Discovering gravitationally lensed quasars using supervised machine learning.
Lead Research Organisation:
University of Cambridge
Department Name: Institute of Astronomy
Abstract
We will adopt a morphology independent supervised machine learning approach to classify quasars and improve the gravitationally lensed quasar search. A Gaussian Mixture Model (GMM) will first be used to select candidates in DES (Dark Energy Survey) Y3 data using features from DES, VHS and WISE.
The Gaia satellite provides high resolution multi-epoch (around 70 epochs) positional measurements in the optical for 1 billion astrophysical sources with a complex data model due to the on-board image analysis. Additional features (10 out of 100) measured by Gaia will be added to the GMM analysis, in April 2018, to improve the lensed quasar selection and classification.
The Gaia satellite provides high resolution multi-epoch (around 70 epochs) positional measurements in the optical for 1 billion astrophysical sources with a complex data model due to the on-board image analysis. Additional features (10 out of 100) measured by Gaia will be added to the GMM analysis, in April 2018, to improve the lensed quasar selection and classification.
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| ST/P006787/1 | 01/10/2017 | 30/09/2024 | |||
| 1966440 | Studentship | ST/P006787/1 | 01/10/2017 | 30/09/2021 | Christopher Desira |