit, it’s probably not as cool as the Higgs, but it’s a hell of a lot easier to see). I was pretty old by the time I figured this out. The first time I noticed it was during graduate school in the courtyard of a building at the Institute for Advanced Study, during a partial eclipse of the Sun. The moon was blocking about 2/3 of the Sun, making a tidy little cresent. All the tree-dappled sunlight on the ground turned into cresents as well. The effect was spectacular, if not a little odd. I wound up watching the rest of the eclipse on the ground, rather than through my carefuly prepared piece of mylar (i.e., the wrapper off a Poptart). Mmmmmmmm….Poptarts!

Being a cosmologist is a dream job. We spend our days (and nights) wondering about this vast and surprising and beautiful Universe we find ourselves in. Every now and then we make our own contributions, uncovering a clue, seeing just a little bit further. But we don’t often reflect on the miracles that are required for the field of cosmology to exist at all.

Human beings are natural-born cosmologists. Essentially all human societies have wondered about the origins of the cosmos. It is easy to imagine a world in which nobody cared about the age of the Universe; such questions have nothing to do with everyday experience, and are completely irrelevant for evolutionary success (in the strict ‘eating-sleeping-procreating’ sense). Nonetheless, part of being human seems to entail asking these “big” questions.

Perhaps even more surprising, contemporary society backs up its cosmological interest with cold, hard cash. Through gifts and taxes individuals make financial sacrifices, allowing science to exist and prosper. And this is not a trifling contribution. The total US investment in the basic physical sciences in 2006 was roughly $20 billion. US taxpayers spend about $70 per person per year, or $150 per household. Europe and Japan contribute similar amounts, and almost all nations contribute at some level. This is what pays for the beautiful images from Hubble. This is what allows us to figure out that there was a Big Bang, that the Universe is expanding, and that this expansion is accelerating. This is what allows us to continue to do what we do. Although funding is a struggle (especially this year, which is turning into a disastrous one for the sciences), the fact that it exists at all is wondrous.

But the greatest miracle is that cosmology works. The Universe appears to be comprehensible. It is possible to ask a question such as “How old is the Universe?”, and actually find an answer. Why are there so many clues? Why is it that, given enough careful observation and quiet thinking, we can actually figure this stuff out? We now have a description of the Universe which works remarkably well from a fraction of a second after the Big Bang to today, 14 billion years later. It’s truly astonishing.

Maybe the string of successes is about to end? It is conceivable that dark matter and dark energy, those mysterious elements which make up 95% of the energy density of the Universe, will remain “dark” to us forever. That we’ll never understand how the first stars formed, nor why galaxies look the way they do. Although this is possible, most of us are optimistic that there’s a long way yet to go. And we have history on our side. Thus far, nature, although coy, does seem to yield her secrets eventually. Science marches forward. Questions which once resided in the realm of metaphysics now have definitive physical answers. Why is the Universe comprehensible? As far as we know, it didn’t have to be this way.

However, without the generous contributions of society much of the Universe would remain incomprehensible. It thus seems appropriate to express thanks to our fellow citizens. It is through your munificence that this whole glorious enterprise is funded. This science belongs to you. From the preface to Gravitation, by Charlie Misner, Kip Thorne, & John Wheeler (affectionately known as MTW, or the telephone book):

We dedicate this book
To our fellow citizens
Who, for love of truth,
Take from their own wants
By taxes and gifts,
And now and then send forth
One of themselves
As dedicated servant,
To forward the search
Into the mysteries and marvelous simplicities
Of this strange and beautiful Universe,
Our home.

Continuing our recent servings of fresh blogging meat, I am delighted to announce the addition of another new member of the Cosmic Variance team. Daniel Holz is a Richard Feynman Fellow in the theoretical astrophysics and particle physics groups at Los Alamos National Laboratory, working on the interplay between general relativity, astrophysics, and cosmology. Dan is a particular expert on gravitational lensing and gravitational waves, but his interests are wonderfully broad and I know he’s going to bring a great new perspective here. As a good friend of some of us already, he’s been mentioned in at least one of our previous posts.

In addition to his scientific expertise, Dan adds important non-Californian balance to the blog, although his history at Santa Barbara, and his obsession with surfing worry me a little.

Despite the impression one gets from visiting Dan’s home page, I can assure you that his face does not, in real life, look like an apple, as his Cosmic Variance page will no doubt soon show.

Welcome Dan!

Our crack team of blog experts has finally identified the problem that was causing the alignment problems in the latest version of Internet Explorer. So if you’ve stopped reading the blog because it didn’t look right, you can come back now! Of course, since you’re not reading this, you’ll never know.

We’ve also taken steps to decrease the number of “CPU allocation exceeded” errors, and to prevent people with blogspot.com domains to comment without being labeled as spam. But there are still some issues there — especially if you can’t comment, please do let us know.

Was it real?

I stared up at the atrium of Building 40, two things becoming apparent. First, with a result with a little bump like this, there was going to be a lot more work to do. Second, I had better get myself back to Fermilab soon rather than remain at CERN.

I changed my ticket to the next Monday as there was nothing available the next day. This meant I had the weekend at CERN to work. I sent email to my still-asleep colleagues in the US, and got busy.

When you see a spectrum like the one we had, with a few bins having more than normal rate, it could be a number of things. It’s always possible that you forgot some background, that the simulation of the detector and the analysis is not perfect, or simply a statistical fluctuation.

High energy particle collisions are governed by quantum mechanics, and therefore intrinsically random processes. If you expect to see a certain number of events with certain characteristics after running for some time, you don’t get exactly that number when you run the experiment. If you expect 10, you might observe 8, or 12, or, more rarely, 20. That was the case here: in the spectrum of what we called “visible mass,” a few of the bins had more events than expected. So was this a statistical fluctuation, a foul-up in the simulation, or the beginning of a real signal?

As the apocryphal student lab report said “our experiment didn’t have enough data so we had to use statistics”. In our field we use statistics all the time. We want to make quantitative statements about the things that make the hair on the back of your neck stand up, or perhaps yawn. Being quantitative means that we can be as objective as possible, and offer to our colleagues and to the world a rational interpretation of the data. Doing these sorts of analyses is my particular specialty in particle physics. Now it was time to do it here.

As a common language in our field we try to relate all our statistical questions back to the good old “normal” distribution, the familiar bell-shaped gaussian. A gaussian is peaked in the middle with long tails on the high and low side. It is quite proper to think of it as an indication of how often you expect some measurement to give you a certain result: most often in the middle, and less often out in the tails. It’s characterized by a “standard deviation” or, since we use the Greek letter sigma to represent that, “1 sigma.” If your measurement is described by a Gaussian, about 2/3 of the time it will lie within plus or minus one sigma of the peak. In 1/6 if the cases, then, it will be more than one sigma on the high side. The more sigma away from the peak, the less likely it is that it happens.

In particle physics we see statistical fluctuations all the time, and so to make a discovery of something really new, we have to demand that the probability of a random fluctuation causing the excess is very, very small. Usually we demand five sigma, that is five standard deviations from the peak, to claim “discovery.” With less statistical significance, we hedge and use words like “evidence” or “indication.”

So how significant was this? I quickly fired up my program to fit the spectrum including a Higgs signal, and, sure enough, the data preferred a SUSY Higgs with a mass of about 160 GeV, or about 170 times the mass of the proton. The peak rate was quite consistent with our sensitivity. And, to boot, it appeared that the statistical significance for that mass was about 2.5 standard deviations. Below that level people are not very interested, but at this level it starts to get intriguing.

The problem is that this is a bump hunt, and we didn’t know where the bump might show up in terms of the mass of the Higgs, since we don’t know what the Higgs mass is! That means that the real question is this: could a random fluctuation give us a bump like this anywhere in the spectrum, not just at 160 GeV.

This calculation was going to take a while. I needed to run a random simulation that generated lots of possible experimental outcomes with no Higgs signal present, and then fit the spectrum for all possible Higgs masses, and see how often I got a 2.5-sigma result. I set about coding this up.

Fortunately I had all the pieces I needed to perform the calculation, and I stitched them together, testing as I went. Then it was time to launch the job. I moved the code to our computer cluster back in California and set up scripts to split the calculation across multiple computers. With a single command, I set 20 CPUs going, generating random spectra and looking for the Higgs where there was no Higgs. It looked like it would be a number of days to get the answer.

It is a source of continual amazement to me that a mere 15 years ago the state of the art in computing at CERN was our big VAX 9000, really the last of the mainframes. Windows desktop PCs were useless to us since we couldn’t program them, and Unix workstations were coming into vogue but still not that fast. So now I had just launched a job which was utilizing about 2000 times more computing power than the old VAX. And this was only 20 CPUs! We now have a global grid with tens of thousands of nodes accessible, and more every day.

So in the mean time, it was time to assess the damage the bump had done to our limits. When I had opened the box, I had expected that we’d see no sign of a Higgs signal, and then proceed to rule out certain regions of supersymmetric parameter space where, if nature had chosen to live there, we would have seen a Higgs signal. Our usual statistical standard is somewhat loose in this regard: we require “95% confidence” to rule out a certain parameter set. In fact that means that there is less than a 5% chance that, if that parameter set is true, we’d have seen what we saw or an even larger excess of Higgs. You kind of have to think about that for a good while to see why such inside-out logic is needed. The problem is that there is a crucial thing we don’t know: the probability that the Higgs exists or not!

With this excess, we’d be able to rule out a lot less territory than we had hoped. I ran the short jobs to calculate the rate limits, and sent them off to Amit to convert into the final SUSY parameter limits, and started writing it all up.

In our collaboration we have a very formal internal review process for getting out results. We need to document everything in advance of two presentations to the appropriate physics analysis meeting. The first presentation is called a “pre-blessing” and is where the real knives come out. The presenter is peppered with deep, probing questions about every aspect of the analysis, usually for an hour or more. Though it can seem like a blood sport at times, this is an absolutely essential part of the scientific process: if we aren’t our own worst skeptics then someone else will do it for us.

Anton was awake now, got the message about the excess, and started asking all the right questions. Amit chimed in and we quickly formed a plan: we had until Sunday at midnight to get the note posted, and we divided up the work. He shot back parameter limits to me and I turned it into a nice plot with Illustrator:

The dark purple region in the plot shows the paramter values we exclude with 95% confidence. The region labeled “expected” is what we thought we’d be able to exclude. Clearly we had not reached our expected sensitivity due to the fluctuation. As we get more and more data, we expect the excluded region to cover more and more of the plot, to lower and lower “tan beta” which is one of the main SUSY parameters. We illustrated the somewhat mild dependence of our result on input assumptions, and compared with the limits obtained years ago at the LEP 2 accelerator at CERN. We’d made progress, but the little bump was hurting us.

I returned to Fermilab on Monday, and met with Anton and Cris to brainstorm about possible problems, and questions that might arise. Anton was going to make the presentation on Thursday, and we needed to armor-plate the result, which was sure to elicit lots of tough questions. He had done an amazing job at documenting everything - our note ran to over 70 pages, much of it plot after plot comparing the data and simulation in our control samples and in other subsamples of the data. Everything checked out - there was no reason to believe that the little bump was anything other than either a statistical fluctuation or something new.

My statistical significance jobs came back with an answer: there was about a 2% chance that a random fluctuation could have given us a bump of the magnitude we saw. That’s about a 2 sigma effect, and those come and go every day in our field. Still, we are all human and have to wonder whether this is real or not.

The presentation went fine - we were “preblessed” and scheduled for final blessing the first week of the new year, the week before my talk at Aspen. We really did not receive any difficult questions, as we’d done our homework well. Over the holidays we could expect questions and comments from our collaborators, and had some more time to do cross checks.

But by the blessing talk three weeks later, a number of people who had not been at the preblessing presentation caught wind of the excess, and showed up at the meeting to ask tough questions. In then end, though, we were blessed and I had a green light to show the result at Aspen the following Tuesday. I worked all weekend on the talk, in which I now focused a lot of attention on the new result, which would be the thing of most interest to the crowd.

It’s pretty rare these days to have a high profile talk like one at Aspen and also your own brand new result to show - sort of a perfect storm. My attitude was to show the facts and draw the obvious conclusion: we need more data! As anticipated, the part of my talk that drew the most interest and questions was our new result. It was about as fun as it gets in this field!

Afterward though, on the ski slopes, in fact, a colleague from our competitor experiment Dzero at the Tevatron, Greg Landsberg, a professor at Brown, intimated to me that their experiment had a result nearing completion. This occupied my thoughts the rest of the afternoon. What had they seen? Did they have an excess too? Last year there had been a small hint of one, but nothing exciting.

At the evening session Greg let the cat out of the bag: Dzero had a deficit where we had an excess! I’d have rather he waited a day or two to let me know, but, eventually the world would find out. Their method was quite similar to ours, so it was hard to escape the conclusion that if they really had a deficit, our excess is more likely to be a statistical fluctuation. We’re still waiting to see the answer from them, which should happen soon. The nice thing is that we have another factor of two more data to analyze soon. By early summer we should have a much better idea what it is we are seeing.

A week from today Anton will present the result at a Friday Fermilab “Wine and Cheese” seminar which, despite the genteel name, is one of the toughest audiences I know of. A single question there, with 150 people in the audience, can quickly erupt into a barrage, with the speaker barely able to catch his or her breath. But Anton is a real pro, and it will be very interesting to be there and see how it all goes.

We’ve caused quite a stir considering how insignificant the bump really is. I’ve gotten requests from all over to see my Aspen slides, to answer questions. When I was back at CERN last week to carry new pixel detectors to CERN, I got stopped by lots of people who had heard about the result and wanted to know more. To me it shows that there is intense interest in finally nailing the Higgs, whatever its nature, and also it shows that the Tevatron has a real shot at making big discoveries before the LHC turns on. Real physics data will come from the LHC by the middle of next year!

In the end, some day we are going to have something new right there in our data, and we cannot shrink from it. We’ve gone a very long time with no truly new discovery in particle physics, no observation that truly changes the paradigm. We’ve gotten used to fluctuations coming and going, and are justly skeptical of any new ones that come along. But I think I got a glimpse that Saturday morning of what it will feel like when we do have something new, and real, and it’s a sensation that I hope I’ll have again some day soon.