Finding the Missing Baryons Using CMB as a Backlight
Shirley Ho (LBNL/Princeton), Simon Dedeo (KICP/IPMU), David Spergel, (Princeton)

TL;DR
This paper introduces a novel method to detect missing baryons by cross-correlating galaxy momentum templates with CMB data, enabling direct detection of ionized gas in the intergalactic medium with significant signal-to-noise ratios.
Contribution
The paper presents a new technique for detecting the kinematic Sunyaev-Zel'dovich effect using galaxy momentum templates derived from surveys and CMB data, improving detection prospects.
Findings
Signal-to-noise ratios of 4, 9, and 12 for ACT with different galaxy surveys.
Signal-to-noise ratios of 11, 23, and 32 for PLANCK with different galaxy surveys.
Availability of galaxy momentum templates for direct cross-correlation with CMB maps.
Abstract
We present a new method for detecting the missing baryons by generating a template for the kinematic Sunyaev-Zel'dovich effect. The template is computed from the product of a reconstructed velocity field with a galaxy field; we find that the combination of a galaxy redshift survey such as SDSS and a CMB survey such as ACT and PLANCK can detect the kSZ, and thus the ionized gas, at significant signal-to-noise. Unlike other techniques that look for hot gas or metals, this approach directly detects the electrons in the IGM through their signature on the CMB. The estimated signal-to-noise for detecting the galaxy-momentum kSZ cross-correlation is 4, 9, and 12 for ACT (with survey area of 2000 ) with SDSS-DR4, SDSS3 and ADEPT respectively. The estimated signal-to-noise for PLANCK with SDSS-DR4, SDSS3 and ADEPT is 11, 23, and 32. Our method provides a new mean for…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsGalaxies: Formation, Evolution, Phenomena · Astronomy and Astrophysical Research · Computational Physics and Python Applications
