Show simple item record

dc.contributor.authorCurti, Mirko
dc.contributor.authorHayden-Pawson, Connor
dc.contributor.authorMaiolino, Roberto
dc.contributor.authorBelfiore, Francesco
dc.contributor.authorMannucci, Filippo
dc.contributor.authorConcas, Alice
dc.contributor.authorCresci, Giovanni
dc.contributor.authorMarconi, Alessandro
dc.contributor.authorCirasuolo, Michele
dc.date.accessioned2022-03-11T00:30:39Z
dc.date.available2022-03-11T00:30:39Z
dc.date.issued2022-04-06
dc.identifier.issn0035-8711
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/334868
dc.description.abstractWe investigate which physical properties are most predictive of the position of local star forming galaxies on the BPT diagrams, by means of different Machine Learning (ML) algorithms. Exploiting the large statistics from the Sloan Digital Sky Survey (SDSS), we define a framework in which the deviation of star-forming galaxies from their median sequence can be described in terms of the relative variations in a variety of observational parameters. We train artificial neural networks (ANN) and random forest (RF) trees to predict whether galaxies are offset above or below the sequence (via classification), and to estimate the exact magnitude of the offset itself (via regression). We find, with high significance, that parameters primarily associated to variations in the nitrogen-over-oxygen abundance ratio (N/O) are the most predictive for the [N II]-BPT diagram, whereas properties related to star formation (like variations in SFR or EW[H$\alpha$]) perform better in the [S II]-BPT diagram. We interpret the former as a reflection of the N/O-O/H relationship for local galaxies, while the latter as primarily tracing the variation in the effective size of the S$^{+}$ emitting region, which directly impacts the [S II]emission lines. This analysis paves the way to assess to what extent the physics shaping local BPT diagrams is also responsible for the offsets seen in high redshift galaxies or, instead, whether a different framework or even different mechanisms need to be invoked.
dc.publisherOxford University Press (OUP)
dc.rightsAll Rights Reserved
dc.rights.urihttp://www.rioxx.net/licenses/all-rights-reserved
dc.subjectastro-ph.GA
dc.subjectastro-ph.GA
dc.titleWhat drives the scatter of local star-forming galaxies in the BPT diagrams? A Machine Learning based analysis
dc.typeArticle
dc.publisher.departmentDepartment of Physics
dc.publisher.departmentDepartment of Physics Student
dc.date.updated2022-03-09T22:23:21Z
prism.publicationNameMONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
dc.identifier.doi10.17863/CAM.82305
rioxxterms.versionofrecord10.1093/mnras/stac544
rioxxterms.versionAM
dc.contributor.orcidCurti, Mirko [0000-0002-2678-2560]
dc.contributor.orcidHayden-Pawson, Connor [0000-0001-7964-1027]
dc.contributor.orcidMaiolino, Roberto [0000-0002-4985-3819]
dc.identifier.eissn1365-2966
dc.publisher.urlhttp://dx.doi.org/10.1093/mnras/stac544
rioxxterms.typeJournal Article/Review
pubs.funder-project-idScience and Technology Facilities Council (ST/M001172/1)
pubs.funder-project-idEuropean Research Council (695671)
pubs.funder-project-idSTFC (2120607)
pubs.funder-project-idRoyal Society (RSRP\R1\211056)
cam.issuedOnline2022-03-02
cam.orpheus.successTue Apr 12 08:22:44 BST 2022 - Embargo updated
cam.orpheus.counter2
cam.depositDate2022-03-09
pubs.licence-identifierapollo-deposit-licence-2-1
pubs.licence-display-nameApollo Repository Deposit Licence Agreement
rioxxterms.freetoread.startdate2022-03-02


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record