Realizing the potential of astrostatistics and astroinformatics

Abstract

This Astro2020 State of the Profession Consideration White Paper highlights the growth of astrostatistics and astroinformatics in astronomy, identifies key issues hampering the maturation of these new subfields, and makes recommendations for structural improvements at different levels that, if acted upon, will make significant positive impacts across astronomy.

arXiv comment14 pages, 1 figure; submitted to the Decadal Survey on Astronomy and Astrophysics (Astro2020) on 10 July 2019; see https://sites.nationalacademies.org/DEPS/Astro2020/DEPS_192906

References in corpus (15)

• Statistics: Handle with Care, Detecting Multiple Model Components with the Likelihood Ratio Test
• On Machine-Learned Classification of Variable Stars with Sparse and Noisy Time-Series Data
• Photometric Supernova Classification With Machine Learning
• Photometric redshifts from SDSS images using a Convolutional Neural Network
• Image Analysis for Cosmology: Results from the GREAT10 Galaxy Challenge
• Results of the GREAT08 Challenge: An image analysis competition for cosmological lensing
• Radio Galaxy Zoo: ClaRAN - A Deep Learning Classifier for Radio Morphologies
• Image Analysis for Cosmology: Results from the GREAT10 Star Challenge
• Deep-Learnt Classification of Light Curves
• Detecting outliers and learning complex structures with large spectroscopic surveys - a case study with APOGEE stars
• Software Use in Astronomy: an Informal Survey
• How proper are Bayesian models in the astronomical literature?
• A Compressed Sensing Approach to 3D Weak Lensing
• Deep Learning for Multi-Messenger Astrophysics: A Gateway for Discovery in the Big Data Era
• PURIFY: a new algorithmic framework for next-generation radio-interferometric imaging