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SUMMARY:Spectral classification of white dwarfs by dimensionality reductio
 n - Xander Byrne\, IoA
DTSTART:20241009T121500Z
DTEND:20241009T124000Z
UID:TALK222481@talks.cam.ac.uk
CONTACT:Xander Byrne
DESCRIPTION:As a suite of large-sky spectroscopic surveys comes online\, a
 utomated spectral classification techniques are needed more than ever. For
  white dwarfs -- the evolutionary endpoint of the vast majority of stars -
 - spectral classification is vital for understanding their properties\, ye
 t still almost exclusively done by eye. Upcoming surveys will return of or
 der 10^5 white dwarf spectra\, highlighting the need for automated tools t
 hat are fast\, but do not miss rare or unique objects\, as supervised mach
 ine learning models often do. We present the use of dimensionality reducti
 on\, an unsupervised method\, on white dwarf spectra from the DESI EDR. I 
 will outline the theory behind dimensionality reduction\, as well as resul
 ts showing its effectiveness in classifying white dwarf spectra. I will al
 so discuss two extensions of the technique: the highlighting of spectral r
 egions\, and its use in a pseudo-supervised manner.
LOCATION:The Hoyle Lecture Theatre + Zoom 
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