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.
Machine Learning in Astronomy: a practical overview
7 Pith papers cite this work. Polarity classification is still indexing.
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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.
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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.
Supervised ML models achieve R^2 > 0.90 when mapping multi-frequency radio data to 0.1-10 GeV gamma-ray intensity and attribute high-frequency radio bands to hadronic processes and low-frequency bands to leptonic processes.
C-rich AGB stars trace the Galactic warp with larger amplitudes than Cepheids at intermediate ages of about 1 Gyr.
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
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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.
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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.
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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.
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Data-driven modeling of Galactic diffuse emission with multi-wavelength observations
Supervised ML models achieve R^2 > 0.90 when mapping multi-frequency radio data to 0.1-10 GeV gamma-ray intensity and attribute high-frequency radio bands to hadronic processes and low-frequency bands to leptonic processes.
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Milky Way's warped disc traced by AGB stars
C-rich AGB stars trace the Galactic warp with larger amplitudes than Cepheids at intermediate ages of about 1 Gyr.
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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.
- Nested Sampling for ARIMA Model Selection in Astronomical Time-Series Analysis