WFC3, exoplanet searching

At Computer-vision-meets-astronomy today, Fadely showed us all some example HST WFC3 images, some models of the PSF, and some comparisons between model and observed stars. I had never put two-and-two together, but the PHAT project (on the periphery of which I lie) has taken some absolutely awesome WFC3 images for the purposes of array calibration: The PHAT images (being of M31) are absolutely teeming with stars. Indeed, it is impressive that the PHAT team can photometer them at all. We discussed strategies for flat-field determination given that we have a good but not perfect PSF model and a lot of heterogeneous data.

After that but before lunch, we more-or-less decided that while Foreman-Mackey works on a Kepler light-curve likelihood function paper, Angus (Oxford) should start work on a Kepler light-curve exoplanet search paper, making use of the same machinery. This is a great division of labor (I hope) and might eventually bring us close to the goal of everything we have been doing with Kepler, to wit, finding Earth-like planets on year-ish orbits around Sun-like stars. Pleased.

the sub-pixel flat field

I took a risk up at Columbia's Pizza Lunch forum by talking about the Kepler flat-field. I also was exceedingly rude and talked through Price-Whelan's spot (he was supposed to follow me). I apologize! Well, you can't say I didn't try to bore the pants off of everyone: I talked about the (novel, and exciting to almost no-one other than me) result, published in our white paper, that it is possible to infer the properties of the flat field at higher than pixel resolution.

That is, the team (meaning, in this case, Lang) made simulated data with drifting stars, a PSF that varies slowly with position (and is well understood), and no prior knowledge about how the stars in the field are drifting. We find (meaning Lang finds) that he can simultaneously figure out the pointing of the satellite and the flat-field, even when the flat-field is both created and fit with models that have multiple sub-pixels per pixel. The reason it works is that as the star moves, it illuminates each pixel differently, and is therefore differently sensitive to the different parts of each pixel. It is not clear yet whether we can do this accurately enough to recover the Kepler sub-pixel flat-field, but damn I want to try. Unfortunately, we need lots of data taken in the two-wheel mode, and (as far as I know) they aren't yet taking any new data. Kepler: Please?

talking about exoplanets

Discussed MCMC convergence with Jeffrey Gertler (NYU), Bayesian evidence (fully marginalized likelihood) with Hou and Goodman, and data-science projects with Mike O'Neil (NYU). O'Neil is co-teaching a course at NYU for the new Data Science program, where the idea is that Masters students will do research projects on real research topics. Foreman-Mackey and I are happy to provide; we discussed several ideas, most of which involve the Kepler data, which we have on the brain right now. One idea is to find all the single transits and see if you can use them to place limits on (or measure!) the frequency (suitably defined) of Jupiter analogs (suitably defined). That's a great problem to post on my Ideas Blog. Hou is computing the marginalized likelihoods of various qualitatively different explanations of radial velocity data, including stellar oscillation models and multi-planet scenarios. Gertler is preparing to find exoplanets in the Galex photon (time-resolved) data.

KIPAC@10, day 4

The morning session was about dark energy, with Sawicki (Heidelberg) taking the theory side and various observers taking the data side. Highlights for me were the weak lensing talks, with von der Linden (DARK) talking about measuring cluster masses and Bard (KIPAC) talking about cosmic shear. During Bard's talk I came up with three possible short papers about weak lensing methodologies, which Marshall, Bard, Meyers (KIPAC) and various others refined over lunch:

The first paper obviates all the problems with transferring stellar PSFs to galaxies by measuring the PSF using the galaxies. LSST can do this because it takes many images under different PSFs. The second paper uses the configurational separations of features (think peaks) in galaxy images to measure shear, independently of, or in concert with, ellipticity measurements. In principle this might be useful, because point separations depend only on astrometric calibration, not PSF determination. The third is to use image-to-image variations in astrometric distortions to infer image-to-image changes in the PSF. I think these two things have to be related, no? This latter project has probably already been done; it requires a literature search.

KIPAC@10, day 3

Although there were very amusing and useful talks this morning from Bloom (Berkeley), Boutigny (CNRS), Marshall, and Wecshler (KIPAC), the highlight for me was a talk by Stuart Lynn (Adler) about the Zooniverse family of projects. He spent a lot of time talking about the care they take of their users; he not only demonstrated that they are doing great science in their new suite of projects, but also that they are treating their participants very ethically. He also emphasized my main point about the Zoo, which is that the rich communication and interaction on the forums of the site is in many ways what's most interesting about the projects.

In the afternoon, we had the "unconference" session. Marshall and I led a session on weak lensing. We spent the entire afternoon tweaking and re-tweaking and arguing about a single graphical model! It was useful and fun, though maybe a bit less pragmatic than we wanted.

KIPAC@10, day 2

Today was the second day (I missed the first) of the KIPAC@10 meeting at KIPAC at SLAC. There was a whirlwind of talks on compact objects and galaxy evolution, too many to summarize, but some highlights for me were the following:

Steiner (UW) showed neutron-star mass and radius measurements and discussed their implications for the properties of matter at extreme density. He showed some very noisy likelihood functions (ish) in mass–radius space, one per measured neutron star (and there are 8-ish with measurements) and tried to draw a curve through them. I have opinions about how to do that and he seems to be doing it right; each time we tried to discuss this over coffee something interrupted us.

Perna (Colorado) talked about magnetars; I hadn't appreciated how extremely short-lived these stars must be; their lifetimes are measured in kyr, which is not a unit you see every day. Romani (Stanford) made a pitch that Fermi-discovered gamma-ray pulsars are the bees knees. He didn't show folded light-curves but apparently there are now hundreds where you can see the periodicity in the (sparse) Fermi data. Tomsick (Berkeley) showed some outrageously awesome NuSTAR data, making me want to hear much more about that mission. It's PI is my old friend from graduate school, Fiona Harrison (Caltech), to drop a name.

Cordes (Cornell) talked about pulsar timing and gravitational radiation, a subject on which I have opinions (from a precision measurement perspective). He, like is common in that business, concentrated on the stochastic gravitational wave background; I would like to hear or think more about coherent source detection. It is usually easier! Along those lines, at one point Blandford (KIPAC) asked Aarons (Berkeley) if physical models of pulsar emission were likely to help in measurements of pulsar timing. Aarons didn't commit either way, but I think the answer has to be yes. Indeed, I have suggested previously that modeling the emission almost has to improve the measurements.

Stark (Arizona) showed very nice new data on galaxies at extremely high redshifts. He noted that almost every result at redshifts beyond six depends entirely on photometric redshifts. That's true, but is it a concern? I guess it is because there could be interloping lower-redshift objects (or stars) having a big effect on the conclusions. Kriek (Berkeley) and Lu (KIPAC), in separate talks, showed that it is difficult to explain the evolution of galaxies in sizes and stellar populations with simple models of star formation and merging. Also, Kriek called into question the star-formation-rate estimates people have been using, which is interesting; she finds a factor-of-two-ish range in the mistakes that could be being made, and this is the same order of magnitude as the amplitude of the variation in specific star-formation rate with galaxy mass. She didn't claim that there is an error there.

In the discussions at lunch, Stuart Lynn (Adler) pitched an idea from David Harris (NAS) that we start a journal of short contributions. Marshall was all over that; it might get launched tomorrow in the unconference session.

exoplanets

First thing in the morning, I tweaked up our Kepler two-wheel white paper and submitted it. I am very proud, because it contains at least three novel results in astronomical image modeling and calibration. I very much hope I can keep momentum and get my team to publish these results. If you want to read the white paper (see if you can spot the three novel results), it is here.

At a very leisurely lunch, Foreman-Mackey, Fadely, Ruth Angus (Oxford), and I discussed possible projects for this month, in which Angus is visiting CampHogg. We more-or-less settled on long-period exoplanet search, with heavy use of Gaussian Processes.

image modeling for Kepler

I spent the whole long holiday weekend (it is Labor Day here in the US) working on our Kepler white paper, in response to the two-wheel call (PDF). Foreman-Mackey came through with some auto-regressive-like data-driven models for extant Kepler data, Michael Hirsch (UCL, MPI-IS) came through with some PSF-photometry tests for insane (drifted) PSFs, and Lang came through with simulations of toy Kepler data in the two-wheel era, along with models thereof. Lang was able to show that we can (in principle) infer a flat-field that is higher resolution than the data! That is, we can infer heterogeneous intra-pixel sensitivity variations, pixel by pixel. That is awesome, and we ought to apply it to every data set out there! We will post the white paper on arXiv after we submit it.

probabilistic judgements about habitability

I had a long conversation this morning with Rix and Lisa Kaltenegger (MPIA) about how you would losslessly propagate observational noise in exoplanet observations into probabilistic judgements about habitability. We decided to take a principled (Bayesian, hierarchical) approach in setting up the problem and then to make approximations as necessary to make the calculation possible given current knowledge and technology. We worked through the problem in the form of a probabilistic graphical model, and then discussed how to translate that PGM into code. And then sample it.

reverberation mapping, modeling Kepler

In Galaxy Coffee this morning, William Bethune (Univ. J. Fourier) spoke about his project to look at reverberation mapping of a variable AGN at two-ish microns. He and his collaborators can do reverberation mapping on the dusty torus around the AGN, which was novel (to me). They can see temperature changes as well as brightness changes, which opens up new possibilities for physical modeling. The data are noisy and sparse, so the Bayesian methods of Brewer might make for better results.

In the afternoon, I worked on our white paper for the Kepler two-wheel call. All I have so far is an executive summary, no content! Unfortunately it is due in a few days.

abundances (chemical and planetary)

At Milky Way group meeting I got all crazy about the fact that there are all these multi-element chemical abundance surveys starting, taking great spectra of giant stars, but there is no evidence (yet) that anyone can actually measure the detailed abundances in any giant stars, even given great data. I exhorted everyone to look at the APOGEE data, which are beautiful and plentiful and ought to be good enough to do this science. Any improvement in their ability to measure chemical abundances will be richly rewarded.

In the afternoon I spoke with Beth Biller (MPIA) and Ian Crossfield (MPIA) about possibly constraining the long-period planet distribution using single-transit objects in the Kepler data (that is, stellar light curves for which a single transit is observed and nothing else). This is an idea from the exoSAMSI meeting a few months ago. We decided that it would probably not be a good idea to do this with a single-transit catalog that doesn't also have a good estimate of completeness and purity and so on. That is, Biller and Crossfield were (rightly) suspicious when I said maybe we could just fit the completeness function hyper-parameters along with the population hyper-parameters! That said, they were both optimistic that this could work. I wrote to Meg Schwamb (Taipei) about her (possibly existing) single-transit catalog from PlanetHunters.

Kepler

Most of my research time today went into herding the cats involved in my plan to submit a white paper to the Kepler two-wheel call.

imaging survey strategy

I spent some time discussing with Stephanie Wachter (MPIA) the Euclid calibration strategy and survey requirements. I think that Euclid (and all surveys) should do something like the "strategy C" in the paper on survey strategy by Holmes et al. Indeed, I think that strategy C would not only lead to far better calibration of Euclid than any of their current survey strategy ideas, I think it would make for more uniform solid-angular coverage, better time-domain coverage, and greater robustness to spacecraft faults or issues.

off the grid, thinking

I spent the weekend completely off the grid. I didn't even bring my computer or any device. That was a good idea, it turns out, even for doing research. I got in some thinking (and writing) on various projects: I sharpened up my argument (partially helped by conversations with various MPIA people last week) that you never really want to compute the Bayes evidence (fully marginalized likelihood). If it is a close call between two models, it is very prior-dependent and isn't the right calculation anyway (where's the utility?); if it isn't a close call, then you don't need all that machinery.

I worked out a well-posed form for the question "What fraction of Sun-like stars have Earth-like planets on year-ish orbits?". That question is not well-posed but there are various possible well-posed versions of it, and I think some of them might be answerable with extant Kepler data.

Along the same lines, I wrote up some kind of outline and division of responsibilities for our response to the Kepler call for white papers related to repurpose in the two-wheel era. I decided that our main point is about image modeling, even though we have many thoughts and many valuable things to say about target selection, field selection, cadence, and so on. When I get back to civilization I have to email everyone with marching orders to get this done.

Rix and I have a side project to find streams or kinematic substructures in Milky-Way stellar data of varying quality. It works by building a sampling of the possible integrals of motion for each star given the observations, as realistically as possible, and then finding consensus among different stars' samplings. I worked on scoping that project and adjusting its direction. I am hoping to be able to link up stars in big Halo-star surveys into substructures.

cosmography, dust mapping, null data, discrete optimization

In a very full day, I learned about quasar-absorption-line-based mapping of the density field in large volumes of the Universe from K. G. Lee (MPIA), I discussed non-parametric methods for inferring the three-dimensional dust map in the Milky Way from individual-star measurements with Richard Hanson (MPIA), I was impressed by work by Beth Biller (MPIA) that constrains the exoplanet population by using the fact (datum?) that there are zero detections in a large direct-detection experiment, and I helped Beta Lusso (MPIA) get her discrete optimization working for maximum-likelihood quasar SED fitting. On the latter, we nailed it (Lusso will submit the paper tomorrow of course) but before nailing it we had to do a lot of work choosing the set of models (discrete points) over which fitting occurred. This reminds me of two of my soap-box issues: (a) Construction of a likelihood function is as assumption-laden as any part of model fitting, and (b) we should be deciding which models to include in problems like this using hierarchical methods, not by fitting, judging, and trimming by hand. But I must say that doing the latter does help one develop intuition about the problem! If nothing else, Lusso and I are left with a hell of a lot of intuition.