ORCID Profile
0000-0002-8560-4449
Current Organisation
CNRS
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Publisher: EDP Sciences
Date: 07-2022
DOI: 10.1051/0004-6361/202142715
Abstract: Context. The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will produce a continuous stream of alerts made of varying sources in the sky. This data flow will be publicly advertised and distributed to scientists via broker systems such as F INK , whose task is to extract scientific information from the stream. Given the complexity and volume of the data to be generated, LSST is a prime target for machine learning (ML) techniques. One of the most challenging stages of this task is the construction of appropriate training s les which enable learning based on a limited number of spectroscopically confirmed objects. Aims. We describe how the F INK broker early supernova Ia (SN Ia) classifier optimizes its ML classifications by employing an active learning (AL) strategy. We demonstrate the feasibility of implementing such strategies in the current Zwicky Transient Facility (ZTF) public alert data stream. Methods. We compared the performance of two AL strategies: uncertainty s ling and random s ling. Our pipeline consists of three stages: feature extraction, classification, and learning strategy. Starting from an initial s le of ten alerts, including five SNe Ia and five non-Ia, we let the algorithm identify which alert should be added to the training s le. The system was allowed to evolve through 300 iterations. Results. Our data set consists of 23 840 alerts from ZTF with a confirmed classification via a crossmatch with the SIMBAD database and the Transient Name Server (TNS), 1600 of which were SNe Ia (1021 unique objects). After the learning cycle was completed, the data configuration consisted of 310 alerts for training and 23 530 for testing. Averaging over 100 realizations, the classifier achieved ~89% purity and ~54% efficiency. From 01 November 2020 to 31 October 2021 F INK applied its early SN Ia module to the ZTF stream and communicated promising SN Ia candidates to the TNS. From the 535 spectroscopically classified F INK candidates, 459 (86%) were proven to be SNe Ia. Conclusions. Our results confirm the effectiveness of AL strategies for guiding the construction of optimal training s les for astronomical classifiers. It demonstrates in real data that the performance of learning algorithms can be highly improved without the need of extra computational resources or overwhelmingly large training s les. This is, to our knowledge, the first application of AL to real alert data.
Publisher: Oxford University Press (OUP)
Date: 19-11-2020
Abstract: fink is a broker designed to enable science with large time-domain alert streams such as the one from the upcoming Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). It exhibits traditional astronomy broker features such as automatized ingestion, annotation, selection, and redistribution of promising alerts for transient science. It is also designed to go beyond traditional broker features by providing real-time transient classification that is continuously improved by using state-of-the-art deep learning and adaptive learning techniques. These evolving added values will enable more accurate scientific output from LSST photometric data for erse science cases while also leading to a higher incidence of new discoveries which shall accompany the evolution of the survey. In this paper, we introduce fink, its science motivation, architecture, and current status including first science verification cases using the Zwicky Transient Facility alert stream.
Publisher: American Astronomical Society
Date: 11-08-2015
Publisher: Oxford University Press (OUP)
Date: 25-07-2022
Abstract: We present our follow-up observations with GRANDMA of transient sources revealed by the Zwicky Transient Facility (ZTF). Over a period of six months, all ZTF alerts were examined in real time by a dedicated science module implemented in the Fink broker, which will be used in filtering of transients discovered by the Vera C. Rubin Observatory. In this article, we present three selection methods to identify kilonova candidates. Out of more than 35 million alerts, a hundred sources have passed our selection criteria. Six were then followed-up by GRANDMA (by both professional and amateur astronomers). The majority were finally classified either as asteroids or as supernovae events. We mobilized 37 telescopes, bringing together a large s le of images, taken under various conditions and quality. To complement the orphan kilonova candidates, we included three additional supernovae alerts to conduct further observations during summer 2021. We demonstrate the importance of the amateur astronomer community that contributed images for scientific analyses of new sources discovered in a magnitude range r′ = 17 − 19 mag. We based our rapid kilonova classification on the decay rate of the optical source that should exceed 0.3 mag d−1. GRANDMA’s follow-up determined the fading rate within 1.5 ± 1.2 d post-discovery, without waiting for further observations from ZTF. No confirmed kilonovae were discovered during our observing c aign. This work will be continued in the coming months in the view of preparing for kilonova searches in the next gravitational-wave observing run O4.
Publisher: American Physical Society (APS)
Date: 09-07-2014
Publisher: American Astronomical Society
Date: 05-2023
Abstract: Object GRB 221009A is the brightest gamma-ray burst (GRB) detected in more than 50 yr of study. In this paper, we present observations in the X-ray and optical domains obtained by the GRANDMA Collaboration and the Insight Collaboration. We study the optical afterglow with empirical fitting using the GRANDMA+HXMT-LE data sets augmented with data from the literature up to 60 days. We then model numerically using a Bayesian approach, and we find that the GRB afterglow, extinguished by a large dust column, is most likely behind a combination of a large Milky Way dust column and moderate low-metallicity dust in the host galaxy. Using the GRANDMA+HXMT-LE+XRT data set, we find that the simplest model, where the observed afterglow is produced by synchrotron radiation at the forward external shock during the deceleration of a top-hat relativistic jet by a uniform medium, fits the multiwavelength observations only moderately well, with a tension between the observed temporal and spectral evolution. This tension is confirmed when using the augmented data set. We find that the consideration of a jet structure (Gaussian or power law), the inclusion of synchrotron self-Compton emission, or the presence of an underlying supernova do not improve the predictions. Placed in the global context of GRB optical afterglows, we find that the afterglow of GRB 221009A is luminous but not extraordinarily so, highlighting that some aspects of this GRB do not deviate from the global known s le despite its extreme energetics and the peculiar afterglow evolution.
Publisher: American Astronomical Society
Date: 23-10-2017
Publisher: American Physical Society (APS)
Date: 02-04-2014
Publisher: American Physical Society (APS)
Date: 08-12-2015
Publisher: SPIE
Date: 23-07-2014
DOI: 10.1117/12.2055611
Publisher: SPIE
Date: 25-08-2022
DOI: 10.1117/12.2630240
Publisher: American Physical Society (APS)
Date: 04-2020
Publisher: EDP Sciences
Date: 09-2023
Publisher: Springer Science and Business Media LLC
Date: 06-01-2016
No related grants have been discovered for Julien Peloton.