Related papers: The Cannon: A data-driven approach to stellar labe…
The discrepancies between theoretical and observed spectra, and the systematic differences between various spectroscopic parameter estimates, complicate the determination of atmospheric parameters of M-type stars. In this work, we present…
Detailed chemical studies of F/G/K -- or Solar-type -- stars have long been routine in stellar astrophysics, enabling studies in both Galactic chemodynamics, and exoplanet demographics. However, similar understanding of the chemistry of M…
The cool temperatures of M dwarf atmospheres enable complex molecular chemistry, making robust characterization of M dwarf compositions a long-standing challenge. Recent modifications to spectral synthesis pipelines have enabled more…
Machine learning has been widely applied to clearly defined problems of astronomy and astrophysics. However, deep learning and its conceptual differences to classical machine learning have been largely overlooked in these fields. The broad…
We present a novel approach to deriving stellar labels for stars observed in MUSE fields making use of data-driven machine learning methods. Taking advantage of the comparable spectral properties (resolution, wavelength coverage) of the…
Machine learning allows efficient extraction of physical properties from stellar spectra that have been obtained by large surveys. The viability of ML approaches has been demonstrated for spectra covering a variety of wavelengths and…
We set out to determine stellar labels from low-resolution survey spectra of hot, OBA stars with effective temperature (Teff) higher than 7500K. This fills a gap in the scientific analysis of large spectroscopic stellar surveys such as…
Large stellar surveys are revealing the chemodynamical structure of the Galaxy across a vast spatial extent. However, the many millions of low-resolution spectra observed to date are yet to be fully exploited. We employ The Cannon, a…
Data-driven models, which apply machine learning to infer physical properties from large quantities of data, have become increasingly important for extracting stellar properties from spectra. In general, these methods have been applied to…
Aims. This paper introduces LRPayne, a novel algorithm designed for the efficient determination of stellar parameters and chemical abundances from low-resolution optical spectra, with a primary focus on data from large-scale galactic…
Open clusters are key chemical and age tracers of Milky Way evolution. While open clusters provide significant constraints on galaxy evolution, their use has been limited due to discrepancies in measuring abundances from different studies.…
Stellar parameters for large samples of stars play a crucial role in constraining the nature of stars and stellar populations in the Galaxy. An increasing number of medium-band photometric surveys are presently used in estimating stellar…
The large volume of spectroscopic data available now and from near-future surveys will enable high-dimensional measurements of stellar parameters and properties. Current methods for determining stellar labels from spectra use physics-driven…
The element abundances of local group galaxies connect enrichment mechanisms to galactic properties and serve to contextualise the Milky Way's abundance distributions. Individual stellar spectra in nearby galaxies can be extracted from…
The Sloan Digital Sky Survey has recently initiated its 5th survey generation (SDSS-V), with a central focus on stellar spectroscopy. In particular, SDSS-V Milky Way Mapper program will deliver multi-epoch optical and near-infrared spectra…
Stars born from the same molecular cloud should be nearly homogeneous in their element abundances. The concept of chemical tagging is to identify members of disrupted clusters by their clustering in element abundance space. Chemical tagging…
A deep understanding of our Galaxy desires detailed decomposition of its stellar populations via their chemical fingerprints. This requires precise stellar abundances of many elements for a large number of stars. Here we present an updated…
Stellar spectra depend on the stellar parameters and on dozens of photospheric elemental abundances. Simultaneous fitting of these $\mathcal{N}\sim 10-40$ model labels to observed spectra has been deemed unfeasible, because the number of ab…
Large-scale surveys will provide spectroscopy for $\sim$50 million resolved stars in the Milky Way and Local Group. However, these data will have a high degree of heterogeneity and most will be low-resolution ($R<10000$), posing challenges…
Chemical abundance determinations from stellar spectra are challenged by observational noise, limitations in stellar models, and departures from simplifying assumptions. While traditional and supervised machine learning methods have made…