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Clustering the Orion B giant molecular cloud based on its molecular emission

hal.structure.identifierInstituto de Ciencia de Materiales de Madrid [ICMM]
hal.structure.identifierLERMA Cergy [LERMA]
dc.contributor.authorBRON, Emeric
hal.structure.identifierLERMA Cergy [LERMA]
dc.contributor.authorDAUDON, Chloé
hal.structure.identifierInstitut de RadioAstronomie Millimétrique [IRAM]
hal.structure.identifierLERMA Cergy [LERMA]
dc.contributor.authorPETY, Jérôme
hal.structure.identifierLERMA Cergy [LERMA]
dc.contributor.authorLEVRIER, François
hal.structure.identifierLERMA Cergy [LERMA]
dc.contributor.authorGERIN, Maryvonne
hal.structure.identifierAMOR 2018
dc.contributor.authorGRATIER, P.
hal.structure.identifierInstitut de RadioAstronomie Millimétrique [IRAM]
hal.structure.identifierLERMA Cergy [LERMA]
hal.structure.identifierUniversité Grenoble Alpes [2016-2019] [UGA [2016-2019]]
dc.contributor.authorORKISZ, Jan
hal.structure.identifierJoint ALMA Observatory [JAO]
dc.contributor.authorGUZMAN, Viviana
hal.structure.identifierInstitut de RadioAstronomie Millimétrique [IRAM]
dc.contributor.authorBARDEAU, Sebastien
hal.structure.identifierInstituto de Ciencia de Materiales de Madrid [ICMM]
dc.contributor.authorGOICOECHEA, Javier R.
hal.structure.identifierNational Radio Astronomy Observatory [NRAO]
dc.contributor.authorLISZT, Harvey
hal.structure.identifierHarvard-Smithsonian Center for Astrophysics [CfA]
dc.contributor.authorÖBERG, Karin
hal.structure.identifierSchool of Physics and Astronomy [Cardiff]
dc.contributor.authorPERETTO, Nicolas
hal.structure.identifierInstitut de RadioAstronomie Millimétrique [IRAM]
dc.contributor.authorSIEVERS, Albrecht
hal.structure.identifierMaison de la Simulation [MDLS]
dc.contributor.authorTREMBLIN, Pascal
dc.date.issued2018
dc.identifier.issn0004-6361
dc.description.abstractEnContext. Previous attempts at segmenting molecular line maps of molecular clouds have focused on using position-position-velocity data cubes of a single molecular line to separate the spatial components of the cloud. In contrast, wide field spectral imaging over a large spectral bandwidth in the (sub)mm domain now allows one to combine multiple molecular tracers to understand the different physical and chemical phases that constitute giant molecular clouds (GMCs). Aims. We aim at using multiple tracers (sensitive to different physical processes and conditions) to segment a molecular cloud into physically/chemically similar regions (rather than spatially connected components), thus disentangling the different physical/chemical phases present in the cloud.Methods. We use a machine learning clustering method, namely the Meanshift algorithm, to cluster pixels with similar molecular emission, ignoring spatial information. Clusters are defined around each maximum of the multidimensional probability density function (PDF) of the line integrated intensities. Simple radiative transfer models were used to interpret the astrophysical informationuncovered by the clustering analysis.Results. A clustering analysis based only on the $J$ = 1–0 lines of three isotopologues of CO proves sufficient to reveal distinct density/column density regimes ($n_H$ ∼ 100 cm$^{−3}$, ∼500 cm$^{−3}$, and >1000 cm$^{−3}$), closely related to the usual definitions of diffuse, translucent and high-column-density regions. Adding two UV-sensitive tracers, the $J$ = 1–0 line of HCO$^+$ and the $N$ = 1–0 line of CN, allows us to distinguish two clearly distinct chemical regimes, characteristic of UV-illuminated and UV-shielded gas. The UV-illuminated regime shows overbright HCO$^+$ and CN emission, which we relate to a photochemical enrichment effect. We alsofind a tail of high CN/HCO$^+$ intensity ratio in UV-illuminated regions. Finer distinctions in density classes ($n_H$ ∼ 7 × 10$^3$ cm$^{−3}$ , ∼4 × 10$^4$ cm$^{−3}$) for the densest regions are also identified, likely related to the higher critical density of the CN and HCO$^+$ (1–0) lines. These distinctions are only possible because the high-density regions are spatially resolved.Conclusions. Molecules are versatile tracers of GMCs because their line intensities bear the signature of the physics and chemistry at play in the gas. The association of simultaneous multi-line, wide-field mapping and powerful machine learning methods such as the Meanshift clustering algorithm reveals how to decode the complex information available in these molecular tracers.
dc.language.isoen
dc.publisherEDP Sciences
dc.subject.enastrochemistry
dc.subject.enISM: molecules
dc.subject.enISM: clouds
dc.subject.enISM: structure
dc.subject.enmethods: statistical
dc.subject.enISM: individual objects: Orion B
dc.title.enClustering the Orion B giant molecular cloud based on its molecular emission
dc.typeArticle de revue
dc.identifier.doi10.1051/0004-6361/201731833
dc.subject.halPlanète et Univers [physics]/Astrophysique [astro-ph]/Astrophysique galactique [astro-ph.GA]
dc.identifier.arxiv1710.07288
dc.description.sponsorshipEuropeGas and Dust from the Stars to the Laboratory: Exploring the NanoCosmos
bordeaux.journalAstronomy and Astrophysics - A&A
bordeaux.pageA12
bordeaux.volume610
bordeaux.peerReviewedoui
hal.identifierhal-01621390
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-01621390v1
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