[en] In the era of sky surveys like PTF, ZTF and the upcoming Vera Rubin Observatory and ILMT, a plethora of imaging data will become available. ZTF scans the sky with a field of view of 48 deg2 and the upcoming Rubin Observatory will have a FoV of 9.6 deg2 but with much larger aperture. The 4m ILMT covers a 22’ wide strip. Transient detection requires all these imaging data to be processed through a Difference Imaging Algorithm and subsequent identification and classification. The ILMT is also expected to discover several known and unknown astrophysical objects including transients. Here, we propose an image subtraction algorithm and a convolutional neural network based automated transient discovery system which will be integrated in the ILMT transient detection and classification pipeline in the future.
Disciplines :
Space science, astronomy & astrophysics
Author, co-author :
Pranshu, Kumar; Aryabhatta Research Institute of Observational sciencES, Nainital, India ; University of British Columbia, Vancouver, Canada
Ailawadhi, Bhavya; Aryabhatta Research Institute of Observational sciencES, India ; Deen Dayal, Upadhyay Gorakhpur University, Gorakhpur, India
Akhunov, Talat; National University of Uzbekistan, Tashkent, Uzbekistan ; Ulugh Beg Astronomical Institute, Tashkent, Uzbekistan
Borra, Ermanno; Laval University, Canada
Dubey, Monalisa; Aryabhatta Research Institute of Observational sciencES, India ; Mahatma Jyotiba Phule Rohilkhand University, Bareilly, India
Dukiya, Naveen; Aryabhatta Research Institute of Observational sciencES, India ; Mahatma Jyotiba Phule Rohilkhand University, Bareilly, India
Fu, Jiuyang; University of British Columbia, Vancouver, Canada
Grewal, Baldeep; University of British Columbia, Vancouver, Canada
Hickson, Paul; University of British Columbia, Vancouver, Canada
Kumar, Brajesh; Aryabhatta Research Institute of Observational sciencES, India
Misra, Kuntal; Aryabhatta Research Institute of Observational sciencES, India
Negi, Vibhore; Aryabhatta Research Institute of Observational sciencES, India ; Deen Dayal, Upadhyay Gorakhpur University, Gorakhpur, India
Ethen, Sun; University of British Columbia, Vancouver, Canada
Surdej, Jean ; Université de Liège - ULiège > Département d'astrophysique, géophysique et océanographie (AGO) ; Institut d'Astrophysique et de Géophysique, Liège University, Belgium
scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.
Bibliography
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., Kudlur, M., Levenberg, J., Monga, R., Moore, S., Murray, D. G., Steiner, B., Tucker, P., Vasudevan, V., Warden, P., Wicke, M., Yu, Y. and Zheng, X. (2016) TensorFlow: A system for large-scale machine learning. In 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16), pp. 265–283. USENIX Association, Savannah (GA). https://www.usenix.org/system/files/conference/osdi16/osdi16-abadi.pdf.
Alard, C. and Lupton, R. H. (1998) A method for optimal image subtraction. ApJ, 503(1), 325. https://doi.org/10.1086/305984.
Bellm, E. C., Kulkarni, S. R., Graham, M. J., Dekany, R., Smith, R. M., Riddle, R., Masci, F. J., Helou, G., Prince, T. A., Adams, S. M., Barbarino, C., Barlow, T., Bauer, J., Beck, R., Belicki, J., Biswas, R., Blagorodnova, N., Bodewits, D., Bolin, B., Brinnel, V., Brooke, T., Bue, B., Bulla, M., Burruss, R., Cenko, S. B., Chang, C.-K., Connolly, A., Coughlin, M., Cromer, J., Cunningham, V., De, K., Delacroix, A., Desai, V., Duev, D. A., Eadie, G., Farnham, T. L., Feeney, M., Feindt, U., Flynn, D., Franckowiak, A., Frederick, S., Fremling, C., Gal-Yam, A., Gezari, S., Giomi, M., Goldstein, D. A., Golkhou, V. Z., Goobar, A., Groom, S., Hacopians, E., Hale, D., Henning, J., Ho, A. Y. Q., Hover, D., Howell, J., Hung, T., Huppenkothen, D., Imel, D., Ip, W.-H., Ivezić, Ž., Jackson, E., Jones, L., Juric, M., Kasliwal, M. M., Kaspi, S., Kaye, S., Kelley, M. S. P., Kowalski, M., Kramer, E., Kupfer, T., Landry, W., Laher, R. R., Lee, C.-D., Lin, H. W., Lin, Z.-Y., Lunnan, R., Giomi, M., Mahabal, A., Mao, P., Miller, A. A., Monkewitz, S., Murphy, P., Ngeow, C.-C., Nordin, J., Nugent, P., Ofek, E., Patterson, M. T., Penprase, B., Porter, M., Rauch, L., Rebbapragada, U., Reiley, D., Rigault, M., Rodriguez, H., van Roestel, J., Rusholme, B., van Santen, J., Schulze, S., Shupe, D. L., Singer, L. P., Soumagnac, M. T., Stein, R., Surace, J., Sollerman, J., Szkody, P., Taddia, F., Terek, S., Van Sistine, A., van Velzen, S., Vestrand, W. T., Walters, R., Ward, C., Ye, Q.-Z., Yu, P.-C., Yan, L. and Zolkower, J. (2019) The Zwicky Transient Facility: System overview, performance, and first results. PASP, 131, 018002. https://doi.org/10.1088/1538-3873/aaecbe.
Beroiz, M., Cabral, J. and Sánchez, B. (2020) Astroalign: A Python module for astronomical image registration. A&C, 32, 100384. https://doi.org/10.1016/j.ascom.2020.100384.
Bradley, L., Sipocz, B., Robitaille, T., Tollerud, E., Deil, C., Vinícius, Z., Barbary, K., Günther, H. M., Bostroem, A., Droettboom, M., Bray, E., Bratholm, L. A., Pickering, T. E., Craig, M., Pascual, S., Greco, J., Donath, A., Kerzendorf, W., Littlefair, S., Barentsen, G., D’Eugenio, F. and Weaver, B. A. (2016) Photutils: Photometry tools. Astrophysics Source Code Library, record ascl:1609.011.
Bramich, D. M. (2008) A new algorithm for difference image analysis. MNRAS, 386(1), L77–L81. https://doi.org/10.1111/j.1745-3933.2008.00464.x.
Cabrera-Vives, G., Reyes, I., Förster, F., Estévez, P. A. and Maureira, J.-C. (2016) Supernovae detection by using convolutional neural networks. In 2016 IJCNN, pp. 251–258. https://doi.org/10.1109/IJCNN.2016.7727206.
Carrasco-Davis, R., Reyes, E., Valenzuela, C., Förster, F., Esté vez, P. A., Pignata, G., Bauer, F. E., Reyes, I., Sánchez-Sáez, P., Cabrera-Vives, G., Eyheramendy, S., Catelan, M., Arredondo, J., Castillo-Navarrete, E., Rodríguez-Mancini, D., Ruz-Mieres, D., Moya, A., Sabatini-Gacitúa, L., Sepúlveda-Cobo, C., Mahabal, A. A., Silva-Farfán, J., Camacho-Iñiguez, E. and Galbany, L. (2021) Alert classification for the ALeRCE broker system: The real-time stamp classifier. AJ, 162(6), 231. https://doi.org/10.3847/1538-3881/ac0ef1.
Duev, D. A., Mahabal, A., Masci, F. J., Graham, M. J., Rusholme, B., Walters, R., Karmarkar, I., Frederick, S., Kasliwal, M. M., Rebbapragada, U. and Ward, C. (2019) Real-bogus classification for the zwicky transient facility using deep learning. MNRAS, 489(3), 3582–3590. https://doi.org/10.1093/mnras/stz2357.
Huckvale, L., Kerins, E. and Sale, S. E. (2014) Reference image selection for difference imaging analysis. MNRAS, 442(1), 259–272. https://doi.org/10.1093/mnras/stu835.
Kumar, B., Dangwal, K., Rawat, H., Misra, K., Negi, V., Jaiswar, M., Dukiya, N., Ailawadhi, B., Hickson, P. and Surdej, J. (2023) First light preparations of the 4m ILMT. JAI, 11(04), 2240003. https://doi.org/10.1142/S2251171722400037.
Kumar, B., Pandey, K. L., Pandey, S. B., Hickson, P., Borra, E. F., Anupama, G. C. and Surdej, J. (2018) The zenithal 4-m International Liquid Mirror Telescope: a unique facility for supernova studies. MNRAS, 476(2), 2075–2085. https://doi.org/10.1093/mnras/sty298.
Lecun, Y., Bottou, L., Bengio, Y. and Haffner, P. (1998) Gradient-based learning applied to document recognition. IEEEP, 86(11), 2278–2324. https://doi.org/10.1109/5.726791.
Mahabal, A., Rebbapragada, U., Walters, R., Masci, F. J., Blagorodnova, N., van Roestel, J., Ye, Q.-Z., Biswas, R., Burdge, K., Chang, C.-K., Duev, D. A., Golkhou, V. Z., Miller, A. A., Nordin, J., Ward, C., Adams, S., Bellm, E. C., Branton, D., Bue, B., Cannella, C., Connolly, A., Dekany, R., Feindt, U., Hung, T., Fortson, L., Frederick, S., Fremling, C., Gezari, S., Graham, M., Groom, S., Kasliwal, M. M., Kulkarni, S., Kupfer, T., Lin, H. W., Lintott, C., Lunnan, R., Parejko, J., Prince, T. A., Riddle, R., Rusholme, B., Saunders, N., Sedaghat, N., Shupe, D. L., Singer, L. P., Soumagnac, M. T., Szkody, P., Tachibana, Y., Tirumala, K., van Velzen, S. and Wright, D. (2019) Machine learning for the Zwicky Transient Facility. PASP, 131, 038002. https://doi.org/10.1088/1538-3873/aaf3fa.
Sánchez-Sáez, P., Reyes, I., Valenzuela, C., Förster, F., Eyheramendy, S., Elorrieta, F., Bauer, F. E., Cabrera-Vives, G., Estévez, P. A., Catelan, M., Pignata, G., Huijse, P., Cicco, D. D., Arévalo, P., Carrasco-Davis, R., Abril, J., Kurtev, R., Borissova, J., Arredondo, J., Castillo-Navarrete, E., Rodriguez, D., Ruz-Mieres, D., Moya, A., Sabatini-Gacitúa, L., Sepúlveda-Cobo, C. and Camacho-Iñiguez, E. (2021) Alert classification for the ALeRCE broker system: The light curve classifier. AJ, 161(3), 141. https://doi.org/10.3847/1538-3881/abd5c1.
Sedaghat, N. and Mahabal, A. (2018) Effective image differencing with convolutional neural networks for real-time transient hunting. MNRAS, 476(4), 5365–5376. https://doi.org/10.1093/mnras/sty613.
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van der Walt, S. J., Brett, M., Wilson, J., Millman, K. J., Mayorov, N., Nelson, A. R. J., Jones, E., Kern, R., Larson, E., Carey, C. J., Polat, İ., Feng, Y., Moore, E. W., VanderPlas, J., Laxalde, D., Perktold, J., Cimrman, R., Henriksen, I., Quintero, E. A., Harris, C. R., Archibald, A. M., Ribeiro, A. H., Pedregosa, F., van Mulbregt, P., Vijaykumar, A., Bardelli, A. P., Rothberg, A., Hilboll, A., Kloeckner, A., Scopatz, A., Lee, A., Rokem, A., Woods, C. N., Fulton, C., Masson, C., Häggström, C., Fitzgerald, C., Nicholson, D. A., Hagen, D. R., Pasechnik, D. V., Olivetti, E., Martin, E., Wieser, E., Silva, F., Lenders, F., Wilhelm, F., Young, G., Price, G. A., Ingold, G.-L., Allen, G. E., Lee, G. R., Audren, H., Probst, I., Dietrich, J. P., Silterra, J., Webber, J. T., Slavič, J., Nothman, J., Buchner, J., Kulick, J., Schönberger, J. L., de Miranda Cardoso, J. V., Reimer, J., Harrington, J., Rodríguez, J. L. C., Nunez-Iglesias, J., Kuczynski, J., Tritz, K., Thoma, M., Newville, M., Kümmerer, M., Bolingbroke, M., Tartre, M., Pak, M., Smith, N. J., Nowaczyk, N., Shebanov, N., Pavlyk, O., Brodtkorb, P. A., Lee, P., McGibbon, R. T., Feldbauer, R., Lewis, S., Tygier, S., Sievert, S., Vigna, S., Peterson, S., More, S., Pudlik, T., Oshima, T., Pingel, T. J., Robitaille, T. P., Spura, T., Jones, T. R., Cera, T., Leslie, T., Zito, T., Krauss, T., Upadhyay, U., Halchenko, Y. O. and and, Y. V.-B. (2020) SciPy 1.0: fundamental algorithms for scientific computing in Python. NatMe, 17(3), 261–272. https://doi.org/10.1038/s41592-019-0686-2.
Similar publications
Sorry the service is unavailable at the moment. Please try again later.
This website uses cookies to improve user experience. Read more
Save & Close
Accept all
Decline all
Show detailsHide details
Cookie declaration
About cookies
Strictly necessary
Performance
Strictly necessary cookies allow core website functionality such as user login and account management. The website cannot be used properly without strictly necessary cookies.
This cookie is used by Cookie-Script.com service to remember visitor cookie consent preferences. It is necessary for Cookie-Script.com cookie banner to work properly.
Performance cookies are used to see how visitors use the website, eg. analytics cookies. Those cookies cannot be used to directly identify a certain visitor.
Used to store the attribution information, the referrer initially used to visit the website
Cookies are small text files that are placed on your computer by websites that you visit. Websites use cookies to help users navigate efficiently and perform certain functions. Cookies that are required for the website to operate properly are allowed to be set without your permission. All other cookies need to be approved before they can be set in the browser.
You can change your consent to cookie usage at any time on our Privacy Policy page.
