Representing Source Movement in Sequences of Telescopic Images Based on Contrastive Learning for Asteroid Detection-Reference-Cited by-同舟云学术

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Representing Source Movement in Sequences of Telescopic Images Based on Contrastive Learning for Asteroid Detection

Published:2023-09-14 Issue: Volume: Page:
ISSN:
Container-title:2023 27th International Computer Science and Engineering Conference (ICSEC)
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Author:

Kongsathitporn Noppachanin¹,Supratak Akara¹,Awiphan Supachai²,Ackley Kendall³,Dyer Martin J.⁴,Lyman Joe³,Jiminez-Ibarra Felipe⁵,Steeghs Danny³,Galloway Duncan K.⁵,Dhillon Vik⁴,O'Brien Paul⁶,Ramsay Gavin⁷,Kotak Rubina⁸,Breton Rene P.⁹,Nuttall Laura K.¹⁰,Pall'e Enric¹¹,Pollacco Don³,Killestein Tom³,Kumar Amit³,Taka Natthaphong¹²,Rattanasai Rattiyakorn¹²,Noysena Kanthanakorn²

Affiliation:

1. Mahidol University,Faculty of Information and Communication Technology

2. National Astronomical Research Institute of Thailand

3. Warwick University

4. University of Sheffield

5. Monash University

6. Universiry of Leicester

7. Armagh Observatory and Planetarium

8. University of Turku

9. University of Portsmouth

10. University of Manchester

11. Instituto de Astrofisica de Canarias

12. Chiang Mai University

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10329458/10329360/10329669.pdf?arnumber=10329669

Reference9 articles.

1. Discovering faint and high apparent motion rate near-Earth asteroids using a deep learning program

2. The Gravitational-wave Optical Transient Observer (GOTO)

3. Transient-optimized real-bogus classification with Bayesian convolutional neural networks – sifting the GOTO candidate stream

4. Alert Classification for the ALeRCE Broker System: The Real-time Stamp Classifier

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