Nested sampling applied to ARIMA models enables Bayesian order selection and parameter inference that recovers ground truth in simulations and fits stochastic variability in sunspot, Kepler, and TESS light curves.
Machine Learning in Astronomy: a practical overview
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
abstract
Astronomy is experiencing a rapid growth in data size and complexity. This change fosters the development of data-driven science as a useful companion to the common model-driven data analysis paradigm, where astronomers develop automatic tools to mine datasets and extract novel information from them. In recent years, machine learning algorithms have become increasingly popular among astronomers, and are now used for a wide variety of tasks. In light of these developments, and the promise and challenges associated with them, the IAC Winter School 2018 focused on big data in Astronomy, with a particular emphasis on machine learning and deep learning techniques. This document summarizes the topics of supervised and unsupervised learning algorithms presented during the school, and provides practical information on the application of such tools to astronomical datasets. In this document I cover basic topics in supervised machine learning, including selection and preprocessing of the input dataset, evaluation methods, and three popular supervised learning algorithms, Support Vector Machines, Random Forests, and shallow Artificial Neural Networks. My main focus is on unsupervised machine learning algorithms, that are used to perform cluster analysis, dimensionality reduction, visualization, and outlier detection. Unsupervised learning algorithms are of particular importance to scientific research, since they can be used to extract new knowledge from existing datasets, and can facilitate new discoveries.
citation-role summary
background 1
citation-polarity summary
verdicts
UNVERDICTED 5roles
background 1polarities
background 1representative citing papers
High-contrast imaging with PACO and REXPACO reveals a new candidate companion at ~14 au and a tightly wound H-alpha spiral in the inner disk of HD 142527, suggesting ongoing companion-disk interactions.
A new scale-aware diagnostic framework shows that unconstrained diffusion generative models exhibit structural freezing and instability instead of smooth physical responses under multiscale perturbations.
Machine learning classifiers on initial orbital elements and convolutional neural networks on recurrence plots from short integrations classify long-term ejection of near-Earth asteroids with accuracy comparable to full numerical simulations.
A quantile-graph PCA SOM embedding creates a map of 1.5 million TESS light curves where proximity reflects similarity in variability amplitude, timescale, SNR, and shape, with stable positions for repeat observations.
citing papers explorer
-
Nested Sampling for ARIMA Model Selection in Astronomical Time-Series Analysis
Nested sampling applied to ARIMA models enables Bayesian order selection and parameter inference that recovers ground truth in simulations and fits stochastic variability in sunspot, Kepler, and TESS light curves.
-
Exploration of the inner region of the system HD 142527
High-contrast imaging with PACO and REXPACO reveals a new candidate companion at ~14 au and a tightly wound H-alpha spiral in the inner disk of HD 142527, suggesting ongoing companion-disk interactions.
-
Scale-Aware Adversarial Analysis: A Diagnostic for Generative AI in Multiscale Complex Systems
A new scale-aware diagnostic framework shows that unconstrained diffusion generative models exhibit structural freezing and instability instead of smooth physical responses under multiscale perturbations.
-
Long-Term Dynamical Evolution and Ejection of Near-Earth Asteroids
Machine learning classifiers on initial orbital elements and convolutional neural networks on recurrence plots from short integrations classify long-term ejection of near-Earth asteroids with accuracy comparable to full numerical simulations.
-
A useful representation of TESS light curves
A quantile-graph PCA SOM embedding creates a map of 1.5 million TESS light curves where proximity reflects similarity in variability amplitude, timescale, SNR, and shape, with stable positions for repeat observations.