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Determining star formation thresholds from observations

Khullar, Shivan; Krumholz, Mark; Federrath, Christoph; Cunningham, Andrew J.

Description

Most gas in giant molecular clouds is relatively low density and forms star inefficiently, converting only a small fraction of its mass to stars per dynamical time. However, star formation models generally predict the existence of a threshold density above which the process is efficient and most mass collapses to stars on a dynamical time-scale. A number of authors have proposed observational techniques to search for a threshold density above which star formation is efficient, but it is unclear...[Show more]

dc.contributor.authorKhullar, Shivan
dc.contributor.authorKrumholz, Mark
dc.contributor.authorFederrath, Christoph
dc.contributor.authorCunningham, Andrew J.
dc.date.accessioned2020-07-15T23:12:00Z
dc.date.available2020-07-15T23:12:00Z
dc.identifier.issn0035-8711
dc.identifier.urihttp://hdl.handle.net/1885/206226
dc.description.abstractMost gas in giant molecular clouds is relatively low density and forms star inefficiently, converting only a small fraction of its mass to stars per dynamical time. However, star formation models generally predict the existence of a threshold density above which the process is efficient and most mass collapses to stars on a dynamical time-scale. A number of authors have proposed observational techniques to search for a threshold density above which star formation is efficient, but it is unclear which of these techniques, if any, are reliable. In this paper, we use detailed simulations of turbulent, magnetized star-forming clouds, including stellar radiation and outflow feedback, to investigate whether it is possible to recover star formation thresholds using current observational techniques. Using mock observations of the simulations at realistic resolutions, we show that plots of projected star formation efficiency per free-fall time ϵff can detect the presence of a threshold, but that the resolutions typical of current dust emission or absorption surveys are insufficient to determine its value. In contrast, proposed alternative diagnostics based on a change in the slope of the gas surface density versus star formation rate surface density (Kennicutt-Schmidt relation) or on the correlation between young stellar object counts and gas mass as a function of density are ineffective at detecting thresholds even when they are present. The signatures in these diagnostics sometimes taken as indicative of a threshold in observations, which we generally reproduce in our mock observations, do not prove to correspond to real physical features in the 3D gas distribution.
dc.description.sponsorshipMRK and CF both acknowledge support from the Australian Research Council’s (ARC) Discovery Projects and Future Fellowship funding schemes, awards DP160100695 (MRK), DP170100603 (CF), FT180100375 (MRK), and FT180100495 (CF), and from the Australia-Germany Joint Research Cooperation Scheme (UA-DAAD). AJC’s work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DEAC52-07NA27344. The simulations used in this work were made possible by grants of high-performance computing resources from the following: the National Center of Supercomputing Application through grant TGMCA00N020, under the Extreme Science and Engineering Discovery Environment, which is supported by National Science Foundation grant number OCI-1053575; the NASA High-End Computing Program through the NASA Advanced Supercomputing (NAS) Division at Ames Research Center; the Leibniz Rechenzentrum and the Gauss Centre for Supercomputing (grants pr32lo, pr48pi, and GCS Large-scale project 10391); the Partnership for Advanced Computing in Europe (PRACE grant pr89mu); the National Computational Infrastructure, which is supported by the Australian Government (grants ek9 and jh2); and the Pawsey Supercomputing Centre with funding from the Australian Government and the Government of Western Australia.
dc.format.mimetypeapplication/pdf
dc.language.isoen_AU
dc.publisherBlackwell Publishing Ltd
dc.rights© 2019 The Author(s)
dc.sourceMonthly Notices of the Royal Astronomical Society
dc.subjectstars: formation
dc.subjectISM: clouds
dc.subjectdust
dc.subjectextinction
dc.subjectinfrared: ISM
dc.subjectsubmillimetre: ISM
dc.titleDetermining star formation thresholds from observations
dc.typeJournal article
local.description.notesImported from ARIES
local.identifier.citationvolume488
dcterms.dateAccepted2019-06-27
dc.date.issued2019-07-03
local.identifier.absfor020103 - Cosmology and Extragalactic Astronomy
local.identifier.absfor020104 - Galactic Astronomy
local.identifier.ariespublicationu5786633xPUB1754
local.publisher.urlhttps://academic.oup.com/
local.type.statusPublished Version
local.contributor.affiliationKhullar, Shivan, College of Science, ANU
local.contributor.affiliationKrumholz, Mark, College of Science, ANU
local.contributor.affiliationFederrath, Christoph, College of Science, ANU
local.contributor.affiliationCunningham, Andrew J., Lawrence Livermore National Laboratory
dc.relationhttp://purl.org/au-research/grants/arc/DP160100695
dc.relationhttp://purl.org/au-research/grants/arc/DP170100603
dc.relationhttp://purl.org/au-research/grants/arc/FT180100375
dc.relationhttp://purl.org/au-research/grants/arc/FT180100495
local.bibliographicCitation.issue1
local.bibliographicCitation.startpage1407
local.bibliographicCitation.lastpage1415
local.identifier.doi10.1093/mnras/stz1800
local.identifier.absseo970102 - Expanding Knowledge in the Physical Sciences
dc.date.updated2020-03-29T07:17:01Z
local.identifier.thomsonIDWOS:000482319700103
dcterms.accessRightsOpen Access
dc.provenancehttp://v2.sherpa.ac.uk/id/publication/24618..."Publisher's version can be made open access on institutional repository" from SHERPA/RoMEO site (as at 16/7/20).
CollectionsANU Research Publications

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