Related papers: Machine learning in APOGEE: Unsupervised spectral …
The updated H-band spectral line list (from \lambda 15,000 - 17,000\AA) adopted by the Apache Point Observatory Galactic Evolution Experiment (APOGEE) for the SDSS IV Data Release 16 (DR16) is presented here. The APOGEE line list is a…
The availability of highly accurate urban airborne laser scanning (ALS) data will increase rapidly in the future, especially as acquisition costs decrease, for example through the use of drones. Current challenges in data processing are…
We present the first unsupervised classification of spaxels in hyperspectral images of individual galaxies. Classes identify regions by spectral similarity and thus take all the information into account that is contained in the data cubes…
Analyses of stellar spectra often begin with the determination of a number of parameters that define a model atmosphere. This work presents a prototype for an automated spectral classification system that uses a 15 nm-wide region around…
The automatic classification of X-ray detections is a necessary step in extracting astrophysical information from compiled catalogs of astrophysical sources. Classification is useful for the study of individual objects, statistics for…
The discovery of exoplanets has expanded our understanding of planetary systems and opened new avenues for astronomical research. In this study, we present a machine learning (ML) framework for exoplanet identification using a time-series…
This paper explores the application of machine learning methods for classifying astronomical sources using photometric data, including normal and emission line galaxies (ELGs; starforming, starburst, AGN, broad line), quasars, and stars. We…
Hyperspectral imaging techniques have a unique ability to probe the inhomogeneity of material properties whether driven by compositional variation or other forms of phase segregation. In the doped cuprates, iridates, and related materials,…
This paper presents an accelerated spherical K-means clustering algorithm for large-scale and high-dimensional sparse document data sets. We design an algorithm working in an architecture-friendly manner (AFM), which is a procedure of…
We report the discovery of a new, chemically distinct population of relatively high-metallicity ([Fe/H] $> -0.7$) red giant stars with super-solar [N/Fe] ($\gtrsim +0.75$) identified within the bulge, disk, and halo of the Milky Way. This…
Motivation: Structural heterogeneity in single-particle cryo-electron microscopy (cryo-EM) data represents a major challenge for high-resolution structure determination. Unsupervised classification may serve as the first step in the…
The Apache Point Observatory Galactic Evolution Experiment (APOGEE) has observed the H-band spectra of over 200 000 stars with $R\sim22 000$. The main motivation for this work is to test an alternative method to the standard APOGEE pipeline…
The Sloan Digital Sky Survey IV (SDSS-IV) APOGEE-2 primary science goal was to observe red giant stars throughout the Galaxy to study its dynamics, morphology, and chemical evolution. The APOGEE instrument, a high-resolution 300 fiber…
Anomaly detection aims to distinguish observations that are rare and different from the majority. While most existing algorithms assume that instances are i.i.d., in many practical scenarios, links describing instance-to-instance…
Identification of specific stellar populations using photometry for spectroscopic follow-up is a first step to confirm and better understand their nature. In this context, we present an unsupervised machine learning approach to identify…
During the last ten years, a considerable amount of effort has been made to develop algorithms for automatic classification of variable stars. That has been primarily achieved by applying machine learning methods to photometric datasets…
The Sloan Digital Sky Survey (SDSS) has been in operation since 2000 April. This paper presents the tenth public data release (DR10) from its current incarnation, SDSS-III. This data release includes the first spectroscopic data from the…
Identifying anomalies in multi-dimensional datasets is an important task in many real-world applications. A special case arises when anomalies are occluded in a small set of attributes, typically referred to as a subspace, and not…
Dusty stellar point sources are a significant stage in stellar evolution and contribute to the metal enrichment of galaxies. These objects can be classified using photometric and spectroscopic observations with color-magnitude diagrams…
There exist a variety of star-galaxy classification techniques, each with their own strengths and weaknesses. In this paper, we present a novel meta-classification framework that combines and fully exploits different techniques to produce a…