Hogg's Research

software repository, licensing

2012-01-25T22:32:00.000-05:00

Inspired by emails from Stumm and Foreman-Mackey, Lang and I had a long conversation about software repositories: SVN vs GIT, in the cloud vs at home, one repository or many, what level of organization, and so on. It is a difficult set of problems, and different solutions serve and support different kinds of development styles and communities. We have decided to migrate one sub-project from the Astrometry.net SVN repository to a github GIT repository as a test balloon, and that led to a round of the endless discussion of licenses. Someone needs to write the document software licensing for astronomers and be done with it!

In the afternoon, I started to read Bovy's latest manuscript about chemical-abundance sub-populations in the Milky Way disk, but now in velocity space.

segmenting images and inferring motion

2012-01-24T22:44:00.002-05:00

Over in Fergus's computer science group, Deqing Sun (Brown) gave a very nice talk about measuring motion in image sequences (think movies) by building a generative model of moving layers with sharp boundaries. He constructs a prior over image segmentations by segmenting the image using threshold-crossing of a (very local) smooth Gaussian process; this permits an analytic prior. The results are beautiful and effective and conform to common sense and also come close to world-record performance against quantitative benchmark tests (with known ground truth). His system performs well in part because it is a (approximate, simplified, sensible) full generative model for the data: It has a large number of parameters, a proper prior over those parameters, and a sensible likelihood function, and he can optimize it. He didn't try to sample from the posterior PDF, but he has only worked (so far) at very high signal-to-noise.

Python

2012-01-23T17:36:00.001-05:00

Foreman-Mackey returned from his furlough at Queens, where he was finishing a paper with Widrow on Andromeda. I quizzed him about some details of cacheing (very slow computations) in my Python RGB-to-CMYK code; he had good ideas. One thing he noted that instead of doing if rgb in cache.keys(): it might be far faster to do try: cmyk = cache[rgb] and then catch the KeyError exception. Apparently that is the rage and style in Python programming. He also promised to help me Python-package and docstring everything. Looking forward to it!

[Note added a few minutes later: Switching from the keys() check to the try style sped up the cache retrieval by a factor of 40!]

planning and not doing

2012-01-20T23:59:00.000-05:00

I worked on making a somewhat binding schedule for creation of my Large Galaxy Atlas and the associated publications; the rest of the day was spent on various complicated issues related to my position as Director of Undergraduate Studies; that is, forbidden content on this blog.

GALEX-orama

2012-01-19T18:00:00.001-05:00

I spent the entire morning in the Hungarian Pastry Shop (Columbia hangout) with Schiminovich, discussing our various GALEX projects, but especially our discovery of white-dwarf companions (some of which are very low mass, possibly sub-stellar) and our evil plans to extract and distribute the full time-tagged photon list, along with the relevant spacecraft pointing and sensitivity data to make full use of them. I am so excited about this project: It will provide the highest time-resolution that is physically possible given the aperture, optics, and detectors on the spacecraft. If that doesn't lead to interesting time-domain science, I don't know what will.

making a catalog is not easy

2012-01-18T15:46:00.000-05:00

Today David Mykytyn (NYU undergrad) and I specified Mykytyn's project to be the construction of a "Large Galaxy Catalog" from the SDSS imaging, using the Tractor to do the galaxy measurements (sizes, surface brightnesses, colors, and magnitudes). Today we discussed many of the complicating issues, which include (but are not limited to) the facts that: (1) angularly large galaxies often overlap multiple SDSS fields, usually taken on different nights, (2) they often overlap very bright stars, which can dominate the photon count in the face of the galaxy, (3) the SDSS software (optimized for much more angularly small galaxies) shreds them often into many pieces, (4) lumps and bumps in the intensity field can be features of the galaxy or confusing foreground objects (stars) or background objects (distant galaxies). We have hacks for all of these issues (not yet implemented), but we would like some principled approaches. It's hard, because, as I have lamented before, despite a hundred years of expensive and painstaking work by thousands of very bright people, astronomers do not have a generative model for galaxies!

finding spectroscopic supernovae in real time

2012-01-17T19:10:00.002-05:00

Back in November, Or Graur (AMNH, Tel Aviv) came to us at NYU and pitched a method for finding supernovae superimposed on the SDSS-III BOSS spectra of early-type galaxies. His system is essentially a generative model of both galaxies and supernovae, so it appeals to me. He has been successful running other data sets, but if we ran his stuff in BOSS on the mountain each night at the end of observing, we could discover and announce supernovae in real time. My only substantial research today was pitching this to the SDSS-III Collaboration; a pitch is required because we can only do this if the Collaboration accepts Graur as an External Collaborator.

pretty much zip

2012-01-16T23:59:00.000-05:00

It being a holiday, I didn't do much here, except for some planning for my non-written Atlas.

printer calibration test strip

2012-01-14T17:49:00.002-05:00

I made this printer test (8-bit CMYK TIFF file) for my RGB-to-CMYK conversion project. If you can print this out on a CMYK printer (no, there is absolutely no reason to look at it on the screen), and if you can be sure your print driver is not flattening to RGB before doing a reconversion to CMYK (this is hard to know, given the craziness of the print driver world), then printing this and comparing it to a screen view (no, there is absolutely no point in printing it) of the original (8-bit RGB JPEG) leads to a (very rough) printer calibration. In making this test strip, I have reduced my 12-parameter (already simplfied) printer model to only 3 parameters. Paper (arXiv-only, I expect) and open-source code coming soon.

multi-band Tractor

2012-01-13T17:39:00.000-05:00

One of the ways in which the Tractor is qualitatively better than other methods for measuring the properties of stars and galaxies in imaging is that it can fit multiple images—with different seeing, taken on different nights, and taken through different bandpasses—simultaneously, delivering consistent shapes, colors, and variability information despite heterogenous data and with no requirement of "stacking" before measurement. All that is true in theory but until today most of this functionality was vapor-ware. Today, Lang and I (well, really Lang, with me watching) got the Tractor working on multi-band, heterogenous imaging by permitting the "fluxes" or "magnitudes" of the objects to be arrays of values, one per band. In the future, we hope to work in spaces with well-defined priors, learned hierarchically, but we have a start. When we applied the code to a small snippet of SDSS data, we found some tiny band-to-band astrometric offsets, for which (along with photometric calibration and PSF) the Tractor can also fit.

more deconvolution

2012-01-11T23:59:00.000-05:00

Hennawi and I started to write down a real likelihood function and some priors for the combining-without-stacking problem, thinking about quasars and other high-redshift objects. One of the key ideas is that we can deconvolve the spectral information in the low-spectral-resolution broad-band photometry to get a higher spectral-resolution spectral energy distribution, by using the idea that at different redshift we see similar populations but at different rest wavelengths. Blanton pointed us to this paper by Csabai et al.

don't stack your data (UV edition)

2012-01-10T15:27:00.000-05:00

Hennawi is visiting NYU for a couple of days, and pitched to me a few projects all centered around the idea of getting more information out of a set of noisy observations than you can get by just stacking. One cool idea is to get medium-resolution spectral components (or distribution in spectrum space) with only broad-band photometry. Another is to get the same even in the presence of variable and spiky IGM absorption. Another is to get IGM absorption statistics from broad-band photometry alone. And so on. He emphasized that the UV bump in quasars is not well observed (because it is in the UV and either cut off by atmosphere or else IGM), despite the fact that it is the most direct observable created by the accretion flow. All we did today is talk, but tomorrow we will write down likelihood functions (the first step in any project!).

CMYK

2012-01-06T17:08:00.000-05:00

I finally coded up and got running a RGB-to-CMYK conversion that is based on the physical properties of the printing device. The (perhaps insane) idea I have in mind is the following: Our RGB images of SDSS galaxies, if you view them on a normal RGB monitor, have a definite, quantitative relationship between the light hitting your eyes and the intensity hitting the telescope. It is non-trivial and non-linear, but it is quantitatively traceable and (lossy) invertible. When we print these out on a CMYK printer, this is not true, in part because the RGB-to-CMYK conversion is heuristic and doesn't in any way model the physical process of light hitting the page, being attenuated by the ink, and then reflecting. This process, for example, is multiplicative (not subtractive as is usually said). It is multiplicative with multipliers less than one, and (strongly) wavelength-dependent. The model I have built of this process can (in principle) make it once again true that the reflected light from the page (when viewed with a standard room illumination, say) has properties that are quantitatively and (lossy) invertible back to the intensity falling on the telescope. Why do I do these thing?

Popper

2012-01-03T23:10:00.000-05:00

I have been critical of Popper (or really of a cartoon version of Popper who lives in my mind) in the past, so when I passed a copy of his book The logic of scientific discovery in a Wellington, NZ bookstore, I picked it up. I have only just started, but I realize that his really important contribution—and one with which I agree wholeheartedly and as do all probabilistic reasoners (I hope)—is that we should stop trying to solve any kind of problem of induction: We do not generate general rules by making repeated, specific observations! We use repeated observations to test hypotheses. We create generalizations, figure out their consequences, and test those against the data. The data do not create our laws; we create them.

Where I disagree with Popper is in the question of falsification. Popper believes (I think; I haven't read him yet!) that laws can only be falsified when compared with data. I believe that falsification is never absolute, and that falsification of competitors can be effectively confirmatory to the competing hypothesis.

over-estimated error variances

2012-01-02T14:19:00.000-05:00

[On travel, so posting is irregular; see Rules.]

Astronomers spend a lot of their time estimating their errors (meaning the variances or standard deviations of the noise contributions to their measurements), as they should! However, often these error analyses can make a lot of unwanted assumptions. For example, in the case of stellar metallicity measurements (the case I am working on with Bovy and Rix), the errors are estimated by looking at the variation across stellar models, where the range of stellar models is both too large (many of these models are in fact ruled out by the data) and too small (many real observed stars are not well fit by any model), the errors estimated by standard error estimation techniques can be either too small or too large, depending on how the modeling disagrees with reality.

In the limit of large amounts of data, any machine-learner will tell you that if your uncertainty variainces matter (and they do), then you must be able to infer them along with the parameters of true interest. That is, when you have a lot of data, your data themselves probably tell you more about your measurement error properties (your noise model) than do any external or subsequent error analyses. The crazy thing is that it is clear from the informative detail we see in Figure 2 of this paper that the team-reported error variances on the SEGUE metallicity measurements are substantial over-estimates! There might be large biases in these measurements but there simply can't be large scatter. Now how to convince the world?

HMF paper submitted!

2011-12-21T22:25:00.003-05:00

I spent the morning working on Tsalmantza and my HMF method paper. I signed off and in the afternoon she finished and submitted it. I feel pretty good about that.

tractor large

2011-12-20T23:59:00.000-05:00

NYU undergraduate David Mykytyn has been helping Lang and I get the Tractor working on big galaxies, with the thought that we could model big galaxies with the same machinery that we are using to model small ones (and by big I mean angularly big). He got everything working in record time but the model is so simple that it is having trouble dealing with all the real-world craziness at the centers of real, huge galaxies (think dust lanes etc). This on the path to a new Atlas.

mixture-of-Gaussian image descriptions

2011-12-18T21:24:00.000-05:00

In a many-hour delay in Madrid airport at gate U58, I re-hashed the CMML workshop for a group of New-York-bound machine learners who were in other workshops, but who (today) had nothing but time (thanks mechanical issue with airplane). I then tried to do some of the math that Marshall and I had been waving around yesterday. I think we might have some conceptual issues. The plan is sound, way sound, but we might not be able to transfer probabilistic information with quite the fidelity we had in mind yesterday.

a better catalog

2011-12-17T23:59:00.000-05:00

One thing I didn't mention about the awesome day yesterday is that Marshall and I scoped (not scooped, scoped) two papers on PanSTARRS-like data. One involves making a super-simple catalog from the imaging, a catalog made up of a mixture of Gaussians (and nothing else, not even PSF determination). Marshall has the intuition that this catalog—not even really a catalog but more like a lossy but responsible description of the mean properties of the imaging—could be used to transmit to end users far more information than is transferred by traditional astronomical catalogs. We were on a roller coaster of good news, bad news until we cracked it in a nasty cafe across the street from the Grenada bus station.

cosmology meets machine learning

2011-12-16T23:59:00.000-05:00

Today was the first day of the workshops at NIPS, and the day of the Cosmology Meets Machine Learning organized by a group led by Michael Hirsch (UCL and MPK Tübingen). What a day it was! The talks, by astronomers doing cosmology with sophisticated machine tools, were edutaining, with (among others) Lupton doing his best to pretend to be curmudgeonly (okay, he does have a point that some of the stuff I say is not all that practical), Starck showing amazing decompositions of Planck-like maps, and Refregier doing his best to alarm us about the difficulty of the cosmological weak lensing problem. In between these talks were shorts by the poster presenters; all good and all high bandwidth in their four-minute spots. A standout for me was Kaisey Mandel and his hierarchical probabilistic model for the type-Ia SNe, making the cosmological constraints more precise by hierarchically learning the priors over the nuisance parameters you need to marginalize out if you want to do things right!

While many left to ski, Marshall declared the afternoon break to be an un-workshop in which workshop topics self-assembled and self-organized. This evolved to two big un-workshops, one on probabilistic graphical models, with Iain Murray doing the heavy lifting, and one on blind deconvolution with Hirsch throwing down. Hirsch showed some devastating results in blind and non-blind deconvolution, including (in the style of Rob Fergus), outrageous ability to compensate for bad hardware or bad photography. Outrageous.

Despite all that, it was the PGM workshop with Murray that—and I am not exaggerating here—was possibly the most educational ninety minutes of my post-graduate-school life. After some introductory remarks by Murray, we (as a group) tried to build a PGM for Refregier and Bridle's weak-lensing programs. Marshall insisted we use the notation that is common in the field and keep it simple, Murray insisted that we do things that are not blantantly wrong, Stefan Harmeling provided philosophy and background, especially about the relationship between generative modeling and probabilistic modeling, Lupton tried to stay as curmudgeonly as he could, and at the end, Murray broke it all down. It wasn't just science, it was like we were starring in an HBO special about science. We realized that PGMs are very valuable for de-bugging your thinking, structuring the elements of your code, and, of course, making sure you write down not-wrong probability expressions. Aawww Yeah!

At the end of the day, Marshall moderated a (huge) panel, which covered a lot of ground. The crazy thing is that we had some important points of consensus, not limited to the following: (1) As a pair of overlapping communities, our best area of overlap is in structured, physics-informed probabilistic modeling. Many cosmologists are stuck on problems like these, many machine learners have good technology (things like sparse methods, online and stochastic methods, and sampling foo). Neil Lawrence pointed out that the machine learners got their Bayes from astronomers Gauss and Laplace. Now the astronomers are asking for it back. (2) We should be setting up some simple challenges and toy problems. These make it easy to draw machine learners into the field, and help us boil our issues down to the key ideas and problems. That's Murray's big point.

Hirsch, Bridle, Marshall, Murray, and everyone else: Thank you. Absolutely cannot understand why Sam Roweis wasn't there for it. I never really will.

NIPS day 4

2011-12-15T23:59:00.000-05:00

I went to (most of) the NIPS 2011 talks today (my first day at NIPS). Unlike the AAS meetings, the talks are very highly vetted (getting a talk at NIPS is harder—statistically speaking—than getting a prize fellowship in astronomy) and there are no parallel sessions, even though the meeting is almost as large as the AAS (NIPS is 1400; AAS winter is 2000-ish). One standout talk was by Laurent on the strange encoding of olfactory information in insects (and, apparently, humans, which are similar in this respect). There is a part of the olfactory system that looks like a sparse coding of the input, which looks (to my eyes) to be a very inefficient use of neurons. Another was by Feldman on coresets, which are data subsamples (imagine that you have way too many data points to fit in RAM) plus associated weights, chosen such that the weighted sum of log-likelihoods of the coreset points is an epsilon-close approximation to the full-data log-likelihood (or other additive objective function). This concept could be useful for astrophysics; it reminds me of my projects with Tsalmantza on archetypes.

On the bus to Sierra Nevada in the afternoon, Marshall and I tried to scope out our next paper. I put that in quotation marks because we don't have a very good track record of finishing projects! We are going to do something that involves image modeling and approximations thereto that can be performed on catalog (database) quantities.

graphical model

2011-12-14T23:59:00.000-05:00

I have been reading about graphical models for a few days here and there. So on the plane (to NIPS 2011) I tried to draw a graphical model for a large-scale structure and weak-lensing jointly supported model of the density field and cosmological parameters. I think I am close. Marshall and I and anyone who can stand it are going to unconference on Friday and try to work it out.

finding absorption features by machine

2011-12-13T23:59:00.000-05:00

Guangtun Zhu (JHU) stopped by this week (welcome back!) and came for a chat about modeling spectra, in particular quasar spectra that might have absorption-line systems redward of Lyman alpha. Most catalogs of absorption systems involve significant hand-vetting; we discussed methods to ameliorate or eliminate the by-hand aspects of the problem. Guess what I advocated? Justified probabilistic modeling of the spectra, plus some sensible priors.

exam time

2011-12-12T23:59:00.000-05:00

Nothing to report.

space-filling curve

2011-12-11T23:59:00.000-05:00

It is not clear it counts as research, since I did it purely for fun, but I wrote a recursive code to build this kind of space-filling curve. It is slow, but it works for any image (array) that is 2ⁿ×2ⁿ. If it has any research application, it is in image compression, but I officially don't care about that subject (this paper notwithstanding; it isn't about image compression; it is about information!).

I am so proud of this; check out how scale-free it is: The top-left 4×4 block has the same two-dimensional pattern for the ones digit as the whole chart has for the sixteens digit, but with the pixels being sixteen-cell blocks.

what would you do with the PTF data?

2011-12-09T23:59:00.000-05:00

Price-Whelan returned to NYU for a day (from distant Columbia, where he is now a grad student) to discuss methodologies for some of the projects he is thinking about with Palomar Transient Factory imaging data. There are lots of things possible; like most big projects, PTF creates more possible projects than there are collaborators to do them; this is why astrophysics is a great field! I argued that he should use the things we have been thinking about with respect to image combination and source detection to detect—and then analyze—the sources below the single-exposure detection limit. Any project PTF has done at high signal-to-noise can be done for far more sources additionally if you are willing to work at lower signal-to-noise (and at image level rather than catalog level).

non-integrable galaxies

2011-12-08T19:57:00.001-05:00

As my loyal reader knows, I am strongly against modeling non-integrable systems with integrable potentials. And guess what: No galaxy in any kind of realistic dark-matter mass distribution could ever have an integrable potential! During a meeting today (and yesterday also, which perhaps doesn't count according to the implicit parts of The Rules), Fadely, Foreman-Mackey, and I talked about how to generalize things like Schwarzschild modeling to non-integrable potentials and proper probabilistic inference. Glenn van de Ven (MPIA) does these things for a living, so we wrote him email and he sent back some great suggestions for how to proceed. Now the question is: Should we implement something, even if it means reinventing a few wheels? It didn't take us long to decide: Yes!. Ah, Camp Hogg prefers engineering to science these days. One set of ideas I am excited about is how to write down a prior over phase-space distribution functions when you don't have an integrable-potential action–angle language in which to write it. I love that hairball of problems.

why do we have five fingers?

2011-12-07T23:59:00.000-05:00

After I used my five fingers to put my foot squarely in my mouth on the APOGEE Collaboration mailing list (yes folks, apparently you should be careful with that Reply All button), I was treated late in the day to a couple of excellent talks about NYU efforts that border on astrobiology; not astrobiology per se but close: Paul Chaikin (NYU Physics) talked about his work with Pine and Seamans to make artificial structures out of DNA or small particles painted with DNA or the like that can self-assemble and then self-replicate. Bud Mishra (NYU Math and Biology) talked about simulations of the interactions that lead to multi-cellularity, with the thought that multi-cellularity might be inexpensive and generic in the path from first life forms to complex ones.

One of Mishra's most gripping (ha ha) points was about the number of fingers we have: Is five a random accident or is it something you would expect to see on other worlds? He pointed to a nice math result of his that shows that for frictionless fingers to hold any arbitrary solid object under load, you need between 7 and 12 fingers. Given that fingers come at a cost, and that there is friction, five might be very close to optimal for a wide range of utility functions! That's an idea I will be thinking about for a while. I also got him to say a bit about higher dimensions: He said my problem wasn't crazy because an articulated object is like a solid object in higher dimensions!

spectroscopy, calibration

2011-12-05T23:59:00.000-05:00

Douglas Brenner (AMNH) dropped by for a discussion of spectrum extraction, related to the coronography stuff we have been working on. I pointed him to this instant classic, a paper much discussed a few years ago in this forum (for example here). At lunch, Muna talked about software development for stellar population synthesis, which could have a big impact in the large-spectroscopic-survey world we are currently in. In the afternoon, Foreman-Mackey produced color-color plots for stars in his re-calibration (self-calibration) of SDSS Stripe 82 imaging. They are looking very good.

what is the probability of habitability?

2011-12-03T13:27:00.001-05:00

At the end of yesterday, there was a discussion of astrobiology at NYU, and then this morning I had email from Rix about a probability calculation for p(habitable|data) where "habitable" is the bit "yes this exoplanet is habitable" or "no it isn't" and "data" is whatever you have measured about an exoplanetary system. He was inspired by this paper. We discussed briefly by email; one of the issues (of course) is that many of the parameters that might determine habitability do not even indirectly affect the observable (at present day) properties of the exoplanet. That's a nice problem; it reminds me of my conversations about extraterrestrial life: Prior information informs the current debate much more than the data do; it is a playground for Bayesian reasoners.

talking, and occupy!

2011-12-02T23:59:00.000-05:00

In an all-talk day, I talked with Lang about next steps on our various slowly moving projects; I talked with Sjoert van Velzen (Amsterdam) and Foreman-Mackey about large surveys; I talked with Fadely yet again about the scope for paper zero on classification. This was a highlight: This time I think we have done it! The scope will be showing that hierarchical beats likelihood tests no matter how bad the models. In the afternoon, Nissanke (Caltech) gave a seminar about gravitational radiation detection, particularly in the light of coincident electromagnetic signals. At the end, discussion devolved into the insane data-secrecy policies of LIGO, which I abhor. Occupy LIGO!

software engineering for science

2011-11-30T23:59:00.000-05:00

I had lunch today with Demitri Muna (NYU) of scicoder fame. We discussed software engineering, code-writing environments, and the idea that Muna should write a book about what he knows. He has been teaching scicoder workshops at NYU for a couple of years now, and there is no good book for scientists who code.

IDL

2011-11-29T23:59:00.000-05:00

Almost no research got done today, with the exception of being impressed by David Mykytyn (NYU), my new undergraduate researcher, picking up IDL and using it within an hour. Of course we hate IDL here at Camp Hogg, but sometimes we can't avoid it!

editing

2011-11-28T14:16:00.001-05:00

I spent my research time editing Holmes's nearly-ready paper on self-calibration of imaging surveys (what I have called "uber-calibration" previously). The results are so simple and so easy-to-use; I hope we have an influence on the next generation of surveys.

zip

2011-11-23T23:59:00.000-05:00

No research today; gone till Monday.

oscillation model bad?

2011-11-22T23:59:00.000-05:00

Hou, Goodman, and I have a radial-velocity linear damped oscillator model to capture non-exoplanet near-sinusoidal variability in radial velocity surveys (especially for giant stars). Unfortunately, it doesn't seem to be a good fit to the radial-velocity jitter in our test-case real star. So now we have to see if among the data we have there is any nail on which we can use this hammer.

no "N" in "LINER"?

2011-11-21T23:59:00.000-05:00

My only research today was attendance at a nice blackboard talk by Renbin Yan (NYU) on the origin of LINER emission in galaxies. He has kinematic evidence that it is produced by evolved stars rather than nuclear emission from an accreting black hole. If he is right—and I think he is—this will make some waves in the galaxy evolution world, where LINER ratios has been assumed to mean black hole.

cosmology, scattering

2011-11-18T23:59:00.000-05:00

In the morning, Mustafa Amin (MIT) gave a short talk about consequences of potential shapes in inflation for the period between the end of inflation and the beginning of the normal physics of standard-model particles. In the afternoon, Joel Primack (Santa Cruz) gave the astrophysics seminar on the extragalactic background light. For the latter, some of the constraints come from the absorption of high-energy photons from blazars. I asked about scattering—when I was a boy I was told that whenever there is absorption there is always scattering, was that wrong?—but he implied in his answer that the scattering mechanisms are very different from the absorbing mechanisms. I am confused! If you do the classical calculation of a plane wave intercepted by finite absorbers, you have to get scattering too, just as a consequence of physical optics. Maybe my reader will un-confuse me.

Between talks, Jagannath and I discussed the implications of recent literature developments for our paper on fitting streams. I think we have a plan to re-scope it.

clustering likelihood; coronograph mixture

2011-11-17T23:59:00.000-05:00

Sarah Bridle (UCL) and Marshall called me by Skype (tm) this morning (my time) to discuss Phil and my crazy ideas about inferring the mass distribution and the cosmological parameters from weak-lensing data. Instead, we got sidetracked onto clustering measurements, with me making the pitch that we might be sacrificing signal-to-noise on the baryon acoustic feature by fitting models at the point-estimated (pair counts in separation bins) two-point correlation functions. That is, no large-scale structure project ever writes down a likelihood function for the galaxy positions.

At lunch, Fergus showed me his new and improved models for Oppenheimer's coronograph data. He factorizes the data matrix into principal components, and then finds that he can't fit the data well near the companion (exoplanet) locations with the dominant principal components. Furthermore, he finds that if he fits the data as a mixture of (empirical, data-driven) speckle model for the residual star light plus (heuristic, theory-driven) point-source model for the companion light, he does a great job of separating the components and thereby photometering the companion. Beautiful stuff and music to my ears, methodologically.

HMF second draft

2011-11-16T23:59:00.000-05:00

I got Tsalmantza and my HMF method paper up to the second-draft level. I was supposed to finish this back in October! It still needs a bit of work—Tsalmantza and I speak with very different voices—but it is extremely close. One thing the paper lacks is a clear demonstration that optimizing chi-squared is far preferable to optimizing an unscaled mean square error. I guess we always considered that obvious, but I now wonder if everyone else does.

beating confusion

2011-11-15T23:59:00.000-05:00

I spent the day at the Spitzer Science Center as part of my Oversight-Committee duties. This is not research. However, on the airplane to the meeting, I started image simulations to explore an idea that came up while I was at UCLA: Can you use multi-epoch imaging to beat the naive confusion limit if your sources are moving fast? I am sure the answer is yes.

geodesic motion

2011-11-14T23:59:00.000-05:00

Daniel Mortlock (Imperial) dropped in for the day and we spent some quality time talking about data. We don't have any project to work on, but we should! Mortlock holds the current high-redshift quasar redshift record. At lunch time, Gabe Perez-Giz (NYU) talked about computing geodesic orbits around Kerr black holes. He is a big believer in making use of symmetries, and there are additional computational symmetries if you look at closed orbits—orbits where the azimuthal, radial, and polar-angle frequencies are rationally related. If you can build a dense orbit library out of these orbits (and it appears you can), then you might be able to compute a whole bunch of stuff fast. Now show me the money!

random forests; supernova discovery

2011-11-11T21:03:00.001-05:00

In the astro seminar today, Joey Richards (Berkeley), about whom I have been blogging all week, spoke about the methodologies and successes of the Bloom-led Center for Time-Domain Informatics team in automatically classifying time-variable objects in various imaging surveys. He concentrated on random forest (a combination of many decision trees, each of which is made by randomizing in various ways the training data) in part because it is extremely effective in these kinds of problems. He even claimed that it beat well-tuned support-vector machine implementations. I will have to sanity-check that with Schölkopf in Tübingen! Richards did a great job, in particular in explaining and responding to the principal disagreement we have, which is this: I argue that a generative model that can generate the raw pixels will always beat any black-box classifier, no matter how clever; Richards argues that you will never have a generative model that is accurate for all (or even most) real systems. It is this tension that made me invite him (along with Bloom and Long) to NYU this week.

After the seminar, Or Graur (Tel Aviv, AMNH) showed us how he can find supernovae lurking in SDSS spectra and pitched (very successfully) a test with SDSS-III BOSS data. We will get on that next week.

pair-coding

2011-11-10T23:59:00.000-05:00

While Richards worked on faintifying bright Mira-variable light curves and censoring them in the manner of an insane robot (or astronomical imaging pipeline), Long and I worked on Python-ifying, and numpy-ifying some slow marginalized likelihood code. The issue is that our likelihood model has two nuisance parameters per data point (the true uncertainty variance and the true censoring flux value, considered poorly known and different for every datum) which we want to marginalize out inside the repeatedly called likelihood function. Lots of ways to do this slowly; few ways to do this fast. The goal is to have the skeleton of a paper by tomorrow afternoon!

blowing text

2011-11-09T23:59:00.000-05:00

Over a long and hearty breakfast, I turned our equations for fitting lightcurves in the presence of carelessly censored data into a LaTeX document complete with lots of discussion. I handed it to Richards and Long.

measuring the undetectable

2011-11-08T18:13:00.002-05:00

I have published one paper and have two more in the works under the subject measuring the undetectable. My evil plan is panning out; today Joey Richards, James Long (Berkeley), Dan Foreman-Mackey, and I all agreed that we should work together on a very nice and extremely practical project. Here it is:

Imagine you have a catalog of point-source fluxes, measured for a bunch of sources in a badly documented multi-epoch survey. Now imagine that you are looking at one source, which is variable, and it has been detected at some epochs and not at others. Imagine further that at the non-detect epochs, you are not provided with any information about the flux upper limits; all you know is that the source wasn't seen. How do you fit the stellar (lightcurve) properties of this source, using both the detections and the non-detections?

Without priors, this is impossible, I think, because you don't know whether the non-detections are non-detections because the data at those epochs was extremely bad or whether they are non-detections because the star at those epochs was very faint. But we figured out what you could do if you could hierarchically infer a prior over the detection threshold and over the noise properties of the detected sources. We started to write documents and code in the afternoon.

classification etc

2011-11-07T23:59:00.000-05:00

Josh Bloom, James Long, and Joey Richards (Berkeley), all of Palomar Transient Factory and Center for Time-Domain Informatics fame, are visiting Camp Hogg this week. My not-so-secret plan is to come up with a jointly executed project, so that I can get a piece of this talented team for free. We discussed options, ranging over classification, prediction, decision making, and so on. We have a substantial difference of opinion on various matters—Camp Hogg is all about models that generate the raw data, while the CTDI likes data-driven models in feature space—but we are working towards very similar goals in our work on time-domain astrophysics. One shared goal is to measure time-domain properties of sources too faint to identify at any individual epoch; that might be our project.

intensity invariance

2011-11-04T23:59:00.000-04:00

Only research today was a talk by Gwenael Giacinti (APC, Paris) about anisotropies in the local distribution of cosmic rays. It ended with a short argument (in the audience) about lensing of the intensity field, with various people confused and not confused about the meaning of the point that lensing conserves phase-space density of photons. The point is trivial in one sense, but in an equally important sense, it is not at all trivial, as evidenced by confusion among astrophysicists about what it can and can't mean. The argument inspired me to write something about it.

black-hole binaries, image formation

2011-11-03T23:59:00.000-04:00

After getting back on the red-eye, I attended a nice seminar by Mike Eracleous (Penn State) about black-hole binaries. He is a bit more optimistic than I am about finding and confirming them, and about their usefulness. But we had near-simultaneous papers on the subject this year. In the afternoon, Fergus and I continued our debates about how to model speckles in the 1640 coronograph data. The issues are all about how to make the model so flexible that it can fit all the speckles, but so constrained that it does not at the same time want to fit out the companion (think exoplanet). Out most insane idea is to fit for the electric field in the camera! But that, I think, is hard.

UCLA

2011-11-02T23:59:00.000-04:00

I was at UCLA all day today to give a seminar. I had great conversations with many of the faculty, postdocs, and students. One idea (of many, many) that stands out is from Mark Morris, who is thinking about whether time-resolved imaging of the Galactic Center (where the stars move fast) could be used to beat the confusion limit. I am sure it can; this is a great project to think about! Another is from Brad Hansen, who points out that if we do find eclipsing planets with GALEX (as Schiminovich and I hope to), they ought to be around massive (rather than normal) white dwarfs. This is because the massive ones might be formed by mergers that could also bring in post-main-sequence planets.

goose egg

2011-11-01T23:59:00.000-04:00

No research at all today. But I am not ashamed: You should have seen my office at office hours! (Big exam on Thursday.)

supernova progenitors

2011-10-31T23:59:00.000-04:00

The scariest thing about Hallowe'en this year was how little research I got done! Maryam Modjaz (NYU) gave a nice blackboard talk at lunch about the supernova zoo, and the hopes we have for indirect determination of stellar and binary progenitors. For prompt supernovae, local gas-phase metallicity could be extremely informative; however, you often have to wait years to get a good spectrum. In the afternoon I had a short conversation with NYU undergraduate Gregory Lemberskiy, who is looking at some issues in image model initialization and optimization.

mixtures of Gaussians for astronomy

2011-10-28T23:59:00.000-04:00

Phil Marshall and I spent the day pair-coding a tiny Python package that fits a set of mixture-of-Gaussian models of increasing complexity to a PanSTARRS-produced catalog cutout image. It is insanely fast! (We did this outside of Lang's Tractor for reasons that are too annoying to explain here.) Our success means that in principle, any survey that is doing any kind of galaxy fitting can also, very easily, fit general non-parameteric models to everything: That is, we could have a variable-complexity description of every imaging patch. I love this just because I am a geek. Marshall loves this because maybe—just maybe—the mixture-of-Gaussians description of a patch of imaging will be close to a compact sufficient statistic for performing other kinds of fits or analyses. He has weak and strong gravitational lensing in mind, of course. More on this soon, I very much hope!

[In other news, Brian Cox thinks I am guilty of scientific misconduct. Fortunately, Sarah Kendrew (MPIA) doesn't.]

PanSTARRS catalog modeling

2011-10-27T23:59:00.000-04:00

Marshall showed up for a couple of days of hacking. He appealed to my sense of irresponsibility to convince me not to work on much more pressing things, like letters of recommendation, mid-term exams, hallowe'en costumes, and other matters that don't constitute research (consult rules at right). We discovered that the PanSTARRS preliminary catalogs might not have the angular resolution (or, perhaps, deblending aggression) we would like in order to use them to find gravitational lenses. The experience will lead to feedback that will (we hope) improve the PanSTARRS software, but they remind me of the great opportunities that could be afforded if we had a probabilistic framework for producing catalogs from imaging. I should work on that!

open science, importance sampling

2011-10-26T23:59:00.000-04:00

It is Open Access Week and for that reason, SUNY Albany libraries held an afternoon-long event. I learned a lot at the brown-bag discussion about how open access policies could dramatically improve the abilities of librarians to serve their constituents, and dramatically improve the ability of universities to generate and transmit knowledge. The horror stories about copyright, DRM, and unfair IP practices were, well, horrific. In the afternoon I gave a seminar about the openness of our group at NYU, including this blog, our web-exposed SVN repo, and our free data and code policies (obeyed where we are permitted to obey them; see above). It was great, and a great reminder that librarians are currently—in many universities—the most radical intellectuals, with sharp critiques of the conflicts and interactions between institutions of higher learning and institutions of commerce.

On the train home, I tried out importance sampling for my posterior PDF over catalogs project. Not a good idea! The prior is so very, very large.

sparse matrices

2011-10-25T23:59:00.000-04:00

In separate conversations with Hou and with Foreman-Mackey, I found myself discouraging each of them from looking into serious sparse methods for Gaussian processes. Both students are potentially matrix-inversion-limited (or determinant-computation-limited). We could definitely benefit from having matrix respresentations (even if it means approximations) that have sparse inverses (or analytic inverses). But going there is (at this point) a research project in sparse methods, not an astronomy project. So I am going to hold off until we really need it. That said, there is a huge literature now of very promising techniques.

gravity

2011-10-24T23:59:00.000-04:00

Seminars filled my research time today. At lunch, Sergei Dubovsky (NYU) talked about conjectures for the fundamental action of strings in string theory. He gave a tiny bit of motivation for why 26 dimensions is the preferred dimensionality of spacetime in string theory. Apparently the 26 dimensions are expected to be 25 spacelike and one timelike, which seems odd to me, but causality is a bitch (as they say). Causality featured prominently in his talk, because he is experimenting with different causal structures for the dynamics on the worldsheet of the fundamental string.

In the afternoon, Michele Vallisneri (JPL) told us about detecting gravitational radiation with LISA-like missions. They face there the same issue that Lang and I have been talking about for astronomical imaging: It is no longer possible to think about there being the catalog of sources from the data. There will always be many qualitatively different explanations of the data stream, and always secure scientific conclusions will have to be based on marginalizations over that catalog-space probability distribution function. He said interesting things about the hardware injections of false events into the LIGO data stream to stress-test the analyses and about the software engineering aspects of these huge projects.

GSOC Mentor Summit

2011-10-23T23:59:00.000-04:00

Lang and I spent the weekend at the Googleplex for the Google Summer of Code Mentor Summit. We did some work on our source detection paper, and had a lot of conversations about open-source software. Things I learned of relevance to astronomers include: The Software Freedom Conservancy (and other organizations that are similar, like SPI) can provide a non-profit umbrella for your project, making it possible for you to raise money for your project as a non-profit organization. The commercial movie industry uses a lot of open-source computer vision and graphics (which is really physics) open-source software, even in blockbuster films (like Smurfs). The Climate Code Foundation is trying to do some of the things advocated in Weiner's white paper, but for climate science. The semantic web dream of many an astronomer (though not me; I am suspicious of all meta data) has been realized in the music space, in open-source projects like MusicBrainz.

CIDU day 3

2011-10-21T23:59:00.000-04:00

Highlights for me today included El Ghaoui (Berkeley) talking about text document clustering and searching, and Das (NASA Ames) talking about sparse matrix methods in Gaussian processes. In the former, I realized it is incredibly easy to make sparse models of authors' use of words; I have always wanted to launch a project to figure out, in multi-author papers, which authors wrote which paragraphs. If you want to do that project with my consulting, send me email! In the latter, I learned about some inferential techniques for finding sparse approximations to the inverses of sparse matrices. In general the inverse is not sparse if the matrix is sparse, but you can come up with sparse approximations to the matrix and inverse pair. I spoke today, about my comprehensive image modeling projects. I also flashed some of Fergus's results.

CIDU day 2

2011-10-20T23:59:00.000-04:00

Day two (but day one for me) of the 2011 Conference on Intelligent Data Understanding at Mountain View was today. Many of the talks are about airport safety and global climate. I learned, for example, that there are far more forest fires going on in Canada and Russia than in past years, probably as a result of global climate change. Highlights of the day include comments by Basu (Google) about the issue that useful algorithms have to be simple so that they can be scaled up and run on the highly parallel cloud (MapReduce and all that), and comments by Djorgovski (Caltech) about needing to take humans out of the loop for transient follow-up. On both points, I agree completely. At the end of the day there was a panel discussion about data-driven science and interdisciplinary data science with me as one of the panelists. I opened my discussion about how we work successfully with applied mathematicians (like Goodman) by saying that I can't understand any paper that begins with the word "Let".

How does a coronograph work?

2011-10-19T23:59:00.000-04:00

As my loyal reader knows, Fergus and I have been working on data from Oppenheimer's (AMNH) 1640 coronograph. Fergus's model is a data-driven, empirical model of the speckle pattern as a function of wavelength, informed by—but not fully determined by—the physical expectation that the pattern should grow (in an angular sense) with wavelength. Fergus's model is very simple but at the same time competitive with the official data pipeline. Nonetheless, we had to make a lot of decisions about what we can and can't hold fixed, and what we can and can't assume about the observations. We resolved many of these issues today in a long meeting with Oppenheimer and Douglas Brenner (AMNH).

Complications we discussed include the following: Sometimes in the observing program, guiding fails and the star slips off the coronograph stop and into the field. That definitely violates the assumptions of our model! The spectrograph is operating at Cassegrain on the Palomar 200-inch, so as the telescope tracks, the gravitational load on the instrument changes continuously. That says that we can't think of the optics as being rigid (at the wavelength level) over time. When the stars are observed at significant airmass, differential chromatic refraction makes it such that the star cannot be centered on the coronograph stop simultaneously at all wavelengths. The planet or companions to which we are sensitive are not primarily reflecting light from the host star; these are young planets and brown dwarfs that are emitting their own thermal energy; this has implications for our generative model.

One more general issue we discussed is the obvious point made repeatedly in computer vision but rarely in astronomy that astronomical imaging (and spectroscopy too, actually) is a bilinear problem: There is an intensity field created by superposing many sources and an instrumental convolution made by superposing point-spread-function basis functions. The received image is the convolution of these two unknown functions; since convolution is linear, this makes the basic model bilinear—a product of two linear objects. The crazy thing is that any natural model of the data will have far more parameters than pixels, because the PSF and the scene both are (possibly) arbitrary functions of space and time. Astronomers deal with this by artificially reducing the number of free parameters (by, for example, restricting the number of basis functions or the freedom of the PSF to vary), but computer vision types like Fergus (and, famously, my late colleague Sam Roweis) aren't afraid of this situation. There is no problem in principle with having more parameters than data!

yet more talking

2011-10-18T23:59:00.000-04:00

Only conversation today, making me worry about my ability to still actually do anything! Gabe Perez-Giz (NYU) and I spent an inordinate amount of time talking about how GPS works, and whether recent claims about systematic error in the OPERA neutrinos could be correct. We also talked about his work on classifying very odd test-particle orbits in the Kerr spacetime.

In the afternoon, Goodman, Hou, Foreman-Mackey, and I talked about Gaussian Processes, among other things. Hou's model of stellar variability is a very restricted (and physically motivated) GP, while Foreman-Mackey's Stripe 82 calibration model involves a (not physically motivated) GP for interpolation and error propagation. Goodman pointed us to the idea of the copula, which apparently destroyed the world recently.

Konidaris and MacFadyen

2011-10-17T23:59:00.000-04:00

Not much research got done today, but I did have a nice long chat about instrumentation and calibration with Nick Konidaris (Caltech), who is building the SED Machine, and attended a blackboard talk on relativistic turbulence by Andrew MacFadyen (NYU).

writing and neutron stars

2011-10-14T22:26:00.003-04:00

I spent the morning writing in Tsalmantza and my HMF paper. This is my top priority for October. In the afternoon, Feryal Ozel (Arizona) gave a great talk about getting precise neutron-star mass and radius information to constrain the nuclear equation of state at high densities. She is doing very nice data analysis with very nice data and can rule out huge classes of equation-of-state models. She also showed some nice results on neutron-star masses from other areas, and (after the talk) showed me some hierarchical inferences about neutron-star mass distribution functions as a function of binary companion type.

supervised and unsupervised, together at last

2011-10-13T23:59:00.000-04:00

Maayane Soumagnac (UCL) visited for a few hours to discuss her projects on classification and inference in the Dark Energy Survey. She is using artificial neural networks, but wants to compete them or compare them with Bayesian methods that involve modeling the data probabilistically. I told her about what Fadely, Willman, and I are doing and perhaps she will start doing some of the same, but focused on photometric redshifts. The key idea is to make the galaxy type, luminosity, and redshift priors hierarchically; that is, to use the data on many galaxies to construct the best priors to use for each individual galaxy. Any such system makes photometric redshift predictions but also makes strong predictions or precise measurements of many other things, including galaxy metallicities, star-formation rates, and properties as a function of redshift and environment.

One of the things we discussed, which definitely requires a lot more research, is the idea of hybrid methods between supervised and unsupervised. Imagine that you have a training set, but the training set is incomplete, small, or unreliable. Then you want to generate your priors using a mixture of the training data and all the data. Hierarchical methods can be trained on all the data with no supervision—no training set—but they can also be trained with supervision, so hierarchical methods are the best (or simplest, anyway) places to look at hybrid training.

all the catalogs consistent with an image

2011-10-12T23:59:00.000-04:00

I got up exceedingly early in the morning, highly motivated to write a short theory paper—that's theory of data analysis, of course—about the posterior probability distribution over catalogs. I have become motivated to write something like this because I have started to become concerned about various bits of wrongness in my old paper with Turner on faint source photometry. The principal results or conclusions of the paper are not wrong, but the language is all wrong (the terms likelihood and measurement are used wrongly) and the priors are improper. I asked Phil Marshall what you do about a paper that is wrong; he said: Write a new paper correcting it!

One of the key things that has to be fixed in the problem is that the explanation of an image in terms of a catalog is—or should be—properly probabilistic. That means that if the image is not high in signal to noise, there are necessarily many even qualitatively different catalogs that could explain the image. This means describing or sampling a posterior distribution over models with varying complexity (or number of parameters or number of sources). That's not a trivial problem, of course.

The nice thing, if I can do it all, is that the new paper will not just resolve the issues with Hogg & Turner, it will also generalize the result to include positional and confusion noise, all in one consistent framework. The key idea is that for any source population you care about (faint galaxies, say, or Milky-Way stars), it is very easy to write down a proper and informative prior over catalog space (because, as Marshall often reminds me, we can simulate imaging data and the catalogs they imply very accurately).

speckles in coronographs

2011-10-11T23:59:00.000-04:00

After giving a seminar at the Cavendish lab in Cambridge UK by videocon, I spent most of my research day talking with Fergus, discussing his model of residual speckle patterns in coronograph images from Oppenheimer's group. Fergus's model is extremely general, but has strong priors which "regularize" the solution when there isn't much data or the data aren't troublesome. Because it is so general, the model is in fact very simple to write down, has analytic derivatives, and can be optimized quickly by some (pretty clever) Matlab code. We decided that he should run on everything and then we should meet with Oppenheimer's group to decide what's next. I think we might be able to improve the sensitivity of coronographs generally with these models.

exoplanet talk

2011-10-07T23:59:00.000-04:00

Roman Rafikov (Princeton) spent the day at NYU and we had long discussions about the state and future of exoplanets. This in addition to his nice talk on the subject. I realized that between the image modeling I am doing with Fergus, the transit discoveries with Schiminovich, and the MCMC work with Goodman and Hou, more than half my current work is related to exoplanets.

Etsy, photons

2011-10-06T23:06:00.000-04:00

I don't think I can count a nice lunch with Christopher Stumm at Etsy as research. But I did meet up with Schiminovich afterwards to figure out how we are going to write the papers we need to write. I am so stoked to have the full list of time- and angle-tagged photons from the full observing history of GALEX.

response to referee

2011-10-05T23:59:00.000-04:00

I worked with Hou today on the response to the (very good, very detailed, very useful) referee report we got on our expolanet affine-invariant ensemble sampling paper.

stars and sampling

2011-10-04T22:12:00.000-04:00

The highlight of my research day was a long conversation with Hou and Goodman about stars, stellar oscillations (linear and non-linear), modeling those with Gaussian processes and the like, and next-generation Markov-Chain Monte Carlo methods. On the latter, the idea is to use an ensemble sampler (or a pair of them) to perform very high-quality proposals, for applications where posterior (likelihood or prior) calls are espensivo.

fink

2011-10-03T12:59:00.000-04:00

I didn't do much research today, but I did remind myself how to get a new Mac computer working. The answer: Fink. As much as I hate compiling everything from scratch (which fink does), the pre-built binaries never work on new hardware or operating systems, so I found that all shortcuts I wanted to take were useless. The key commands were:

sudo fink selfupdate sudo fink update-all sudo fink install numpy-py27 sudo fink install scipy-py27 sudo fink install matplotlib-py27 sudo fink install texlive sudo fink install imagemagick

(All these run after installing Xcode from the App store.) Setting up a new computer is annoying and a headache, but actually, I think maybe it is research.

other universes

2011-09-30T23:59:00.000-04:00

Stephen Feeney (UCL) gave a nice talk on finding bubble collisions by performing model fits and Bayesian hypothesis tests on the cosmic microwave background maps. He had to make some approximations to do the Bayes integrals. He concludes that the rate of bubble collisions is low (I guess we would be suspicious if he concluded otherwise), but because of resource limitations and decisions made early in the project, he and his team did not test for large-angle signatures, which are expected to be the most common. The good news is that such tests are going to be easy in the near future.

M5 proper motion

2011-09-29T23:26:00.000-04:00

After a few minutes of conversation, Blanton demonstrated to Willman and I that even though the SDSS data are not designed to deal with crowded fields well, they can be used to measure the proper motions of halo globular clusters. Koposov, Rix, and I demonstrated that the proper motions can be used statistically when we looked at the GD-1 Stream, but then I left this subject behind, even though globular clusters are actually much easier to measure than the GD-1 Stream. This relates to my repetitive rant that there are many great measurements waiting to be made in existing data.

local image transformations

2011-09-28T21:50:00.000-04:00

Fergus came by in the morning to discuss modeling speckles as a function of wavelength in coronography, and we spent a while counting parameters. As is usual in these discussions I have with vision people, there are more parameters than data points in the natural models we want to write down. So we either have to apply priors, or else simplify the model; we decided to do the latter. The odd thing (in my mind) is that simplifying the model (that is, reducing the number of free parameters) is actually equivalent to applying extremely strong priors. So the idea that one can "avoid using priors" by choosing a simpler model is straight-up wrong, no? That said, I am very happy with Fergus's beautiful model, which involves an extremely general description of how one might transform an image locally.

ensemble sampling

2011-09-27T21:39:00.001-04:00

Foreman-Mackey and I started work on—and Goodman and I discussed—next-generation ensemble samplers, that update a pair of ensembles in a leapfrog fashion. I still haven't completely understood the rules, but it appears that as long as at every step we satisfy detailed balance, we are fine. If this is true, then I have an intuition that we can design a new sampler with incredible performance on non-trivial probability distribution functions.

Milky Way disk

2011-09-26T23:59:00.000-04:00

Other than a few conversations, my only research today was to talk about Bovy, Rix, and my results on the chemical and position-space structure of the Milky-Way disk. We find that if you look at disk structure as a function of chemical abundances, you get very simple and nice results; it is less messy (for reasons we don't entirely understand) than looking at the chemical abundances as a function of position. Manuscript forthcoming soon; watch this space.

black hole electrodynamics, scoop

2011-09-23T23:59:00.000-04:00

Maxim Lyutikov (Purdue) came through to give a nice seminar about fundamental electrodynamics around black holes. The big deal is that near the horizon, E gets a component along B and then plasma is produced easily out of the vacuum to short the parallel component and stabilize the B field. This is very nice!

I had many conversations with team members about various projects in progress; nothing specific to report, except maybe that Jagannath and I realized that we were semi-scooped but that the scooping paper has some serious errors in it. More on this when we get together our thinking and become confident that we are right in our criticisms.

sampling

2011-09-22T23:59:00.000-04:00

I spent time today talking with Goodman more about his (with Weare) ensemble samplers. He points out that if you have two ensembles, you have a lot of freedom for using one ensemble to inform the proposal distribution for the other ensemble. This could permit non-trivial density modeling of the one ensemble to help sample efficiently the other ensemble. We discussed the possible implications of this for multi-modal probability distribution functions and I am optimistic that we could make a very efficient next-generation sampler. This is all about proposal distribution of course; there are lots of things people want samplers to do; we are concentrating on those things that make the autocorrelation time short or the number of independent samples per likelihood call high.

photometric redshifts

2011-09-21T22:02:00.000-04:00

Fadely, Willman, and I realized today that our data-driven, hierarchical Bayesian discrete classification system for faint sources in current and future optical survey data is also a photometric redshift prediction system. We decided (tentatively) that we should write a paper on that. I also had conversations with Foreman-Mackey about Gaussian processes, and Ben Weaver (NYU) about SDSS-III BOSS redshift fitting.

Delaware

2011-09-20T23:59:00.000-04:00

I spent the day in Delaware, hanging out with John Gizis (Delaware) and talking about finding rare objects. He has found some of the coolest and nearest stars in the WISE data, by very straightforward methods. I think he published the first WISE paper after the WISE public data release; this reminds me of 1996 and the Hubble Deep Field! He has also found hotter stars with strange infrared properties. One of the many things we discussed is understanding the spatial distribution of stars with dust disks relative to the population of very young stars in the local neighborhood. I also gave a seminar.

numerical relativity, cosmological constant

2011-09-19T23:59:00.000-04:00

In most of my research time today I talked with our new NSF postdoc Gabe Perez-Giz (NYU) about his plans for his first year here. These include working through a set of challenging and fundamental problems in numerical methods for computing gravitational radiation. Part of this plan is to produce a quantitative description of test-particle phase space (qualitative orbit properties) around Kerr (spinning) black holes. I think this is a great idea, but it involves a huge amount of literature review, synthesis, and computation.

At lunch, the CCPP brown-bag series was kicked off by Kleban (NYU) who told us about natural properties of the cosmological constant in M-theory. The idea is that one natural (or mathematically equivalent) way of thinking about the cosmological constant is as a high-dimensional analog of electromagnetism, with a vacuum field value. This gets all the stringy or M-y properties of the cosmological constant: Huge number of possible vacua, finite probabilities of transitioning to other (much worse) vacua, no non-trivial dynamics in the cosmological constant sector (except for vacuum-changing dynamics).

speckle imaging; what is a galaxy?

2011-09-16T22:15:00.001-04:00

I spent the morning with Fergus working on fitting a dumb model to Ben Oppenheimer's (AMNH) coronographic imaging of possibly-planet-hosting stars. Oppenheimer's instrument is not just a coronograph but an integral field spectrograph, so there is a huge amount of information in the wavelength-dependence of the residual light from the primary star. Fergus and I worked on building a simple model of it, hoping to increase the sensitivity to faint planets.

At lunch, Beth Willman (Haverford, long-term visitor at NYU) made the case that part of the definition of a galaxy (yes, believe it or not, people are currently arguing about how to define a galaxy; see, eg, Willman 1) ought to involve the chemical abundances of the stars. This is a beautiful, simple, and convincing idea.

posterior on the baryon acoustic feature

2011-09-15T23:59:00.000-04:00

I asked the BOSS galaxy-clustering team about my concerns about measuring the baryon acoustic feature by first making a point estimate of the two-point correlation function of galaxies and then, from that two-point function estimate, inferring the baryon acoustic feature length scale. I got no sympathy. Maybe I am wrong, but I feel like if we are going to move the information from the data to the BAF, we need to write down a likelihood. The reason the BOSSes think I am wrong (and maybe I am) is because on large scales the density field is close to a Gaussian random field, and the two-point function is a sufficient statistic. But the reason I think they might be wrong is (a) the distribution of galaxies is a non-linear sampling of the density field, and (b) the two-point function might be sufficient to describe the density, but a point estimate of the two-point function is not the two-point function. Anyway, I retreated, and resolved to either drop it or do the math.

catalogs to images to models

2011-09-14T23:59:00.000-04:00

Marshall and I spent a phone call talking about how we can model strong gravitational lenses in the PanSTARRS data, given that the data (in the short term, anyway) will be in the form of catalogs. As both my loyal readers know, I am strongly against catalogs. What Marshall and I had independently realized—me for catalog source matching and Marshall for lens modeling—is that the best way to deal with a catalog is to treat it as a lossy compression of the data. That is, use the catalog to synthesize an approximation to the imaging from which it was generated, use the error analysis to infer a reasonable noise model, and then fit better or new models to those synthesized images. I love this idea, and it is very deep; indeed it may solve the combinatoric complexity that makes catalog matching impossible, as well as the lensing problems that Marshall and I are working on.

modeling noise

2011-09-13T21:03:00.000-04:00

Foreman-Mackey and I went over to Fergus's office to discuss with Fergus and Andrew Flockhart (NYU) a few possible projects. One is to perform cosmic-ray identification in imaging without a CR-split. That is, to find the cosmic rays by modeling the data probabilistically rather than by comparing overlapping imaging. This could make HST snapshot surveys and SDSS data more productive at no additional cost, or just as productive at smaller observing intervals. Another project we discussed is one to model the speckle patterns in multi-wavelength coronograph images taken by Oppenheimer's crew. In the latter we talked about priors that could help given that the space of possible solutions is almost unimaginably large.

big surveys

2011-09-12T23:59:00.000-04:00

In the tiny bit of research time I got today, I chatted with Willman, Fadely, and Daniel Mortlock (Imperial) about upcoming large surveys, especially VISTA, which ought to have a huge impact on all the things we work on.

probabilistic factor analysis

2011-09-09T23:59:00.000-04:00

Prompted by some comments yesterday from Iain Murray on my incomprehensible post about PCA, I spent some of the morning looking into the different meanings of factor analysis in the machine-learning literature. At least some of those are very close to Tsalmantza and my HMF method; I think this means some changes to the abstract, introduction, and method sections of our paper.

PCA plus mean

2011-09-08T22:41:00.002-04:00

In the morning I worked on Tsalmantza and my paper on matrix factorization. Specifically, I worked on the initialization, which we perform with a PCA. But before we do the PCA, we do two things: The first is we "isotropize" the space from a measurement-noise point of view. That is, we re-scale the axes (which number in the thousands when running on SDSS spectra) so that the median measurement uncertainty (noise variance) is the same in every direction. The second is that we compute the mean, and then project every spectrum into the subspace that is orthogonal to the mean spectrum. That is, we compute the principal variance directions that are orthogonal to the mean-spectrum direction. Then our K-spectrum initialization is based on the mean spectrum and the first K−1 variance eigenvectors orthogonal to the mean.

Whew! All that work put into the PCA, which is just to initialize our method. As my loyal reader knows, I think PCA is almost never the right thing to do. I really should finish the polemic I started to write about this a few years ago.

[Comments below by Murray make me realize that I am not being very clear here. In the case of our matrix factorization, we are trying to find K orthogonal eigenspectra that can be coadded to explain a large set of data. To initialize, we want to start with K orthogonal eigenspectra that include the mean spectrum. That's why we project and PCA.]

pretty pictures

2011-09-07T23:59:00.000-04:00

I worked on getting SDSS-III DR8 versions of my RC3 galaxies working. I was still finding problems until late in the day when Ben Weaver (NYU), our awesome SDSS-III data guru, stepped in and saved my day with a bug fix. The great thing about working on SDSS and SDSS-III is working with great people.

star-galaxy separation; testing

2011-09-06T23:59:00.000-04:00

Fadely, Willman, and I have a star–galaxy separation method (hierarchical Bayesian discrete classifier) for LSST that can be trained on untagged data; that is, we do not need properly classified objects (any truth table) to learn or optimize the parameters of our model. However, we do need a truth table to test whether our method is working. Not surprisingly, there are not a huge number of astronomical sources at faint magnitudes (think 24) where confident classification (that is, with spectroscopy or very wide-wavelength SED measurements) has been done. So our biggest problem is to find such a sample for paper zero (the method paper). One idea is to run on a bunch of data and just make the prediction, which is gutsy (so I like it), but really who will believe us that our method is good if we haven't run the test to the end?

speckles

2011-09-02T17:06:00.000-04:00

A combination of illness, travel, and preparation for class prevented much research getting done in the last few days. One exception is that Fergus and I met to discuss modeling planet-obscuring speckle in coronograph images, where Fergus thinks he may have a breakthrough method that is simple and physically motivated. It seems promising, so I am about to ask the Oppenheimer team for some example data!

matrix factorization paper

2011-08-29T23:59:00.000-04:00

Tsalmantza and I worked on our matrix factorization method paper, plus some applications. We are trying to finish before I depart Heidelberg.

just like in grad school

2011-08-26T23:59:00.003-04:00

Marshall and I (nearly) finished writing the lens-equation-solving part of our plug-in to the Tractor. This reminded me of graduate school, where I spent a lot of time solving the lens equation. One of my first efforts along this line became my first arXiv submission (astro-ph/9311077), which I am proud to say was submitted when the wonderful, wonderful arXiv was in its second year of infancy (and wasn't called arXiv). Today Marshall and I re-discovered that uniquely identifying—by root finding—all the multiple images of a lensed source is non-trivial, especially near cusp caustics! That also reminded me of graduate school, where I worked out and then abandoned dozens of semi-clever techniques.

watch out, Stripe 82!

2011-08-25T18:36:00.002-04:00

Marshall and I continued working on our lens prior PDF code, and we also spent some time looking at candidate double-redshift (maybe lens?) objects found in SDSS spectra by Tsalmantza. But the big news for my day is that Foreman-Mackey's code for re-calibrating SDSS Stripe 82 data and finding variable stars scaled up very well; it looks like running it on a single (okay, 12-core) machine will take only a couple of weeks. I hope that is right! We also confirmed that all the imaging is spinning on NYU disks.

generating lenses from prior PDFs

2011-08-24T17:09:00.004-04:00

Marshall and I wrote a piece of code that draws sets of four point-source positions from a prior over lensing configurations, without explicitly solving the lens equation. This code penalizes configurations by their non-compactness on the source plane, transformed to image-plane units. It works: When we MCMC the prior with Foreman-Mackey's awesome ensemble sampler we get realistic-looking lenses, even though we never actually did any root-finding. The goal is to plug this prior into the Tractor when it is working on images that contain lenses.

position and velocity modeling

2011-08-23T23:59:00.001-04:00

As my reader knows, I have been working on a responsible and probabilistic use of the Jeans equations (which model velocity second moments) for inference. Today I re-built how I simultaneously model the density as a function of position and the velocity variance as a function of position to meet a few desiderata: (1) I want the model to be extremely flexible and data-driven, or non-parametric (meaning huge numbers of parameters). (2) I want the model to strictly enforce the Jeans equation given a parametric (or non-parametric) density or gravitational potential model. (3) I want the model to be parameterized so that I don't have to put harsh or ugly barriers in parameter space that will cause my optimizer to choke. (4) I want the parameterization to be relatively stable in the sense that I want small parameter changes to lead to small, smooth changes to the density and velocity moment models. I got all of this working with a very odd parameterization: I build a non-parametric model of the derivative with respect to position of the number density times the velocity second moment! This gets divided by a potential gradient to give the number density model, and it gets integrated and divided by the density model to get the velocity moment model. Crazy, but it works. I am sure there are much better solutions for my desiderata, but I found that I was much more willing to write code than go to the library!

RC3 galaxies

2011-08-22T23:59:00.001-04:00

I have a huge collection of RC3 galaxy images on the web; these appear in many talks, some even occasionally credited to me. Keep those cards and letters rolling in! Today I started to re-make them with the (better and bigger) DR8 data set, which should make the collection larger, if not far better. Blanton and Ben Weaver (NYU) helped me with some of the crucial technical details.

Phil Marshall showed up at MPIA today for a week. He wants to work on lensing. I told him I will work on lensing provided that it involves image modeling. He was puzzled, but agreed.

phase-space modeling

2011-08-21T23:59:00.000-04:00

I spent all weekend madly coding up my likelihood or Bayesian moment modeling and full phase-space modeling for one-dimensional dynamical systems. I can't see any easy, simple ways to make the code fast so it is super-slow. I have a method for inferring the gravitational potential using the position distribution and second moments of the velocity distribution and then I have one using the full position-and-velocity two-dimensional distribution function. Although there is a lot of computation involved, the problem is technically easy because all one-dimensional potentials are integrable. That makes it a nice toy system for learning about inference with different noise models and different selection functions.

Python, Gaia, and the disk

2011-08-19T13:22:00.003-04:00

I spent the morning pair-coding a simple stellar phase-space distribution modeling code with Rix, and the afternoon solo-coding the likelihood functions for my one-dimensional dynamics code. In both cases, the attempt is to have the code benefit strongly from the things Python can do for us.

In the afternoon, I had a fruitful conversations with Coryn Bailer-Jones (MPIA) about the internals and outputs of the Gaia pipelines for stellar parameter estimation and source classification. For the former, we are discussing three-dimensional models of the Milky Way, and for the latter we are working out methods that make use of discrete models.

I also spoke with Glenn van de Ven (MPIA) about what's been done in one-dimensional dynamical modeling, and Bovy sent Rix and me some beautiful plots of the Milky Way structural parameters as a function of stellar chemical abundances.