Dust Inference


I have been working with Andy Miller (Columbia) on building a 3D dust map of the Milky Way galaxy using a Gaussian process prior on 1e9 stars from Gaia. Of course we always want to solve all of astronomy, and infer the dust map and the color magnitude diagram simultaneously. But for a first step we want to model a dust-free CMD, infer individual dust estimates for stars, and then build the GP model on these individual estimates. Here I’m showing a first attempt at our dust-free CMD model, built using XD (a gaussian mixture model that can include heteroskedastic noise written by Jo Bovy) on GAIAx2MASSxWISE stars with sfd dust estimates < 0.005 and parallax S/N > 20. Some issues that pop out to me: (1) I find it very strange that all of Gaussians look so isotropic, (2) having such a tight constraint on the dust also minimizes support of the prior, especially for the brightest stars, and (3) although XD is a nice non parametric model, it feels like there are better options, something that is more inclusive of our prior knowledge of the CMD, i.e. that stars are tightly correlated in this space along a line with some thickness. We’re looking into using a normalizing flow, which is a non linear transformation of a Gaussian PDF, something that Miles Cranmer (Princeton) has been thinking about. Here I’m showing the log likehood of the CMD prior (left panel), some intitial posterior estimates in red (center panel), and the inferred dust estimates (left panel).

Welcome to our blog!


This is the first post for our site and it’s pretty meta. This week at group meeting, I presented this blog to the group (pictured above). Our goal is to use this as a way to highlight some of the things that we work on in our group, to give ourselves some amount of accountability, and to practice open and transparent science. After this post, the plan is that each week one group member will post the figure that they presented in group meeting with a short description of what they’re working on, what they’ve recently learned, and (perhaps) where they’re stuck. We hope you enjoy it and we hope that we all learn something.

Hello World


This is our website where one member of the group will post a research update every week. This is meant to be work in progress so keep your expectations set accordingly.

Photo by Anthony DELANOIX on Unsplash.