Paper Explainer: Dark Radiation Sticks Together: Dark QCD and the Hubble Tension

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Paper Explainer: Dark Radiation Sticks Together: Dark QCD and the Hubble Tension

This is an explainer for my new paper "Dark Radiation Sticks Together: Dark QCD and the Hubble Tension" with Rutgers postdocs Nico Fernandez and Eric Putney. In this paper, we propose a model of dark matter that has self-interactions (creating a "dark sector" of physics) which are similar to the physics we see in quantum chromodynamics (QCD). This proposed physics of the dark sector modifies how the history of the Universe unfolds, changing it in a way that we show resolves some long-standing tensions in the data. In particular, we reduce the "Hubble tension" to a $3\sigma$ problem, which is about how well as any of the best new physics models can do.

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Paper Explainer: ClearPotential: Revealing Local Dark Matter in Three Dimensions

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Paper Explainer: ClearPotential: Revealing Local Dark Matter in Three Dimensions

This is a paper explainer for my recent work with my graduate student Eric Putney, previous Rutgers postdoc (now at Institute of Basic Science in Korea), and my Rutgers colleague David Shih. This is the culmination of a series of papers, which I’ve written about here.

The goal of this paper and the larger project is to use the collective motion of the stars near the Earth, as measured by the Gaia Space Telescope, to measure the gravitational potential, acceleration, and mass density of the Milky Way galaxy. In particular, once we have the total mass density, we can subtract off the visible matter (that is, stars and gas, what we call “baryonic” material) to determine the density and distribution of dark matter in the Galaxy.

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Paper Explainer: Mapping Dark Matter Through the Dust of the Milky Way Part I

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Paper Explainer: Mapping Dark Matter Through the Dust of the Milky Way Part I

This is work that I did with my student, Eric Putney, then-Rutgers-postdoc Sung Hak Lim (now at the Institute for Basic Science in Daejeon), and my colleague David Shih. This is actually a continuation of work we’ve been doing for a while, starting with a paper that tested the idea on synthetic data, and then a later paper applying it to real data. I didn’t write blog posts on those because I fell behind on everything starting in 2020 and I’m only just now digging myself out. This new paper is one of a pair, Part II will be coming out in the new year.

So what’s the big idea, and what are we doing now?

In short, we have a new method that takes the motion of stars in the Milky Way and learns the gravitational potential of all the stars and gas and dark matter in the Galaxy. It does this even in the regions where we can’t see most of the stars, due to dust obscuring their light, which is the new development above and beyond the previous work. This paper is about the method, and the next paper will give the results for the gravitational potential and the dark matter density we can learn from it.

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Paper Explainer: Inferring the Morphology of the Galactic Center Excess with Gaussian Processes

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Paper Explainer: Inferring the Morphology of the Galactic Center Excess with Gaussian Processes

This is a paper I wrote with Tracy Slatyer at MIT, her student Yitian Sun (now a postdoc in McGill), Sidd Mishra-Sharma (previously a postdoc at IAIFI, newly hired as a professor at Boston University), and my student Ed Ramirez. I think it is fair to say Ed did the majority of the analysis and coding on this (quite extensive) project, and was instrumental to the project from beginning to end..

This paper is a contribution to a long-running debate within the fields of particle physics and astrophysics, so it is fairly technical in parts, but the debate itself is very interesting and — I think — very important for the field of dark matter.

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Paper Explainer: Via Machinae

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Paper Explainer: Via Machinae

This is an explainer for my most [recent paper][1], with my Rutgers colleague David Shih, Lina Necib at Caltech, and UCSC grad student (and a Rutgers undergrad alum) John Tamanas. It’s a project that we’ve been working on for a while (some of this being for obvious, 2020-related reasons), and I’m very happy to finally have it see the light of day, as it’s a really interesting convergence of a number of my interests in dark matter-motivated astrophysics, big data, and machine learning.

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