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Machine Learning in Astronomy (IAU S368): Possibilities and Pitfalls [Kõva köide]

Edited by (University of British Columbia, Vancouver), Edited by (Swinburne University of Technology, Victoria), Edited by (California Institute of Technology)
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Machine Learning in Astronomy (IAU S368): Possibilities and PitfallsSuurem pilt
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Püsilink: https://www.kriso.ee/db/9781009345194.html
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IAU S368 addresses graduate students and professional astronomers who wish to leverage machine learning to unlock the potential of modern data-rich surveys and deep images, as well as archival data. Researchers at the frontiers share best practices in applied machine learning that are relevant to astronomy and other data-rich fields.

Today's astronomical observatories are generating more data than ever, from surveys to deep images. Machine learning methods can be a powerful tool to harness the full potential of these new observatories, as well as large archives that have accumulated. However, users should beware of common pitfalls, including bias in data sets and overfitting. IAU Symposium 368 addresses graduate students, teachers and professional astronomers who would like to leverage machine learning to unlock these huge volumes of data. Researchers pushing the frontiers of these methods share best practices in applied machine learning. While this volume is focused on astronomy applications, the methodological insights provided are relevant to all data-rich fields. Machine learning novices and expert users will find and benefit from these fresh new insights.

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Researchers at the frontiers share best practices in applied machine learning that are relevant to astronomy and other data-rich fields.
Enhancing exoplanet surveys via physics-informed machine earning Eric
Ford; How do we design data sets for machine learning in astronomy? Renee
Hlozek; Deep machine learning in cosmology: Evolution or revolution? Ofer
Lahav; An astronomers guide to machine learning Sara Webb; Panel discussion:
practical problem solving for machine learning David Parkinson; Panel
discussion: methodology for fusion of large datasets Kai Polsterer; The
entropy of galaxy spectra Ignacio Ferreras; Unsupervised classification: a
necessary step for deep learning? Didier Fraix-Burnet; Spectral identication
and classication of dusty stellar sources using spectroscopic and
multiwavelength observations through machine learning Sepideh Ghaziasgar;
Simulating transient burst noise with gengli Melissa Lopez; Detecting complex
sources in large surveys using an apparent complexity measure David
Parkinson; Machine learning in the study of star clusters with Gaia EDR3
Priya Shah; Assessing the quality of massive spectroscopic surveys with
unsupervised machine learning John Suárez-Pérez; Neural networks for
meteorite and meteor recognition Aisha Alowais; Unsupervised clustering
visualisation tool for Gaia DR3 Marco Alvarez Gonzalez; Kinematic Planetary
Signature Finder (KPSFinder): Convolutional neural network-based tool to
search for exoplanets in ALMA data Jaehan Bae; Predicting physical parameters
of Cepheid and RR Lyrae variables in an instant with machine learning Anupam
Bhardwaj; Bayesian deconvolution of a rotating spectral line profile to a
non-rotating one Michel Curé; A short study on the representation of
gravitational waves data for convolutional neural network Margherita Grespan;
Search for microlensing signature in gravitational waves from binary black
hole events Kyungmin Kim; Deep learning and numerical simulations to infer
the evolution of MaNGA galaxies Johan Knapen; Data pre-extraction for better
classification of galaxy mergers William Pearson; Stellar spectra
classification and clustering using deep learning Tomasz Róaski; Is GMM
effective in membership determination of open clusters? Priya Shah; Deep
radio image segmentation Hattie Stewart; Computational techniques for high
energy astrophysics and medical image processing Nicolás Vásquez; Deep
learning proves to be an effective tool for detecting previously undiscovered
exoplanets in Kepler data Amelia Yu.