dc.contributor.author Curti, Mirko dc.contributor.author Hayden-Pawson, Connor dc.contributor.author Maiolino, Roberto dc.contributor.author Belfiore, Francesco dc.contributor.author Mannucci, Filippo dc.contributor.author Concas, Alice dc.contributor.author Cresci, Giovanni dc.contributor.author Marconi, Alessandro dc.contributor.author Cirasuolo, Michele dc.date.accessioned 2022-03-11T00:30:39Z dc.date.available 2022-03-11T00:30:39Z dc.date.issued 2022-04-06 dc.identifier.issn 0035-8711 dc.identifier.uri https://www.repository.cam.ac.uk/handle/1810/334868 dc.description.abstract We 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.publisher Oxford University Press (OUP) dc.rights All Rights Reserved dc.rights.uri http://www.rioxx.net/licenses/all-rights-reserved dc.subject astro-ph.GA dc.subject astro-ph.GA dc.title What drives the scatter of local star-forming galaxies in the BPT diagrams? A Machine Learning based analysis dc.type Article dc.publisher.department Department of Physics dc.publisher.department Department of Physics Student dc.date.updated 2022-03-09T22:23:21Z prism.publicationName MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY dc.identifier.doi 10.17863/CAM.82305 rioxxterms.versionofrecord 10.1093/mnras/stac544 rioxxterms.version AM dc.contributor.orcid Curti, Mirko [0000-0002-2678-2560] dc.contributor.orcid Hayden-Pawson, Connor [0000-0001-7964-1027] dc.contributor.orcid Maiolino, Roberto [0000-0002-4985-3819] dc.identifier.eissn 1365-2966 dc.publisher.url http://dx.doi.org/10.1093/mnras/stac544 rioxxterms.type Journal Article/Review pubs.funder-project-id Science and Technology Facilities Council (ST/M001172/1) pubs.funder-project-id European Research Council (695671) pubs.funder-project-id STFC (2120607) pubs.funder-project-id Royal Society (RSRP\R1\211056) cam.issuedOnline 2022-03-02 cam.orpheus.success Tue Apr 12 08:22:44 BST 2022 - Embargo updated cam.orpheus.counter 2 cam.depositDate 2022-03-09 pubs.licence-identifier apollo-deposit-licence-2-1 pubs.licence-display-name Apollo Repository Deposit Licence Agreement rioxxterms.freetoread.startdate 2022-03-02
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