Observational constraints on the origin of the elements. X. Combining NLTE and machine learning for chemical diagnostics of 4 million stars in the 4MIDABLE-HR survey
Nicholas Storm, Maria Bergemann, Tomasz R’o.za’nski, Victor F. Ksoll, Thomas Bensby, Gregor Traven, Georges Kordopatis, Ross P. Church, Mingjie Jian, Weijia Sun, Guillaume Guiglion, Grav{z}ina Tautvaiv{s}ien.e
arXiv:2512.15888v2 Announce Type: replace
Abstract: We present the 4MOST-HR resolution Non-Local Thermal Equilibrium (NLTE) Payne artificial neural network (ANN), trained on $404,793$ new FGK spectra with 16 elements computed in NLTE. This network will be part of the Stellar Abundances and atmospheric Parameters Pipeline (SAPP), which will analyse 4 million stars during the five year long 4MOST consortium 4: 4MOST MIlky way Disc And BuLgE High-Resolution (4MIDABLE-HR) survey. A fitting algorithm using this ANN is also presented that is able to fully-automatically and self-consistently derive both stellar parameters and elemental abundances. The ANN is validated by fitting 121 observed spectra of low-mass FGKM type stars, including main-sequence dwarf, subgiant and giant stars down to [Fe/H] $approx -3.3$ degraded to 4MOST-HR resolution of $Rapprox20,000$, and comparing the derived abundances with the output of the classical radiative transfer code TSFitPy. We are able to recover all 18 elemental abundances with a bias~$arXiv:2512.15888v2 Announce Type: replace
Abstract: We present the 4MOST-HR resolution Non-Local Thermal Equilibrium (NLTE) Payne artificial neural network (ANN), trained on $404,793$ new FGK spectra with 16 elements computed in NLTE. This network will be part of the Stellar Abundances and atmospheric Parameters Pipeline (SAPP), which will analyse 4 million stars during the five year long 4MOST consortium 4: 4MOST MIlky way Disc And BuLgE High-Resolution (4MIDABLE-HR) survey. A fitting algorithm using this ANN is also presented that is able to fully-automatically and self-consistently derive both stellar parameters and elemental abundances. The ANN is validated by fitting 121 observed spectra of low-mass FGKM type stars, including main-sequence dwarf, subgiant and giant stars down to [Fe/H] $approx -3.3$ degraded to 4MOST-HR resolution of $Rapprox20,000$, and comparing the derived abundances with the output of the classical radiative transfer code TSFitPy. We are able to recover all 18 elemental abundances with a bias~$

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