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SUMMARY:Characterising Polluted White Dwarfs with Machine Learning to Prob
 e Extrasolar Geochemistry - Mariona Badenas-Agusti (MIT)
DTSTART:20230516T120000Z
DTEND:20230516T130000Z
UID:TALK200926@talks.cam.ac.uk
CONTACT:Dr Emily Sandford
DESCRIPTION:A large fraction of white dwarfs (between 25-50%) exhibit trac
 es of heavy elements in their atmospheres\, likely from the recent or ongo
 ing accretion of rocky or icy extrasolar material. Through spectroscopic o
 bservations of these “polluted” white dwarfs\, it is possible to infer
  the bulk composition of their accreted material and learn about extrasola
 r compositions more broadly. To date\, there are more than 1000 known poll
 uted white dwarfs\, yet less than a few dozen have been characterised in d
 etail with high-resolution spectroscopy. The scarcity of polluted white dw
 arfs with well-known photospheric abundances is partly due to the nature o
 f conventional spectral modelling techniques\, which typically involve man
 ual\, time-intensive\, and iterative work\, as well as proprietary atmosph
 eric models that are not easily available to the astrophysical community. 
 In our group\, we aim to overcome these limitations by developing a fast a
 nd reliable Machine Learning (ML) pipeline to accurately determine the mai
 n physical and chemical properties of polluted white dwarfs from their spe
 ctra. In addition to designing new ML techniques for the study of photosph
 eric pollution\, we also seek to expand the population of polluted white d
 warfs and increase the number of such objects with precise spectra. To thi
 s end\, we are leveraging massive databases such as Gaia EDR3 and LAMOST a
 nd are acquiring high-resolution data with multiple astronomical facilitie
 s\, including Magellan/MIKE and Keck/ESI. In this talk\, I will give an ov
 erview of how polluted white dwarfs can be used to learn about extrasolar 
 compositions\, present our LAMOST/Gaia catalogue of polluted white dwarfs\
 , discuss our ML pipeline\, and justify how ML tools can open the door to 
 a statistical understanding of extrasolar geochemistry.
LOCATION:Ryle seminar room + ONLINE - Details to be sent by email
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