Dr Kate Storey-Fisher

Kate Storey-Fisher (NYU) defended her PhD here at NYU today. She killed it! She talked about emulating cosmological simulations (at the level of statistics, not maps), making invariant scalars that encode the shapes and dynamics of dark-matter halos, and her awesome 1.2 million all-sky quasar catalog from ESA Gaia and NASA WISE. It was all things my loyal reader knows lots about but I loved it. It has been an honor and a privilege to work with KSF these years, and I will miss her very very much.


Dr Irina Espejo

Today it was my honor to serve on the PhD defense committee of Irina Espejo (NYU), who is one of the first (ever in the world, actually!) PhDs in Data Science. Her PhD research involved making real, practical, scalable, reproducible tools for the (late-in-pipeline) analysis of high-energy physics data from the Large Hadron Collider. She built tools to speed up likelihood-free inferences, and she built a tool to find exclusion regions (upper limits) in complex parameter spaces. She used the latter to put constraints on a (real, not toy) proposed modification to the standard model.

On the first project, the tools that she built (and built on) make the LHC more sensitive to new physics, because they find better test statistics for distinguishing models. They make some searches far better, which makes me wonder whether particle physics is using our money efficiently??


how to extract XP spectra from raw Gaia data?

On the plane home from meetings at Cambridge, Warwick, and Paris, I worked on a long document I am writing for Gaia DPAC CU5, which is the organization responsible for calibrating and extracting the Gaia XP spectra. They are doing a beautiful self-calibration to extract all the spectra on the same system, in the sense of resolution, dispersion, and throughput. But their system has some pathologies, which we discussed last week. I think I know how to solve some of them. My document is reporting those thoughts.

Writing like this reminds me of graduate school: One of my advisors (Blandford) often encouraged me to write up thoughts, ideas, projects, and proposals, even when we had no intention of submitting them anywhere. It's good practice, I think, because you can't understand anything if you don't write about it.


how to maximize the yield of planets?

There were discussions this week at University of Warwick about the Terra Hunting Experiment strategy and likely detection capability. Various take-homes include that we need to mitigate lots of stellar noise, and that we care deeply about the covariance (as a function of separation in time) of adjacent measurements. I advocated that we split our ten-year survey into two or three surveys, of varying length. In the first, we learn about the stars, and in the last, we go to town on the very most promising targets. There was general agreement that this is a good idea. But now we need a very specific plan for what this means. As my loyal reader knows, in my view, the decisions must be based on repeatable operations, so that we have some hope of learning statistical things about populations in the end.


predicting RVs from SDO imaging

I'm at the Terra Hunting annual Science Working Group meeting, held this year at University of Warwick. There were many great talks today, some technical and some science. My mind was blown by Ben Lakeland (Exeter), who showed Solar Dynamics Orbiter data of the Sun, and then showed that, from these images, he can predict the magnetic-activity-generated RV signals in simultaneous EPRV measurements of the Solar RV. That's pretty exciting. He also showed that much of the time, the RV variations are dominated not by magnetic activity per se. If we are going to beat one meter per second, we are going to have to correct for convective shifts. Somehow!?


distances between point clouds

I spent the last two days working at Apple Paris, which was fun! I worked with the open-source ott-jax package, which can do some amazing things. I worked with Soledad Villar (Apple & JHU) to generalize the k-means algorithm to point clouds! It can cluster point clouds morphologically, even if the different point clouds have different numbers of points, and even if the different point clouds live in spaces of different dimensions! Everything obeys permutation and rotation symmetries.


calibrating Gaia

I was honored today by being invited to a meeting of the group at Cambridge (UK) that extracts and calibrates the low-resolution ESA Gaia XP spectra. The model is bilinear: Each source is represented as a linear sum of basis functions, and each observation of each source has an expectation which is that linear sum multiplied by a convolution kernel, which is also represented as a sum of components. These components are smooth functions of position in the device and wavelength. It's a very nice system! I went through it all with them and said what I would have done differently (which is not much, I have to admit).