Let’s use qubits to look for dark matter.

Examining Akash Dixit’s qubit-coupled cavity in the Schuster Laboratory (http://schusterlab.uchicago.edu/). Photo credit: Reidar Hahn (http://vms.fnal.gov/asset/detail?recid=1955845)

We’re starting a new research program and I’m excited to tell you about it!

Let’s start with the big picture, and then we’ll use subsequent posts to zoom in on the details. The big picture goes like this:

  1. 95% of the mass-energy in the universe is a huge mystery. Everything we see around us — pizza, dinosaur bones, galactic clusters — is made of atoms. But as best we can tell, atoms account for only 5% of the mass-energy in the observable universe. The rest is a big mystery that we call dark matter and dark energy. It would be fair to say that physicists are very interested in sorting out the remaining 95%. Aren’t you? If your everyday senses only reveal 5% of the known universe, what are you missing out on?
  2. Dark matter is very probably made of particles, and we think a great candidate is a particle called the axion. The axion is cool because it solves other problems in physics in addition to dark matter. (We’ll talk about it later, but you can skip ahead and look up the “strong CP problem” if you want.) You may not have heard of axions yet, but the scrabble game on my telephone sure has.
  3. Axions are hard to detect. They’re a bit like neutrinos, in that they’re (probably) all around us but they hardly ever interact with normal matter. You detect them by converting them into photons and then looking for the photons. (Physicists are pretty good at counting photons; we’ve had a century of practice at it.) To further complicate matters, our theorists have narrowed down the axion mass to a window that spans three orders of magnitude. We have to design an axion search that is sensitive to potential masses (or equivalently, photon energies) between meV and μeV.
  4. So: a metaphor. Imagine you’re driving down a desert highway in an old car and you want to listen to the radio. Since you’re way out in the middle of nowhere, the radio is mostly static. How do you find a station? You’d probably tune the radio dial a little bit, listen for a while to see if you could pick out any signal in the noise, tune the dial, listen, tune, and so on. Eventually, if you started to pick out some faint music in the static, you’d know you were getting close. Same for us! In fact, our colleagues have an experiment called the Dark Matter Radio. The key point here is distinguishing signal from noise.

    By Riberto Frederico [CC BY 2.0 (http://creativecommons.org/licenses/by/2.0)], via Wikimedia Commons

  5. Reduce, reduce, reduce the noise that competes with the signal. If you give me $100M for a 30-tesla magnet, I can give you a real strong, healthy axion signal. If you don’t have that kind of magnet money laying around, I’ll have to figure out some way to keep noise out of my experiment. One way to do this is to use quantum bits. I’ll get into this in a later post, but the technology we borrow from quantum information science gives us the ability to suppress experimental noise by four orders of magnitude. Not bad!

    An qubit.

So there you go: the broad strokes for finding dark matter using quantum bits. Slowly but surely, we’ll take all those individual menu items above and expand on them in future posts. Exciting times!

John vs Jennifer: In which the journal club discusses gender bias and has a collective epiphany

Data from the American Institute of Physics, “Women Among Physics Faculty Members”, https://www.aip.org/statistics/physics-trends/women-among-physics-faculty-members-0

Will I shock you, dear reader, if I tell you that physics has a gender problem? Women are underrepresented in physics at every career stratum. Whether you look at undergraduate students or faculty, it’s mostly dudes.

American Physical Society, “Fraction of PhDs earned by women, by major”, https://www.aps.org/programs/education/statistics/fraction-phd.cfm

Since we’re talking about gender today, I’ll say right up front that with a few notable exceptions, lots of the data out there about STEM & gender assumes that there are only two genders. My nonbinary folks out there: we see you. Maybe one day the data will see you too.

Women make up about 50% of the population, so you don’t have to do much work to see how screwed up our ratios are. As of 2015, women made up 20% of all physics majors and only about 10% of all tenured physics faculty. You can look at other metrics and see the same story. Holman, Stuart-Fox, and Hauser just published a study in PLOS suggesting that at the rate we’re going, it will take 256 years before women in physics are published at parity with men.

There’s no shortage of work that tries to explain the sources of this imbalance, and the forefront of this research is an interesting place to watch. If you want to understand a complex problem though, it’s a good move to break it up into little sub-problems. So some folks study the role that family care plays in this imbalance, or overt sexism in the workplace. Today’s post is about gender bias in STEM hiring. A few meetings ago, our Inclusivity Journal Club read and discussed “Science faculty’s subtle gender biases favor male students” by Moss-Racusin et al. Here’s a link to the article and the supplemental material.

The authors put together a job application package for a fictional aspiring lab manager and sent it to 127 physics, biology, and chemistry professors. These application packages were all identical, except that for half of them the candidate was named John, and for half of them the candidate was named Jennifer. The professors were asked to rate the competence and hireability of this fictional candidate, to rate their own willingness to mentor this candidate, and to propose a starting salary for the position.

I bet you can guess what happened!

What happened was that the male version of this candidate was rated significantly better across all metrics, and the average proposed starting salary for male candidates was about $4k higher. Maybe a little more surprising: this bias towards male applicants was about the same, regardless of the gender of the person doing the evaluation.

This study gets a lot of play in the popular science press, and for good reason. It’s hard to do a double-blind study on gender bias! Or bias of any kind, really. It’s an interesting exercise to try and design a study like this in your head. Would you just give someone a survey asking whether they’re biased?

Since lots of people have written about this paper already, I’ll focus on our group’s reaction. I noticed the mood of the room move through a couple distinct phases; maybe you’ll recognize those phases in yourself when you read the paper, too. We started off feeling collectively angry and disappointed on behalf of all women scientists. Then, I’m not sure what motivated this, but the mood shifted and we all took turns being very critical. And then once that subsided, there was a self-reflective phase where we all did a little personal growth.

First phase: Sexism is stupid and dumb, and the paper shows its effects to you pretty clearly, with easy-to-understand graphs. It’s no wonder we started off feeling angry.

Next phase: the knives came out. Once we had a handle on the basics of the paper, we started picking at it in the way that physicists are famous and famously mocked for doing. Put more charitably, critical engagement is an important intellectual step and some nerds regard it as a way of showing respect. Our critical engagement looked kind of like this:

  • The physicists in the room, used to using terabyte-scale data sets for high-energy collider analysis, were all a little scandalized that this study relied on so few data points. Only 127 respondents? To us, that seemed like too small a number to do anything really cool. (Our social scientist pal convinced us it was ok.)
  • We took turns noticing that 78% of the respondents were male, and 81% were White. Did this further skew the results? Or is this just “the way it is” in the sciences, making the study actually more representative?
  • In the real world, lots of hiring gets done by committees, in part to help reduce the effects of individual bias. How would the results change if, instead of individual professors doing this rating, it was done by groups?
  • We all had a hard time understanding how these results would translate into actual hiring outcomes. (Maybe you can tell that we started asking quite a lot from this one little five-page paper.)
  • Why weren’t the respondents given any open-ended questions?
  • How would the results change if the fictional applicant was going for a tenure-track job? Would prospective long-term colleagues be evaluated differently from low-level, more transient positions?
  • It would have been interesting to see how geography influenced responses. For example, $25k per year goes a lot farther in St. Louis than it does in San Francisco. Tying the proposed salary to local cost-of-living would have been a neat addition with maybe some explanatory power.

So we went through the phases of appreciating and then being critical of the paper. After we’d gotten all that “critical engagement” out of our systems, we got a little self-aware. Physicists’ papers are built around very specific, narrow arguments. Why should we expect anything different from sociologists? Maybe this paper is not iron-clad proof of pan-institutional sexism, but why should we expect it to be? Moss-Racusin et al. asked a very narrow question, and gave us a very specific answer. That’s why the study is compelling.

There’s even scholarship on the way people react to papers about bias. It’s fascinating! The argument goes like this: Objectivity is a culturally desirable trait in STEM. Studies that discuss bias are very triggering for a particular kind of person who enjoys thinking of themselves as an ice-cold rational scholar. Such people (men more often than women) tend to work extra-hard to trash studies about bias in STEM. Were we all collectively doing this?

Real talk: I caught myself getting a little mentally defensive during this discussion. “I would do better than these other respondents. I’m much more aware of my own biases.” That kind of defensive thinking is part of the problem. So this is one of the reasons I ended up liking this paper so much. I learned something about bias in STEM and about they way social scientists roll, sure. But I also got pushed into being a little more self-aware. Not bad for an hour’s work, huh?

Stay tuned as I get caught up on these blog posts! Next up: Devine et al.‘s “Long-term reduction in implicit race bias: A prejudice habit-breaking intervention”.

You and I are going to use actual data to understand diversity in STEM.

You and I are now going to attempt to have a conversation about diversity and discrimination. We’re going to do it with love and respect, and it’s going to take place here, on the internet.

[NARRATOR: And they were never heard from again.]

In particular, because this is a blog about physics, I want to talk about issues of diversity and inclusivity in physics. First, the facts. It is a fact that various genders and ethnic groups are underrepresented in science, technology, engineering, and mathematics (STEM) disciplines.

In case that graph just looks like a bunch of numbers, I’ve reframed it
below. In a just and equal society, where everyone has the same opportunity
to succeed, you would expect the demographic distribution in STEM to roughly match the national demographic distribution. For example, if 17.1% of the US population identifies as Hispanic/Latino, you might expect that 17.1% of all STEM PhDs would also identify as Hispanic/Latino. A lower percentage would indicate that that group is under-represented in STEM, and a higher percentage would indicate that that group is over-represented. I’ve taken the demographic distributions of various STEM groups and normalized them to the US population to get a rough idea of who is under/over-represented.

By the way, I’ve drawn this data from the US Census [1], from the NSF study on “Women, Minorities, and Persons with Disabilities in Science and Engineering” [2], and from the Fermi National Accelerator Laboratory’s Diversity website [3]. (I think it’s great that Fermilab is tracking this information and displaying it publicly, and I wish more institutions would do the same.)

There are problems with this data, of course. For example, not everybody fits cleanly into one of the categories in the US Census. What if you’re multi-racial? What if you look Black, speak mostly Spanish, and live in the USA? (Actually, if you really want to get right down to it, “race” is a fiction, invented by people who wanted to sell slaves [4, 5, 6].) What if the gender binary isn’t useful to describe you? What box do you check? And what about LGBTQIA+ folks, who aren’t yet counted by the census and may not wish to be “out” at work? There are a lot of ways to be a person — or, per this essay, a person with a STEM job — and not all those ways are described very well by extant demographic data.

It’s interesting to debate whether race and/or gender are social constructs, but a bit outside the scope of this essay. These memes have colonized our brains, whether we want them to or not. People will judge you by the color of your skin, your perceived national origin, the language or accent they hear you speak with, the clothes you wear, etc. The question I want to ask you here is: what are the consequences for STEM institutions?

There are a lot fewer women, Black, Hispanic/Latinx, etc. folks working in scientific jobs than you might expect, given their populations in the US. Talking about this problem (yes, I think it’s a problem) immediately makes some people defensive. Or emotional. Or just scared, as if the mere mention of structural racism is enough to pitch us all onto the social media bonfire. “I just want to hire the best person for this position” is a common refrain. Or, “we care about diversity, but it’s not our fault that mostly white men responded”. Some people will tell you, despite plenty of evidence to the contrary, that the demographic distribution in STEM reflects innate differences in ability between men and women, or between various races.

This is all very lazy.

By “lazy”, I mean that you should expect more from scientists. If you recognize a problem, and the solution to that problem isn’t immediately obvious, shouldn’t this lead to more questions? Shouldn’t you be willing to consider that difficult problems might have complex solutions? If you say “racism is bad but I can’t help anything in my position as a scientist”, why is this any different from saying “this business about `luminiferous ether’ seems fishy, but it sure is a popular theory; I have no choice but to play along”.

There are better, more complete explanations for these phenomena, and they are freely available to us. Social scientists have been attacking the problem of STEM diversity for years. What does their research tell us? What are the sources of inequity, and what can we do to fix things?

This is what I want to tackle in this new series of posts. Some of my particle physics pals have formed a journal club. We meet once a month to pick through an article on diversity and inclusivity in STEM, with some help from our social scientist colleagues. I’m going to write here about what we learn.

I expect that most of what I have to say here will be about the process of learning about this issue. What does the literature say? How should we interpret what the literature says? (This is a big one for us. We’re used to enormous data sets and meticulous systematic error analyses. How should we understand studies with, like, 200 respondents?) And what can we take away from these studies and apply in our own lives?

Did we make it? Everybody still standing? Good, we’ll get into the literature next time, when I discuss our discussion of “Science faculty’s subtle gender biases favor male students” by C.A. Moss-Racusin et al., PNAS 109, no. 41, 10/9/2012, pp. 16474-16479 [7].

star trek ok GIF

[1] https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=bkmk

[2] https://www.nsf.gov/statistics/2017/nsf17310/

[3] http://diversity.fnal.gov/laboratory-demographics/

[4] https://www.scientificamerican.com/article/race-is-a-social-construct-scientists-argue/

[5] https://www.theatlantic.com/national/archive/2013/05/what-we-mean-when-we-say-race-is-a-social-construct/275872/


[6] http://www.pbs.org/race/000_About/002_04-background-02-09.htm

[7] http://www.pnas.org/content/109/41/16474.full.pdf+htm


Gratitude for Larry Phillips

Larry Phillips is one of the most interesting people I have ever met. I consider him a role model, and I learned today that we lost him to cancer. He was my PhD advisor.

Larry was and is well-regarded in the field of particle accelerator technology. I worked with him on problems related to thin-film superconductivity for accelerating cavities, but he was one of those smart, versatile people who ends up getting involved in all sorts of fun problems. You’ll find that he’s made contributions to the design of cryomodules, RF windows, electropolishing of niobium accelerator components, plasma surface treatments, … it’s a long list. He also had an encyclopedic knowledge of how to actually get things built. He “played against type” that way; he could keep up with condensed matter theorists, metallurgists, machinists, and grad students. In recent years he had gotten involved in the design of accelerator-driven nuclear reactors, and novel methods for detecting dark matter.

This diversity of interests and competence is maybe not surprising if you know anything about his life. Larry lived in New York in the 60s, partying with bohemians and working as a motorcycle courier. He blew glass professionally; he got his PhD working with Hans Meissner (son of Walther Meissner, who discovered the eponymous Meissner effect); he lived on a sailboat for a while; he not only designed his own house, but the construction methods used to build it; he was a devoted husband and father; and he was an excellent mentor for young physicists.

This is the part of the essay where I talk about gratitude. Larry took me on as a student at a time when I was feeling very unsure about myself, academically. I couldn’t have been luckier, in retrospect. He was always available when I needed support, and he also seemed to know when I needed to be left alone to sort things out for myself.

He went out of his way to make opportunities for me. There were the kinds of things you’d expect from a PhD advisor, like professional networking or experimental support. But he also made sure I didn’t graduate without learning how to weld, braze, run the hydraulic press, and how to talk to a machinist. (You’d be surprised how many scientists are not good at this.) And he set an example for me and all the people in our group with his positive attitude. He was always getting enthusiastic for new projects, and declaring that they would be fun. (He would also declare that new projects would be “easy”, but we all knew that when he said “easy” he meant “probably not impossible”.) I had a great time with him.

If you have never been a graduate student, you might not see this last statement as an appropriately big deal. Some of my grad school pals had advisors who worked them like dogs, or completely ignored them, or (in a few terrible cases) were outright emotionally abusive. This sort of thing is unfortunately not uncommon. And while I could never claim that the process of graduating wasn’t stressful, I came out of it feeling like a “real” scientist, with an appreciation for the state of my field and the ways I might contribute to it. I came out feeling positive and ready to go.

That’s why I’m talking about gratitude right now. It’s hard to overstate the enormity of the gift that Larry gave me. And not just me. We threw a surprise party for his 80th birthday just a few months ago, and the crowd there was full of people who will tell you the same things I’ve been telling you now.

Thanks, Larry, from all of us.

Gender bias in STEM fields helps maintain the “gender gap”.

We’ve got an accelerator conference coming up soon. I volunteered to help prepare material addressing issues faced by women in STEM (science, technology, engineering, and mathematics) fields. The title says it all, really.

I suspect that every scientist would benefit from doing this sort of service work now and again. I read some very interesting papers and I learned a ton.

Problems of bias can seem nebulous and impossible to describe, let alone to solve. Something I particularly appreciated about my experience making this poster: the problems are clear and often quantitatively described. What’s more, there are data-driven, concrete solutions to these problems! Assuming your department has the political will to implement these solutions, you can help fix things. Not bad!


Coffee vs. neutrinos


Anaïs took this photo while I was writing this post. I am drinking *tea* here because, after the reading I’ve been doing (see below), I just can’t handle the question of whether I should be drinking coffee. Like, on a philosophical level. It’s right up there with “what is truth” and “does free will exist”.

Let’s start with some numbers. According to the International Coffee Organization, the USA bought enough coffee beans in 2014 to brew something like 100 billion cups of coffee. That’s roughly one cup of coffee per day for every single US resident. Including babies. Coffee is everywhere, all the time.

So here’s a perfectly reasonable question: is coffee good for you? Humans have been drinking coffee for at least 600 years — plenty of time to come up with an answer.

The answer is a resounding “maybe”. Consider:

  • A 1985 article in the New York Times suggested that 5 or more cups a day increases risk of heart disease. Specifically, it described a study which concluded that drinking that much coffee will triple the risk of heart disease, relative to people who drink none. (Coffee bad.)
  • A 2012 article in the New England Journal of Medicine reported a correlation between increased coffee consumption and a decrease in “all-cause mortality” rates, essentially a measure of the likelihood of death. (Coffee good!)
  • A 2013 study by the Mayo Clinic concluded that 4 cups per day increases the likelihood of all-cause mortality. (Coffee bad.) They recommend that young people limit their consumption of coffee to less than 4 cups per day.
  • A 2014 study in The Annals of Internal Medicine concluded that “Regular coffee consumption was not associated with an increased mortality rate in either men or women. The possibility of a modest benefit of coffee consumption on all-cause and CVD mortality needs to be further investigated.” (Coffee good?)

I could go on. And on. And on. There’s an overwhelming supply of studies that will support either side of this argument. We haven’t even started talking about coffee’s effect on specific medical conditions; there are studies out there addressing coffee’s effect on the incidence and/or severity of Parkinson’s, liver disease, diabetes, Alzheimer’s, anemia, depression, dementia, athletic performance …

Superficially, this is crazy. Coffee is everywhere. If you are not currently drinking coffee, you probably could be five minutes from now if you put your mind to it. I’m writing this post while sitting in a coffee shop. How can we still have so much uncertainty about something so ubiquitous?

The answer is typically science-y, of course. Coffee’s effect on the human body is complex because the human body is complex. These confusing and contradictory experimental results can motivate scientists to seek deeper, more complete answers to difficult questions. In the case of coffee, some very recent work suggests that your genes determine whether “coffee good” or “coffee bad”. Brave readers with lots of free time might want to stick around for the epilogue.

Anyway, I assert that coffee is hard to understand, despite its ubiquity. Do you know what else is ubiquitous and hard to understand?

Did you just yell “NEUTRINOS!” at the top of your lungs? Yeah, that’s the answer I was going for.

Subatomic particles, generally, are so, so far outside our everyday experience as human beings. Chances are, you’ve never had any reason to care about muons in your daily life. The subject just doesn’t come up, right? What about cosmic ray flux? Or neutrino oscillation rates? Not as often as you drink coffee, amirite?

I refuse to apologize for my puns. This is no exception.

I’m willing to bet that this is the only Quark that you have any substantial experience with.

So here you are, minding your own business, when a physicist starts blogging at you about neutrinos. They’re all around you, he says. Trillions of them pass through your body every second, he says. What are you supposed to do with that? To be blunt, how are you supposed to believe something so far outside your daily experience, when you don’t even know whether “coffee bad”?

Here, I don’t mean “believe” in the sense of truth vs. lies. I mean, how can you know that your body is permeated by neutrinos in the same way you know that gravity pulls you down to Earth, or that snow is made of frozen water, or that Daniel is handsome? You have direct, personal experience, through your senses, that these things are true. There are no intermediate steps. You don’t have to consult a scientific instrument to know that things fall down — you can feel the pull of gravity and you can see its effects on everything around you. Likewise, you don’t need to read a book in a library to know that coffee tastes amazing at 8 am.

But are your senses the only reliable source of truth? Are you skeptical about neutrinos because you can’t see them? You haven’t seen live dinosaurs either, and your day-to-day experience suggests that the Earth is flat, not round. Maybe you should limit your appreciation of truth to what you can sense for yourself.

More than two thousand years ago, the ancient Greeks kicked that idea right in its butt.This is a long discussion and I can’t do it justice in an already-long blog post. Essentially, your perception can change depending on circumstances. For example, maybe a fig tastes sweet to you. But if you eat honey before you eat figs, maybe those figs won’t seem so sweet anymore. What can you say that you know (like, really really know) about the taste of figs?

Your perceptions can be unreliable. Just think about the last time you got hangry. You skipped breakfast, maybe, and then right around 11 am the world started to suck, right? The line at the coffee shop started to seem unreasonably long, or the barista’s haircut seemed unreasonably annoying, or the guy behind you in line was talking unreasonably loud on his phone. In that moment, are you really perceiving an objective reality? Do you have well-deserved, righteous indignation about the barista’s haircut? Maybe you should get a muffin with that coffee.

Our senses, by themselves, are not the sole arbiters of truth. They are vital, beautiful, and useful, but they are not the whole story. Humans reach for truth in ways besides immediate sensory experience. One of those ways is called science. We have built tools and systems of thought in order to help us reliably, repeatably demonstrate complex and obscure phenomena.

Leon Lederman is a Nobel laureate, a former director of Fermilab, and the co-author of a truly enjoyable book with an admittedly silly name: The God Particle: If the Universe is the Answer, What Is the Question? (1993, Bantam Press). Here’s a particularly relevant excerpt. (Note for young people: TVs used to be bulky vacuum tubes with electron beams inside.)

The lady in the audience was stubborn. “Have you ever seen an atom?” she insisted.  … My attempts to answer this thorny question always begin with trying to generalize the word “see”. Do you “see” this page if you are wearing glasses? … If you are reading the text on a computer screen? Finally, in desperation, I ask, “Have you ever seen the pope?”

“Well, of course,” is the usual response. “I saw him on television.” Oh, really? What she saw was an electron beam striking phosphorous painted on the inside of a glass screen. My evidence for the atom, or the quark, is just as good.

Sometimes, you need to reach for truth through a pair of glasses. Or a television. Or a particle accelerator.

I’ll be talking about these ideas in more depth on December 6th at an event called Ask A Scientist. It should be fun! Hope to see you there.


Epilogue for Sticklers

For those of you still reading, I should admit to being a little glib for rhetorical reasons. As I said before, the human body is an incredibly complex system. To pose a binary question about whether coffee is categorically good or bad is to be ridiculously reductive.

Sometimes, the questions worth asking have complex answers. The questions we ask should allow for answers complex enough to be correct. As they say on Twitter, you should want better for yourself.

Arguments about all-cause mortality are statistical in nature and difficult to apply to a specific individual with her own specific physiology, metabolism, gut flora, lifestyle, etc. And in fact, there are a couple studies I’ve seen recently that bear this out.

  • Does coffee increase your risk of heart disease? Well, you’ve got a gene called CYP1A2 that tells your liver how to make enzymes that help to metabolize caffeine. If you’ve got the CYP1A2*1A allele, your liver will make enzymes that help you to metabolize caffeine quickly; in that case, “coffee good”. But if you’ve got the CYP1A2*1F allele instead, you metabolize caffeine slowly and coffee might increase your risk of a heart attack. (Coffee bad.) This is hard to summarize in a paragraph-friendly way. Check out the article for better information.
  • Likewise, there seem to be genetic factors that influence the effect of coffee on the risk of Parkinson’s disease.

Probably, then, the question “is coffee healthy” is a bad question to ask since the answer depends so much on individual factors. Perhaps a better question would be, “will I personally benefit from drinking coffee?” And perhaps you can’t answer that question without doing some of your own research, listening to your body, … I’ve even heard of people ordering genetic tests for themselves so that they can have some certainty about this.

Tip your baristas, ladies & gentlemen.

On the craziness of neutrinos, or: Why is there stuff?

I’m going to try to blow your mind two separate times in this post. Stay with me while things get weird, ok?


Today’s photos are of MINOS, a neutrino experiment at Fermilab.

Let’s have some fun with neutrinos. For this very-sophisticated physics demonstration you’ll need your hand and one second of time. I’ll wait while you collect those supplies.


Ok, first hold out your hand, palm up. Now wait for one second. Are you done? Did you notice anything freaky happening?

What if I told you that about a trillion neutrinos passed through your hand in that one second? Not figuratively, the same way you might say “I’m so hungry I could eat, like, a trillion pizzas”. I mean that several thousand billion particles — called neutrinos — pass through your body every single second of every day and night.

Don’t freak out! Well, go ahead and freak out a little if you want. A trillion is a freaky-big number. But the thing about neutrinos is that they’re guaranteed not to bother you. They basically never interact with other matter. You could shoot a neutrino through a brick of lead one light-year long, and that neutrino would only have a 50% chance of colliding with one of the atoms in that lead brick. They’re like the ghosts of the particle physics world.

Wait wait, come back! I’m sorry I made you think about trillions of ghosts whooshing silently through your body. I promise they won’t hurt you.

All those neutrinos are coming from the sun, by the way. The sun is so hot and massive that individual solar protons will squish together and fuse into helium. One of the by-products of that solar fusion is some neutrinos. And by “some” I mean “a number so big it hurts to think about it”.


Neutrino experiments tend to happen in underground caves. Burying your experiment under a hundred meters of rock and dirt is a great way to keep unwanted radio waves, cosmic rays, and other surface dreck from confusing your data. And the neutrinos don’t care where they are, of course.

Now let’s change gears slightly. Remember the Big Bang? When the universe was a crazy-hot pinprick of horrendous energy? As the infant universe expanded and cooled, little globs of matter — particles! — started to condense out of that energy. That happened the way Einstein said: some energy E would turn into some mc2, and then all of a sudden there’d be a particle (with mass m) where before there was just a wad of energy.

The thing is, Einstein’s E = mc2 doesn’t say anything about particles vs. anti-particles. A wad of Big Bang energy should be just as likely to make an anti-electron as to make an electron. (Or whatever, pick your favorite particle.) Statistically, then, you would expect equal amounts of matter and anti-matter to form after the Big Bang.

But! Any Trekkie will tell you that when matter and anti-matter collide, they annihilate in a puff of energy. (That’s how the Enterprise runs!) So in an early, hot universe with particles zooming around helter-skelter, you’d expect matter to collide with anti-matter, leaving nothing. That’s right folks, if physics was simple and things generally made sense, the universe would be filled with nothing. Instead of which, we have not-nothing! There is stuff, and all of the stuff we’ve ever found in the universe is made of matter. Nobody has ever seen an anti-matter galaxy full of anti-matter stars.

If I’ve done my job right, you’re freaking out again right about now.


The experiment was RUNNING when I took this tour! That guy is sticking his hand into AN ACTIVE NEUTRINO BEAM. No problem! The white spot is where the neutrinos leave the experimental hall and pass through 450 miles of rock. They emerge at the bottom of a mineshaft in Minnesota, where another set of particle detectors are situated. All of what you just read is real.

Physicists have worked out a theoretical model that explains this matter/anti-matter asymmetry. Theoretically, there’s some mechanism that biased the early universe in favor of matter. In order to test that model, we need to study some pretty esoteric things about neutrinos. That brings us to the final question of this post: If neutrinos are so insubstantial, how can you possibly study them?

The answer has to do with statistics. Let’s say you build a particle detector and then you start throwing neutrinos at it. If you only throw one neutrino at a time, there’s basically no chance of that particular neutrino interacting with your detector. What if you throw a million trillion neutrinos at once? Each individual neutrino still has a vanishingly small chance of interacting with your detector, but now statistics is starting to work in your favor. This is kind of like the lottery. If you buy one lottery ticket, I promise you won’t win the million-dollar jackpot. But if you buy a million lottery tickets, you might have a decent chance of winning.


Computer hardware for data acquisition. If you like things that look cool, you might like to tour a particle physics laboratory.

There are some experiments that manage to use only solar neutrinos to answer very specific questions. The Ice Cube experiment in Antarctica is a notably, amazingly hardcore example of this. But really big questions (“Why is there stuff?”) require way more neutrinos than the sun alone can provide. Another experimental approach is to make your own neutrinos in way, way larger quantities, and to throw them all at your particle detector as fast as you possibly can. (This is equivalent to buying all the lottery tickets.) And what’s the best way to do that? Why, with a particle accelerator of course! This is just what I was talking about in last week’s post.

I want to build a neutrino factory. Don’t you?

Makin’ those Big Decisions


cloudsI want to be able to tell myself a story about the future. If I have no idea what the next month will bring me (like when I was applying for jobs this past spring) I can get a little stressed. If I have a story to tell myself, then I have a goal I can work towards. But if I don’t know enough to put together a story, then in my mind every future is equally likely. I could get my dream job or I could get no job. We could move to Illinois or we could get sucked through a rogue cosmic wormhole and end up on Planet Squizznonks. I have a good imagination! But my imagination needs some structure or it will freak out, like a middle school student who’s too smart for his own good.

Right now, I can tell myself a pretty convincing story about the next year or two. Anaïs might graduate and start looking for jobs. I might be working like crazy on my new experimental program. Maybe we’ll make it through a couple Chicago winters and they won’t seem so insane anymore. Story: check. No freak-outs: check.

But if I look a little farther out, there’s some pretty big stuff I just don’t know about yet. Are we going to buy a house like real grown-ups? Are we going to start having kids? Can I plan on staying at my new job that I love, or will we need to solve the “two-body problem” again when people start showering Anaïs with super-amazing job offers? (Anaïs will certainly get showered with job offers because she is brilliant and hard-working and beautiful.)

So for now, I’m trying to focus on the present. The present is pretty good. And I haven’t noticed any rogue cosmic wormholes in my neighborhood yet, so that’s something.

Now let’s peer into our copper cauldron to ponder the phuture of particle physics…

cauldronSame story, different characters. When you talk about the future of particle physics, there’s some near-term stuff that’s easier to plan and talk about, and some long-term stuff that’s hazy and hard to imagine.

In the near-term, there’s a lot of exciting questions about neutrinos that we can answer with today’s technology. For example, some people think there might be new, weird flavors of neutrino we haven’t observed yet. Also, a careful study of neutrinos could help us answer this question: “why is there stuff?” Don’t you want to know why there’s stuff?

Let’s leave that as a teaser for the next blog post: the mystery of the existence of stuff. But for now we’re talking about the future. In my artful and clever allegory, all this neutrino business is in the easy-to-imagine, anxiety-mitigating near-future. It’s good to know we have some important work ready to be done right now.

Beyond that neutrino stuff, though, it’s harder to tell ourselves the story of the next big accelerator. The problem is that right now, we don’t know enough about the next big questions. Is there only one Higgs boson, or are there a bunch of them? And what about supersymmetry? Is that a thing, or what?

Those are big, big questions in physics that will be answered by building a big, big accelerator. And until the LHC generates more data, we really don’t know what kind of accelerator we’ll need. Should we even build a new, giant accelerator? (Yes.) Should it collide protons or muons, should it be circular or linear, and who should build it? Right now we just don’t have enough data. It’s hard to say what the next big machine will be like because we’re not quite sure — yet — how to ask the next round of big questions.

That uncertainty about the future of particle physics is fuel today for a lot of meetings and powerpoint slides and general hand-wringing. My physics pals and I are working just as fast as we can to put together the next big story in a way that makes sense to us all. And that’s what I’ll spend my next few blog posts talking about. Stay tuned!

New job, new post!

hoorayI took a few months off of blogging so I could focus all my energies on worrying about job applications. And I guess all that worrying paid off because I got a job! I work at Fermilab now, doing basically the same stuff as before.

I want to tell you all about it! But not all at once, because (a) I respect you and your limited free time too much for that; and (b) oh man, Anaïs and I still have so much unpacking to do.

I’m very happy to be blogging at you again. Hello!

Friday Physics Photos: ???



Ok you guys, I’m doing that thing where as I write my next post, I discover that I have more and more things I want to talk about and the post gets longer and longer … Right now it’s an unreadable mess. While you wait for me to carve that mess up into several smaller messes, here’s a little bit of fun.

On my bike ride into work, I passed by another department’s lab. In their parking lot was this totally inexplicable vignette. I have absolutely no idea what’s going on here and I love it. Somebody write me a short story about this.