Show simple item record

dc.contributor.authorAudenaert, Jeroen
dc.date.accessioned2025-10-16T21:17:35Z
dc.date.available2025-10-16T21:17:35Z
dc.date.issued2025-07-24
dc.identifier.urihttps://hdl.handle.net/1721.1/163194
dc.description.abstractLarge-scale photometric surveys are revolutionizing astronomy by delivering unprecedented amounts of data. The rich data sets from missions such as the NASA Kepler and TESS satellites, and the upcoming ESA PLATO mission, are a treasure trove for stellar variability, asteroseismology and exoplanet studies. In order to unlock the full scientific potential of these massive data sets, automated data-driven methods are needed. In this review, I illustrate how machine learning is bringing asteroseismology toward an era of automated scientific discovery, covering the full cycle from data cleaning to variability classification and parameter inference, while highlighting the recent advances in representation learning, multimodal datasets and foundation models. This invited review offers a guide to the challenges and opportunities machine learning brings for stellar variability research and how it could help unlock new frontiers in time-domain astronomy.en_US
dc.publisherSpringer Netherlandsen_US
dc.relation.isversionofhttps://doi.org/10.1007/s10509-025-04460-5en_US
dc.rightsCreative Commons Attributionen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.sourceSpringer Netherlandsen_US
dc.titleFrom stellar light to astrophysical insight: automating variable star research with machine learningen_US
dc.typeArticleen_US
dc.identifier.citationAudenaert, J. From stellar light to astrophysical insight: automating variable star research with machine learning. Astrophys Space Sci 370, 72 (2025).en_US
dc.contributor.departmentMIT Kavli Institute for Astrophysics and Space Researchen_US
dc.relation.journalAstrophysics and Space Scienceen_US
dc.identifier.mitlicensePUBLISHER_CC
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2025-10-08T14:34:46Z
dc.language.rfc3066en
dc.rights.holderThe Author(s)
dspace.embargo.termsN
dspace.date.submission2025-10-08T14:34:46Z
mit.journal.volume370en_US
mit.licensePUBLISHER_CC
mit.metadata.statusAuthority Work and Publication Information Neededen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record