Mapping Distances Across the Perseus Molecular Cloud Using CO Observations, Stellar Photometry, and Gaia DR2 Parallax Measurements
Catherine Zucker, Edward F. Schlafly, Joshua S. Speagle, Gregory M., Green, Stephen K. N. Portillo, Douglas P. Finkbeiner, and Alyssa A. Goodman

TL;DR
This paper introduces a novel method combining stellar photometry, Gaia parallaxes, and CO spectral data to accurately map distances to star-forming regions in the Perseus Molecular Cloud, revealing a significant distance gradient across the cloud.
Contribution
The paper develops a new technique that integrates CO spectral slices with stellar data to determine cloud distances, improving spatial resolution and accuracy over previous methods.
Findings
Distances to Perseus regions are approximately 275-300 pc.
A 25 pc distance gradient is observed across the cloud.
The average distance to Perseus is 294±17 pc, higher than previous estimates.
Abstract
We present a new technique to determine distances to major star-forming regions across the Perseus Molecular Cloud, using a combination of stellar photometry, astrometric data, and spectral-line maps. Incorporating the Gaia DR2 parallax measurements when available, we start by inferring the distance and reddening to stars from their Pan-STARRS1 and 2MASS photometry, based on a technique presented in Green et al. 2014; Green et al. 2015 and implemented in their 3D "Bayestar" dust map of three-quarters of the sky. We then refine the Green et al. technique by using the velocity slices of a CO spectral cube as dust templates and modeling the cumulative distribution of dust along the line of sight towards these stars as a linear combination of the emission in the slices. Using a nested sampling algorithm, we fit these per-star distance-reddening measurements to find the…
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.
