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Pipeline for Detection of Transient Objects in Optical Surveys

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Data Analytics and Management in Data Intensive Domains (DAMDID/RCDL 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1620))

Abstract

Identification and following study of optical transients (OTs) associated with cosmic gamma-ray bursts (GRBs) and gravitational wave (GWs) events is a relevant research problem of multi-messenger astronomy. Since their first discovery, one of the greatest challenges is the localisation uncertainty. The sources of OTs are initially localised with space gamma and X-ray telescopes or ground-based laser interferometers LIGO, Virgo and KAGRA having the poor positional accuracy on average. A joint localisation area typically covers about 1000 deg\(^{2}\) of the sky based on previous runs of LIGO and Virgo. The last 25 years has seen a rapid development of the robotic optical surveys. Such instruments equipped with wide-field cameras allow to cover the entire localisation area in several scans. As the result, a massive amount of scientific products is generated, including bulky series of astronomical images. After their processing, large object catalogues that may contain up to \(10^5\) celestial objects are created. It is necessary to identify the peculiar objects among other in the formed catalogues. Both data processing and identification of OTs must be carried out in real-time due to steep decay of brightness. To response pointed problems, the software pipelines are becoming a relevant solution. This paper provides a complete overview of the units of the actively developed pipeline for OT detection. The accuracy and performance metrics of the pipeline units, estimated for two wide-field telescopes are given. In conclusions, the future plans for the development are briefly discussed.

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Notes

  1. 1.

    https://gcn.gsfc.nasa.gov/gcn3_archive.html.

  2. 2.

    A technique to estimate a redshift by constructing a SED from imaging observations.

  3. 3.

    https://www.lsst.org.

  4. 4.

    https://classic.sdss.org/dr7/algorithms/sdssUBVRITransform.html.

  5. 5.

    https://vizier.u-strasbg.fr/viz-bin/VizieR.

  6. 6.

    https://github.com/astropy/astroscrappy.

  7. 7.

    http://astrometry.net.

  8. 8.

    https://www.scipy.org/scipylib/index.html.

  9. 9.

    In a similar way like SExtractor does, see the documentation: https://sextractor.readthedocs.io/en/latest/Position.html.

  10. 10.

    All objects are assumed to be elliptical.

  11. 11.

    DASK is a python library https://dask.org.

  12. 12.

    See Kosik’s paper http://www.iki.rssi.ru/seminar/virtual/kosik.doc.

  13. 13.

    Description of the SIP https://fits.gsfc.nasa.gov/registry/sip.html.

  14. 14.

    https://sqlite.org/index.html.

  15. 15.

    https://www.sqlalchemy.org/.

  16. 16.

    https://github.com/SAOImageDS9/SAOImageDS9.

  17. 17.

    https://astropy-regions.readthedocs.io/en/stable/index.html.

  18. 18.

    https://fermi.gsfc.nasa.gov/ssc/data/analysis/rmfit/gbm_data_tools/gdt-docs/index.html.

  19. 19.

    https://github.com/healpy/healpy.

  20. 20.

    https://github.com/acbecker/hotpants.

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Authors and Affiliations

  1. National Research University Higher School of Economics, Staraya Basmannaya 21/4, Moscow, 105066, Russia

    Nicolai Pankov, Alexei Pozanenko & Sergey Belkin

  2. Space Research Institute of the Russian Academy of Sciences, Profsoyuznaya 84/32, Moscow, 117997, Russia

    Nicolai Pankov, Alexei Pozanenko & Sergey Belkin

  3. University of North Carolina at Chapel Hill, 310 South Road, Chapel Hill, NC, 27514, USA

    Vladimir Kouprianov

  4. The Central Astronomical Observatory of the Russian Academy of Sciences at Pulkovo, Pulkovskoye Chaussee 65, Saint-Petersburg, 196140, Russia

    Vladimir Kouprianov

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    Alexei Pozanenko

  2. Federal Research Center “Computer Science and Control” of RAS, Moscow, Russia

    Sergey Stupnikov

  3. Christian-Albrecht University of Kiel, Kiel, Germany

    Bernhard Thalheim

  4. Universidad Carlos III de Madrid, Getafe, Spain

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Pankov, N., Pozanenko, A., Kouprianov, V., Belkin, S. (2022). Pipeline for Detection of Transient Objects in Optical Surveys. In: Pozanenko, A., Stupnikov, S., Thalheim, B., Mendez, E., Kiselyova, N. (eds) Data Analytics and Management in Data Intensive Domains. DAMDID/RCDL 2021. Communications in Computer and Information Science, vol 1620. Springer, Cham. https://doi.org/10.1007/978-3-031-12285-9_7

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