Titlebook: Intelligent Astrophysics; Ivan Zelinka,Massimo Brescia,Dalya Baron Book 2021 The Editor(s) (if applicable) and The Author(s), under exclus

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Ensemble Classifiers for Pulsar Detection,e learning methods can be adopted. One challenge here though is that training such methods is hampered by the inherent imbalance in the available data since signals related to actual pulsars are relatively rare. In this chapter, we show that ensemble classification methods that specifically address

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Periodic Astrometric Signal Recovery Through Convolutional Autoencoders,th-mass planets in temperate orbits around nearby sun-like stars. The TOLIMAN space telescope [.] is a low-cost, agile mission concept dedicated to narrow-angle astrometric monitoring of bright binary stars. In particular the mission will be optimised to search for habitable-zone planets around . Ce

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Comparison of Outlier Detection Methods on Astronomical Image Data, . objects. Unsupervised outlier detection algorithms may provide a viable solution. In this work we compare the performances of six methods: the Local Outlier Factor, Isolation Forest, k-means clustering, a measure of novelty, and both a normal and a convolutional autoencoder. These methods were ap

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Anomaly Detection in Astrophysics: A Comparison Between Unsupervised Deep and Machine Learning on K beginning. The ongoing and future large and complex multi-messenger sky surveys impose a wide exploiting of robust and efficient automated methods to classify the observed structures and to detect and characterize peculiar and unexpected sources. We performed a preliminary experiment on KiDS DR4 da

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Large Astronomical Time Series Pre-processing for Classification Using Artificial Neural Networks,ies (a.k.a. light curves containing usually flux or magnitude on one axis and Julian date on the other axis) are a bit more challenging to classify. As they comes from multiple observational devices and observatories (designed for e.g. variable stars detection, stellar system analysis or extra-solla

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