Skip to main content

Lund University Publications

LUND UNIVERSITY LIBRARIES

Observational Constraints on the Origin of the Elements. X. Combining Non–Local Thermodynamic Equilibrium and Machine Learning for Chemical Diagnostics of 4 Million Stars in the 4MIDABLE-HR Survey

Storm, Nicholas ; Bergemann, Maria ; Różański, Tomasz ; F. Ksoll, Victor ; Bensby, Thomas LU orcid ; Traven, Gregor LU ; Kordopatis, Georges ; Church, Ross P. LU orcid ; Jian, Mingjie and Sun, Weijia , et al. (2026) In Astrophysical Journal 1003(1).
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 analyze 4 million stars during the 5 yr 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] ≈... (More)

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 analyze 4 million stars during the 5 yr 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] ≈ −3.3 degraded to a 4MOST-HR resolution of R ≈ 20,000 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 of <0.13 and spread of <0.16 dex, although the typical values are <0.09 dex for most elements. These abundances are compared to the OMEGA+ Galactic chemical evolution model, showcasing for the first time the expected performance and results obtained from high-resolution spectra of the quality expected to be obtained with 4MOST. The expected Galactic trends are recovered, and we highlight the potential of using many chemical elements to constrain the formation history of the Galaxy.

(Less)
Please use this url to cite or link to this publication:
author
; ; ; ; ; ; ; ; and , et al. (More)
; ; ; ; ; ; ; ; ; ; and (Less)
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Galaxy chemical evolution (580), Neural networks (1933), Stellar abundances (1577)
in
Astrophysical Journal
volume
1003
issue
1
article number
27
publisher
American Astronomical Society
external identifiers
  • scopus:105038701187
ISSN
0004-637X
DOI
10.3847/1538-4357/ae6108
language
English
LU publication?
yes
id
907d84c7-5d71-4e00-b37f-743f79ba4859
date added to LUP
2026-08-13 11:04:12
date last changed
2026-08-13 11:04:51
@article{907d84c7-5d71-4e00-b37f-743f79ba4859,
  abstract     = {{<p>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 analyze 4 million stars during the 5 yr 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] ≈ −3.3 degraded to a 4MOST-HR resolution of R ≈ 20,000 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 of &lt;0.13 and spread of &lt;0.16 dex, although the typical values are &lt;0.09 dex for most elements. These abundances are compared to the OMEGA+ Galactic chemical evolution model, showcasing for the first time the expected performance and results obtained from high-resolution spectra of the quality expected to be obtained with 4MOST. The expected Galactic trends are recovered, and we highlight the potential of using many chemical elements to constrain the formation history of the Galaxy.</p>}},
  author       = {{Storm, Nicholas and Bergemann, Maria and Różański, Tomasz and F. Ksoll, Victor and Bensby, Thomas and Traven, Gregor and Kordopatis, Georges and Church, Ross P. and Jian, Mingjie and Sun, Weijia and Guiglion, Guillaume and Tautvaišienė, Gražina}},
  issn         = {{0004-637X}},
  keywords     = {{Galaxy chemical evolution (580); Neural networks (1933); Stellar abundances (1577)}},
  language     = {{eng}},
  number       = {{1}},
  publisher    = {{American Astronomical Society}},
  series       = {{Astrophysical Journal}},
  title        = {{Observational Constraints on the Origin of the Elements. X. Combining Non–Local Thermodynamic Equilibrium and Machine Learning for Chemical Diagnostics of 4 Million Stars in the 4MIDABLE-HR Survey}},
  url          = {{http://dx.doi.org/10.3847/1538-4357/ae6108}},
  doi          = {{10.3847/1538-4357/ae6108}},
  volume       = {{1003}},
  year         = {{2026}},
}