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Connor, Liam, van Leeuwen, Joeri (2018) Applying Deep Learning to Fast Radio Burst Classification. The Astronomical Journal, 156. 256pp. doi:10.3847/1538-3881/aae649

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Reference TypeJournal (article/letter/editorial)
TitleApplying Deep Learning to Fast Radio Burst Classification
JournalThe Astronomical Journal
AuthorsConnor, LiamAuthor
van Leeuwen, JoeriAuthor
Year2018 (November 9)Volume156
PublisherAmerican Astronomical Society
DOIdoi:10.3847/1538-3881/aae649Search in ResearchGate
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Mindat Ref. ID3615331Long-form Identifiermindat:1:5:3615331:4
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Full ReferenceConnor, Liam, van Leeuwen, Joeri (2018) Applying Deep Learning to Fast Radio Burst Classification. The Astronomical Journal, 156. 256pp. doi:10.3847/1538-3881/aae649
Plain TextConnor, Liam, van Leeuwen, Joeri (2018) Applying Deep Learning to Fast Radio Burst Classification. The Astronomical Journal, 156. 256pp. doi:10.3847/1538-3881/aae649
In(2018) The Astronomical Journal Vol. 156. American Astronomical Society

See Also

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Law, Casey J., Connor, Liam, Aggarwal, Kshitij (2022) On the Fast Radio Burst and Persistent Radio Source Populations. The Astrophysical Journal, 927 (1) 55pp. doi:10.3847/1538-4357/ac4c42
Ravi, Vikram; Catha, Morgan; Chen, Ge; Connor, Liam; Cordes, James M.; Faber, Jakob T.; Lamb, James W.; Hallinan, Gregg; Harnach, Charlie; Hellbourg, Greg; et al. (2025) Deep Synoptic Array Science: A 50 Mpc Fast Radio Burst Constrains the Mass of the Milky Way Circumgalactic Medium. The Astronomical Journal, 169 (6). doi:10.3847/1538-3881/adc725
Pastor-Marazuela, Inés, van Leeuwen, Joeri, Bilous, Anna, Connor, Liam, Maan, Yogesh, Oostrum, Leon, Petroff, Emily, Vohl, Dany, Hess, Kelley M., Orrù, Emanuela, et al. (2025) Comprehensive analysis of the Apertif fast radio burst sample. Astronomy & Astrophysics, 693. doi:10.1051/0004-6361/202450953
Liu, Lei, Tong, Fengxian, Zheng, Weimin, Zhang, Juan, Tong, Li (2018) A Fast Radio Burst Search Method for VLBI Observation. The Astronomical Journal, 155. 98pp. doi:10.3847/1538-3881/aaa6c5
Hinners, Trisha A., Tat, Kevin, Thorp, Rachel (2018) Machine Learning Techniques for Stellar Light Curve Classification. The Astronomical Journal, 156. 7pp. doi:10.3847/1538-3881/aac16d
Sekhar, Srikrishna, Athreya, Ramana (2018) Two Procedures to Flag Radio Frequency Interference in the UV Plane. The Astronomical Journal, 156. 9pp. doi:10.3847/1538-3881/aac16e
Zucker, Shay, Giryes, Raja (2018) Shallow Transits—Deep Learning. I. Feasibility Study of Deep Learning to Detect Periodic Transits of Exoplanets. The Astronomical Journal, 155. 147pp. doi:10.3847/1538-3881/aaae05
Connor, Liam, Petroff, Emily (2018) On Detecting Repetition from Fast Radio Bursts. The Astrophysical Journal, 861 (1). doi:10.3847/2041-8213/aacd02
Lawrence, Earl, Wiel, Scott Vander, Law, Casey, Spolaor, Sarah Burke, Bower, Geoffrey C. (2017) The Nonhomogeneous Poisson Process for Fast Radio Burst Rates. The Astronomical Journal, 154. 117pp. doi:10.3847/1538-3881/aa844e
Maan, Yogesh, Bassa, Cees, van Leeuwen, Joeri, Krishnakumar, M. A., Joshi, Bhal Chandra (2018) A Search for Pulsars in Steep Spectrum Radio Sources. The Astrophysical Journal, 864 (1). 16pp. doi:10.3847/1538-4357/aad4ad
Harris, William E. (2018) Transformation ofHSTWFC3/UVIS Filters to the StandardBVISystem. The Astronomical Journal, 156. 296pp. doi:10.3847/1538-3881/aaedb8
 
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