The census of dense cores in the Serpens region from the Herschel Gould Belt Survey
E. Fiorellino, D. Elia, Ph. Andr\'e, A. Men'shchikov, S. Pezzuto, E., Schisano, V. K\"onyves, D. Arzoumanian, M. Benedettini, D. Ward-Thompson, A., Bracco, J. Di Francesco, S. Bontemps, J. Kirk, F. Motte, S. Molinari

TL;DR
This study provides a comprehensive census of dense cores in the Serpens star-forming region using Herschel data, revealing core properties, their distribution, and relation to filamentary structures, contributing to understanding star formation processes.
Contribution
It presents the first complete census of dense cores in Serpens, including core classification, mass function analysis, and core-filament spatial correlation, using multi-wavelength Herschel observations.
Findings
833 sources identified, including 709 starless and 124 proto-stellar cores.
Prestellar core mass function follows a log-normal distribution up to 2 Msun.
81% of prestellar cores are located on filamentary structures.
Abstract
The Herschel Gould Belt survey mapped the nearby (d < 500 pc) star-forming regions to understand better how the prestellar phase influences the star formation process. Here we report a complete census of dense cores in a 15 deg2 area of the Serpens star-forming region located between d=420 pc and 484 pc. The PACS and SPIRE cameras imaged this cloud from 70micron to 500micron. With the multi-wavelength source extraction algorithm getsources, we extract 833 sources, of which 709 are starless cores and 124 are candidate proto-stellar cores. We obtain temperatures and masses for all the sample, classifying the starless cores in 604 prestellar cores and 105 unbound cores. Our census of sources is 80% complete for masses larger than 0.8 Msun overall. We produce the core mass function (CMF) and compare it with the initial mass function (IMF). The prestellar CMF is consistent with log-normal…
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.
