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DTSTART:20221107T000000
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SUMMARY:Developing an efficient large-scale machine learning pipeline to c
 lassify the millions of NASA TESS light curves in search for variable star
 s - Jeroen Audenaert
DTSTART;TZID=US/Arizona:20231107T133000
DTEND;TZID=US/Arizona:20231107T134500
DTSTAMP:20260809T011214Z
UID:pretalx-adass2023-PPXGH8@pretalx.com
DESCRIPTION:The NASA Transiting Exoplanet Survey Satellite (TESS) is obser
 ving millions of stars each month. The vast amounts of light curves that a
 re being generated from these photometric observations contain a wealth of
  information for asteroseismology\, binarity and rotation studies. However
 \, before these light curves can be used for stellar structure and evoluti
 on studies\, we first need to be able to identify the relevant stars in th
 is massive data set. The TESS Data for Asteroseismology (T’DA) working g
 roup therefore created an automated open-source machine learning pipeline 
 to classify the millions of light curves delivered by TESS according to th
 eir stellar variability types. The pipeline is highly-parallelized and has
  been optimized for large-scale computing infrastructures. Furthermore\, i
 t has been developed in a modular way such that new state-of-the-art class
 ifiers in search for other variability types can easily be added. In this 
 contribution\, we will present the pipeline and the structure of the machi
 ne learning classifiers\, and explore how the pipeline can be used for oth
 er space missions and large ground-based observatories.
LOCATION:Talks
URL:https://pretalx.com/adass2023/talk/PPXGH8/
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