The application of machine learning to astronomical imaging surveys
Lead Research Organisation:
Durham University
Department Name: Physics
Abstract
The aim of this project is to apply a range of machine learning and data mining algorithms to aid the classification of objects from current and future astronomical imaging surveys. With the start of the LSST 10 year survey only 4 years away, the need to have robust and reliable tools to select the rarest objects, e.g. distant quasars, low mass stars, rich clusters of galaxes, is pressing. Using the UKIDSS/PanSTARRS and VIKING/KiDS matched NIR and optical surveys as a training set, these techniques will be optimised. The emphasis will be on exploiting the photometric catalogues to address a wide range of science goals from low mass stars to the most distant quasars.
People |
ORCID iD |
Iohn Norberg (Primary Supervisor) | |
Aidan Sedgewick (Student) |
Studentship Projects
Project Reference | Relationship | Related To | Start | End | Student Name |
---|---|---|---|---|---|
ST/P006744/1 | 01/10/2017 | 30/09/2024 | |||
1944971 | Studentship | ST/P006744/1 | 01/10/2017 | 31/12/2021 | Aidan Sedgewick |
Title | Reduced UKIDSS-DXS mosaics and catalogues |
Description | mosaics constructed from ukidss dxs individual stacks for 4 dxs fields (elais-n1, lockman hole, sa22, xmm-lss), reduced using SWarp. catalogues produced using SExtractor, matched to PanSTARRS mds catalogues and subaru HSC strategic program (only available in EN1 and XM). |
Type Of Material | Database/Collection of data |
Year Produced | 2020 |
Provided To Others? | No |
Impact | N/A |
Description | INSPIRE-HEP secondment |
Organisation | inSPIRE High Energy Physics (HEP) Information System |
Country | Global |
Sector | Public |
PI Contribution | research in (machine learning) models to classify articles for inclusion in INSPIRE-HEP database - classify as 'core', 'non-core', 'reject' suggestions on features to use for future models (when producing a model for production) |
Collaborator Contribution | use of the inspire database (and all of the knowledge of how it worked) techinical discussions on models data handling strategies |
Impact | N/A |
Start Year | 2018 |