VHE Gamma-ray astronomy with the Cherenkov Telescope Array
Lead Research Organisation:
UNIVERSITY OF OXFORD
Department Name: Oxford Physics
Abstract
The student will contribute to three aspects of CTA design, construction and early science. The aims and objectives of the project are two-fold, having both a technical and an astronomical component. On the technical side student will investigate novel methods for the classification of air showers in Cherenkov telescope images (i.e., whether they are leptonic or hadronic in origin) using machine learning techniques. The scientific side of the project is aimed at investigating the populations of relativistic particles in the so-called "Fermi bubbles" in the Milky Way using a broad-band multi-frequency approach from radio to gamma-rays.
This project lies in the STFC science areas:
D:1. How do the laws of physics work when driven to the extremes?
D:2. How can high energy particles and gravitational waves tell us about the extreme universe?
D:3. How do ultra-compact objects form, what is their nature and how does extreme gravity impact on their surroundings?
Collaborators include the Universities of Liverpool, Leicester and Durham and MPIK Heidelberg. It is envisaged that the student will have a long-term attachment at MPIK.
The machine learning aspects of the project may have impact in application to very-high-speed image classification in areas such as e.g. flame chemistry.
This project lies in the STFC science areas:
D:1. How do the laws of physics work when driven to the extremes?
D:2. How can high energy particles and gravitational waves tell us about the extreme universe?
D:3. How do ultra-compact objects form, what is their nature and how does extreme gravity impact on their surroundings?
Collaborators include the Universities of Liverpool, Leicester and Durham and MPIK Heidelberg. It is envisaged that the student will have a long-term attachment at MPIK.
The machine learning aspects of the project may have impact in application to very-high-speed image classification in areas such as e.g. flame chemistry.
Publications
Abdalla H
(2021)
H.E.S.S. Follow-up Observations of Binary Black Hole Coalescence Events during the Second and Third Gravitational-wave Observing Runs of Advanced LIGO and Advanced Virgo
in The Astrophysical Journal
Abdalla H
(2021)
Search for Dark Matter Annihilation Signals from Unidentified Fermi-LAT Objects with H.E.S.S.
in The Astrophysical Journal
Abdalla H
(2020)
Probing the Magnetic Field in the GW170817 Outflow Using H.E.S.S. Observations
in The Astrophysical Journal Letters
Abdalla H
(2021)
Evidence of 100 TeV ? -ray emission from HESS J1702-420: A new PeVatron candidate
in Astronomy & Astrophysics
Abdalla H
(2021)
TeV Emission of Galactic Plane Sources with HAWC and H.E.S.S.
in The Astrophysical Journal
Abdalla H
(2021)
Sensitivity of the Cherenkov Telescope Array for probing cosmology and fundamental physics with gamma-ray propagation
in Journal of Cosmology and Astroparticle Physics
Abdalla H
(2021)
Searching for TeV Gamma-Ray Emission from SGR 1935+2154 during Its 2020 X-Ray and Radio Bursting Phase
in The Astrophysical Journal
Abdallah H
(2021)
Search for dark matter annihilation in the Wolf-Lundmark-Melotte dwarf irregular galaxy with H.E.S.S.
in Physical Review D
Abdallah H
(2020)
Search for dark matter signals towards a selection of recently detected DES dwarf galaxy satellites of the Milky Way with H.E.S.S.
in Physical Review D
Acharyya A
(2021)
Sensitivity of the Cherenkov Telescope Array to a dark matter signal from the Galactic centre
in Journal of Cosmology and Astroparticle Physics
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| ST/R505006/1 | 30/09/2017 | 29/09/2021 | |||
| 1947714 | Studentship | ST/R505006/1 | 30/09/2017 | 30/03/2021 | Samuel Spencer |
| Description | We investigated the use of photosensor timing information in combinations with deep learning as an event classification method for CTA, investigated the complex task of transferring deep learning models trained on simulations to real data, and modelled the effect of night sky background and moonlight on SSTCAM observations. |
| Exploitation Route | Code is all open source, pipelines developed can be used for other deep learning methods on IACT data. |
| Sectors | Aerospace Defence and Marine Energy Other |
| Description | CTA |
| Organisation | Cherenkov Telescope Array Consortium |
| Country | Germany |
| Sector | Academic/University |
| PI Contribution | Working on new analysis methods for CTA |
| Collaborator Contribution | Pre-construction phase |
| Impact | See website |
| Description | Talk on new machine learning methods at university of Namibia |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Undergraduate students |
| Results and Impact | Gave a talk on 'new machine learning methods in astronomy' at university of Namibia. |
| Year(s) Of Engagement Activity | 2020 |