Big Universe, Big Data: Machine Learning and Image Analysis for Astronomy

Abstract

Astrophysics and cosmology are rich with data. The advent of wide-area digital cameras on large aperture telescopes has led to ever more ambitious surveys of the sky. Data volumes of entire surveys a decade ago can now be acquired in a single night and real-time analysis is often desired. Thus, modern astronomy requires big data know-how, in particular it demands highly efficient machine learning and image analysis algorithms. But scalability is not the only challenge: Astronomy applications touch several current machine learning research questions, such as learning from biased data and dealing with label and measurement noise. We argue that this makes astronomy a great domain for computer science research, as it pushes the boundaries of data analysis. In the following, we will present this exciting application area for data scientists. We will focus on exemplary results, discuss main challenges, and highlight some recent methodological advancements in machine learning and image analysis triggered by astronomical applications.

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• Machine learning technique for morphological classification of galaxies from the SDSS. I. Photometry-based approach
• ASTROMER: A transformer-based embedding for the representation of light curves
• Deep Learning Based Detection of Cosmological Diffuse Radio Sources
• Galaxy Morphology Classification using Neural Ordinary Differential Equations
• Streaming Classification of Variable Stars
• Machine learning technique for morphological classification of galaxies from the SDSS. III. Image-based inference of detailed features
• Machine learning technique for morphological classification of galaxies from SDSS. II. The image-based morphological catalogs of galaxies at 0.02<z<0.1
• On the application of dimensionality reduction and clustering algorithms for the classification of kinematic morphologies of galaxies
• Astronomical observations: a guide for allied researchers
• Bayesian decomposition of the Galactic multi-frequency sky using probabilistic autoencoders
• Solar Radio Burst Detection Based on Deformable DETR
• The Complexity and Information Content of Simulated Universes
• QPOML: A Machine Learning Approach to Detect and Characterize Quasi-Periodic Oscillations in X-ray Binaries
• Selecting Clusters and Protoclusters via Stellar Mass Density: II. Application to HSC-SSP Observations
• A machine learning approach to estimate mid-infrared fluxes from WISE data
• Finding Fast Transients in Real Time Using Novel Light Curve Analysis Algorithm
• DeepRed: an architecture for redshift estimation