Rest-frame optical galaxy SEDs from a 16-parameter SPS model are captured by five disentangled VAE latents (mass, young stars, dust, soft/hard ionization); metallicity and age are not independent drivers.
Data Mining and Machine Learning in Astronomy
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
abstract
We review the current state of data mining and machine learning in astronomy. 'Data Mining' can have a somewhat mixed connotation from the point of view of a researcher in this field. If used correctly, it can be a powerful approach, holding the potential to fully exploit the exponentially increasing amount of available data, promising great scientific advance. However, if misused, it can be little more than the black-box application of complex computing algorithms that may give little physical insight, and provide questionable results. Here, we give an overview of the entire data mining process, from data collection through to the interpretation of results. We cover common machine learning algorithms, such as artificial neural networks and support vector machines, applications from a broad range of astronomy, emphasizing those where data mining techniques directly resulted in improved science, and important current and future directions, including probability density functions, parallel algorithms, petascale computing, and the time domain. We conclude that, so long as one carefully selects an appropriate algorithm, and is guided by the astronomical problem at hand, data mining can be very much the powerful tool, and not the questionable black box.
years
2026 5representative citing papers
SPENDER autoencoder plus k-d tree nearest-neighbor classification on DESI spectra identifies AGN and broad-line AGN at accuracies 0.952 and 0.965, recovering sources missed by single-line diagnostics.
Galaxy size-mass relations exhibit double power-law breaks at different pivot masses for quiescent versus bulge-dominated samples, coinciding with AGN activity scales.
C-rich AGB stars trace the Galactic warp with larger amplitudes than Cepheids at intermediate ages of about 1 Gyr.
ML regressors trained on APOGEE DR17 red giants predict C, O, Mg, Si abundances from kinematics and [Fe/H] more accurately than [Fe/H] baseline, with external validation on HARPS FGK dwarfs and reproduction of Galactic chemical evolution trends.
citing papers explorer
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pop-cosmos: Disentangling galaxy properties from observables using data-driven approaches
Rest-frame optical galaxy SEDs from a 16-parameter SPS model are captured by five disentangled VAE latents (mass, young stars, dust, soft/hard ionization); metallicity and age are not independent drivers.
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Beyond traditional emission-line diagnostics: using autoencoders to uncover active galactic nuclei in DESI spectra
SPENDER autoencoder plus k-d tree nearest-neighbor classification on DESI spectra identifies AGN and broad-line AGN at accuracies 0.952 and 0.965, recovering sources missed by single-line diagnostics.
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pop-cosmos: Galaxy size evolution across structural and star-formation classifications in COSMOS-Web
Galaxy size-mass relations exhibit double power-law breaks at different pivot masses for quiescent versus bulge-dominated samples, coinciding with AGN activity scales.
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Milky Way's warped disc traced by AGB stars
C-rich AGB stars trace the Galactic warp with larger amplitudes than Cepheids at intermediate ages of about 1 Gyr.
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Inferring stellar metallicity and elemental abundances from kinematic and spectroscopic data using machine learning -- Implications for exoplanet host stars
ML regressors trained on APOGEE DR17 red giants predict C, O, Mg, Si abundances from kinematics and [Fe/H] more accurately than [Fe/H] baseline, with external validation on HARPS FGK dwarfs and reproduction of Galactic chemical evolution trends.