J/MNRAS/511/5578ivo://CDS.VizieR/J/MNRAS/511/5578doi:10.26093/cds/vizier.75115578CDSReyes C.Stello D.Hon M.Zinn J.C.2024-08-22T20:15:19Z2022-03-25T11:46:49ZCDS support team

CDS, Observatoire de Strasbourg, 11 rue de l'Universite, F-67000 Strasbourg, France

cds-question@unistra.frasteroseismologyPrecise asteroseismic parameters allow one to quickly estimate radius and mass distributions for large samples of stars. A number of automated methods are available to calculate the frequency of maximum acoustic power ({nu}_max_) and the frequency separation between overtone modes ({Delta}{nu}) from the power spectra of red giants. However, filtering through the results requires either manual vetting, elaborate averaging across multiple methods, or sharp cuts in certain parameters to ensure robust samples of stars free of outliers. Given the importance of ensemble studies for Galactic archaeology and the surge in data availability, faster methods for obtaining reliable asteroseismic parameters are desirable. We present a neural network classifier that vets {Delta}{nu} by combining multiple features from the visual {Delta}{nu} vetting process. Our classifier is able to analyse large numbers of stars determining whether their measured {Delta}{nu} are reliable thus delivering clean samples of oscillating stars with minimal effort. Our classifier is independent of the method used to obtain {nu}_max_ and {Delta}{nu}, and therefore can be applied as a final step to any such method. Tests of our classifier's performance on manually vetted {Delta}{nu} measurements reach an accuracy of 95%. We apply the method to giants observed by K2 Galactic Archaeology Program and find that our results retain stars with astrophysical oscillation parameters consistent with the parameter distributions already defined by well-characterised Kepler red giants.2022MNRAS.511.5578Rhttps://cdsarc.cds.unistra.fr/viz-bin/cat/J/MNRAS/511/5578CatalogResearchIsServedByTAP VizieR generic servicerelated-toIV/34 : K2 Ecliptic Plane Input Catalog (EPIC) (Huber+, 2017)https://cds.unistra.fr/vizier-org/licences_vizier.htmlhttps://vizier.cds.unistra.fr/viz-bin/VizieR-2?-source=J/MNRAS/511/5578https://vizier.iucaa.in/viz-bin/VizieR-2?-source=J/MNRAS/511/5578http://vizieridia.saao.ac.za/viz-bin/VizieR-2?-source=J/MNRAS/511/5578https://vizier.cds.unistra.fr/viz-bin/votable?-source=J/MNRAS/511/5578https://vizier.iucaa.in/viz-bin/votable?-source=J/MNRAS/511/5578http://vizieridia.saao.ac.za/viz-bin/votable?-source=J/MNRAS/511/5578GETtext/xml+votablehttps://tapvizier.cds.unistra.fr/TAPVizieR/tapdefaultJ/MNRAS/511/5578/tableg1Neural network outputs from SYD resultsrecnoRecord number assigned by the VizieR team. Should Not be used for identification.meta.recordintEPICK2 object identifier (K2 ECLIPTIC PLANE INPUT CATALOG)meta.id;meta.mainlongcamp[1/18] K2 campaign numbermeta.id;obsintnumax[3.36/271.26] Numax delivered by the SYD PipelineuHzem.freqfloate_numaxNumax uncertainty as given by the SYD pipelineuHzstat.errorfloatdnu[0.59/21.8] Deltanu delivered by the SYD PipelineuHzem.freq;src.varfloate_dnuDeltanu uncertainty as given by the SYD pipelineuHzstat.errorfloatdnuProb[0.5/1.0] Probability assigned by the neural network classifier from this work to the raw deltanu valuestat.fit.goodnessfloatRCRGB[0/1] Evolutionary phase (1)src.classintJ/MNRAS/511/5578/tableg2Neural network vetted results from five pipelinesrecnoRecord number assigned by the VizieR team. Should Not be used for identification.meta.recordintPipelineName of the pipeline which delivered the values of numax, e_numax, dnu and e_dnumeta.id;stat.fitcharEPICK2 object identifier (K2 ECLIPTIC PLANE INPUT CATALOG)meta.id;meta.mainlongcamp[1/18] K2 campaign numbermeta.id;obsintnumax[2.81/274.5] Numax delivered by the corresponding pipelineuHzem.freqfloate_numaxNumax uncertainty as given by the corresponding pipelineuHzstat.errorfloatdnu[0.59/23.71] Deltanu delivered by the corresponding pipelineuHzem.freq;src.varfloate_dnu? Deltanu uncertainty as given by the corresponding pipelineuHzstat.errorfloatnullableEVensemble? Evolutionary phase for ensemble-corrected values of numax and deltanu (1)src.classcharEV? Evolutionary phase for raw values of numax and deltanu given by the corresponding pipeline (1)src.classchardnuProb[0.5/1.0] Probability assigned by the neural network classifier from this work to the deltanu valuestat.fit.goodnessfloat