Real-time Detection of Anomalies in Multivariate Time Series of Astronomical Data

Muthukrishna, Daniel; Mandel, Kaisey S.; Lochner, Michelle; Narayan, Gautham

doi:10.48550/arxiv.2112.08415

Cited by 3 publications

(3 citation statements)

References 16 publications

(20 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ref. [70] combines a probabilistic deep NN model and a Bayesian approach to identify rare transients such as kilonovae and tidal disruption events, whose lightcurves are distinct from those of supernovae. Models based on unsupervised learning are also able to predict future fluxes from time-series data together with the associated uncertainties.…”

Section: Anomaly Detectionmentioning

confidence: 99%

Machine Learning for Observational Cosmology

Moriwaki¹,

Nishimichi²,

Yoshida³

2023

Preprint

View full text Add to dashboard Cite

An array of large observational programs using ground-based and spaceborne telescopes is planned in the next decade. The forthcoming wide-field sky surveys are expected to deliver a sheer volume of data exceeding an exabyte. Processing the large amount of multiplex astronomical data is technically challenging, and fully automated technologies based on machine learning and artificial intelligence are urgently needed. Maximizing scientific returns from the big data requires communitywide efforts. We summarize recent progress in machine learning applications in observational cosmology. We also address crucial issues in high-performance computing that are needed for the data processing and statistical analysis.

show abstract

Section: Anomaly Detectionmentioning

confidence: 99%

Machine Learning for Observational Cosmology

Moriwaki¹,

Nishimichi²,

Yoshida³

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Unsupervised ML has been applied to astronomical transients [69], and sophisticated methods have already been developed. Muthukrishna et al [70] combines a probabilistic deep NN model and a Bayesian approach to identify rare transients such as kilonovae and tidal disruption events, whose lightcurves are distinct from those of SNe. Models based on unsupervised learning are also able to predict future fluxes from time-series data together with the associated uncertainties.…”

Section: Anomaly Detectionmentioning

confidence: 99%

Machine learning for observational cosmology

2023

View full text Add to dashboard Cite

An array of large observational programs using ground-based and space-borne telescopes is planned in the next decade. The forthcoming wide-field sky surveys are expected to deliver a sheer volume of data exceeding an exabyte. Processing the large amount of multiplex astronomical data is technically challenging, and fully automated technologies based on machine learning and artificial intelligence are urgently needed. Maximizing scientific returns from the big data requires community-wide efforts. We summarize recent progress in machine learning applications in observational cosmology. We also address crucial issues in high-performance computing that are needed for the data processing and statistical analysis.

show abstract

“…Currently, this approach is the state-of-the-art method for SN light-curve preprocessing (see, e.g. Qu et al 2021;Alves et al 2022;Burhanudin &Maund 2022 for classification andPruzhinskaya et al 2019;Villar et al 2021;Ishida et al 2021;Muthukrishna et al 2021 for anomaly detection) and we use it as a baseline result below.…”

Section: Introductionmentioning

confidence: 99%

Toward an understanding of the properties of neural network approaches for supernovae light curve approximation

Mariia¹,

Malanchev²,

Samorodova³

et al. 2022

Preprint

View full text Add to dashboard Cite

The modern time-domain photometric surveys collect a lot of observations of various astronomical objects, and the coming era of large-scale surveys will provide even more information. Most of the objects have never received a spectroscopic follow-up, which is especially crucial for transients e.g. supernovae. In such cases, observed light curves could present an affordable alternative. Time series are actively used for photometric classification and characterization, such as peak and luminosity decline estimation. However, the collected time series are multidimensional, irregularly sampled, contain outliers, and do not have well-defined systematic uncertainties. Machine learning methods help extract useful information from available data in the most efficient way. We consider several light curve approximation methods based on neural networks: Multilayer Perceptrons, Bayesian Neural Networks, and Normalizing Flows, to approximate observations of a single light curve. Tests using both the simulated PLAsTiCC and real Zwicky Transient Facility data samples demonstrate that even few observations are enough to fit networks and achieve better approximation quality than other state-of-the-art methods. We show that the methods described in this work have better computational complexity and work faster than Gaussian Processes. We analyze the performance of the approximation techniques aiming to fill the gaps in the observations of the light curves, and show that the use of appropriate technique increases the accuracy of peak finding and supernova classification. In addition, the study results are organized in a Fulu Python library available on GitHub, which can be easily used by the community.

show abstract

Real-time Detection of Anomalies in Multivariate Time Series of Astronomical Data

Cited by 3 publications

References 16 publications

Machine Learning for Observational Cosmology

Machine Learning for Observational Cosmology

Machine learning for observational cosmology

Toward an understanding of the properties of neural network approaches for supernovae light curve approximation

Contact Info

Product

Resources

About