1. Machine Learning for Pulsar Detection
- Author
-
Stephen J. Roberts, Rebecca McFadden, and Aris Karastergiou
- Subjects
Signal processing ,Data processing ,010308 nuclear & particles physics ,Event (computing) ,business.industry ,Computer science ,Astronomy and Astrophysics ,Machine learning ,computer.software_genre ,01 natural sciences ,Radio telescope ,Pulsar ,Space and Planetary Science ,0103 physical sciences ,Computer data storage ,Transient (computer programming) ,Artificial intelligence ,Sensitivity (control systems) ,business ,010303 astronomy & astrophysics ,computer - Abstract
The next generation of radio telescopes will have unprecedented sensitivity and time-resolution offering exciting new capabilities in time-domain science. However, this will result in very large numbers of pulsar and transient event candidates and the associated data rates will be technically challenging in terms of data storage and signal processing. Automated detection and classification techniques are therefore required and must be optimized to allow high-throughput data processing in real time. In this paper we provide a summary of the emerging machine learning techniques being applied to this problem.
- Published
- 2017