Machine-learning analysis of AGN variability in large-scale optical surveys

Lead Research Organisation: Liverpool John Moores University
Department Name: Astrophysics Research Institute

Abstract

LSST will revolutionise time domain astrophysics, detecting over 1 million transient objects per night that will require high efficiency classification to enable astrophysical interpretation. Code and algorithms currently used for time domain analysis are computationally intensive and typically implemented in an interpreted language. There is a need for algorithmic improvements within a modern, cloud based computing environment, e.g. exploiting technologies such as Apache SPARK to provide parallelism, performance and fault-tolerance. The student will be tasked to do this and thereby gain exposure to the most up-to-date techniques as well as participating in LSST science.

Publications

10 25 50

Studentship Projects

Project Reference Relationship Related To Start End Student Name
ST/P006752/1 01/10/2017 30/09/2024
2027357 Studentship ST/P006752/1 01/10/2017 23/12/2021 Tricia Sullivan