A Deep Learning Approach for Active Anomaly Detection of Extragalactic Transients

• URL: http://arxiv.org/abs/2103.12102v1
• Date: Mon, 22 Mar 2021 18:02:19 GMT
• Title: A Deep Learning Approach for Active Anomaly Detection of Extragalactic Transients
• Authors: V. Ashley Villar, Miles Cranmer, Edo Berger, Gabriella Contardo, Shirley Ho, Griffin Hosseinzadeh, Joshua Yao-Yu Lin
• Abstract summary: We present a variational recurrent autoencoder neural network to encode simulated Rubin Observatory extragalactic transient events. We rank 1,129,184 events based on an anomaly score estimated using an isolation forest. Our algorithm is able to identify these transients as anomalous well before peak, enabling real-time follow up studies.
• Score: 1.7152709285783647
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: There is a shortage of multi-wavelength and spectroscopic followup capabilities given the number of transient and variable astrophysical events discovered through wide-field, optical surveys such as the upcoming Vera C. Rubin Observatory. From the haystack of potential science targets, astronomers must allocate scarce resources to study a selection of needles in real time. Here we present a variational recurrent autoencoder neural network to encode simulated Rubin Observatory extragalactic transient events using 1% of the PLAsTiCC dataset to train the autoencoder. Our unsupervised method uniquely works with unlabeled, real time, multivariate and aperiodic data. We rank 1,129,184 events based on an anomaly score estimated using an isolation forest. We find that our pipeline successfully ranks rarer classes of transients as more anomalous. Using simple cuts in anomaly score and uncertainty, we identify a pure (~95% pure) sample of rare transients (i.e., transients other than Type Ia, Type II and Type Ibc supernovae) including superluminous and pair-instability supernovae. Finally, our algorithm is able to identify these transients as anomalous well before peak, enabling real-time follow up studies in the era of the Rubin Observatory.

Related papers

• A Classifier-Based Approach to Multi-Class Anomaly Detection Applied to Astronomical Time-Series [0.0]
  anomaly detection is an open problem in many scientific fields. Most anomaly detection algorithms for astronomical time-series rely either on hand-crafted features or on features generated through unsupervised representation learning. We introduce a novel approach that leverages the latent space of a neural network classifier for anomaly detection.
  arXiv  Detail & Related papers  (2024-08-05T18:00:00Z)
• A Classifier-Based Approach to Multi-Class Anomaly Detection for Astronomical Transients [0.0]
  Real-time anomaly detection is essential for identifying rare transients in the era of large-scale astronomical surveys. Currently, most anomaly detection algorithms for astronomical transients rely on hand-crafted features extracted from light curves. We introduce an alternative approach to detecting anomalies: using the penultimate layer of a neural network classifier as the latent space for anomaly detection.
  arXiv  Detail & Related papers  (2024-03-21T18:00:00Z)
• From Chaos to Clarity: Time Series Anomaly Detection in Astronomical Observations [6.903396830919462]
  We propose a two-stage framework for unsupervised anomaly detection in astronomical observations. In the first stage, we employ a Transformer-based encoder-decoder architecture to learn the normal temporal patterns on each star. In the second stage, we enhance the graph neural network with a window-wise graph structure learning to tackle the occurrence of concurrent noise.
  arXiv  Detail & Related papers  (2024-03-15T11:39:12Z)
• Graph Spatiotemporal Process for Multivariate Time Series Anomaly Detection with Missing Values [67.76168547245237]
  We introduce a novel framework called GST-Pro, which utilizes a graphtemporal process and anomaly scorer to detect anomalies. Our experimental results show that the GST-Pro method can effectively detect anomalies in time series data and outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-01-11T10:10:16Z)
• Video Anomaly Detection via Spatio-Temporal Pseudo-Anomaly Generation : A Unified Approach [49.995833831087175]
  This work proposes a novel method for generating generic Video-temporal PAs by inpainting a masked out region of an image. In addition, we present a simple unified framework to detect real-world anomalies under the OCC setting. Our method performs on par with other existing state-of-the-art PAs generation and reconstruction based methods under the OCC setting.
  arXiv  Detail & Related papers  (2023-11-27T13:14:06Z)
• CARLA: Self-supervised Contrastive Representation Learning for Time Series Anomaly Detection [53.83593870825628]
  One main challenge in time series anomaly detection (TSAD) is the lack of labelled data in many real-life scenarios. Most of the existing anomaly detection methods focus on learning the normal behaviour of unlabelled time series in an unsupervised manner. We introduce a novel end-to-end self-supervised ContrAstive Representation Learning approach for time series anomaly detection.
  arXiv  Detail & Related papers  (2023-08-18T04:45:56Z)
• Are we certain it's anomalous? [57.729669157989235]
  Anomaly detection in time series is a complex task since anomalies are rare due to highly non-linear temporal correlations. Here we propose the novel use of Hyperbolic uncertainty for Anomaly Detection (HypAD) HypAD learns self-supervisedly to reconstruct the input signal.
  arXiv  Detail & Related papers  (2022-11-16T21:31:39Z)
• Semi-Supervised Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection [57.85347204640585]
  We develop a Universal Domain Adaptation method DeepAstroUDA. It can be applied to datasets with different types of class overlap. For the first time, we demonstrate the successful use of domain adaptation on two very different observational datasets.
  arXiv  Detail & Related papers  (2022-11-01T18:07:21Z)
• Real-time Detection of Anomalies in Multivariate Time Series of Astronomical Data [0.0]
  Astronomical transients are stellar objects that become temporarily brighter on various timescales. New astronomical sky surveys are observing unprecedented numbers of multi-wavelength transients. We present two novel methods that aim to quickly and automatically detect anomalous transient light curves in real-time.
  arXiv  Detail & Related papers  (2021-12-15T19:02:54Z)
• Real-time detection of anomalies in large-scale transient surveys [0.0]
  We present two novel methods of automatically detecting 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.
  arXiv  Detail & Related papers  (2021-10-29T18:29:25Z)
• TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks [73.01104041298031]
  TadGAN is an unsupervised anomaly detection approach built on Generative Adversarial Networks (GANs) To capture the temporal correlations of time series, we use LSTM Recurrent Neural Networks as base models for Generators and Critics. To demonstrate the performance and generalizability of our approach, we test several anomaly scoring techniques and report the best-suited one.
  arXiv  Detail & Related papers  (2020-09-16T15:52:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.