It is the summer, and it feels like I am getting lots done! That said, I only got in a small amount of serious research time today. I spent most of it commenting on a draft manuscript from Price-Whelan on chaos in the halo of the Milky Way. Yes, dynamical chaos. This is the coming to fruition of a pretty old idea at CampHogg: What is the difference between stellar streams on regular orbits and those on chaotic orbits? It turns out that the differences are bigger than we expected!
I read (or skimmed, really) some classic papers on the star-formation history of the Milky Way, in preparation for re-asking this question with APOGEE data this summer. Papers I skimmed included Prantzos & Silk, which infers the SFH from (mainly) abundance distributions and Gizis, Reid, and Hawley, which infers it from M-dwarf chromospheric activity. I also wrote myself a list of all the possible ways one might infer the SFH. I realized that not all of the ways I can think of have actually been executed. So now I have a whole set of projects to pitch!
How can a 6-page proposal take more than six days to write? Also signed off on Dun Wang's paper on his pixel-level self-calibration of the Kepler Mission. Submit!
In my research time today, I kept on the K2 proposal. I made the argument that we want K2 to point less well in Campaign 9 than it has in previous Campaigns, because we want the crowded field (which is in the Bulge of the Milky Way) to move significantly relative to the pixel grid. We need that redundancy (or heterogeneity?) for self-calibration. I hope that—if we get this proposal funded—we will also get influence over the spacecraft attitude management!
I spent my research time today working on my proposal for K2 Campaign 9. The proposal is to self-calibrate to get the flat-field, which is critical for crowded-field photometry (even if done via image differencing).
At group meeting, Fadely showed us plots that show that he can do what I call “radical” self-calibration with realistic (simulated) data from fields of stars. This is the kind of calibration where we figure out the flat-field and PSF simultaneously by insisting that the images we have could have been generated by point sources convolved with some pixel-convolved PSF. He also showed how the results degrade as our knowledge of the PSF gets wrong. We can withstand percent-ish problems with our PSF model, but we can't withstand tens-of-percent. That's interesting, and useful. I feel like we are pretty safe for our HST WFC3 calibration project though: We know the PSF very well and have a great first guess at the flat too.
At the same meeting, we bitched about the Astronomers' Telegram, looked at an outburst from a black-hole source, argued about mapping the sky with Fermi GBM, and looked at K2 data on a Sanchis-Ojeida planet. Oh and right after group meeting, Malz demonstrated to me conclusively that our Bayesian hierarchical inference of the redshift distribution—given probabilistic photometric redshifts—will work!
The only research part of the day was a great lunch with ex-Camp-Hogger (is it possible to be "ex" from CampHogg?) Ekta Patel (Arizona), where she talked to us about research and graduate curriculum at Arizona. She is doing awesome research (on the LMC and Local Group satellites) right off the bat and loving the research focus of the course schedule at Arizona. I couldn't agree more! I pitched my ideas that sub-pixel flat issues could in principle be messing with the incredibly small proper motion measurements for the Local-Group satellites.
Today was #K2proposalSprint day. At group meeting, MJ gave us a review of a new paper on probabilistic approaches to weak lensing, which made many harsh (but useful) approximations. Then we pitched our K2 proposals and started writing. Price-Whelan pitched a proposal to find extragalactic exoplanets! One of the K2 fields touches the Sagittarius stream and therefor will contain (at Kepler sensitivity) some good red giants that might be planet hosts delivered by an accreted galaxy!. Fed Bianco pitched a proposal to do lucky imaging (and improve lucky-imaging pipelines) to follow up microlensing events in the K2 Campaign 9 field (which is a bulge-imaging project aimed at microlensing). I pitched a proposal to determine the PSF and flat-field in Campaign 9, where the field will be so crowded that, for one, the flat-field and PSF will be infer-able in the data, and, for another, the two things will need to be known at good precision to do any useful data analysis. We then spent the day working, but I have to admit I didn't get very far!
We had a star-studded group meeting today. It kicked off with Charlie Conroy (Harvard) talking about some of his recent projects. In one, he looks at the time dependence of pixel brightnesses in M87, because the long-period variables in the stellar population lead to long-period variations in brightness. In principle these variations are a function of stellar population age and density. He showed data from a huge but under-exploited HST program. In another project, he is working on varying unknown physical properties of atomic transitions within a stellar atmosphere model to make an interpretable but data-driven model for stellar atmospheres. This is a great project, but involves coding up all one's prior beliefs about what can vary and how and in what ways. That's a very complicated prior pdf! In another, he discusses the limits of chemical tagging (with Yuan-Sen Ting, MPIA, with whom I will be working this summer. In this project, they find that even a small change in the precision with which chemical abundances can be measured might have a huge impact on any tagging project.
In the second half of group meeting, Andrew Gordon Wilson (CMU) spoke about his new work on kernel learning, in which he optimizes the likelihood of a Gaussian Process in which the kernel is represented as a mixture of Gaussians in spectral space. He has some amazing demos which show that the kernel learning gets a very different covariance matrix than the empirical covariance, which is highly relevant to modern cosmology (where the empirical covariance is all we ever use!). He also talked about some important philosophy about model complexity: For every simple model that works well (in a Bayesian sense), there are other, more complicated models that will always work better (also in that same Bayesian sense). This plays well with my disagreement with all the Mackay-like arguments that Bayes encapsulates Occam's Razor. It just doesn't!
First thing in the morning I spoke with Scott Singer (NYU) about confirming or checking the ultra-short-period exoplanets shown to use by Sanchis-Ojeida. He is new to it all, so we talked about where the data are, how they are indexed and named, and how to plot them.
At the very end of the day, I pitched the project of making probabilistic models that can generate stellar variability (consistent with observations) to our visitor Andrew Gordon Wilson (CMU). The idea is to use all the light curves we have ever seen to build a family of non-trivial kernels (what's called “kernel learning” in the machine-learning literature) and a prior over those, so that we can model (in my sense, which involves a likelihood function) any stellar variability with a bespoke Gaussian Process. This is the key missing piece in our plans to take Kepler (or Kepler-like) light curves and separate them into the component generated by stellar variability, the component generated by spacecraft variability, and the component generated by any transiting companion: We need a good model of what stars can do!
There is growing evidence that Ness, Rix, and I can determine stellar masses (and therefore ages) on the red-giant branch using The Cannon, trained on asteroseismology results from Kepler and spectra from APOGEE. I am stoked. There is some debate among our team about what is going to be the spectral signature that is delivering the mass/age information. Last week, Ness showed me that at least some of the information is coming from very weak emission lines. The Cannon has discovered chromospheric activity! Today, Ness and I worked on visualizing the spectral regions that are delivering label information. We came up with a pretty novel plot, one draft example of which is below.
Also today Andrew Gordon Wilson (CMU) showed up to school us on Gaussian Processes. He said that he would have no trouble computing the determinant of the covariance matrix for the CMB real-space likelihood function even for Planck-sized data; he said the determinant would take seconds! His methods involve a regular grid of inducing points and interpolation to the irregular grid. So we threw down the challenge; now Foreman-Mackey and I have to get the problem set up for him. We'll see!
At group meeting, Fadely showed us evidence that the radical self-calibration that we are executing for the HST WFC3 instrument can work: He showed that if you know the PSF—but nothing about any individual exposure—you can indeed infer the flat-field to some precision. Also and related, Vakili showed that he is getting pretty good estimates of the PSF in real HST WFC3 imaging. So we are getting close to going end-to-end on this project. I call this self-calibration “radical” because it doesn't rely on stars being observed more than once; it only relies on stable enough (or dense enough) imaging that the PSF can be accurately inferred. It works by asking what flat-field is required in order to generate good predictions for the data. One thing we are hoping: The quality of the results might depend more on the center of the PSF (the easy part) than the outskirts (the hard part); we are trying to understand that now. The long-term goal of this project is to save the asses of projects that took their data in violation of the principles for self-calibration.
In a low-research day I got some quality time in at the end with Foreman-Mackey, reviewing the open threads and unfinished papers. There are five high priority unfinished projects, all related to exoplanets (meaning: we aren't even counting the other stuff). Uh oh.