Engineering topics

Volnova, A. A.

Publications and source records attributed to Volnova, A. A..

3 records

DOE OSTI2022

GRB 110213A: A Study of Afterglow Electromagnetic Cascade Radiation

We obtained well-sampled optical photometry of GRB 110213A, including Swift/UVOT and XRT. Combining our data from those of other ground-based telescopes, we present 15 optical multicolor light curves showing similar shapes with two peaks. In contrast, in the X-ray band, only a single peak is observed between the two optical peaks. Temporal and spectral analysis of GRB 110213A shows that the X-rays differ from the optical for Phases I–III (before the second peak of the optical band at ~5.6 ks). Moreover, they have the same spectral behavior at late times (Phases IV–VI). These data indicate that the optical and X-ray emission are dominated by different components. The synchrotron-supported pair cascade emission is included in the standard external forward-shock model, which is dominated by synchrotron radiation and synchrotron self-Compton (SSC). We find that the optical bands of GRB 110213A are dominated by the cascade emission from synchrotron radiation + SSC at the early stage, while the primary synchrotron + SSC radiation dominates the X-ray band. At late stages, both the X-ray and optical bands are dominated by emission from primary synchrotron + SSC radiation. The cascade component can reasonably explain the first optical peak. In contrast, the primary synchrotron + SSC emission mainly contributes to the second peak.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

Active anomaly detection for time-domain discoveries

Aims. We present the first evidence that adaptive learning techniques can boost the discovery of unusual objects within astronomical light curve data sets. Methods. Our method follows an active learning strategy where the learning algorithm chooses objects which can potentially improve the learner if additional information about them is provided. This new information is subsequently used to update the machine learning model, allowing its accuracy to evolve with each new information. For the case of anomaly detection, the algorithm aims to maximize the number of scientifically interesting anomalies presented to the expert by slightly modifying the weights of a traditional Isolation Forest (IF) at each iteration. In order to demonstrate the potential of such techniques, we apply the Active Anomaly Discovery (AAD) algorithm to 2 data sets: simulated light curves from the Photometric LSST Astronomical Time-Series Classification Challenge (PLAsTiCC) and real light curves from the Open Supernova Catalog. We compare the AAD results to those of a static IF. For both methods, we performed a detailed analysis for all objects with the ~2% highest anomaly scores. Results. We show that, in the real data scenario , AAD was able to identify ~80% more true anomalies than the IF. This result is the first evidence that AAD algorithms can play a central role in the search for new physics in the era of large scale sky surveys.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

Anomaly detection in the Zwicky Transient Facility DR3

We present results from applying the SNAD anomaly detection pipeline to the third public data release of the Zwicky Transient Facility (ZTF DR3). The pipeline is composed of three stages: feature extraction, search of outliers with machine learning algorithms, and anomaly identification with followup by human experts. Our analysis concentrates in three ZTF fields, comprising more than 2.25 million objects. A set of four automatic learning algorithms was used to identify 277 outliers, which were subsequently scrutinized by an expert. From these, 188 (68 per cent) were found to be bogus light curves – including effects from the image subtraction pipeline as well as overlapping between a star and a known asteroid, 66 (24 per cent) were previously reported sources whereas 23 (8 per cent) correspond to non-catalogued objects, with the two latter cases of potential scientific interest (e.g. one spectroscopically confirmed RS Canum Venaticorum star, four supernovae candidates, one red dwarf flare). Moreover, using results from the expert analysis, we were able to identify a simple bi-dimensional relation that can be used to aid filtering potentially bogus light curves in future studies. We provide a complete list of objects with potential scientific application so they can be further scrutinised by the community. These results confirm the importance of combining automatic machine learning algorithms with domain knowledge in the construction of recommendation systems for astronomy. Our code is publicly available.

79 ASTRONOMY AND ASTROPHYSICS↗