A Deep-learning Approach for Live Anomaly Detection of Extragalactic Transients Article Swipe
V. Ashley Villar
,
Miles Cranmer
,
E. Berger
,
Gabriella Contardo
,
Shirley Ho
,
G. Hosseinzadeh
,
Joshua Yao-Yu Lin
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3847/1538-4365/ac0893
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3847/1538-4365/ac0893
There is a shortage of multiwavelength and spectroscopic follow-up capabilities given the number of transient and variable astrophysical events discovered through wide-field optical surveys such as the upcoming Vera C. Rubin Observatory and its associated Legacy Survey of Space and Time. 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 data set 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.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3847/1538-4365/ac0893
- https://iopscience.iop.org/article/10.3847/1538-4365/ac0893/pdf
- OA Status
- bronze
- Cited By
- 39
- References
- 90
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3137069764
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- OpenAlex ID
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https://openalex.org/W3137069764Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3847/1538-4365/ac0893Digital Object Identifier
- Title
-
A Deep-learning Approach for Live Anomaly Detection of Extragalactic TransientsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-08-01Full publication date if available
- Authors
-
V. Ashley Villar, Miles Cranmer, E. Berger, Gabriella Contardo, Shirley Ho, G. Hosseinzadeh, Joshua Yao-Yu LinList of authors in order
- Landing page
-
https://doi.org/10.3847/1538-4365/ac0893Publisher landing page
- PDF URL
-
https://iopscience.iop.org/article/10.3847/1538-4365/ac0893/pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
- OA URL
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https://iopscience.iop.org/article/10.3847/1538-4365/ac0893/pdfDirect OA link when available
- Concepts
-
Haystack, Autoencoder, Anomaly detection, Observatory, Anomaly (physics), Supernova, Computer science, Artificial neural network, Artificial intelligence, Physics, Astrophysics, Condensed matter physicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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39Total citation count in OpenAlex
- Citations by year (recent)
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2025: 7, 2024: 9, 2023: 12, 2022: 7, 2021: 4Per-year citation counts (last 5 years)
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90Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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