ELSA Conference on Gaia, day four

Today I gave Bovy's talk on inferring dynamical properties from kinematic snapshots. The talk noted that in our April Fools' paper, the planets with velocity closest to perpendicular to position do the most work. After, Juric (Harvard) asked if there are similarly important stars for the Milky Way? If so, we could just observe the hell out of those and be done! My only response is that if we had a sample of realizations of the Milky Way that generate the data—that is, detailed dynamical models—the stars whose properties vary the most across the sampling would be the critical stars. Not a very satisfying answer, since that chain would be almost impossible to produce, maybe even by 2020. Interestingly, my attitude about this turns out to be very similar to that of Pfenniger (Geneva), who gave a very nice philosophical talk about how our vision of the simplicity or complexity of the Milky Way has evolved extremely rapidly. He argued that no smooth models are likely to give right answers, not even approximately right. I don't think everyone in the business would agree with that, though I do. Before either of our talks, Juric gave a summary of his three-dimensional Milky Way modeling using photometric data. He, of course, finds that simple models don't work, but he has some great tools to precisely model the substructure.

Ludwig (Heidelberg) argued that either there are systematic issues with chemical abundance indicators or else even single stellar populations have metallicity diversity. This would do damage to the chemical tagging plans. In conversation afterwards, Freeman (ANU) told me that he thinks that the issue is with the models not the populations and that the models will improve or are improved already. I suggested to Freeman an entirely data-driven approach, but I couldn't quite specify it. This is a project for the airplane home.

Pasquato (Brussels) made very clearly the point that if stars have strong surface features that evolve (as when they have highly convective exteriors), there will be astrometric jitter if the star has an angular size that is significant relative to Gaia's angular precision. This is obvious, of course (and I mean that in the best possible way), but it suggests that maybe even parallax needs to be defined like radial velocity was yesterday!

Antoja (Barcelona), Minchev (Strasbourg), and Babusiaux (Paris) gave talks about the influence of the bar and spiral structure on the Galaxy and galaxies in general. Minchev presented his now-famous results on radial mixing, which relate in various ways to things we have been doing with the moving groups. Antoja was more focused on understanding the specific impact of specific spiral arm and bar models on the local distribution functions. Babusiaux nearly convinced me that the bulge of the Milky Way was created entirely from the disk by the action of the (or a) bar. Bars rule! After dinner, Minchev and I found ourselves at a bar.

ELSA Conference on Gaia, day three

It was spectroscopy all morning, with the Gaia Radial Velocity Spectrograph worked-over by Cropper (UCL), Katz (Paris), and Jasniewicz (Montpellier). Two things that were of great interest to me: The first is that the satellite can only do relative measurements of velocity (really of logarithmic Doppler Shift, I think) so they need absolute velocity standards. They will use a combination of asteroids and team-calibrated (from the ground) radial velocity standards that span the sphere and spectral type. The issue is more fundamental, of course: To compare stars of different types, you need to tie together radial velocity standards that are based on different lines with different gravitational redshifts and convective corrections. But the issue is even more fundamental, and that is the second thing that was of great interest to me: Lindegren (Lund) has a paper defining radial velocity that shows that it is not really a spectroscopically measured quantity: There are many physical effects that affect stellar lines. Indeed, this relates in my mind to an old conversation with Geha (Yale) and Tremaine (IAS) about whether it is possible—even in principle—to measure a velocity dispersion smaller than Geha's smallest measurements. I think it is but it is because God has been kind to us; this was not something we could expect just by dint of hard work.

Freeman (ANU) gave a talk about HERMES, an ambitious project to take detailed enough spectra of about a million stars to do full chemical tagging and separate them into thousands of unique sub-groups by detailed chemical properties. He made an argument that the chemical space will be larger than (though not much larger than) a seven-dimensional space. I have been hearing about chemical tagging for years and seen nothing, but with this it really looks like it might happen. This project is explicitly motivated by the science that it enables in the context of Gaia. Where are the American projects of this kind? Also, when will we have the technology or money to take spectra of a billion stars?

In the afternoon, there were Solar-System and asteroid talks; these are always impressive because precisions in this field are so much higher than anywhere else in astronomy. Oszkiewicz (Helsinki) gave a nice talk about fully Bayesian orbit fitting for asteroids, showing that they could have predicted the asteroid hit in Sudan in 2008 with high confidence (did they predict that?). She also showed that they can model asteroid shapes from Gaia lightcurve data with the same kind of MCMC machinery.

ELSA Conference on Gaia, day two

There is far more to report here from today's than I easily can, so I will just blurt some highlights once again:

Lindegren (Lund University) gave a beautiful talk about fundamental issues in astrometry, especially with spinning satellites. Gaia—unlike Hipparcos will work in the limit in which the spin is more strongly constrained by the density of informative stellar transits than it could be by any reasonable dynamical model of the spinning satellite subject to torques. That is, there is only a very weak dynamical model and the data do the talking. This means that any measurement by the satellite that can be accommodated by an attitude change is not constraining on the global astrometric solution! For this reason, with each of the fast rotations (scans) of the satellite, the only constraining measurements made on the global astrometric solution are comparisons between stellar separations and the basic angle of the satellite, along the direction of the scan. It really is a one-dimensional machine, at least on large scales. The two-dimensional images off the focal plane will be useful transverse only on small scales. He followed these beautiful fundamental arguments with discussions of self-calibration of the satellite, which is really what all the talks have been about these two days, in some sense.

Bombrun (ARI, Heidelberg) and Holl (Lund) gave back-to-back talks about the optimization of the linearized system of equations and the error propagation. Optimization (after the Collaboration found conjugate-gradient method) is trivial, but exact error propagation—even for the linearized system—is impossible. That's because the sparse matrix of the linear system becomes very non-sparse when you square and invert it. Holl has some very nice analytic approximations to the inverted matrix, made by expanding around invertible parts, and by making simplifying assumptions. This is key for the first generation of error propagation. In my talk I will emphasize that if the Collaboration can expose something that looks like the likelihood function, error propagation is trivial and it becomes the burden of the user, not the Collaboration. However, there is no chance of this in the short run.

Eyer (Geneva) gave an electrifying talk about variable stars, making clear what should have been obvious: Gaia will be the finest catalog of variable stars ever made, and contain in almost every class of variability hundreds of times more stars than are currently known. This opens up the possibility for all kinds of novel discovery, and enormous improvements in our understanding of the variables we now know. His group is computing observability of various kinds of variables and the numbers are simply immense. He noted that Gaia might discover WD–WD eclipsing-binary gravitational wave sources.

At the end of the day Mahabal (Caltech) spoke of automated transient classification. They are doing beautiful things in the VO/semantic framework. Of course I am a critic of this framework, because I want meta-data to be probabilistic and computer-generated, not human-designed and combinatoric (as in this is a Type IIP Supernova; much more useful to be given relative likelihoods of all non-zero possibilities). But there is no doubt that within this framework they are doing beautiful stuff.

ELSA Conference on Gaia, day one

Today was the first day of the ELSA Conference. Talks focused on the status of Gaia hardware and software, with a lot of concentration on the expected effects of radiation damage to the CCDs and strategies for dealing with that. There were many great contributions; a few facts that stuck in my mind are the following:

Prusti (ESA) emphasized the point that the intermediate or preliminary catalog released in 2015 will only be slightly less good than the final catalog of 2020, if all goes well. He argued that Gaia should release preliminary catalogs since surveys like SDSS have prepared the astronomical community for non-permanent and non-final and buggy catalogs. I agree.

Charvet (EADS) showed the spacecraft design and assembly. It is made from silicon carbide, not metal, for rigidity and stability. Apparently this makes fabrication much more difficult. The mirrors are silvered, polished silicon carbide, attached to a silicon carbide skeleton. The machining of the parts is good to a few microns and there is an on-board interferometer that continuously measures the internal distances relevant to the basic angle (between the two lines of sight) at the picometer level. It also has an on-board atomic clock. He strongly implied that this is the most challenging thing anyone at EADS has worked on.

van Leeuwen (Cambridge) spoke about spacecraft spin and attitude. He showed that his re-reduction of the Hipparcos catalog (published in book form) came from permitting the spacecraft to be jerked or clanked by configurational changes (related to temperature changes?). He found 1600 such events in the time-stream and when he modeled them, the systematic errors in the data set went down by a factor of five. I commented after his talk that the real message from his talk was not about spacecraft attitude but about the fact that it was possible to re-analyze the raw data stream. The Gaia position seems to be that raw data will be preserved and that re-analyses will be permitted, as long as they don't cost the Consortium anything. That's fine with me.

O'Mullane (ESA) gave a romp through the computer facilities and made some nice points about databases (customizability and the associated customer service is everything). He does not consider it crucial to go with open-source, in part because there is an abstraction layer between the software and the database, so change of vendor is (relatively) cheap. He then went on to say how good his experience has been with Amazon EC2, which doesn't surprise me, although he impressed me (and the crowd) by noting that while it takes months for ESA to buy him a computer, he can try out the very same device on EC2 in five minutes. That's not insignificant. From a straight-up money point of view, he is right that EC2 beats any owned hardware, unless you really can task it 24-7-365.

proposal

I worked on the proposal—and other writing projects—on the airplane and in my room at the ELSA conference on Gaia. Still not far enough along!

too little

I wrote way too little in my NSF Software Infrastructure for Scientific Innovation (NSF's words) grant proposal; it is due on Monday!

NIPS paper submitted

Lang pulled out all the stops and got our NIPS paper submitted, three minutes before deadline. In the process, he found a large number of great examples of situations in which an image-modeling or catalog-as-model approach to the SDSS data improves the catalog substantially. The paper is pretty rough around the edges, but we will tune it up and post in on arXiv, in a few weeks, I very much hope.

trusting robots; SciCoder

I worked on an upcoming NSF proposal (with Lang) on building robots that calibrate (and verify calibration meta-data for) astronomical imaging. The idea is to produce, validate, and make trustable the meta-data available through VO-like data interfaces. Right now an astronomer who makes a VO query and gets back hundreds of images spends the next two weeks figuring out if they really are useful; what will it be like when a VO query returns tens of thousands?

In the afternoon, Demetri Muna (NYU), Blanton, and I discussed Muna's SciCoder workshop, where Muna is going to have the participants learn about databases, code, and data interaction by doing real scientific projects with public SDSS data. I am pretty excited about the idea. Right now Muna is doing some of the projects ahead of time so he is prepared for all the bumps in the road.

perturbed streams

Kathryn Johnston appeared in my office today and we spent a few hours discussing simple calculations of perturbations to tidal streams by CDM-substructure-like satellites. She has greatly simplified the confused thoughts I had about them a year or two ago.

proposing on holiday

On the Memorial Day long weekend here, I outlined Lang and my next NSF proposal.

deconvolution and hierarchical Bayes

I wrote english prose and LaTeX equations like the wind on our brand-new exoplanet project. In particular, I spent some time working out the difference between hierarchical bayesian approaches to distribution estimation and deconvolution or forward-modeling approaches. There is a lot of overlap, but the key difference is that if all you care about is the distribution itself, in the Bayesian approaches you integrate out all of the individual measurements (which, in this context, should be thought of as fits to more "raw" data). That is, if you are deconvolving (forward modeling) you are trying to explain the individual-object fit results; if you are hierarchical Bayesian, you are trying to obliterate them. As I wrote text, Myers wrote code, and Lang (who came into town) worked on image modeling in preparation for the NIPS deadline.

archetypes

Blanton, Guantun Zhu (NYU), and I discussed the possibility of writing a paper on the archetypes system we set up for PRIMUS. The idea of the paper would be to split the archetypes finding and optimization out of any PRIMUS data paper because it has much wider applicability. The idea is to model a distribution of d-dimensional data by a set of delta functions in the d-dimensional space, with the set chosen to be the minimal set that adequately represents every data point. The nice thing is you can choose whatever operation you want to decide what represents what, and it can handle any kind of crazy degeneracies, missing data, or marginalization over nuisance parameters (think calibration, or extinction). The hard thing is that the search for the minimal set of archetypes is hard (in the technical algorithmic sense of the term) but Roweis cast the problem for us as a binary programming task, which is incredibly well handled by any number of open-source and commercial packages. For PRIMUS we used the IBM CPLEX code, which was astoundingly fast.

Spitzer and exoplanets

I spent a big chunk of the day working on the Spitzer Oversight Committee, which is helping the Spitzer Science Center react to funding realities and the slow shut-down of an incredibly productive and successful but finite observatory mission. We spent some time in the meeting talking about Spitzer's capabilities for (and successes in) exoplanet science, and the possibility of encouraging that even more in the future. This post bends the rules but oddly I truly find the work I do on this committee to be of great intellectual interest.

marginalized likelihood

You can't marginalize over a parameter in the likelihood without a prior because the units are wrong! The likelihood is the probability of the data given the model, and therefore has units of inverse data. If you want to marginalize out some nuisance parameter, you have to multiply the likelihood by a prior probability distribution for that parameter and then integrate. So, as I like to point out, only Bayesians can marginalize.

Adam Myers and I are using marginalization to get the likelihood for parameters of a distribution for a quantity (in this case exoplanet mass), marginalizing out every individual quantity (mass) estimate. You have to marginalize out the individual mass determinations because they are all terribly biased individually, and it is only the underlying or uncertainty-deconvolved distribution that you really care about. More soon, especially if we succeed!

time domain

Adam Myers (UIUC) showed up today for a week of work; we spent (too little) time today planning what it is we are going to accomplish by Friday. At the same time, several of my students are writing papers and I am getting way behind on comments and feedback. In the afternoon there was a nice talk by Suvi Gezari (JHU) about shock breakout SNe and tidal disruption events discovered in GALEX repeat-visit data. I had seen these results before, but she shocked us (or me at least) by noting that there are essentially no plans for future surveys in the ultraviolet. I knew this but it really is crazy to place all of our (community's) bets on the infrared. That's so 2006!