J/AJ/165/95ivo://CDS.VizieR/J/AJ/165/95doi:10.26093/cds/vizier.51650095CDSTey E.Moldovan D.Kunimoto M.Huang C.X.Shporer A.Daylan T.,Muthukrishna D.Vanderburg A.Dattilo A.Ricker G.R.Seager S.2024-09-03T20:09:40Z2024-01-12T06:48:35ZCDS support team

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

cds-question@unistra.frexoplanetsvisible-astronomystellar-massesstellar-radiiThe TESS mission produces a large amount of time series data, only a small fraction of which contain detectable exoplanetary transit signals. Deep-learning techniques such as neural networks have proved effective at differentiating promising astrophysical eclipsing candidates from other phenomena such as stellar variability and systematic instrumental effects in an efficient, unbiased, and sustainable manner. This paper presents a high-quality data set containing light curves from the Primary Mission and 1st Extended Mission full-frame images and periodic signals detected via box least-squares. The data set was curated using a thorough manual review process then used to train a neural network called Astronet-Triage-v2. On our test set, for transiting/eclipsing events, we achieve a 99.6% recall (true positives over all data with positive labels) at a precision of 75.7% (true positives over all predicted positives). Since 90% of our training data is from the Primary Mission, we also test our ability to generalize on held-out 1st Extended Mission data. Here, we find an area under the precision-recall curve of 0.965, a 4% improvement over Astronet-Triage. On the TESS object of interest (TOI) Catalog through 2022 April, a shortlist of planets and planet candidates, Astronet-Triage-v2 is able to recover 3577 out of 4140 TOIs, while Astronet-Triage only recovers 3349 targets at an equal level of precision. In other words, upgrading to Astronet-Triage-v2 helps save at least 200 planet candidates from being lost. The new model is currently used for planet candidate triage in the Quick-Look Pipeline.2023AJ....165...95Thttps://cdsarc.cds.unistra.fr/viz-bin/cat/J/AJ/165/95CatalogResearchIsServedByTAP VizieR generic servicerelated-toIV/38 : TESS Input Catalog - v8.0 (TIC-8) (Stassun+, 2019)I/352 : Distances to 1.47 billion stars in Gaia EDR3 (Bailer-Jones+, 2021)J/ApJS/224/12 : Kepler planetary candidates. VII. 48-month (Coughlin+, 2016)J/AJ/156/102 : TESS Input Catalog and Candidate Target List (Stassun+, 2018)J/ApJS/235/38 : Kepler planetary cand. VIII. DR25 reliability (Thompson+, 2018)J/AJ/157/169 : Identifying exoplanets with deep learning K2 (Dattilo+, 2019)J/AJ/158/25 : Automated triage and vetting of TESS candidates (Yu+, 2019)J/A+A/633/A53 : TESS planet candidates classification (Osborn+, 2020)J/ApJS/254/39 : Exoplanet candidates from TESS first 2yr obs (Guerrero+, 2021)https://cds.unistra.fr/vizier-org/licences_vizier.htmlhttps://vizier.cds.unistra.fr/viz-bin/VizieR-2?-source=J/AJ/165/95https://vizier.iucaa.in/viz-bin/VizieR-2?-source=J/AJ/165/95http://vizieridia.saao.ac.za/viz-bin/VizieR-2?-source=J/AJ/165/95https://vizier.cds.unistra.fr/viz-bin/votable?-source=J/AJ/165/95https://vizier.iucaa.in/viz-bin/votable?-source=J/AJ/165/95http://vizieridia.saao.ac.za/viz-bin/votable?-source=J/AJ/165/95GETtext/xml+votablehttps://tapvizier.cds.unistra.fr/TAPVizieR/tapOpticaldefaultJ/AJ/165/95/table2*Threshold-crossing events (TCE) tablerecnoRecord number assigned by the VizieR team. Should Not be used for identification.meta.recordintTICTESS Input Catalog identifiermeta.id;meta.mainEpoch[1198/2366] BLS-detected transit center in BTJDdtime.epochdoubleindexedPer[0.06/167] BLS-detected perioddtime.perioddoubleDurBLS-detected durationdtime.durationcharDepth[0/396326348810] BLS-detected depth(10-6)phys.absorption.opticalDepthMass[0.16/3.69]? Stellar mass via TIC 8.2solMassphys.massdoublenullableRad[0.18/151]? Stellar radius via TIC 8.2solRadphys.size.radiusdoublenullableeRad[0.1/1000]? Estimated stellar radius; Section 3.2solRadphys.size.radiusdoublenullableTmag[0.57/13.6] TESS-band magnitudemagphot.mag;em.optdoubleYear[1/3] TESS Cycle signal was detectedtime.epochintMinT[1325/2362] Minimum BTJD used from the light curvedtime.epochdoubleMaxT[1541/2390] Maximum BTJD used from the light curvedtime.epochdoubleSplitDataset signal was used for; test, train, or valmeta.id;meta.datasetcharCLabelConsensus label (1)meta.notecharL1Label assigned by labeller L1meta.codecharL2Label assigned by labeller L2meta.codecharL3Label assigned by labeller L3meta.codecharL4Label assigned by labeller L4meta.codecharL5Label assigned by labeller L5meta.codecharL6Label assigned by labeller L6meta.codecharL7Label assigned by labeller L7meta.codecharL8Label assigned by labeller L8meta.codechar