The mean optical and radio flux density of our sample of radio variables is over the underlying distribution of astrophysical classes (Stewart et al. 2018). Black crosses indicate parallels in the MeerLICHT database, while gray triangles are upper limits. The diagonal lines represent a constant ratio between the radio and optical flux densities, while the 𝐴𝑅 symbol represents the horizontal displacement caused by the 5 magnitudes of optical extinction. Most of our radio sources are extragalactic because they overlap in parameter space with quasars and GRBs. – astro-ph.IM
New generations of radio telescopes can probe large areas with high sensitivity and cadence, generating data volumes that require new methods to better understand the transient sky.
Here, we describe the results of the first citizen science project dedicated to early radio stations, using data from the Meerkat telescope with weekly cadences. Explosions from space: MeerKAT was launched in late 2021 and received ~89000 classifications from over 1000 volunteers in 3 months. Our volunteers discovered 142 new variable sources that, along with known transients in our fields, allowed us to estimate that at least 2.1 percent of radio sources vary in sampling cadence and sensitivity by 1.28 GHz, consistent with previous work.
We present a full list of these sources, the largest of candidate radio variables to date. In addition to recovering known starbursts and X-ray binary jets in our observations, transient sources found with archival counterparts include a pulsar (B1845-01) and an OH maser star (OH 30.1-0.7). Data from the MeerLICHT optical telescope, along with estimates of long time-scale variations induced by scintillation, indicate that most of the new variables are active galactic nuclei. This tells us that citizen scientists can detect phenomena that vary in timescales from weeks to years.
The success of both volunteer engagement and scientific merit guarantees the continued development of the project, while we use the volunteers’ classifications to develop machine learning techniques to detect transients.
Alex Anderson, Chris Lindott, Rob Fender, Joe Bright, Francesco Carotenuto, Laura Driesen, Mathilde Espinas, Ian Heywood, Alexander J. Van der Horst, Sarah Motta, Lauren Rhodes, Evangelia Tremo, David R.A. Williams, Xian Juyang, Geode Steven Blumen, Paul Grudt, Paul Vrieswijk, Stefano Giarratana, Clone Saigea, Jonas Anderson, Lizeth Ruiz Arroyo, Loic Baird, Matthew Baumann, Wilfried Dominko, Thorsten Schweiler, Tim Forsinho, David Gauden, Sauro Gauden , Sauro Cowden, Kyle J. Melville, Marianne de Souza Nascimento, Leticia Navarro, Sai Parthasarathy, Philonen, Najma Rahman, Jeffrey Smith, B. Stewart, Newton Demock, Chloe Turek, Isabelle Whittle
Comments: Accepted for MNRAS, 14 pages + appendix containing our main data table
Subjects: High Energy Astrophysical Phenomena (astro-ph.HE); Astrophysics of the Galaxies (astro-ph.GA); Astrophysical Instruments and Methods (astro-ph.IM)
Citation: arXiv:2304.14157 [astro-ph.HE] (or arXiv:2304.14157v1 [astro-ph.HE] for this version)
Submission history
Posted by: Alex Anderson
[v1] Thu, 27 Apr 2023 12:53:38 UTC (3,453 KB)
https://arxiv.org/abs/2304.14157