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

arXiv: https://arxiv.org/abs/2112.08415 · HTML: https://mlsanity.com/paper/2112.08415/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 23 references that are in the map.
- First posted on arXiv 15 Dec 2021.

## Builds on (most cited first, 6 of 6)

- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [Dropout as a Bayesian Approximation: Appendix](https://mlsanity.com/paper/1506.02157/) (2015)
- [A Deep Learning Approach for Active Anomaly Detection of Extragalactic Transients](https://mlsanity.com/paper/2103.12102/) (2021)
- [Active Anomaly Detection for time-domain discoveries](https://mlsanity.com/paper/1909.13260/) (2019)
- [Real-Time Detection of Anomalies in Large-Scale Transient Surveys](https://mlsanity.com/paper/2111.00036/) (2021)

## Built on by (most cited first, 1 of 1)

- [AstroPT: Scaling Large Observation Models for Astronomy](https://mlsanity.com/paper/2405.14930/) (2024)

## Abstract (arXiv)

Astronomical transients are stellar objects that become temporarily brighter on various timescales and have led to some of the most significant discoveries in cosmology and astronomy. Some of these transients are the explosive deaths of stars known as supernovae while others are rare, exotic, or entirely new kinds of exciting stellar explosions. New astronomical sky surveys are observing unprecedented numbers of multi-wavelength transients, making standard approaches of visually identifying new and interesting transients infeasible. To meet this demand, we present two novel methods that aim to quickly and automatically detect anomalous transient light curves in real-time. Both methods are based on the simple idea that if the light curves from a known population of transients can be accurately modelled, any deviations from model predictions are likely anomalies. The first approach is a probabilistic neural network built using Temporal Convolutional Networks (TCNs) and the second is an interpretable Bayesian parametric model of a transient. We show that the flexibility of neural networks, the attribute that makes them such a powerful tool for many regression tasks, is what makes them less suitable for anomaly detection when compared with our parametric model.