CV’s spam filter has been a tad bit overenthusiastic these days, so I’ve recently had to troll through the spam to retrieve misfiled comments. As expected, the spam is a morass of viagra ads and truly horrid lists of porn-related search terms (where “horrid” means “things that Dan Savage would not approve of”). But lurking in there is a new breed of affirmation spam:

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Moreover, now that they’re tired of thinking only of on-line casino gambling, spammers seem to wish to join the CV conversation:

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I couldn’t understand some parts of this article Post: Juan Collar on Dark Matter Detection | Cosmic Variance, but I guess I just need to check some more resources regarding this, because it sounds interesting.

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Sometimes, though, the spammers enthusiasm for our work transcends their usual respectful admiration:

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And at least among the spammers, our work is being appreciated.

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Reflecting on his earlier speech on faith at the height of his campaign, Mitt decides to stand up for atheists:

But upon reflection, I realized that while I could defend their absence from my address, I had missed an opportunity…an opportunity to clearly assert that non-believers have just as great a stake as believers in defending religious liberty.

If a society takes it upon itself to prescribe and proscribe certain streams of belief — to prohibit certain less-favored strains of conscience — it may be the non-believer who is among the first to be condemned. A coercive monopoly of belief threatens everyone, whether we are talking about those who search the philosophies of men or follow the words of God.

We are all in this together. Religious liberty and liberality of thought flow from the common conviction that it is freedom, not coercion, that exalts the individual just as it raises up the nation.

(from a speech at the “Beckett Fund for Religious Liberty’s Canterbury dinner”). He loses a lot of ground with me on the rest of the speech, where he elaborates on his earlier claim that “freedom requires religion” and argues that without religion keeping us all well behaved, the US would have descended into anarchy or facism. All the same, it’s nice to see someone tied so closely with both politics and faith demonstrating understanding of why atheists get a bit squicked out with the notion of theocracies.

UPDATE: This was recovered from before the recent site troubles. As Sean mentioned, earlier comments have been lost.

I just received the following workshop announcement:

11th Birmingham-Nottingham Extragalactic Workshop - 1st Announcement

“Semi-analytic models - are we kidding ourselves?“

Refeshingly honest conference title aside, this is a terrific topic for a workshop. Semi-analytic galaxy formation models are extremely useful tools, which consist of (1) an underlying prescription for the growth of dark matter halos and (2) a set of knobs for grafting complicated baryonic physics onto those halos. The first step is well-understood analytically, and has been reliably calibrated with N-body simulations. The second step, however, contains a lot of crafty juju (how much gas winds up inside the dark matter halos? At what rate does that gas cool? How and when does that gas convert into stars? How does the formation of stars and the subsequent supernovae affect the surrounding gas? How do mergers between dark matter halos change the spatial distribution of the stars and the temperature structure of the gas?). The developers of these models are smart folks, and make reasonably well-informed assumptions about all of these baryonic processes, leading to results that are decent matches to the ensemble properties of the galaxy populations. (Note that I didn’t say “predict results” — these models are ex post facto in most usages.) However, just because the models can be tuned to produce a rough statistical match to observations in no way means that the input assumptions are correct or unique descriptions of what actually happens. Moreover, there are serious discrepancies between the models and the observed properties of very low mass galaxies — when the models are tuned to match the properties of relatively massive galaxies, they predict that the low mass galaxies are red and gas poor, whereas the observations say they’re blue and gas rich. It’s great that the community is looking at these issues head on, given the usefulness the semi-analytic galaxy models.

Today Steinn followed up a nice little post on the role of luck in science (is “lucky” an insult?) with a spot-on description of how being ahead of the curve can damn you in grant proposals:

selected referee comments on “A bold proposal to do something new and interesting”, years 1-3, with added bonus translation

1. very speculative, no track record in this area, would be helped by showing preliminary results proving methodology and showing that results will be forthcoming [trans: come on, first do the research then ask for funding, don’t you know anything?]

   trans: - huh, I never heard of this! Some new stuff. Speculative.
   Oy! He wants full postdoc for three years?
   Who does this guy think he is?

(Years 2 and 3 are equally good).

I think the reaction that Steinn describes can be summarized in Dalcanton’s Lemma of Proposal Writing: “It is nearly impossible to change a referee’s mind about something they think they already know“. If the reviewer comes to the problem with no preconceived notions (i.e. they’ve never read a single paper in your field), you can really make progress in educating them. Same deal if you’re moving forward on well-trodden ground (say, pushing SN Type Ia surveys to larger distances). However, if the reviewer knows something about your topic and thinks the problem is already solved, or uninteresting, or technically unfeasible, or crazy, 10 pages of perfectly formatted prose and elegant figures may still not be enough to change their minds, even if you are 100% completely and totally correct.

Shifting a referee’s frame requires that you first realize that most readers aren’t going to believe you if you’re talking about something that no one else is. Jumping into a bunch of details that seem sensible to you gets you nowhere, when the referee still can’t figure out why you’d bother even thinking about the question. You’ve got to knock down their frame before you stand a chance of getting anywhere. For example: “Although everyone assumes that stars form from gas, here’s a series of three plots demonstrating that that’s completely false when looked at in detail.” Or, “It may seem that the velocity requirements for measuring doppler shifts to detect extrasolar planets are beyond current technical capabilities, but here’s a series of plots where I show that current detection limits are indeed at a level where a monitoring campaign could detect shifts due to Jupiter-mass planets.” Or, “While the theoretical idea that the moon could indeed be made of cheese does not initially seem compelling, here are three analytical calculations suggesting that the properties of cheese could indeed be superior to rock in explaining the observed lunar properties, and thus that further work on the lunar cheese model (LCM) is warranted.” The frame breaking can’t be just a throwaway line, but must be direct acknowldegement of and attack on the paradigm that a likely reviewer would bring to the proposal.

Under the above Lemma, Steinn got shafted in Year 1 because the reviewer came with a frame that said “This can’t possibly work”, and, by not completing enough of the work before submitting the proposal, Steinn didn’t have enough ammo to break the frame. He’s also right in the “Who does this guy think he is?” comment, since the other way you can break a frame is to have enough of a rep that people know never to bet against you. The whole business is another example of Aspects of the Running of Science That Are True, Probably Unavoidable, But Not Necessarily Fair or Optimal.

The classic three pillars of an academic position are teaching, research, and service. While the University Administration sometimes seems to think of “service” as being synonymous with “sitting on committees”, many of us enjoy taking the broader view.

As part of my service activities, this weekend I had the pleasure to talk with a roomful of fantastic young scholars from the McNair program (officially known as the Ronald E. McNair Postbaccalaureate Achievement Program). The program was named after one of the astronauts who was killed in the Challenger disaster). He was also a physicist with a Ph.D. from MIT.

The McNair program identifies promising undergraduates who either are low-income, are first-generation college students, or are from an underrepresented minority group. It then provides extensive mentoring to encourage the students to continue on to graduate school. The mentoring takes the form of supporting the students in research projects in their own departments, guiding them through the steps involved in preparing a strong graduate application, providing an additional resource for academic and personal advising, and waiving application fees.

If you haven’t run across this program, keep an eye out for it. If you know a student who might be a candidate, encourage them to apply. Even more importantly, if you have a chance to work with a McNair scholar, jump at the chance. These kids are phenomenal. They’re interesting and driven, and a pleasure to know.

I’m usually not one to go all blog-happy about the latest press-release, but a recent one happened to be about a paper I had just read, so, I’ll dive on in.

To give a little background, one game that astronomers like to play is “Find the oldest galaxy around at some time”. There are a number of reasons this game has so many eager practitioners. First, the oldest galaxies at any time are probably the locations of the some of the very first collapsed structures in the universe, which make them cool. Second, the most massive galaxies we see today seem to be those that that host the oldest stellar populations — turning this around, if you hunt for the oldest things at a given time, you’re hopefully finding the most massive things at that time. Finally, it’s very tough to figure out how the population of galaxies you see at one time is related to the one you see at the present day (i.e., just because a galaxy is blue and distorted 10 Gigayears (Gyr) ago, doesn’t mean it’s not red and symmetric today). However, if you stick to studying massive galaxies that already looked old 10 Gyr ago, it’s probably a reasonably good guess that they’re comparable to the predecessors of the most massive old galaxies today, giving you one of the few cases where you can match up the two populations with reasonable confidence.

Which brings us to the latest paper by Pieter van Dokkum and his collaborators, on the spectroscopic and HST/NICMOS follow-up of 9 massive red galaxies from the MUSYC NIR imaging survey. These galaxies are at redshifts of 2.5-ish, corresponding to about 2-3 Gyr after the Big Bang for WMAP cosmology. They are very red, and have spectra consistent with a dormant stellar population (i.e. one that stopped forming stars roughly a Gyr previously). An earlier paper by Kriek et al estimated masses for these galaxies (assuming reasonable models of the kinds of stars and dust that are in the galaxies), and found that not only are the galaxies dormant, they’re also extremely massive.

So, now you’ve got yourself a bunch of massive galaxies, that in spite of being no more than 2-3 Gyr old, already finished all their star formation a gigayear previously. Pretty nifty, and what you’d hope to find as the precursors of the old massive galaxies we see today. That’s all well and good, but the problem is that the galaxies are just too dang small. They’re tiny. The most massive galaxies today are typically much larger than the Milky Way, but the galaxies van Dokkum is reporting on are much much smaller. (On the plot at right, the solid scale bar shows a size of 10 kpc. On the plot below, the big solid dots are van Dokkum’s sample, and the little dots are the galaxies seen today in the Sloan Digital Sky Survey.)

Ok fine, you might say. A lot can happen in the intervening 10 Gyr. However, not that much can happen, since these galaxies are already pushing the mass limit of the most massive galaxies we see today. You therefore can’t grow the galaxies by padding them out with new stars from accreted satellite galaxies, without taking them over the limit (i.e. looking at the plot on the upper left, accreting new galaxies would take the galaxies up, but would also move them to the right). Even if you could pull this off, the centers would still be too dense. You also can’t magically puff the galaxies up over time. You could imagine perhaps driving a bunch of mass out of the center of the galaxy, maybe through stellar winds, but it would take an absurd amount of mass loss, even if you believed you could get all that mass out of such a dense honking galaxy. You could also imagine trying to disrupt the galaxies somehow by puffing them up kinematically, maybe through violent mergers. However, the problem here is that the galaxies are waaaaaay too dense. If the galaxies really are as dense as the plot above suggests, then they’re going to be almost impossible to disrupt. It would be like trying to disrupt an iron ball by dropping it into a bowl of pudding.

My take on this is that some of the assumptions that went into making the plot above are wrong, because it’s very hard to imagine hiding the descendants of incredibly massive, incredibly dense galaxies somewhere in the local universe. The authors understand this, and argue that it might be a combination of issues related to measuring the radius (loss of diffuse light at large radii, radial gradients in the conversion of light to stellar mass), and possibly the initial mass function (the great “here be dragons” of all extragalactic astronomy).

I actually think the errant step might wind up being the assumed conversion of light to mass. The standard lore is that when you observe galaxies in the NIR, the light is dominated by old red giant branch stars, giving you a reasonably robust conversion from light into stellar mass. However, at a redshift of 2.5, there’s no way that any star in the galaxy is older than 3 Gyr. In this case, most of the red light from the galaxy will be coming from asymptotic giant branch stars, which are notoriously difficult to model. Thus, the calibration of light to mass can easily be off. AGB stars can also give you a nice red spectrum like those observed, and can be potentially centrally concentrated in response to a central burst of star formation. I think the authors have done the best that they could with their analysis, but I suspect we may have run into some of the real limits of our current modeling.

While deeply held feelings about string theory (”Genius!” “Total Bunk!”) may sometimes drive us apart, all of us can certainly get behind the theory that chocolate is a net good. However, in spite of its appeal as a tasty eatable (with or without bacon), it’s actually a bit of a pain to work with. If you’ve ever tried to use chocolate in its melted form, you’ve probably discovered that chocolate has a number of peculiarities that frequently thwart your best culinary efforts. For example, if your melted chocolate becomes contaminated with an errant drop of water, the chocolate siezes up. If you try to reharden chocolate that’s been melted (say, in making chocolate covered strawberries), you’re frequently left with a matte finish and crumbly texture that in no way resembles the dark glossy chocolate you began with.

The reasons for this should be familiar to any solid state physicist (or at least, they were to the one who made my wedding cake and first clued me in). Cocoa butter, one of the dominant ingredients in chocolate, contains several triglycerides that lock into a crystal form when cooled. However, there is not just one form that the triglycerides can lock into, but six of them (β(I) through β(VI)). Each successive form is more stable and has a higher melting point. Almost all commercial chocolate is in the β(V) form — from what I can tell, you only get to sample β(VI) in the afterlife, if you’ve been very, very good. When chocolate goes all wrong, it is usually a failure of the melted and cooled chocolate to recrystallize into the β(V) state. Similar problems can affect commercial chocolate suppliers as well, leading to chocolate that develops that unsightly chalky film we associate with old chocolate. Even previously stable β(V) chocolate can wind up with the same unsightly film after temperature fluctuations break down the crystal structure, or melt and reharden a thin layer on the surface. Given the commercial implications, there’s been some solid technical work on the structure of the magical β(V) form, which has been studied with x-ray diffraction using synchrotron radiation (more technical data here).

Given the above, when cooking with chocolate, one’s goal is to coax the cooled chocolate back into the β(V) form if one wants the end product to look glossy, be solid at room temperature, and have a nice crisp snap when bitten. The traditional mechanism for this is known as tempering (video here). Traditional tempering involves carefully controlling the temperature of the chocolate as it cools, so that the chocolate favors the preferred crystalline state. However, there is a vastly simpler mechanism, namely, seeding the crystal. If you take a lump of unmelted commercial chocolate, toss it into your bowl of melted chocolate, and stir for a bit, you’ll melt the new lump while cooling the melted chocolate. The cooling chocolate will then prefer the same crystal structure as the melting lump, such that when it hardens completely, you’ll find it in the luscious β(V) state.

PS. I can verify that the above works exactly as advertised. Last weekend I made the wedding cake above for the same solid state physicist who made mine a decade ago. (The cake was alternately described as looking like the Heatmiser’s hair, Mordor, and Garrett Lisi’s E₈ symmetry group, so you can imagine it was a pretty techie crowd). Making the thin chocolate sheets from which I cut the decorations, I got huge swaths of perfectly glossy chocolate. Occasionally, though, there’d be a small section with a matte surface, that was clearly a different crystalline form. Science. It works, bitches.

Much of the April 15th angst that Sean described comes from student’s questioning “Will I be a success if I go to this particular graduate school?”. They place a tremendous weight on this decision (and rightly so, given the 5+ year duration of a typical PhD). The decision of where to go to school presents a clean well-defined juncture, where you can imagine two clear paths before you, one that leads to a happy land filled with unicorns and flowers and all night coffee shops and independent record stores, and another that leads to a sad grey land where you spend your time shuffling piles of paper for The Man. However, having been in the game from the faculty side for nearly a decade, I can say that much of what determines whether one is a “success” is largely independent from this decision. (An aside: for this discussion I’m going to assume “success” equals working as a research scientist, which is the typical goal of an entering grad student. I don’t mean this as a value judgement, since “success” is really “whatever career path you find fulfilling”, and I’m just as happy to train phenomenal future high school science teachers as future faculty at Harvard.)

I think the essence of what determines your long-term success as a scientist is your ability to influence the scientific discussion. When you’re at a point in your career when people pay attention to your work, and want to know “What does <your name > think about this?”, you are on a near certain path to a stable position as a research scientist. Instead, if no one is reading your papers (to the extent that you’ve published them at all), or wants to hear what you say at conferences, or calls you up to ask you about your area of expertise, then you’re in danger of drifting out of the field.

Now, the factors that lead to having scientific influence are many. Among the most important are:

• Writing lots of papers
• Writing interesting papers
• Writing papers using novel or superior data sets
• Writing papers on a timely topic
• Being recognized as leading the above papers, rather than being directed by others
• Communicating your ideas with clarity
• Being socially well-connected in your field
• Being really, really, really, unusually smart and/or creative
• Having influential mentors promoting you

To be scientifically successful, you don’t need to have all of these factors, or even most of these factors. You just need to have enough of them, or a long enough suit in one or two of them, that people can’t ignore what you’re doing.

Of this list, there are at least half that are almost entirely under a student’s own control, no matter where they go to graduate school. You can pick inspiring mentors, write lots of papers on interesting, timely topics, and give riveting talks about them, no matter where you are. You can fail to write any papers (on topics boring or not) and give lousy talks, under the negative guidance of ineffective advisors, even if you go to a top-ranked school. Some of the other factors do probably have some correlation with top-ranked programs, in that such programs are more likely to have the luxury to admit only students with early evidence of brains and creativity, and they tend to have more of the resources that lead to superior data access, or a larger pool of productive theorists (postdocs & faculty). [However, in astronomy at least, there is sufficiently rich access to public resources (SDSS, NASA’s Great Observatories, 2MASS, etc) that one can usually have sufficient access to create “novel or superior data sets” no matter where you are. For lab-based physics, this is likely less true.] In this list, the relative “prestige” of one’s graduate program has little direct impact on your eventual scientific impact. When I hire postdocs, or evaluate fellowship applications, I am drastically more impressed by what someone actually did, than where they went to school.

Besides the import for deciding where to attend school, the above elucidates why “climate” issues can have such a large impact on your eventual career success. If you’re at an institution that places obstacles in your path that make it difficult for you to write papers, to find good mentors, and to make scientific connections in your field, then you’ve got a problem. You’re going to be struggling uphill.

However, the same list also provides the recipe for climbing that hill, if you find that you’re on it. The number one thing you can do is to write papers (and preferably interesting and timely ones). People cannot ignore a large body of high quality work for long. Sometimes it takes a while before they notice, it’s true. But the more you publish, the more likely it is that people will begin to notice your work, and be influenced by it. As that happens, they will start noticing you as well, and will tend to deem you “someone worth having around”, whether as a postdoc, or at their conference, or as their next faculty colleague. This process is vastly easier with a good mentor behind you, but if you wound up without one (or gawd forbid with an anti-mentor), it’s going to be your only route out.

I think the clearest evidence of this is a relatively jaw-dropping preprint that was recently posted to the arXiv (h/t to Zuska). A former particle-physics postdoc (and current grad student in statistics) carried out a very detailed analysis of the productivity of postdocs on the Run II Dzero experiment, and how that translated into giving conference presentations, and being hired into faculty positions. The paper found that the postdocs’ success in eventually landing faculty jobs were highly correlated to productivity (as measured by internal papers), to conference presentations (which were awarded by the leadership of the project), and to the degree of “physics socialization”. These correlations are all what you would expect, and reinforce the above list of what leads to being scientifically influential.

The jaw-dropping aspect of the paper is that the awarding of conference presentations was grossly gender biased (as was the fraction of service work assigned to the women). The female postdocs had drastically higher levels of productivity (indeed, half the men were less productive than the least productive woman), but were allocated far fewer conference presentations than men with comparable productivity. (Note: this is a paper you actually have to read, rather than just flipping to the table at the end. It’s a very well-done piece of statistical analysis, and can’t be fully appreciated from just comparing two means in a table.)

In this exercise, we see the influence game writ large. You need to be productive and visible. If some sort of bias (against women, or shy people, or people from state schools, or whomever) is present that conspires to make you less visible, you’re going to have to be even more productive. It’s not fair, and people in positions to fight against the bias in their institution should do so. But, at least it’s something that you have a chance of controlling.

The comments on Sean’s post below brought to mind a conversation I had long ago. I had been a postdoc at the Carnegie Observatories, which was a research foundation funded by donors. We were having a meet-n-greet with the folks who had given money to the institute — showing them the machine shop, the offices, etc. I was sitting down with one of the more elderly donors, who announced, “Women’s lib killed the public school system.”

When I picked my jaw off the floor, I encouraged him to expand on his thesis, and found that he wasn’t completely nuts. Back in the day, women of brains, talent, and ambition had two acceptable career options: nursing, and teaching. If I had been born 50 years earlier, I would not have a PhD in astrophysics. Instead, I would probably have grown up to be a school teacher, just like my grandmother. It didn’t have to pay that well, since really, what would have my other options have been? Not law school, not physics, not mechanical engineering, not finance. Today, the brightest women have far more options beyond teaching, and while some still teach, the vast majority of us work in other fields. The salaries in teaching remain low, as for many fields that have been dominated by women, guaranteeing that teaching is not as competitive with other career options available.

To clarify, I don’t 100% buy the premise that the public school system is a disaster. My dad was a public high school teacher, I went to urban public schools, and my daughter is in the public schools. Are there problems and failures? Sure. But I don’t accept that all public schools or schools systems are “bad”. Even if I’m not teaching in one.

I’ve written before about my husband’s affection, or rather, obsession with Apple. Like all good converts, he feels compelled to proselytize, particularly about my perceived need for an iPhone. “But honey, you can check your email!” “Hey look! Google Maps knows where you are!”. I remain unconvinced.

However, the other day, he nearly got me:

“Did you know it can emulate the HP-15C?”

Be. Still. My. Heart.

The HP-15C is simply the finest piece of handheld computing technology ever. (Take that Steve Jobs). I got my first 15C back in high school, and it was the only calculator I used for the next couple of decades. I could operate it in the dark. I lost it in an airplane seat back pocket and have never gotten over it.

I suppose in the intervening years we’ve gotten used to irrational devotion to electronic gadgets, but the 15C had to have been one of the first targets, at least in geeky circles. If you mention the 15C to a nerds of a certain age, our eyes grow misty at the utter perfection of it. It was a calculator that simply got everything right.

The genius of the 15C is multifold. First is the form factor. It’s essentially the same as an iPhone, held in landscape mode, with a nice weight that fits well in the hand. The buttons are large and well separated, and there are no more or no fewer than you could want. (In comparison, modern HP calculators are crammed with a thicket of unusable little buttons. Ick.) Second is the glory of reverse polish notation. The 15C operates with a memory stack, which when operating with RPN allows you to perform complex calculations with no need for parentheses. Third is the 15C’s unnatural durability. A former dog of mine literally mangled a friend’s 15C, and it continued to work in spite of the large teeth marks denting the keys. Fourth (and most critical for getting me through years of physics labs and observing runs) was that it’s programmable. That’s no big deal these days, but huge in the early 80’s. Spreadsheets were hardly widespread, and when one timed balls going down ramps or any other such repeated trial, doing repetitive calculations was a breeze on the 15C.

Now, am I alone if my love for the 15C? No, indeed. On Ebay, a 15C in good shape can go for hundreds of dollars. (And if you buy one, it’ll still work. I’m guessing one will not say the same about the iPod in 30 years.). There’s an on-line petition begging HP to bring the 15C back.

And, there are people out there writing emulators for it to run on the iPhone. If you ever see me with an iPhone, this will be why.