SuperRAENN: A Semisupervised Supernova Photometric Classification Pipeline Trained on Pan-STARRS1 Medium-Deep Survey Supernovae
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Abstract Automated classification of supernovae (SNe) based on optical photometric light-curve information is essential in the upcoming era of wide-field time domain surveys, such as the Legacy Survey of Space and Time (LSST) conducted by the Rubin Observatory. Photometric classification can enable real-time identification of interesting events for extended multiwavelength follow-up, as well as archival population studies. Here we present the complete sample of 5243 “SN-like” light curves (in g P1 r P1 i P1 z P1) from the Pan-STARRS1 Medium-Deep Survey (PS1-MDS). The PS1-MDS is similar to the planned LSST Wide-Fast-Deep survey in terms of cadence, filters, and depth, making this a useful training set for the community. Using this data set, we train a novel semisupervised machine learning algorithm to photometrically classify 2315 new SN-like light curves with host galaxy spectroscopic redshifts. Our algorithm consists of an RF supervised classification step and a novel unsupervised step in which we introduce a recurrent autoencoder neural network (RAENN). Our final pipeline, dubbed SuperRAENN, has an accuracy of 87% across five SN classes (Type Ia, Ibc, II, IIn, SLSN-I) and macro-averaged purity and completeness of 66% and 69%, respectively. We find the highest accuracy rates for SNe Ia and SLSNe and the lowest for SNe Ibc. Our complete spectroscopically and photometrically classified samples break down into 62.0% Type Ia (1839 objects), 19.8% Type II (553 objects), 4.8% Type IIn (136 objects), 11.7% Type Ibc (291 objects), and 1.6% Type I SLSNe (54 objects).
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Villar et al. (2021b) used a Variational Recurrent Autoencoder (VRAE) network described in Villar et al. (2020) to identify anomalous light curves from the simulated PLAsTiCC dataset.
“Villar et al. (2021b) used a Variational Recurrent Autoencoder (VRAE) network described in Villar et al. (2020) to identify anomalous light curves from the simulated PLAsTiCC dataset.”
From The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys (2023) · cites this paper as Villar et al. (2020)
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Bronder, T. J., Hook, I., Astier, P., Balam, D., Balland, C., Basa, S., Carlberg, R. G., Conley, A., Fouchez, D., Guy, J., et al. (2007) · Astronomy and Astrophysics · doi:10.1051/0004-6361:20077655 · Cited by 90 · see citing works
Graur, O., Bianco, F. B., Huang, S., Modjaz, M., Shivvers, I., Filippenko, A. V., Li, W., & Eldridge, J. J. (2017) · The Astrophysical Journal · doi:10.3847/1538-4357/aa5eb8 · Cited by 88 · see citing works
PS1-10afx ATz= 1.388: PAN-STARRS1 DISCOVERY OF A NEW TYPE OF SUPERLUMINOUS SUPERNOVA
Chornock, R., Berger, E., Rest, A., Milisavljevic, D., Lunnan, R., Foley, R. J., Soderberg, A. M., Smartt, S. J., Burgasser, A. J., Challis, P., et al. (2013) · The Astrophysical Journal · doi:10.1088/0004-637x/767/2/162 · Cited by 75 · see citing works
Self-citationSystematically Bridging the Gap Between Novae and Supernovae
Kasliwal, M. M. (2012) · Publications of the Astronomical Society of Australia · doi:10.1071/as11061 · Cited by 73 · see citing works
THE EFFECT OF DUST GEOMETRY ON THE Lyα OUTPUT OF GALAXIES
Scarlata, C., Colbert, J., Teplitz, H., Panagia, N., Hayes, M., Siana, B., Rau, A., Francis, P., Caon, A., Pizzella, A., et al. (2009) · The Astrophysical Journal · doi:10.1088/0004-637x/704/2/l98 · Cited by 71 · see citing works
Pasquet, J., & Pasquet, J. (2017) · Astronomy and Astrophysics · doi:10.1051/0004-6361/201731106 · Cited by 70 · see citing works
Kernel PCA for Type Ia supernovae photometric classification
Ishida, É. E. O., & de Souza, R. S. (2013) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/sts650 · Cited by 69 · see citing works
Semi-supervised learning for photometric supernova classification★
Richards, J. W., Homrighausen, D., Freeman, P. E., Schafer, C. M., & Poznanski, D. (2011) · Monthly Notices of the Royal Astronomical Society · doi:10.1111/j.1365-2966.2011.19768.x · Cited by 63 · see citing works
Ma, Z., Xu, H., Zhu, J., Hu, D., Li, W., Shan, C., Zhu, Z., Gu, L., Li, J., Liu, C., et al. (2019) · The Astrophysical Journal Supplement Series · doi:10.3847/1538-4365/aaf9a2 · Cited by 61 · see citing works
Sanders, N., Soderberg, A., Foley, R. J., Chornock, R., Milisavljevic, D., Margutti, R., Drout, M. R., Moe, M., Berger, E., Brown, W. R., et al. (2013) · The Astrophysical Journal · doi:10.1088/0004-637x/769/1/39 · Cited by 61 · see citing works
Self-citationJones, D. O., Scolnic, D. M., Riess, A. G., Kessler, R., Rest, A., Kirshner, R. P., Berger, E., Ortega, C. A., Foley, R. J., Chornock, R., et al. (2017) · The Astrophysical Journal · doi:10.3847/1538-4357/aa767b · Cited by 60 · see citing works
Self-citationNarayan, G., Foley, R. J., Berger, E., Botticella, M. T., Chornock, R., Huber, M. E., Rest, A., Scolnic, D. M., Smartt, S. J., Valenti, S., et al. (2011) · The Astrophysical Journal Letters · doi:10.1088/2041-8205/731/1/l11 · Cited by 60 · see citing works
Self-citationMultiscale probability mapping: groups, clusters and an algorithmic search for filaments in SDSS
Smith, A. G., Hopkins, A. M., Hunstead, R. W., & Pimbblet, K. A. (2012) · Monthly Notices of the Royal Astronomical Society · doi:10.1111/j.1365-2966.2012.20400.x · Cited by 59 · see citing works
DASH: Deep Learning for the Automated Spectral Classification of Supernovae and Their Hosts
Muthukrishna, D., Parkinson, D., & Tucker, B. E. (2019) · The Astrophysical Journal · doi:10.3847/1538-4357/ab48f4 · Cited by 54 · see citing works
PELICAN: deeP architecturE for the LIght Curve ANalysis
Pasquet, J., Pasquet, J., Chaumont, M., & Fouchez, D. (2019) · Astronomy and Astrophysics · doi:10.1051/0004-6361/201834473 · Cited by 52 · see citing works
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Radio Galaxy Zoo: Unsupervised Clustering of Convolutionally Auto-encoded Radio-astronomical Images
Ralph, N. O., Norris, R. P., Fang, G., Park, L. A. F., Galvin, T. J., Alger, M. J., Andernach, H., Lintott, C. J., Rudnick, L., Shabala, S. S., et al. (2019) · Publications of the Astronomical Society of the Pacific · doi:10.1088/1538-3873/ab213d · Cited by 50 · see citing works
Obscured and unobscured AGN populations in a hard-X-ray selected sample of the XMDS survey
Tajer, M., Polletta, M. D. C., Chiappetti, L., Maraschi, L., Trinchieri, G., Maccagni, D., Andreon, S., Garcet, O., Surdej, J., Pierre, M., et al. (2007) · Astronomy and Astrophysics · doi:10.1051/0004-6361:20066667 · Cited by 44 · see citing works
CLASSIFYING SUPERNOVAE USING ONLY GALAXY DATA
Foley, R. J., & Mandel, K. (2013) · The Astrophysical Journal · doi:10.1088/0004-637x/778/2/167 · Cited by 43 · see citing works
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STACCATO: a novel solution to supernova photometric classification with biased training sets
Revsbech, E. A., Trotta, R., & van Dyk, D. A. (2017) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stx2570 · Cited by 41 · see citing works
Optical identification of XMM sources in the Canada–France–Hawaii Telescope Legacy Survey
Stalin, C. S., Petitjean, P., Srianand, R., Fox, A. J., Coppolani, F., & Schwope, A. (2009) · Monthly Notices of the Royal Astronomical Society · doi:10.1111/j.1365-2966.2009.15636.x · Cited by 41 · see citing works
A CNN adapted to time series for the classification of Supernovae
Brunel, A., Pasquet, J., PASQUET, J., Rodriguez, N., Comby, F., Fouchez, D., & Chaumont, M. (2019) · Electronic Imaging · doi:10.2352/issn.2470-1173.2019.14.color-090 · Cited by 32 · see citing works
Luminous red galaxy clustering atz≃ 0.7 - first results using AAOmega
Ross, N. P., Shanks, T., Cannon, R. D., Wake, D. A., Sharp, R. G., Croom, S. M., & Peacock, J. A. (2008) · Monthly Notices of the Royal Astronomical Society · doi:10.1111/j.1365-2966.2008.13332.x · Cited by 30 · see citing works
Low-redshift quasars in the SDSS Stripe 82. The local environments
Karhunen, K., Kotilainen, J. K., Falomo, R., & Bettoni, D. (2014) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stu688 · Cited by 29 · see citing works
A PLETHORA OF ACTIVE GALACTIC NUCLEI AMONG Lyα GALAXIES AT LOW REDSHIFT
Finkelstein, S. L., Cohen, S. H., Malhotra, S., Rhoads, J. E., Papovich, C., Zheng, Z.-Y., & Wang, J. (2009) · The Astrophysical Journal · doi:10.1088/0004-637x/703/2/l162 · Cited by 29 · see citing works
Photometric Classification of 2315 Pan-STARRS1 Supernovae with Superphot
Hosseinzadeh, G., Dauphin, F., Villar, V. A., Berger, E., Jones, D. O., Challis, P., Chornock, R., Drout, M. R., Foley, R. J., Kirshner, R. P., et al. (2020) · The Astrophysical Journal · doi:10.3847/1538-4357/abc42b · Cited by 26 · see citing works
Self-citationUnsupervised feature-learning for galaxy SEDs with denoising autoencoders
Frontera–Pons, J., Sureau, F. C., Bobin, J., & Floc'h, E. L. (2017) · Astronomy and Astrophysics · doi:10.1051/0004-6361/201630240 · Cited by 24 · see citing works
Real-time, Value-driven Data Augmentation in the Era of LSST
Sravan, N., Milisavljevic, D., Reynolds, J. M., Lentner, G., & Linvill, M. (2020) · The Astrophysical Journal · doi:10.3847/1538-4357/ab8128 · Cited by 24 · see citing works
Self-citationThe WiggleZ Dark Energy Survey
Blake, C., Brough, S., Couch, W. J., Glazebrook, K., Poole, G., Davis, T. M., Drinkwater, M. J., Jurek, R. J., Pimbblet, K. A., Colless, M., et al. (2008) · Astronomy & Geophysics · doi:10.1111/j.1468-4004.2008.49519.x · Cited by 22 · see citing works
Optical and infrared properties of active galactic nuclei in the Lockman Hole
Rovilos, E., Fotopoulou, S., Salvato, M., Burwitz, V., Egami, E., Hasinger, G., & Szokoly, G. (2011) · Astronomy and Astrophysics · doi:10.1051/0004-6361/201015763 · Cited by 21 · see citing works
Photometric Light Curves Classification with Machine Learning
Gabruseva, T., Евгеньевич, З. С., & Wang, P. (2020) · Journal of Astronomical Instrumentation · doi:10.1142/s2251171720500051 · Cited by 16 · see citing works
THE DEEP SWIRE FIELD. III. WIYN SPECTROSCOPY
Owen, F. N., & Morrison, G. E. (2009) · The Astrophysical Journal Supplement Series · doi:10.1088/0067-0049/182/2/625 · Cited by 11 · see citing works
Light Curves of Pan-STARRS1 SN-like Transients
Villar, V. A., Hosseinzadeh, G., Berger, E., Ntampaka, M., Jones, D. O., Challis, P., Chornock, R., Drout, M. R., Foley, R. J., Kirshner, R., et al. (2020) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.3974949 · Cited by 4 · see citing works
Self-citationvillrv/SuperRAENN: v1.0 Release
Villrv (2020) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.3968714 · Cited by 2 · see citing works
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Unveiling the Universe with emerging cosmological probes
Moresco, M., Amati, L., Amendola, L., Birrer, S., Blakeslee, J. P., Cantiello, M., Cimatti, A., Darling, J., Valle, M. D., Fishbach, M., et al. (2022) · Living Reviews in Relativity · doi:10.1007/s41114-022-00040-z · Cited by 312 · see citing works
Perley, D. A., Fremling, C., Sollerman, J., Miller, A. A., Dahiwale, A., Sharma, Y., Bellm, E. C., Biswas, R., Brink, T. G., Bruch, R., et al. (2020) · Liverpool John Moores University · Cited by 181 · see citing works
Gaussian Process Regression for Astronomical Time Series
Aigrain, S., & Foreman-Mackey, D. T. (2023) · Annual Review of Astronomy and Astrophysics · doi:10.1146/annurev-astro-052920-103508 · Cited by 156 · see citing works
SALT3: An Improved Type Ia Supernova Model for Measuring Cosmic Distances
Kenworthy, W. D., Jones, D. O., Dai, M., Kessler, R., Scolnic, D. M., Brout, D., Siebert, M. R., Pierel, J. D. R., Dettman, K., Dimitriadis, G., et al. (2021) · The Astrophysical Journal · doi:10.3847/1538-4357/ac30d8 · Cited by 148 · see citing works
Self-citationThe Young Supernova Experiment: Survey Goals, Overview, and Operations
Jones, D. O., Foley, R. J., Narayan, G., Hjorth, J., Huber, M. E., Aleo, P. D., Alexander, K. D., Angus, C. R., Auchettl, K., Baldassare, V. F., et al. (2021) · The Astrophysical Journal · doi:10.3847/1538-4357/abd7f5 · Cited by 111 · see citing works
Self-citationThe Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys
Huertas-Company, M., & Lanusse, F. (2023) · Publications of the Astronomical Society of Australia · doi:10.1017/pasa.2022.55 · Cited by 102 · see citing works
“Villar et al. (2021b) used a Variational Recurrent Autoencoder (VRAE) network described in Villar et al. (2020) to identify anomalous light curves from the simulated PLAsTiCC dataset.”
MethodJones, D. O., Mandel, K. S., Kirshner, R., Thorp, S., Challis, P., Avelino, A., Brout, D., Burns, C. R., Foley, R. J., Pan, Y. C., et al. (2022) · The Astrophysical Journal · doi:10.3847/1538-4357/ac755b · Cited by 57 · see citing works
Self-citationTesting the consistency of dust laws in SN Ia host galaxies: a BayeSN examination of Foundation DR1
Thorp, S., Mandel, K. S., Jones, D. O., Ward, S. M., & Narayan, G. (2021) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stab2849 · Cited by 54 · see citing works
Self-citationA Deep-learning Approach for Live Anomaly Detection of Extragalactic Transients
Villar, V. A., Cranmer, M., Berger, E., Contardo, G., Ho, S., Hosseinzadeh, G., & Lin, J. Y.-Y. (2021) · The Astrophysical Journal Supplement Series · doi:10.3847/1538-4365/ac0893 · Cited by 49 · see citing works
Self-citationSuperluminous supernovae: an explosive decade
Nicholl, M. (2021) · Astronomy & Geophysics · doi:10.1093/astrogeo/atab092 · Cited by 48 · see citing works
Aleo, P., Malanchev, K. L., Sharief, S. N., Jones, D. O., Narayan, G., Foley, R. J., Villar, V. A., Angus, C. R., Baldassare, V., Bustamante-Rosell, M. J., et al. (2023) · The Astrophysical Journal Supplement Series · doi:10.3847/1538-4365/acbfba · Cited by 48 · see citing works
Self-citationSánchez-Sáez, P., Lira, H., Martí, L., Sánchez-Pi, N., Arredondo, J., Bauer, F. E., Bayo, A., Cabrera-Vives, G., Donoso-Oliva, C., Estévez, P. A., et al. (2021) · The Astronomical Journal · doi:10.3847/1538-3881/ac1426 · Cited by 47 · see citing works
Pierel, J. D. R., Jones, D. O., Kenworthy, W. D., Dai, M., Kessler, R., Ashall, C., Do, A., Peterson, E. R., Shappee, B. J., Siebert, M. R., et al. (2022) · The Astrophysical Journal · doi:10.3847/1538-4357/ac93f9 · Cited by 47 · see citing works
Self-citationGomez, S., Berger, E., Nicholl, M., Blanchard, P., & Hosseinzadeh, G. (2022) · Astrophysical Journal · doi:10.3847/1538-4357/ac9842 · Cited by 27 · see citing works
Self-citationA review of unsupervised learning in astronomy
Fotopoulou, S. (2024) · Astronomy and Computing · doi:10.1016/j.ascom.2024.100851 · Cited by 45 · see citing works
ParSNIP: Generative Models of Transient Light Curves with Physics-enabled Deep Learning
Boone, K. (2021) · The Astronomical Journal · doi:10.3847/1538-3881/ac2a2d · Cited by 38 · see citing works
“This approach has previously been demonstrated in Pasquet et al. (2019) and Villar et al. (2020).”
BackgroundMethodResultInfluentialMachine learning for observational cosmology
Moriwaki, K., Nishimichi, T., & Yoshida, N. (2023) · Reports on Progress in Physics · doi:10.1088/1361-6633/acd2ea · Cited by 37 · see citing works
The dark energy survey 5-yr photometrically identified type Ia supernovae
Möller, A., Smith, M., Sako, M., Sullivan, M., Vincenzi, M., Wiseman, P., Armstrong, P., Asorey, J., Brout, D., Carollo, D., et al. (2022) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stac1691 · Cited by 37 · see citing works
Self-citationGómez, S., Nicholl, M., Berger, E., Blanchard, P. K., Villar, V. A., Rest, S., Hosseinzadeh, G., Aamer, A., Ajay, Y., Athukoralalage, W., et al. (2024) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stae2270 · Cited by 36 · see citing works
Self-citationResults of the Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC)
Hložek, R., Malz, A. I., Ponder, K. A., Dai, M., Narayan, G., Ishida, É. E. O., Allam, T., Bahmanyar, A., Bi, X., Biswas, R., et al. (2023) · The Astrophysical Journal Supplement Series · doi:10.3847/1538-4365/accd6a · Cited by 35 · see citing works
Self-citationTranslating Neutron Star Observations to Nuclear Symmetry Energy via Deep Neural Networks
Krastev, P. G. (2022) · Galaxies · doi:10.3390/galaxies10010016 · Cited by 33 · see citing works
Identifying Tidal Disruption Events with an Expansion of the FLEET Machine-learning Algorithm
Gómez, S., Villar, V. A., Berger, E., Gezari, S., van Velzen, S., Nicholl, M., Blanchard, P. K., & Alexander, K. D. (2023) · The Astrophysical Journal · doi:10.3847/1538-4357/acc535 · Cited by 31 · see citing works
Self-citationRehemtulla, N., Miller, A. A., du Laz, T. J., Coughlin, M. W., Fremling, C., Perley, D. A., Qin, Y.-J., Sollerman, J., Mahabal, A. A., Laher, R. R., et al. (2024) · The Astrophysical Journal · doi:10.3847/1538-4357/ad5666 · Cited by 30 · see citing works
A Search for Relativistic Ejecta in a Sample of ZTF Broad-lined Type Ic Supernovae
Corsi, A., Ho, A. Y. Q., Cenko, S. B., Kulkarni, S. R., Anand, S., Yang, S., Sollerman, J., Srinivasaragavan, G. P., Omand, C. M. B., Balasubramanian, A., et al. (2023) · The Astrophysical Journal · doi:10.3847/1538-4357/acd3f2 · Cited by 28 · see citing works
Gagliano, A. T., Contardo, G., Foreman-Mackey, D. T., Malz, A. I., & Aleo, P. (2023) · The Astrophysical Journal · doi:10.3847/1538-4357/ace326 · Cited by 27 · see citing works
Photometric Classification of 2315 Pan-STARRS1 Supernovae with Superphot
Hosseinzadeh, G., Dauphin, F., Villar, V. A., Berger, E., Jones, D. O., Challis, P., Chornock, R., Drout, M. R., Foley, R. J., Kirshner, R. P., et al. (2020) · The Astrophysical Journal · doi:10.3847/1538-4357/abc42b · Cited by 26 · see citing works
Self-citationPhotometric Classification of Early-time Supernova Light Curves with SCONE
Qu, H., & Sako, M. (2022) · The Astronomical Journal · doi:10.3847/1538-3881/ac39a1 · Cited by 24 · see citing works
Spectroscopic Studies of Type Ia Supernovae Using LSTM Neural Networks
Hu, L., Chen, X., & Wang, L. (2022) · The Astrophysical Journal · doi:10.3847/1538-4357/ac5c48 · Cited by 24 · see citing works
Superphot+: Real-time Fitting and Classification of Supernova Light Curves
de Soto, K., Villar, V. A., Berger, E., Gómez, S., Hosseinzadeh, G., Branton, D., Campos, S., DeLucchi, M., Kubica, J., Lynn, O., et al. (2024) · The Astrophysical Journal · doi:10.3847/1538-4357/ad6a4f · Cited by 20 · see citing works
Self-citationSun, Z., Ting, Y.-S., & Cai, Z. (2023) · The Astrophysical Journal Supplement Series · doi:10.3847/1538-4365/acf2f1 · Cited by 20 · see citing works
Sheng, X., Nicholl, M., Smith, K., Young, D. R., Williams, R. D., Stevance, H. F., Smartt, S. J., Srivastav, S., & Moore, T. N. (2024) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stae1253 · Cited by 19 · see citing works
Alves, C. S., Peiris, H. V., Lochner, M., McEwen, J. D., Allam, T., & Biswas, R. (2022) · The Astrophysical Journal Supplement Series · doi:10.3847/1538-4365/ac3479 · Cited by 18 · see citing works
Möller, A., Wiseman, P., Smith, M., Lidman, C. E., Davis, T. M., Kessler, R., Sako, M., Sullivan, M., Galbany, L., Lee, J., et al. (2024) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stae1953 · Cited by 17 · see citing works
Carnegie Supernova Project. II. Near-infrared Spectral Diversity and Template of Type Ia Supernovae
Lü, J., Hsiao, E. Y., Phillips, M. M., Burns, C. R., Ashall, C., Morrell, N., Ng, L., Kumar, S., Shahbandeh, M., Hoêflich, P., et al. (2023) · The Astrophysical Journal · doi:10.3847/1538-4357/acc100 · Cited by 15 · see citing works
Self-citationKumar, A., Sharma, K. K., Vinkó, J., Steeghs, D. T. H., Gompertz, B. P., Lyman, J. D., Dastidar, R., Singh, A., Ackley, K., & Pursiainen, M. (2024) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stae901 · Cited by 14 · see citing works
Soumagnac, M. T., Nugent, P., Knop, R. A., Ho, A. Y. Q., Hohensee, W., Awbrey, A., Andersen, A., Aldering, G., Ventura, M., Aguilar, J., et al. (2024) · The Astrophysical Journal Supplement Series · doi:10.3847/1538-4365/ad76ae · Cited by 14 · see citing works
Predicting the Spectrum of UGC 2885, Rubin’s Galaxy with Machine Learning
Holwerda, B. W., Wu, J. F., Keel, W. C., Young, J., Mullins, R., Hinz, J., Ford, K. E. S., Barmby, P., Chandar, R., Bailin, J., et al. (2021) · The Astrophysical Journal · doi:10.3847/1538-4357/abffcc · Cited by 14 · see citing works
Classifying Astronomical Transients Using Only Host Galaxy Photometry
Kisley, M., Qin, Y.-J., Zabludoff, A. I., Barnard, K., & Ko, C.-L. (2023) · The Astrophysical Journal · doi:10.3847/1538-4357/aca532 · Cited by 14 · see citing works
Thorp, S., Mandel, K. S., Jones, D. O., Kirshner, R., & Challis, P. (2024) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stae1111 · Cited by 14 · see citing works
Self-citationMachine Learning and CosmologyPreprint
Dvorkin, C., Mishra-Sharma, S., Nord, B., Villar, V., Avestruz, C., Bechtol, K., Ciprijanovic, A., Connolly, A. J., Garrison, L., Narayan, G., et al. (2022) · arXiv.org · doi:10.48550/arxiv.2203.08056 · Cited by 21 · see citing works
Self-citationUnveiling the Diversity of Type IIn Supernovae via Systematic Light-curve Modeling
Ransome, C. L., & Villar, V. A. (2025) · The Astrophysical Journal · doi:10.3847/1538-4357/adce03 · Cited by 13 · see citing works
Self-citationNtampaka, M., & Vikhlinin, A. A. (2022) · The Astrophysical Journal · doi:10.3847/1538-4357/ac423e · Cited by 13 · see citing works
Self-citationSearching for Anomalies in the ZTF Catalog of Periodic Variable Stars
Chan, H.-S., Villar, V. A., Cheung, S.-H., Ho, S., O’Grady, A. J. G., Drout, M. R., & Renzo, M. (2022) · The Astrophysical Journal · doi:10.3847/1538-4357/ac69d4 · Cited by 12 · see citing works
Self-citationThe simulated catalogue of optical transients and correlated hosts (SCOTCH)
Lokken, M., Gagliano, A. T., Narayan, G., Hložek, R., Kessler, R., Crenshaw, J. F., Salo, L., Alves, C. S., Chatterjee, D., Vincenzi, M., et al. (2023) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stad302 · Cited by 12 · see citing works
Survey2Survey: a deep learning generative model approach for cross-survey image mapping
Buncher, B., Sharma, A. N., & Kind, M. C. (2021) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stab294 · Cited by 12 · see citing works
Maven: a multimodal foundation model for supernova science
Zhang, G., Helfer, T., Gagliano, A. T., Mishra-Sharma, S., & Villar, V. A. (2024) · Machine Learning Science and Technology · doi:10.1088/2632-2153/ad990d · Cited by 11 · see citing works
Self-citationRehemtulla, N., Jacobson-Galán, W. V., Singh, A., Miller, A. A., Kilpatrick, C. D., Hinds, K.-R., Liu, C., Schulze, S., Sollerman, J., du Laz, T. J., et al. (2025) · The Astrophysical Journal · doi:10.3847/1538-4357/adcf1e · Cited by 11 · see citing works
Self-citationAnomaly Detection and Approximate Similarity Searches of Transients in Real-time Data Streams
Aleo, P., Engel, A. W., Narayan, G., Angus, C. R., Malanchev, K. L., Auchettl, K., Baldassare, V., Berres, A., de Boer, T. J. L., Boyd, B. M., et al. (2024) · The Astrophysical Journal · doi:10.3847/1538-4357/ad6869 · Cited by 10 · see citing works
Self-citationGómez, S., & Gezari, S. (2023) · The Astrophysical Journal · doi:10.3847/1538-4357/acefbc · Cited by 9 · see citing works
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Impact of Rubin Observatory Cadence Choices on Supernovae Photometric Classification
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Nugent, A. E., Villar, V. A., Gagliano, A. T., Jones, D. O., Horowicz, A., de Soto, K., Wang, B., & Margalit, B. (2026) · The Astrophysical Journal · doi:10.3847/1538-4357/ae247b · Cited by 5 · see citing works
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The automation of optical transient discovery and classification in Rubin-era time-domain astronomy
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TLW: A Real-Time Light Curve Classification Algorithm for Transients Based on Machine Learning
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Multivariate time series transformer embeddings for light curves of periodic variable stars
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DMTN-118: Review of Timeseries Features
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Magnetic Activity of Ultracool Dwarfs in the LAMOST DR11
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A Review of Machine Learning in Astronomical Plate Digitization and Survey Data
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NOMAI: A real-time photometric classifier for superluminous supernova identification
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Artificial Intelligence in Multi-Messenger Astronomy
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Interstellar Intelligence: A Survey of AI-Powered Real-Time Detection of Astronomical Events
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- Authors
- Villar, V. A., Hosseinzadeh, G., Berger, E., Ntampaka, M., Jones, D. O., Challis, P., Chornock, R., Drout, M. R., Foley, R. J., Kirshner, R. P., Lunnan, R., Margutti, R., Milisavljevic, D., Sanders, N., Pan, Y.-C., Rest, A., Scolnic, D. M., Magnier, E., Metcalfe, N., Wainscoat, R., & Waters, C.
- Affiliations
- Center for Astrophysics Harvard & Smithsonian (US)
- Harvard University (US)
- University of California, Santa Cruz (US)
- Northwestern University (US)
- University of Toronto (CA)
- Gordon and Betty Moore Foundation (US)
- Stockholm University (SE)
- Purdue University West Lafayette (US)
- National Central University (TW)
- Space Telescope Science Institute (US)
- Duke University (US)
- University of Hawaiʻi at Mānoa (US)
- Durham University (GB)
- Princeton University (US)
- ORCID
- Villar, V. Ashley
- Hosseinzadeh, Griffin
- Berger, Edo
- Ntampaka, Michelle
- Jones, David O.
- Chornock, Ryan
- Drout, Maria R.
- Foley, Ryan J.
- Kirshner, Robert P.
- Lunnan, Ragnhild
- Margutti, Raffaella
- Milisavljevic, Dan
- Pan, Yen-Chen
- Rest, Armin
- Scolnic, Daniel M.
- Magnier, Eugene
- Metcalfe, Nigel
- Wainscoat, Richard
- Waters, Christopher
- Venue
- The Astrophysical Journal
- Publisher
- American Astronomical Society
- Published
- 2020-12-01
- Volume
- 905
- Issue
- 2
- Pages
- 94
- ISSN
- 0004-637X (ISSN-L)
- 1538-4357
- OpenAlex
- W3048419233
- Cited by
- 92
- References
- 110
- Citations by year
- 2026: 13 · 2025: 14 · 2024: 19 · 2023: 16 · 2022: 15 · 2021: 12 · 2020: 3
- FWCI
- 5.13
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- The Astrophysical Journal (publishedVersion)
- Durham Research Online (Durham University) (publishedVersion, other-oa)
- arXiv (Cornell University) (submittedVersion)
- Durham Research Online (Durham University) (submittedVersion, cc-by)
- arXiv (Cornell University)
- Funders
- National Science Foundation (1914448, 1518052, 1829740, 2037297, 1815935, 1714498, 1238877, NAS 5-26555)
- Smithsonian Institution
- National Aeronautics and Space Administration (NNG16PJ34C, 14-WPS14-0048, NAS 526555, NNX08AR22G, AST-1238877, 526555, NNG-17PX03C, AR-14296)
- Alfred P. Sloan Foundation (NNX08AR22G)
- Gordon and Betty Moore Foundation
- Harvard University
- Johns Hopkins University (NAS 5-26555)
- Space Telescope Science Institute (NAS5- 26555, 5-26555, NNX08AR22G, 26555)
- European Commission (H2020-MSCA-IF-2017., 794467, H2020‐MSCA‐IF, H2020-MSCA)
- Queen's University Belfast
- Durham University
- Queen's University
- National Central University
- Eötvös Loránd Tudományegyetem
- Max-Planck-Institut für Astronomie
- Smithsonian Astrophysical Observatory
- Horizon 2020 Framework Programme (MSCA-IF-2017-794467, H2020-MSCA, H2020-MSCA-IF-2017, MSCA-IF-2017)
- Science Mission Directorate (NAS 5-26555, NNX08AR22G)
- Harvard Data Science Initiative, Harvard University
- Science and Technology Facilities Council (ST/T000244/1)
- FAS Division of Science, Harvard University
- Planetary Science Division (NNX08AR22G)
- Los Alamos National Laboratory (NAS 5-26555)
- Topics
- Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Gamma-ray bursts and supernovae
- Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Galaxies: Formation, Evolution, Phenomena
- Physical Sciences › Engineering › Computational Mechanics › Astronomical Observations and Instrumentation
- Keywords
- supernova light curves, semi-supervised learning, Type Ia supernovae, Type Ib/c supernovae, Type II supernovae, Type IIn supernovae, superluminous supernovae
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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