Zobrazeno 1 - 10
of 766
pro vyhledávání: '"Gangler, A."'
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:
Kornilov, M. V., Korolev, V. S., Malanchev, K. L., Lavrukhina, A. D., Russeil, E., Semenikhin, T. A., Gangler, E., Ishida, E. E. O., Pruzhinskaya, M. V., Volnova, A. A., Sreejith, S.
Publikováno v:
proceeding from Data Analytics and Management in Data Intensive Domains (DAMDID) 2024
We present coniferest, an open source generic purpose active anomaly detection framework written in Python. The package design and implemented algorithms are described. Currently, static outlier detection analysis is supported via the Isolation fores
Externí odkaz:
http://arxiv.org/abs/2410.17142
Autor:
Semenikhin, T. A., Kornilov, M. V., Pruzhinskaya, M. V., Lavrukhina, A. D., Russeil, E., Gangler, E., Ishida, E. E. O., Korolev, V. S., Malanchev, K. L., Volnova, A. A., Sreejith, S.
In the task of anomaly detection in modern time-domain photometric surveys, the primary goal is to identify astrophysically interesting, rare, and unusual objects among a large volume of data. Unfortunately, artifacts -- such as plane or satellite tr
Externí odkaz:
http://arxiv.org/abs/2409.10256
Autor:
Voloshina, A. S., Lavrukhina, A. D., Pruzhinskaya, M. V., Malanchev, K. L., Ishida, E. E. O., Krushinsky, V. V., Aleo, P. D., Gangler, E., Kornilov, M. V., Korolev, V. S., Russeil, E., Semenikhin, T. A., Sreejith, S., Volnova, A. A.
Publikováno v:
Monthly Notices of the Royal Astronomical Society, Volume 533, Issue 4, October 2024, Pages 4309-4323
Most of the stars in the Universe are M spectral class dwarfs, which are known to be the source of bright and frequent stellar flares. In this paper, we propose new approaches to discover M-dwarf flares in ground-based photometric surveys. We employ
Externí odkaz:
http://arxiv.org/abs/2404.07812
Autor:
Russeil, Etienne, de França, Fabrício Olivetti, Malanchev, Konstantin, Burlacu, Bogdan, Ishida, Emille E. O., Leroux, Marion, Michelin, Clément, Moinard, Guillaume, Gangler, Emmanuel
Symbolic regression (SR) searches for analytical expressions representing the relationship between a set of explanatory and response variables. Current SR methods assume a single dataset extracted from a single experiment. Nevertheless, frequently, t
Externí odkaz:
http://arxiv.org/abs/2402.04298
Autor:
Russeil, E., Malanchev, K. L., Aleo, P. D., Ishida, E. E. O., Pruzhinskaya, M. V., Gangler, E., Lavrukhina, A. D., Volnova, A. A., Voloshina, A., Semenikhin, T., Sreejith, S., Kornilov, M. V., Korolev, V. S.
We present Rainbow, a physically motivated framework which enables simultaneous multi-band light curve fitting. It allows the user to construct a 2-dimensional continuous surface across wavelength and time, even in situations where the number of obse
Externí odkaz:
http://arxiv.org/abs/2310.02916
Autor:
Aldoroty, L., Wang, L., Hoeflich, P., Yang, J., Suntzeff, N., Aldering, G., Antilogus, P., Aragon, C., Bailey, S., Baltay, C., Bongard, S., Boone, K., Buton, C., Copin, Y., Dixon, S., Fouchez, D., Gangler, E., Gupta, R., Hayden, B., Karmen, Mitchell, Kim, A. G., Kowalski, M., Küsters, D., Léget, P. -F., Mondon, F., Nordin, J., Pain, R., Pecontal, E., Pereira, R., Perlmutter, S., Ponder, K. A., Rabinowitz, D., Rigault, M., Rubin, D., Runge, K., Saunders, C., Smadja, G., Suzuki, N., Tao, C., Thomas, R. C., Vincenzi, M.
Publikováno v:
The Astrophysical Journal, 948:10 (15pp), 2023 May 1
We apply the color-magnitude intercept calibration method (CMAGIC) to the Nearby Supernova Factory SNe Ia spectrophotometric dataset. The currently existing CMAGIC parameters are the slope and intercept of a straight line fit to the first linear regi
Externí odkaz:
http://arxiv.org/abs/2210.06708
Exploring the expansion history of the universe, understanding its evolutionary stages, and predicting its future evolution are important goals in astrophysics. Today, machine learning tools are used to help achieving these goals by analyzing transie
Externí odkaz:
http://arxiv.org/abs/2210.00869
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:
Stein, George, Seljak, Uros, Bohm, Vanessa, Aldering, G., Antilogus, P., Aragon, C., Bailey, S., Baltay, C., Bongard, S., Boone, K., Buton, C., Copin, Y., Dixon, S., Fouchez, D., Gangler, E., Gupta, R., Hayden, B., Hillebrandt, W., Karmen, M., Kim, A. G., Kowalski, M., Kusters, D., Leget, P. F., Mondon, F., Nordin, J., Pain, R., Pecontal, E., Pereira, R., Perlmutter, S., Ponder, K. A., Rabinowitz, D., Rigault, M., Rubin, D., Runge, K., Saunders, C., Smadja, G., Suzuki, N., Tao, C., Thomas, R. C., Vincenzi, M.
We construct a physically-parameterized probabilistic autoencoder (PAE) to learn the intrinsic diversity of type Ia supernovae (SNe Ia) from a sparse set of spectral time series. The PAE is a two-stage generative model, composed of an Auto-Encoder (A
Externí odkaz:
http://arxiv.org/abs/2207.07645