Related papers: Machine learning in APOGEE: Unsupervised spectral …
The growth of sky surveys and the large amount of stellar spectra in the current databases, has generated the necessity of developing new methods to estimate atmospheric parameters, a fundamental task on stellar research. In this work we…
Due to the ever-expanding volume of observed spectroscopic data from surveys such as SDSS and LAMOST, it has become important to apply artificial intelligence (AI) techniques for analysing stellar spectra to solve spectral classification…
We employ unsupervised machine learning to enhance the accuracy of our recently presented scaling method for wave confinement analysis [1]. We employ the standard k-means++ algorithm as well as our own model-based algorithm. We investigate…
The SDSS-III/APOGEE survey operated from 2011-2014 using the APOGEE spectrograph, which collects high-resolution (R~22,500), near-IR (1.51-1.70 microns) spectra with a multiplexing (300 fiber-fed objects) capability. We describe the survey…
We describe the Sloan Digital Sky Survey IV (SDSS-IV), a project encompassing three major spectroscopic programs. The Apache Point Observatory Galactic Evolution Experiment 2 (APOGEE-2) is observing hundreds of thousands of Milky Way stars…
We derive distances and masses of stars from the Sloan Digital Sky Survey (SDSS) Apache Point Observatory Galactic Evolution Experiment (APOGEE) Data Release 17 (DR17) using simple neural networks. Training data for distances comes from…
Generating dense point clouds from sparse raw data benefits downstream 3D understanding tasks, but existing models are limited to a fixed upsampling ratio or to a short range of integer values. In this paper, we present APU-SMOG, a…
Galaxy morphology offers significant insights into the evolutionary pathways and underlying physics of galaxies. As astronomical data grows with surveys such as Euclid and Vera C. Rubin , there is a need for tools to classify and analyze…
Along the life of the IUE project, a large archive with spectral data has been generated, requiring automated classification methods to be analyzed in an objective form. Previous automated classification methods used with IUE spectra were…
We have developed a novel technique based on a clustering algorithm which searches for kinematically- and chemically-clustered stars in the APOGEE DR12 Cannon data. As compared to classical chemical tagging, the kinematic information…
Aims. The present study aims at providing a deeper insight into the power and limitation of an unsupervised classification algorithm (called Fisher-EM) on spectra of galaxies. This algorithm uses a Gaussian mixture in a discriminative…
We conduct a systematic robustness analysis of the hybrid machine learning framework \texttt{USmorph}, which integrates unsupervised and supervised learning for galaxy morphological classification. Although \texttt{USmorph} has already been…
In modern astrophysics, the machine learning has increasingly gained more popularity with its incredibly powerful ability to make predictions or calculated suggestions for large amounts of data. We describe an application of the supervised…
We present a catalog of fundamental stellar properties for 7,673 evolved stars, including stellar radii and masses, determined from the combination of spectroscopic observations from the Apache Point Observatory Galactic Evolution…
This study explores the application of autoencoder-based machine learning techniques for anomaly detection to identify exoplanet atmospheres with unconventional chemical signatures using a low-dimensional data representation. We use the…
The first generations of stars left their chemical fingerprints on metal-poor stars in the Milky Way and its surrounding dwarf galaxies. While instantaneous and homogeneous enrichment implies that groups of co-natal stars should have the…
We present stellar age distributions of the Milky Way (MW) bulge region using ages for $\sim$6,000 high-luminosity ($\log(g) < 2.0$), metal-rich ($\rm [Fe/H] \ge -0.5$) bulge stars observed by the Apache Point Observatory Galactic Evolution…
The Euclid Space Telescope will provide deep imaging at optical and near-infrared wavelengths, along with slitless near-infrared spectroscopy, across ~15,000 sq deg of the sky. Euclid is expected to detect ~12 billion astronomical sources,…
Anomaly detection has been considered under several extents of prior knowledge. Unsupervised methods do not require any labelled data, whereas semi-supervised methods leverage some known anomalies. Inspired by mixture-of-experts models and…
Gaia Bp/Rp spectra for over two hundred million stars have great potential for mapping metallicity across the Milky Way. We aim to construct an alternative catalog of atmospheric parameters from Gaia Bp/Rp spectra by fitting them with…