Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Pruzhinskaya, Maria V."'
Autor:
Volnova, Alina A., Aleo, Patrick D., Lavrukhina, Anastasia, Russeil, Etienne, Semenikhin, Timofey, Gangler, Emmanuel, Ishida, Emille E. O., Kornilov, Matwey V., Korolev, Vladimir, Malanchev, Konstantin, Pruzhinskaya, Maria V., Sreejith, Sreevarsha
Publikováno v:
In: Baixeries, J., Ignatov, D.I., Kuznetsov, S.O., Stupnikov, S. (eds) Data Analytics and Management in Data Intensive Domains. DAMDID/RCDL 2023. Communications in Computer and Information Science, vol 2086. Springer, Cham
SNAD is an international project with a primary focus on detecting astronomical anomalies within large-scale surveys, using active learning and other machine learning algorithms. The work carried out by SNAD not only contributes to the discovery and
Externí odkaz:
http://arxiv.org/abs/2410.18875
Autor:
Malanchev, Konstantin, Kornilov, Matwey V., Pruzhinskaya, Maria V., Ishida, Emille E. O., Aleo, Patrick D., Korolev, Vladimir S., Lavrukhina, Anastasia, Russeil, Etienne, Sreejith, Sreevarsha, Volnova, Alina A., Voloshina, Anastasiya, Krone-Martins, Alberto
We describe the SNAD Viewer, a web portal for astronomers which presents a centralized view of individual objects from the Zwicky Transient Facility's (ZTF) data releases, including data gathered from multiple publicly available astronomical archives
Externí odkaz:
http://arxiv.org/abs/2211.07605
Autor:
Pruzhinskaya, Maria V., Ishida, Emille E. O., Novinskaya, Alexandra K., Russeil, Etienne, Volnova, Alina A., Malanchev, Konstantin L., Kornilov, Matwey V., Aleo, Patrick D., Korolev, Vladimir S., Krushinsky, Vadim V., Sreejith, Sreevarsha, Gangler, Emmanuel
Publikováno v:
A&A 672, A111 (2023)
We provide the first results from the complete SNAD adaptive learning pipeline in the context of a broad scope of data from large-scale astronomical surveys. The main goal of this work is to explore the potential of adaptive learning techniques in ap
Externí odkaz:
http://arxiv.org/abs/2208.09053
Autor:
Balakina, Elena A., Pruzhinskaya, Maria V., Moskvitin, Alexander S., Blinnikov, Sergei I., Wang, Xiaofeng, Xiang, Danfeng, Lin, Han, Rui, Liming, Wang, Huijuan
In this work we present the photometric and spectroscopic observations of Type IIb Supernova 2017gpn. This supernova was discovered in the error-box of LIGO/Virgo G299232 gravitational-wave event. We obtained the light curves in B and R passbands and
Externí odkaz:
http://arxiv.org/abs/2008.07934
Autor:
Ishida, Emille E. O., Kornilov, Matwey V., Malanchev, Konstantin L., Pruzhinskaya, Maria V., Volnova, Alina A., Korolev, Vladimir S., Mondon, Florian, Sreejith, Sreevarsha, Malancheva, Anastasia, Das, Shubhomoy
Publikováno v:
A&A 650, A195 (2021)
We present the first evidence that adaptive learning techniques can boost the discovery of unusual objects within astronomical light curve data sets. Our method follows an active learning strategy where the learning algorithm chooses objects which ca
Externí odkaz:
http://arxiv.org/abs/1909.13260
Autor:
Pruzhinskaya, Maria V., Malanchev, Konstantin L., Kornilov, Matwey V., Ishida, Emille E. O., Mondon, Florian, Volnova, Alina A., Korolev, Vladimir S.
In the upcoming decade large astronomical surveys will discover millions of transients raising unprecedented data challenges in the process. Only the use of the machine learning algorithms can process such large data volumes. Most of the discovered t
Externí odkaz:
http://arxiv.org/abs/1905.11516
Publikováno v:
Monthly Notices of the Royal Astronomical Society, Volume 455, Issue 1, p.423-430 (2016)
It has been suggested that Type II supernovae with rapidly fading light curves (a.k.a. Type IIL supernovae) are explosions of progenitors with low-mass hydrogen-rich envelopes which are of the order of 1 Msun. We investigate light-curve properties of
Externí odkaz:
http://arxiv.org/abs/1510.01656
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