Zobrazeno 1 - 10
of 37
pro vyhledávání: '"Aleo, Patrick"'
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:
Gagliano, Alexander, Contardo, Gabriella, Foreman-Mackey, Daniel, Malz, Alex I., Aleo, Patrick D.
Substantial effort has been devoted to the characterization of transient phenomena from photometric information. Automated approaches to this problem have taken advantage of complete phase-coverage of an event, limiting their use for triggering rapid
Externí odkaz:
http://arxiv.org/abs/2305.08894
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:
Jacobson-Galán, Wynn, Dessart, Luc, Jones, David, Margutti, Raffaella, Coppejans, Deanne, Dimitriadis, Georgios, Foley, Ryan J., Kilpatrick, Charles D., Matthews, David J., Rest, Sofia, Terreran, Giacomo, Aleo, Patrick D., Auchettl, Katie, Blanchard, Peter K., Coulter, David A., Davis, Kyle W., de Boer, Thomas, DeMarchi, Lindsay, Drout, Maria R., Earl, Nicholas, Gagliano, Alexander, Gall, Christa, Hjorth, Jens, Huber, Mark E., Ibik, Adaeze L., Milisavljevic, Danny, Pan, Yen-Chen, Rest, Armin, Ridden-Harper, Ryan, Rojas-Bravo, César, Siebert, Matthew R., Smith, Ken W., Taggart, Kirsty, Tinyanont, Samaporn, Wang, Qinan, Zenati, Yossef
We present panchromatic observations and modeling of supernova (SN) 2020tlf, the first normal type II-P/L SN with confirmed precursor emission, as detected by the Young Supernova Experiment transient survey with the Pan-STARRS1 telescope. Pre-explosi
Externí odkaz:
http://arxiv.org/abs/2109.12136
Autor:
Chatterjee, Deep, Narayan, Gautham, Aleo, Patrick D., Malanchev, Konstantin, Muthukrishna, Daniel
As gravitational-wave (GW) interferometers become more sensitive and probe ever more distant reaches, the number of detected binary neutron star mergers will increase. However, detecting more events farther away with GWs does not guarantee correspond
Externí odkaz:
http://arxiv.org/abs/2108.04166
Autor:
Gagliano, Alexander, Izzo, Luca, Kilpatrick, Charles D., Mockler, Brenna, Jacobson-Galán, Wynn Vincente, Terreran, Giacomo, Dimitriadis, Georgios, Zenati, Yossef, Auchettl, Katie, Drout, Maria R., Narayan, Gautham, Foley, Ryan J., Margutti, R., Rest, Armin, Jones, D. O., Aganze, Christian, Aleo, Patrick D., Burgasser, Adam J., Coulter, D. A., Gerasimov, Roman, Gall, Christa, Hjorth, Jens, Hsu, Chih-Chun, Magnier, Eugene A., Mandel, Kaisey S., Piro, Anthony L., Rojas-Bravo, César, Siebert, Matthew R., Stacey, Holland, Stroh, Michael Cullen, Swift, Jonathan J., Taggart, Kirsty, Tinyanont, Samaporn
We present photometric and spectroscopic observations of Supernova 2020oi (SN 2020oi), a nearby ($\sim$17 Mpc) type-Ic supernova (SN Ic) within the grand-design spiral M100. We undertake a comprehensive analysis to characterize the evolution of SN 20
Externí odkaz:
http://arxiv.org/abs/2105.09963
Autor:
Matheson, Thomas, Stubens, Carl, Wolf, Nicholas, Lee, Chien-Hsiu, Narayan, Gautham, Saha, Abhijit, Scott, Adam, Soraisam, Monika, Bolton, Adam S., Hauger, Benjamin, Silva, David R., Kececioglu, John, Scheidegger, Carlos, Snodgrass, Richard, Aleo, Patrick D., Evans-Jacquez, Eric, Singh, Navdeep, Wang, Zhe, Yang, Shuo, Zhao, Zhenge
We describe the Arizona-NOIRLab Temporal Analysis and Response to Events System (ANTARES), a software instrument designed to process large-scale streams of astronomical time-domain alerts. With the advent of large-format CCDs on wide-field imaging te
Externí odkaz:
http://arxiv.org/abs/2011.12385
Autor:
Aleo, Patrick D., Lock, Simon J., Cox, Donna J., Levy, Stuart A., Naiman, J. P., Christensen, A. J., Borkiewicz, Kalina, Patterson, Robert
Scientific visualization tools are currently not optimized to create cinematic, production-quality representations of numerical data for the purpose of science communication. In our pipeline \texttt{Estra}, we outline a step-by-step process from a ra
Externí odkaz:
http://arxiv.org/abs/2006.00084
Autor:
Burke, Colin J., Aleo, Patrick D., Chen, Yu-Ching, Liu, Xin, Peterson, John R., Sembroski, Glenn H., Lin, Joshua Yao-Yu
Publikováno v:
Mon.Not.Roy.Astron.Soc. 490 (2019) 3952-3965
We apply a new deep learning technique to detect, classify, and deblend sources in multi-band astronomical images. We train and evaluate the performance of an artificial neural network built on the Mask R-CNN image processing framework, a general cod
Externí odkaz:
http://arxiv.org/abs/1908.02748