Grandma: A Network to Coordinate Them All

Agayeva, S.; Alishov, S.; Antier, S.; Ayvazian, V.; Bai, Jinming; Baransky, A.; Barynova, K.; Basa, S.; Beradze, S.; Bertin, E.; Berthier, J.; Błażek, M.; Boër, M.; Бурхонов, О.; Burrell, A. G.; Cailleau, A.; Chabert, B.; Chen, J.C.; Christensen, N.; Coleiro, A.; Corre, D.; Coughlin, M. W.; Coward, D. M.; Crisp, H.; Delattre, C.; Dietrich, Tim; Ducoin, J.-G.; Duverne, P.-A.; Eymar, L.; Fock-Hang, P.; Gendre, B.; Hello, P.; Howell, E. J.; Inasaridze, R.; Исмаилов, Н. З.; Канн, Д. А.; Kapanadze, G. V.; Карпов, С.; Klotz, A.; Kochiashvili, N.; Lachaud, C.; Leroy, N.; Su, Anlong; Li, W.X.; Lin, Wei; Lognone, P.; Marchal-Duval, G.; Marron, R.; Mašek, M.; Mo, Jun; Moore, J.; Morris, D.; Natsvlishvili, R.; Noysena, K.; Orange, N.; Perrigault, S.; Peyrot, A.; Prouza, M.; Sadibekova, T.; Samadov, D.; Simon, A.; Stachie, C.; Teng, J. P.; Thierry, P.; Thöne, C. C.; Tillayev, Yusufjon; Turpin, D.; Postigo, A. de Ugarte; Vachier, F.; Vardosanidze, M.; Vasylenko, V.; Vidadi, Z.; Wang, C.J.; Wang, X.F.; Yan, Shengyu; Zhang, Juchen; Zhang, And X.H.

doi:10.22201/ia.14052059p.2021.53.39

Cited by 5 publications

(2 citation statements)

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“…Likewise, theoretical forecasts can identify and prioritize follow-up photometry at critical phases of transient evolution, including monitoring the plateau dropoff of SNe II light curves that provides information needed to improve estimates of kinetic energy and ZAMS and 56 Ni masses. Ideally, predicting transient evolution using the underlying physics of transients can be incorporated into a TOM or ORACLE that can efficiently recommend targets for follow-up at information-rich epochs (Djorgovski et al 2016;Street et al 2018;Kasliwal et al 2019;Sravan et al 2020;Agayeva et al 2021).…”

Section: Discussionmentioning

confidence: 99%

Inferencing Progenitor and Explosion Properties of Evolving Core-collapse Supernovae from Zwicky Transient Facility Light Curves

Subrayan

Milisavljevic

Moriya

et al. 2023

ApJ

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We analyze a sample of 45 Type II supernovae from the Zwicky Transient Facility public survey using a grid of hydrodynamical models in order to assess whether theoretically driven forecasts can intelligently guide follow-up observations supporting all-sky survey alert streams. We estimate several progenitor properties and explosion physics parameters, including zero-age main-sequence (ZAMS) mass, mass-loss rate, kinetic energy, 56Ni mass synthesized, host extinction, and the time of the explosion. Using complete light curves we obtain confident characterizations for 34 events in our sample, with the inferences of the remaining 11 events limited either by poorly constraining data or the boundaries of our model grid. We also simulate real-time characterization of alert stream data by comparing our model grid to various stages of incomplete light curves (Δt < 25 days, Δt < 50 days, all data), and find that some parameters are more reliable indicators of true values at early epochs than others. Specifically, ZAMS mass, time of the explosion, steepness parameter β, and host extinction are reasonably constrained with incomplete light-curve data, whereas mass-loss rate, kinetic energy, and 56Ni mass estimates generally require complete light curves spanning >100 days. We conclude that real-time modeling of transients, supported by multi-band synthetic light curves tailored to survey passbands, can be used as a powerful tool to identify critical epochs of follow-up observations. Our findings are relevant to identifying, prioritizing, and coordinating efficient follow-up of transients discovered by the Vera C. Rubin Observatory.

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Section: Discussionmentioning

confidence: 99%

Inferencing Progenitor and Explosion Properties of Evolving Core-collapse Supernovae from Zwicky Transient Facility Light Curves

Subrayan

Milisavljevic

Moriya

et al. 2023

ApJ

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“…The Global Rapid Advanced Network Devoted to the Multi-messenger Addicts (GRANDMA) is an international project, which currently coordinates 25 telescopes with both photometric and spectroscopic facilities [159,160], including the TAROT and FRAM described above, purposed to provide coordinated observations of transients in large regions of the sky. Being primarily aimed at GW alert follow-ups, GRANDMA can follow-up any transient alerts, even with poor spatial localization.…”

Section: Grandmamentioning

confidence: 99%

Key Space and Ground Facilities in GRB Science

et al. 2022

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Gamma-ray bursts (GRBs) are short and intense flashes of γ-rays coming from deep space. GRBs were discovered more than a half century ago and now are observed across the whole electromagnetic spectrum from radio to very-high-energy gamma rays. They carry information about the powerful energy release during the final stage of stellar evolution, as well as properties of matter on the way to the observer. At present, space-based observatories detect on average approximately one GRB per day. In this review, we summarize key space and ground facilities that contribute to the GRB studies.

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O’TRAIN: A robust and flexible ‘real or bogus’ classifier for the study of the optical transient sky

Turpin

Corre

Карпов

et al. 2022

A&A

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Context. Scientific interest in studying high-energy transient phenomena in the Universe has risen sharply over the last decade. At present, multiple ground-based survey projects have emerged to continuously monitor the optical (and multi-messenger) transient sky at higher image cadences and covering ever larger portions of the sky every night. These novel approaches are leading to a substantial increase in global alert rates, which need to be handled with care, especially with regard to keeping the level of false alarms as low as possible. Therefore, the standard transient detection pipelines previously designed for narrow field-of-view instruments must now integrate more sophisticated tools to deal with the growing number and diversity of alerts and false alarms. Aims. Deep machine learning algorithms have now proven their efficiency in recognising patterns in images. These methods are now used in astrophysics to perform different classification tasks such as identifying bogus from real transient point-like sources. We explore this method to provide a robust and flexible algorithm that could be included in any kind of transient detection pipeline. Methods. We built a convolutional neural network (CNN) algorithm in order to perform a ‘real or bogus’ classification task on transient candidate cutouts (subtraction residuals) provided by different kinds of optical telescopes. The training involved human-supervised labelling of the cutouts, which are split into two balanced data sets with ‘true’ and ‘false’ point-like source candidates. We tested our CNN model on the candidates produced by two different transient detection pipelines. In addition, we made use of several diagnostic tools to evaluate the classification performance of our CNN models. Results. We show that our CNN algorithm can be successfully trained on a large and diverse array of images on very different pixel scales. In this training process, we did not detect any strong over- or underfitting with the requirement of providing cutouts with a limited size no larger than 50 × 50 pixels. Tested on optical images from four different telescopes and utilising two different transient detection pipelines, our CNN model provides a robust ‘real or bogus’ classification performance accuracy from 93% up to 98% for well-classified candidates.

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Grandma: A Network to Coordinate Them All

Cited by 5 publications

References 0 publications

Inferencing Progenitor and Explosion Properties of Evolving Core-collapse Supernovae from Zwicky Transient Facility Light Curves

Inferencing Progenitor and Explosion Properties of Evolving Core-collapse Supernovae from Zwicky Transient Facility Light Curves

Key Space and Ground Facilities in GRB Science

O’TRAIN: A robust and flexible ‘real or bogus’ classifier for the study of the optical transient sky

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