Issue |
A&A
Volume 687, July 2024
|
|
---|---|---|
Article Number | A246 | |
Number of page(s) | 19 | |
Section | Numerical methods and codes | |
DOI | https://doi.org/10.1051/0004-6361/202450166 | |
Published online | 19 July 2024 |
Identification of problematic epochs in astronomical time series through transfer learning★,★★
1
INAF – Osservatorio Astronomico di Capodimonte,
via Moiariello 16,
80131
Napoli,
Italy
e-mail: stefano.cavuoti@inaf.it
2
INFN – Section of Naples,
via Cinthia 9,
80126
Napoli,
Italy
3
Department of Physics, University of Napoli “Federico II”,
via Cinthia 9,
80126
Napoli,
Italy
e-mail: demetra.decicco@unina.it
4
Millennium Institute of Astrophysics (MAS),
Nuncio Monseñor Sotero Sanz 100, Providencia,
Santiago,
Chile
5
AIMI, ARTORG Center, University of Bern,
Murtenstrasse 50,
Bern
3008,
Switzerland
6
Department of Physics and Astronomy ‘Augusto Righi’, University of Bologna,
via Piero Gobetti 93/2,
40129
Bologna,
Italy
7
INAF – Osservatorio di Astrofisica e Scienza dello Spazio di Bologna,
via Piero Gobetti 101,
40129
Bologna,
Italy
Received:
28
March
2024
Accepted:
6
May
2024
Aims. We present a novel method for detecting outliers in astronomical time series based on the combination of a deep neural network and a k-nearest neighbor algorithm with the aim of identifying and removing problematic epochs in the light curves of astronomical objects.
Methods. We used an EfficientNet network pretrained on ImageNet as a feature extractor and performed a k-nearest neighbor search in the resulting feature space to measure the distance from the first neighbor for each image. If the distance was above the one obtained for a stacked image, we flagged the image as a potential outlier.
Results. We applied our method to a time series obtained from the VLT Survey Telescope monitoring campaign of the Deep Drilling Fields of the Vera C. Rubin Legacy Survey of Space and Time. We show that our method can effectively identify and remove artifacts from the VST time series and improve the quality and reliability of the data. This approach may prove very useful in light of the amount of data that will be provided by the LSST, which will prevent the inspection of individual light curves. We also discuss the advantages and limitations of our method and suggest possible directions for future work.
Key words: methods: data analysis / techniques: image processing / galaxies: active
Observations were provided by the ESO programs 088.D-4013, 092.D-0370, and 094.D-0417 (PI: G. Pignata).
The code is available at https://github.com/cavuoti/AnomalyInTimeSeries.
© The Authors 2024
Open Access article, published by EDP Sciences, under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
This article is published in open access under the Subscribe to Open model. Subscribe to A&A to support open access publication.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.