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SUMMARY:WEAVE-Pristine survey: NLTE-Informed Machine Learning for Stellar 
 Parameters and Abundances in Pristine-selected metal-poor stars.
DTSTART;VALUE=DATE-TIME:20261028T164500Z
DTEND;VALUE=DATE-TIME:20261028T165500Z
DTSTAMP;VALUE=DATE-TIME:20261027T104452Z
UID:indico-contribution-3663@nyx.physics.mcgill.ca
DESCRIPTION:Speakers: Minjih Kim (University of Victoria)\nHomogeneously a
 nalysing the chemical signatures of the Milky Way’s most pristine metal-
 poor stars is essential to reconstruct its early assembly and first-star n
 ucleosynthetic yields. Over the next few years\, WEAVE in the North and 4M
 OST in the South will enable high-resolution\, multi-object spectroscopic 
 follow-up of millions of Gaia stars. We present a machine learning pipelin
 e for the WEAVE-Pristine survey to automate analysis of high-resolution (R
  ~ 20\,000) spectra of very ([Fe/H] < -2) to extremely ([Fe/H] < -3) metal
 -poor star candidates in the Pristine Data Release 2. The neural network i
 s trained on +100\,000 synthetic spectra interpolated from the MARCS atmos
 phere grid and varying 15 elements of which 9 of them were computed in Non
 -Local Thermal Equilibrium (NLTE) using the MPIA/1DNLTE spectral synthesis
  code. To reduce the synthetic gap\, we augment the spectra on-the-fly dur
 ing training with WEAVE instrumental effects. Thus\, we have an end-to-end
  pipeline that spans from the generation of raw synthetic spectra to the f
 inal prediction of stellar labels and their uncertainties\, including atmo
 spheric parameters (Teff\, log g\, [Fe/H]\, microturbulence) and individua
 l chemical abundances [X/Fe] for 15 species: alpha-elements (O\, Mg\, Si\,
  Ca\, Ti)\, iron-peak (Cr\, Mn\, Ni)\, light elements (Na\, Al)\, neutron-
 capture (Sr\, Ba\, Eu)\, and C/N. The pipeline is tested on both synthetic
  and observed spectra from early internal release of WEAVE observations\, 
 including stars in the very metal poor globular cluster M15 and the solar 
 metallicity open cluster NGC 6819.\n\nhttps://nyx.physics.mcgill.ca/event/
 587/contributions/3663/
LOCATION:Online 
URL:https://nyx.physics.mcgill.ca/event/587/contributions/3663/
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