generalized flat-relative extraction

I asked, in the Astronomical Data Group meeting at Flatiron, about the method of spectral 2D-to-1D extraction known as flat-relative optimal extraction. It's genius, and simple, but it makes strong assumptions about the spectrograph. I asked how we might improve it. And I think I maybe have a plan. The idea (which was thrown out by Megan Bedell) is to make the spectral representation something continuous, and evaluate it individually at every pixel, not just once per column of the detector. This should improve extraction. And it is relevant to the NASA proposal I am writing with Matt Daunt.


wacky shape scalars

Kate Storey-Fisher (NYU) showed me today the results of her work predicting stellar contents of dark-matter halos in hydrodynamic n-body simulations. She is building her shape scalars from geometric properties (scalars, vectors, and tensors) of the position-space and velocity-space distributions of the n-body particles. She did a very principled feature-importance study, including one feature at a time, combinatorically, and seeing how each feature helps, differentially. The most important features are... strange! Why? Because most of the regression work is done by very simple features (halo mass, halo size, halo velocity) so the (dimensionless) shape scalars we have made are fixing up non-trivial problems. Time to write the paper!


Dr Yucheng Zhang

Today Yucheng Zhang (NYU) defended his PhD. He used SDSS eBOSS large-scale structure samples to test gravity on large scales, and also made forecasts for measuring the non-Gaussiany parameter fnl and other very-large-scale-structure measurements in upcoming surveys. Beautiful work and a very nice defense. In the question period, Kate Storey-Fisher (NYU) asked Zhang about his possible forecasts for the upcoming ESA Gaia sample of 6.4 million quasars. Zhang has not considered this sample yet (almost no cosmologists have!) but he said that he does have the technology to make predictions for it. His intuition is that it would be great for measuring baryon acoustic feature and fnl. We plan to take Zhang out to lunch to discuss in the near future!


information loss

I wrote words today about how information is being lost in radial-velocity-spectrograph data-analysis pipelines at the stage of going from 2D spectra to 1D spectra. I am proposing to NASA (with Matt Daunt, NYU) to fix these problems! This is important, in my opinion, but I have to admit that it is not currently considered the tall pole in EPRV.


exoplanet roadmaps, plans, and surveys

Inspired by research by Matt Daunt (NYU), I looked at the various reports, presentations, and papers that have been written by NASA panels, committees, and projects about the tall poles and engineering gaps in the exoplanet research ecosystem. Why? Writing a proposal, of course! Daunt and I are proposing to work very close to the metal in radial-velocity work, so we are looking at the critical infrastructure that's close to the metal.


radio reboot

[Somehow this blog keeps failing. I will try to get back into it, but no promises! I apologize to my loyal reader.]

Today I met with Abby Shaum (IPAC) who worked with me a few years ago making a phase demodulator to find stellar companions. The idea is that if a star is broadcasting a coherent (or even incoherent) asteroseismic or pulsation mode, and if the star is orbiting a companion, the kinematics of the orbit will be imprinted on phase and frequency modulations of the carrier frequency. Like a radio! Indeed we built a signal-processing method that looks just like a radio demodulator. Today we discussed how to reboot this project and write a paper for the refereed literature.



I gave a seminar at lunch today (black-board talk) about how sailboats work. I got lots of great comments and questions, especially about sailing down wind faster than the wind. I vowed to add a paragraph to my paper on sailing (with Matt Kleban) about how to sail this way. I think it is extremely hard to do, technically. So much so that some of the books on sailing say that it is impossible! It isn't, in principle.


extragalactic stellar stream

Sarah Pearson (NYU) is working on modeling a stellar stream (disrupted satellite galaxy) around an external galaxy. The goal is to figure out what observables are most critical, and what properties of the host galaxy are most strongly constrained by a good model. That is, information theory. Pearson showed beautiful results today to Adrian Price-Whelan (Flatiron) and me: She can show that the mass of the galaxy's dark-matter halo is covariant with velocity gradients along the stream. Those would be hard to measure but not impossible. One high-level objective is to understand what would be the scientific merit of a big program with new imaging data and follow-up spectroscopy.


simulating BpRp spectra

I had an early meeting with Maddie Lucey (UT Austin) and Adrian Price-Whelan (Flatiron) about simulating ESA Gaia BpRp spectra. Lucey has this technology and can simulate stars with any parameters. We discussed making a fake-data set that we can use to test ideas and methods we would like to use after Gaia DR3 in June. We ended with a plan to simulate matched BpRp spectra, one for each APOGEE DR17 spectrum. Lucey is on the case. Let us know if you are interested in doing preparatory science with such a data set!


how do clustering results scale with survey size?

I spoke with Abby Williams (NYU) and Kate Storey-Fisher (NYU) today about Williams's forecasts for measuring cosmological-scale gradients in the large-scale structure. We came up (many moons ago) with approximate scalings with survey volume, the number of tracers, and the amplitude of the clustering. Some of these are obeyed by Williams's results and some aren't! What gives? We think it might have to do with the occupation number of the modes. If the number density of tracers is high, the clustering precision depends on volume, not galaxy number density.


distributions of dimensionless quantities

In finishing up our paper on dimensional analysis for machine learning, Soledad Villar and I have been discussing how to talk about out-of-distribution generalization of machine-learning methods. The space of dimensionless quantities is smaller in many ways, but I couldn't figure out how to argue that it is easier to match the test data to the training data in the dimensionless quantities than in the original, dimensional inputs. Villar pointed out that one way to see it is that many different distributions in the dimensional quantities map to the same distribution in the dimensionless quantities. For example, if you multiply all the masses by five, you haven't changed the distribution in the mass ratios, even though your mass distributions will no longer overlap. That's a good argument, and what we ended up arguing in the paper.