Streaming classification of variable stars

Simple item page
dc.contributor.authorZorich, L
dc.contributor.authorPichara Baksai, Karim Elías
dc.contributor.authorProtopapas, P.
dc.date.accessioned2020-05-19T20:43:08Z
dc.date.available2020-05-19T20:43:08Z
dc.date.issued2019
dc.description.abstractIn the last years, automatic classification of variable stars has received substantial attention. Using machine learning techniques for this task has proven to be quite useful. Typically, machine learning classifiers used for this task require to have a fixed training set, and the training process is performed offline. Upcoming surveys such as the Large Synoptic Survey Telescope will generate new observations daily, where an automatic classification system able to create alerts online will be mandatory. A system with those characteristics must be able to update itself incrementally. Unfortunately, after training, most machine learning classifiers do not support the inclusion of new observations in light curves, they need to re-train from scratch. Naively re-training from scratch is not an option in streaming settings, mainly because of the expensive pre-processing routines required to obtain a vector representation of light curves (features) each time we include new observations. In this work, we propose a streaming probabilistic classification model; it uses a set of newly designed features that work incrementally. With this model, we can have a machine learning classifier that updates itself in real time with new observations. To test our approach, we simulate a streaming scenario with light curves from Convention, Rotation and planetary Transits (CoRoT), Orbital Gravitational Lensing Experiment (OGLE), and Massive Compact Halo Object (MACHO) catalogues. Results show that our model achieves high classification performance, staying an order of magnitude faster than traditional classification approaches.
dc.format.extent2909 páginas
dc.fuente.origenOUP
dc.identifier.doi10.1093/mnras/stz3426
dc.identifier.eissn1365-2966
dc.identifier.issn0035-8711
dc.identifier.urihttps://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9114084
dc.identifier.urihttps://doi.org/10.1093/mnras/stz3426
dc.information.autorucEscuela de ingeniería ; Zorich, L ; S/I ; 204158
dc.information.autorucEscuela de ingeniería ; Pichara Baksai, Karim Elías ; S/I ; 6541
dc.issue.numero1
dc.language.isoen
dc.nota.accesoContenido parcial
dc.pagina.final2909
dc.pagina.inicio2897
dc.publisherOUP
dc.revistaMonthly Notices of the Royal Astronomical Societyes_ES
dc.revistaMonthly Notices of the Royal Astronomical Society
dc.rightsacceso restringido
dc.subject.ddc520
dc.subject.deweyAstronomíaes_ES
dc.subject.otherPlanetas - Orbitases_ES
dc.subject.otherEstrellas variableses_ES
dc.subject.otherAstronomía - Observacioneses_ES
dc.titleStreaming classification of variable starses_ES
dc.typeartículo
dc.volumen492
sipa.codpersvinculados6541
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Streaming classification of variable stars.pdf
Size:
285.71 KB
Format:
Adobe Portable Document Format
Description:
Download
Collections
Artículos de revistas