exoplanet search, large galaxies

I worked on large-galaxy photometry with Patel for part of the day. She is dealing with all our problem cases; we have good photometry for almost any large, isolated galaxy, but something of a mess for some of the cases of overlapping or merging galaxies. Not surprising, but challenging. I am also working on how to present the results: What we find is that with simple, (fairly) rigid galaxy models we get excellent photometry. How to explain that, when the models are too rigid to be "good fits" to the data? It has to do with the fact that you don't have to have a good model to make a good photometric measurement, and the fact that simple models are "interpretable".

In the afternoon, we had a breakthrough in which we realized that Foreman-Mackey's exoplanet search (which he sped up by a factor of 10⁴ on the weekend with sparse linear algebra and code tricks) can be sped up by another large factor by separating it into single-transit hypothesis tests and then hypothesis tests that link the single transits into periodic sets of transits. He may try to implement that tomorrow.

halo occupation, Earth analogs

I spent some time with Kilian Walsh (NYU) discussing halo occupation (putting galaxies into dark-matter halos). With Jeremy Tinker (NYU) he is looking at whether enriching the halo parameters (currently only mass and concentration) with environment information will improve halo-occupation models. As per usual, I asked for plots that show how well they are doing without the environment.

In the afternoon, Foreman-Mackey delivered results of a (very limited) search for Earth-like exoplanets in the light-curve of a bright Kepler G dwarf star, using his Gaussian-process likelihood function. In the limited search, he was able to re-find an injected Earth-like transit with a 300-day period. That's extremely promising. It is not clear whether things will get a lot worse when we do a fuller search or go to fainter stars.

out sick

did nothing

Tri-State Astronomy

I spent the research part of my day at the Tri-State Astronomy meeting, beautifully organized by Geha (Yale) and Maller (CUNY) and others. I won't do it justice; there were five great review talks and lots of great posters (and one-minute poster summaries). Alyson Brooks (Rutgers), in her review talk, beautifully summarized the issues with CDM at small scales, and discussed the idea that baryonic processes might explain all of these. My take on her bottom line (that baryons might explain everything) is that it would be a disappointment if we can't see the predicted properties of the dark matter at small scales! But she did point towards isolated dwarf galaxies (that is, very low luminosity galaxies that are not satellites of any more luminous galaxy) as critical tests of the ideas; isolated dwarfs should differ from satellite dwarfs if CDM is going to stay okay at small scales. She also implicitly made many predictions about outflows from small galaxies at intermediate redshifts. And she gave a few shout-outs to Zolotov, who has been deeply involved in these baryon issues since leaving NYU.

inferring spectra, optimizing search

Vy Tran (TAMU) showed up and we discussed inference of spectral energy distributions of high-redshift galaxies given only photometry (no spectroscopy). She showed us some nice results, but Fadely and I think we could actually infer spectral properties at wavelength resolution higher than that implied by the broad-band (or medium-band) photometry. So we more-or-less launched a collaboration.

On the way to lunch, Fadely, Foreman-Mackey, and I had an epiphany: Foreman-Mackey has been trying to set the hyperparameters of a Gaussian Process to optimize our ability to search for and find exoplanets. This has been befuddling because it is so frequentist (or not even frequentist you might say). The issue is that what you want to optimize (hyperparameter-wise) depends on the details of how you are going to decide what is an exoplanet (that is, on your threshold choices). We realized on the way to lunch that we probably should be choosing the hyperparameters to maximize the area under the ROC curve. Foreman-Mackey launched on this in the afternoon. Cool if it works; novel definitely.

search and hyperparameters

In a last-minute clutch play on a low-research day, Foreman-Mackey showed me that our ability to detect exoplanet transits in the lightcurves of stochastically varying stars depends in a non-trivial way on the hyperparameters of the Gaussian Process we use to model the unocculted star. We are hoping we can tune to the most-sensitive-for-search hyperparameters before searching, and only switch back to being agnostic about the hyperparameters at the post-search characterization stage.

marginalized likelihood, goose egg

Having written some stuff that I wasn't happy with a few months ago, thought about it, forgot about it, remembered it, mentioned it on the blog here and there, and then dusted it off today, I got close to being ready to make an argument about when you should and shouldn't compute the marginalized likelihood, even if you are a committed probabilistic reasoner. The fundamental idea is that you shouldn't do model selection based on marginalized likelihoods; these are very challenging integrals but only approximations (and often bad ones) to the integrals you would really want to do to inform a model choice. I guess one way to put it is: Don't spend a huge amount of computer time computing something that is a worse approximation to what you want than something else that might be much easier to compute! I sent my argument for review to Brewer, my guru in all things Bayes. I want to say things that are useful and uncontroversial, so I need to be careful, because on these matters I tend to be like a bull in a china shop.

Late in the day I talked to a NYU Data Science class about possible research projects they could do with the Kepler data. As I was talking about how we search for exoplanets in the data (and how likely it is that the data contain many undiscovered planets), one of the faculty in charge of the class (Mike O'Neil) asked me how many exoplanets we (meaning CampHogg) have found in the data so far. I had to admit that the answer is zero. That's just embarrassing. I need to ride the team hard tomorrow.

exoplanets, exoplanets, exoplanets

I gave the CCPP Brown Bag talk today, about Kepler data and exoplanets. I was going to talk about calibration, flat-field, and our white paper, but I ended up talking about very flexible models, for intrinsic stellar variability and spacecraft-induced variability in the lightcurves. People were shocked that some of our models have hundreds of thousands of parameters. I didn't let them in on the secret that, in some sense, the Gaussian Processes we use have infinite numbers of parameters!

Tim Morton (Caltech) dropped by to talk about various things exoplanet. He has a very nice system for computing and propagating probabilities for various exoplanet and non-exoplanet (false-positive) scenarios, given Kepler data. He produces most of what you need in order to do efficient follow-up and population studies, given uncertain or noisy exoplanet identifications. In other news, today was Angus's last day here at NYU. She has been visiting for a month from Oxford, and started some projects with us on search, using Gaussian Processes under the hood. They told us it couldn't be done (too slow) but we are doing it.

astronomy engineering at TAMU

I talked to Lucas Macri (TAMU) and Lifan Wang (TAMU) about optical observations being made in Antarctica at Dome A. This is a Chinese site that is horrifying to get to and use, but which has amazing observational properties, like excellent seeing, transparency, and sky spectrum. They have taken lots of valuable data with amazing 24/7 time coverage and short cadence. They even have some potential exoplanet discoveries. Wang has lots of ideas for next-generation projects, including some with a hefty component of high-grade robotics.

In the morning, Darren DePoy (TAMU) and Jennifer Marshall (TAMU) showed me the hardware being built for the HETDEX experiment. This involves 150 identical (or near-identical) fiber-fed spectrographs, plus a robot positioner. We spent a lot of time talking about mechanical engineering, because if you want to mass assemble a whole lot of instrumentation, you need it to be simultaneously easy to adjust mechanically, and unnecessary to adjust. This may be the future of astrophysics.

Texas A&M

I spent today at Texas A&M University, where I spoke to the Physics Department. I took a risk and talked entirely about modeling astrophysics data, including even inferring the Kepler flat-field! Nick Suntzeff (TAMU) introduced me with a discussion of astro-statistics and its importance in the future of astrophysics, which made me feel a bit better about choosing such a technical topic. I particularly emphasized that making measurements in astrophysics problems—where we can't do controlled experiments—usually requires building a hybrid model that includes both data-driven components (for the parts of the problem that are complicated but we don't particularly need to understand), and causal-physical components (for the parts where we hope to gain some understanding). My examples were XDQSO, Kepler, Comet Holmes and the kitten, and the Solar System force law. On the first example, all I really said is that a whole lot of bad data can be as good as a small amount of good data, when you have a good noise model. On the last point, all I really said was that we have no idea how to scale up for Gaia.

dotastro, day 3

The third and last day of dotastronomy 5 started with reports of the outcome of the Hack Day. Various extremely impressive hacks happened, way too many to mention, but including a very impressive video about planet naming, by Deacon and Angus and others, an automated astronomer-career mapping app by Foreman-Mackey and others, a XBox-Kinect doppler-shift app by Lynn that got everyone in the room dancing and spinning more than once, and (near and dear to my heart) improved functionality for the Zoonibot by Barentsen and Simmons and others. That latter hack is an extension of the the bot that got started by Beaumont and Price-Whelan (at, I am proud to say, my suggestion) at dotastronomy 4.

Among the talks, one of the highlights for me was Trouille (Adler) talking about the Galaxy Zoo Quench project, in which Zooites are taking the project from soup to nuts, including writing the paper. She spent a time in her talk on the problem of getting the participants to boldly play with the data as professional scientists might. It is a rich and deep piece of public outreach; it takes self-selected people through the full scientific process. Another highlight was Microsoft's Tony Hey talking about open access, open data, open science, libraries, and the fourth paradigm. Very inspiring stuff.

Related to that, there was great unconference action in a session on open or low-page-charge publishing models, led by Lynn (Adler) and Lintott (Oxford), in which Simpson (Oxford; and our fearless dotastronomy leader) got emotional (in all the right ways) about how crazy it is that the professional societies and individual scientists have signed away their right to their own work that they researched, wrote, reviewed, and edited for the literature. Testify!

I ran a short unconference session on combining noisy information coming from Zoo participants (or equivalent) in citizen-science and croud-sourcing situations. A good discussion of many issues came up, including about the graphical model that represents our assumptions about what is going on in the projects, about active learning and adaptive methods, and about exposing the internal data in real time so that external (third-party) systems can participate in the adaptive decision-making. I also advocated for boosting-like methods, based on the idea that there might be classifiers (people) with non-trivial and covariant residual (error) properties.

It has been a great meeting; Rob Simpson (Oxford) and Gus Muench (Harvard) deserve huge thanks for organizing and running it.

taking over the world

For reasons outside my control, I had to play hookey on the second day of dotastro. That is very sad, because it is the Hack Day, and the whole raison d'etre of the meeting, as far as I am concerned. The only (research) compensation for this was a fast meeting with Kathryn Johnston (Columbia) and Robyn Sanderson in which we planned various ways to take over the world. The general idea is that we might be able to build a team, connected by commuter rail, that covers the observational, theoretical, and data analytical sides of measuring the Milky Way potential with cold phase-space structures.

dotastro, day 1

Today was the first day of dotastronomy, the meeting for astronomy and web and outreach and so-on, this time in Cambridge, MA. Stand-out talks included those by Stuart Lynn (Adler) on the Zooniverse and Elisabeth Newton (Harvard) about astronomy blogging in general (she mentioned this blog) and Astrobites in particular. Astrobites has been an incredible resource for astronomy, and it is carefully cultivated, edited, and managed. What a project!

In the afternoon we switched to unconference, some of which I skipped to attend a phonecon about Kepler data with the exoSAMSI crew, organized by Bekki Dawson (Harvard), who is effectively our leader. On that call, we discussed what everyone has been doing since exoSAMSI, which is quite a bit. Barclay (Ames) has been working on inferring the limb-darkening laws using transits as measuring tools. Quarles (Texas) has been searching the real-stars-with-injected-planets that we (read: Foreman-Mackey) made back at exoSAMSI, with some success. Foreman-Mackey and Angus have been searching for long-period systems with a fast Gaussian Process inside the search loop. We also spent some time talking about modeling the pixel-level data, since we at CampHogg have become evangelists about this. The SAMSI program, organized mainly by Eric Ford (PSU) has been incredibly productive and is effectively the basis for a lot of my research these days.

In my dotastro talk this morning, I mentioned the point that in "citizen science" you have to model the behavior of your citizens, and then generalized to "scientist science": If you are using data or results over which you have almost no control, you probably have to build a model of the behavior and interests and decision-making of the human actors involved in the data-generating process. In the afternoon, Lintott (Oxford) suggested that we find a simple example of this and write a short paper about it, maybe in an area where it is obviously true that your model of the scientists impacts your conclusions. That's a good idea; suggestions about how to do this from my loyal reader (you know who you are) are welcome.

origins

In a low-research day, I spoke to the new graduate students about research opportunities among the astrophysicists in the Department. I ended up making a pitch for discovery and for origins: We work in a field in discoveries are daily occurrences. And we have some hope of understanding our origins, of our Universe, of our Galaxy, of our Solar System, and maybe even of our tree of life. I love my job!

Blandford

The highlight of a low-research day was a visit from Roger Blandford (KIPAC), who gave the Physics Colloquium on particle acceleration, especially as regards ultra high-energy particles. He pointed out that the (cosmic) accelerators are almost the opposite of thermal systems: They put all the energy very efficiently into the most energetic particles, with a steep power-law distribution. He made the argument that the highest energy particles are probably accelerated by shocks in the intergalactic media of the largest galaxy clusters and groups. This model makes predicitions, one of which is that the cosmic rays pretty-much must be iron nuclei. In conversations over coffee and dinner we touched on many other subjects, including gravitational lensing and (separately) stellar spectroscopy.