REVIEW 4 cited by
Machine Learning in Stellar Astronomy: Progress up to 2024
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Machine learning (ML) has become a key tool in astronomy, driving advancements in the analysis and interpretation of complex datasets from observations. This article reviews the application of ML techniques in the identification and classification of stellar objects, alongside the inference of their key astrophysical properties. We highlight the role of both supervised and unsupervised ML algorithms, particularly deep learning models, in classifying stars and enhancing our understanding of essential stellar parameters, such as mass, age, and chemical composition. We discuss ML applications in the study of various stellar objects, including binaries, supernovae, dwarfs, young stellar objects, variables, metal-poor, and chemically peculiar stars. Additionally, we examine the role of ML in investigating star-related interstellar medium objects, such as protoplanetary disks, planetary nebulae, cold neutral medium, feedback bubbles, and molecular clouds.
Forward citations
Cited by 4 Pith papers
-
Deep Learning Analysis of Ions Accelerated at Shocks
A convolutional neural network can predict with >90% accuracy whether an ion at a collisionless shock is injected into acceleration, using only the local magnetic field time series from its first few gyrations.
-
Effective temperatures estimation of low-mass stars and identification of T Tauri stars in LAMOST DR10 using machine learning
A GBM regressor trained on PHOENIX synthetic spectra estimates Teff for 1,733,852 LAMOST DR10 low-mass spectra, and a robust logistic classifier identifies 2,534 T Tauri star candidates.
-
Supernova Classification using the Recurrent Neural Network in the CSST Ultra-Deep Field Survey
In mock CSST-UDF data, an LSTM classifier plus JLA-like cuts yields a >99.5% pure Type Ia sample and recovers Ω_M and w to 14% and 18% in a flat wCDM model.
-
Multiple machine-learning as a powerful tool for the star clusters analysis
Five clustering algorithms applied to Gaia EDR3 data support the identification of NGC1605a and NGC1605b as a genuine old binary open cluster pair in an advanced stage of merging.
Discussion (0). Continue with ORCID to comment.