Neural network-based anomaly detection for high-resolution X-ray spectroscopy

Ichinohe, Yuto; Yamada, S.

doi:10.1093/mnras/stz1528

Cited by 18 publications

(10 citation statements)

References 34 publications

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“…Applications of anomaly detection to spectroscopic data include Boroson & Lauer (2010) who detected anomalies in SDSS quasar spectra using the reconstruction error of a Principal Component Analysis (PCA) model of the data, i.e., the residual between the data and best fitting model. A more recent use of reconstruction-based anomaly detection is Ichinohe & Yamada (2019), where the model of the data (in this case X-ray spectra), was built using a variational auto encoder. Meusinger et al (2012) used self organizing maps (SOM, Kohonen 1982) for anomaly detection in SDSS quasar spectra.…”

Section: Anomaly Detectionmentioning

confidence: 99%

Effectively using unsupervised machine learning in next generation astronomical surveys

Reis¹,

Rotman²,

Poznanski³

et al. 2019

Preprint

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In recent years many works have shown that unsupervised Machine Learning (ML) can help detect unusual objects and uncover trends in large astronomical datasets, but a few challenges remain. We show here, for example, that different methods, or even small variations of the same method, can produce significantly different outcomes. While intuitively somewhat surprising, this can naturally occur when applying unsupervised ML to highly dimensional data, where there can be many reasonable yet different answers to the same question. In such a case the outcome of any single unsupervised ML method should be considered a sample from a conceivably wide range of possibilities. We therefore suggest an approach that eschews finding an optimal outcome, instead facilitating the production and examination of many valid ones. This can be achieved by incorporating unsupervised ML into data visualisation portals. We present here such a portal that we are developing, applied to the sample of SDSS spectra of galaxies. The main feature of the portal is interactive 2D maps of the data. Different maps are constructed by applying dimensionality reduction to different subspaces of the data, so that each map contains different information that in turn gives a different perspective on the data. The interactive maps are intuitive to use, and we demonstrate how peculiar objects and trends can be detected by means of a few button clicks. We believe that including tools in this spirit in next generation astronomical surveys will be important for making unexpected discoveries, either by professional astronomers or by citizen scientists, and will generally enable the benefits of visual inspection even when dealing with very complex and extensive datasets. Our portal is available online at galaxyportal.space.

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Section: Anomaly Detectionmentioning

confidence: 99%

Effectively using unsupervised machine learning in next generation astronomical surveys

Reis¹,

Rotman²,

Poznanski³

et al. 2019

Preprint

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“…In physics, machine learning has been successfully used in classification of galaxy morphology [10] and jets in particle colliders [11], to name but two. Machine learning techniques have also been proposed for anomaly detection in X-ray spectra [12].…”

Section: Introductionmentioning

confidence: 99%

Accelerating the search for axion-like particles with machine learning

Day¹,

Krippendorf²

2020

J. Cosmol. Astropart. Phys.

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Machine learning (ML) techniques have been applied with tremendous success in many areas of physics. In this work, we use ML to place bounds on the coupling between photons and axion-like particles (ALPs). This coupling causes ALPs and photons to interconvert in the presence of a background magnetic field. This would lead to modulations in the spectra of point sources shining through the magnetic fields of galaxy clusters. This effect has already been used to place world-leading bounds on the ALP-photon coupling using conventional statistical methods. We train ML classification algorithms on simulated spectra from the Chandra X-ray telescope for a range of point sources and ALP-photon couplings. We then use the response of these algorithms to the real Chandra spectra to place bounds on ALP-photon interactions. We obtain bounds at a similar level to those based on other techniques, but find improvements on an individual source basis. We expect such search techniques to become increasingly important for ALP searches with future telescopes that will offer substantially higher energy resolution.

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“…Zhang & Zou (2018) proposed to look for anomalous transients via a long short-term memory neural network. Ichinohe & Yamada (2019) suggested searching for anomalous X-ray transients using a variational autoencoder. Malanchev et al (2021) extracted features from light curves given by the ZTF Data Release 3 and searched for anomalies using the isolation forest, local outlier factor, Gaussian mixture model, and one-class support vector machines.…”

Section: Anomaly Detection Through Machine Learning In Astronomymentioning

confidence: 99%

Searching for Anomalies in the ZTF Catalog of Periodic Variable Stars

Chan

Villar

Cheung

et al. 2022

ApJ

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Periodic variables illuminate the physical processes of stars throughout their lifetime. Wide-field surveys continue to increase our discovery rates of periodic variable stars. Automated approaches are essential to identify interesting periodic variable stars for multiwavelength and spectroscopic follow-up. Here we present a novel unsupervised machine-learning approach to hunt for anomalous periodic variables using phase-folded light curves presented in the Zwicky Transient Facility Catalogue of Periodic Variable Stars by Chen et al. We use a convolutional variational autoencoder to learn a low-dimensional latent representation, and we search for anomalies within this latent dimension via an isolation forest. We identify anomalies with irregular variability. Most of the top anomalies are likely highly variable red giants or asymptotic giant branch stars concentrated in the Milky Way galactic disk; a fraction of the identified anomalies are more consistent with young stellar objects. Detailed spectroscopic follow-up observations are encouraged to reveal the nature of these anomalies.

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Neural network-based anomaly detection for high-resolution X-ray spectroscopy

Cited by 18 publications

References 34 publications

Effectively using unsupervised machine learning in next generation astronomical surveys

Effectively using unsupervised machine learning in next generation astronomical surveys

Accelerating the search for axion-like particles with machine learning

Searching for Anomalies in the ZTF Catalog of Periodic Variable Stars

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