Suppressed Cosmic-Ray Energy Densities in Molecular Clouds from Streaming Instability-regulated Transport

Margot Fitz Axen, Stella Offner, Philip F. Hopkins, Mark R. Krumholz, Michael Y. Grudić

Research output: Contribution to journalArticlepeer-review

Abstract

Cosmic rays (CRs) are the primary driver of ionization in star-forming molecular clouds (MCs). Despite their potential impacts on gas dynamics and chemistry, no simulations of star cluster formation following the creation of individual stars have included explicit cosmic-ray transport (CRT) to date. We conduct the first numerical simulations following the collapse of a 2000M MC and the subsequent star formation including CRT using the STAR FORmation in Gaseous Environments framework implemented in the GIZMO code. We show that when CRT is streaming-dominated, the CR energy in the cloud is strongly attenuated due to energy losses from the streaming instability. Consequently, in a Milky Way-like environment the median CR ionization rate in the cloud is low (ζ ≲ 2 × 10−19 s−1) during the main star-forming epoch of the calculation and the impact of CRs on the star formation in the cloud is limited. However, in high-CR environments, the CR distribution in the cloud is elevated (ζ ≲ 6 × 10−18), and the relatively higher CR pressure outside the cloud causes slightly earlier cloud collapse and increases the star formation efficiency by 50% to ∼13%. The initial mass function is similar in all cases except with possible variations in a high-CR environment. Further studies are needed to explain the range of ionization rates observed in MCs and explore star formation in extreme CR environments.

Original languageEnglish
Article number16
JournalAstrophysical Journal
Volume973
Issue number1
DOIs
StatePublished - Sep 1 2024
Externally publishedYes

Funding

This research is part of the Frontera computing project at the Texas Advanced Computing Center and used computing award AST21002. Frontera is made possible by the National Science Foundation award OAC-1818253. This research was supported in part by NASA ATP grant 80NSSC20K0507. This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, Department of Energy Computational Science Graduate Fellowship under Award No. DE-SC0021110. Support for P.F.H. was provided by NSF Research Grants 1911233, 20009234, 2108318, NSF CAREER grant 1455342, NASA grants 80NSSC18K0562, HST-AR-15800. M.R.K. acknowledges support from the Australian Research Council with the Discovery Projects and Laureate Fellowship schemes, awards DP 230101055 and FL220100020. Support for M.Y.G. was provided by NASA through the NASA Hubble Fellowship grant #HST-HF2-51479 awarded by the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., for NASA, under contract NAS5-26555.

FundersFunder number
Advanced Scientific Computing Research
U.S. Department of Energy
Office of Science
National Science Foundation2108318, HST-AR-15800, OAC-1818253, 1911233, 80NSSC18K0562, 20009234, 1455342
National Science Foundation
Department of Energy Computational ScienceDE-SC0021110
National Aeronautics and Space Administration80NSSC20K0507
National Aeronautics and Space Administration
Australian Research CouncilFL220100020, #HST-HF2-51479, DP 230101055
Australian Research Council
Space Telescope Science InstituteNAS5-26555
Space Telescope Science Institute

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