Exploring the use of Machine Learning with extragalactic emission-line surveys, in preparation for the Square Kilometre Array
Lead Research Organisation:
Cardiff University
Department Name: School of Physics and Astronomy
Abstract
The Atacama Large Millimeter/submillimeter Array (ALMA) is the largest scale operating telescope project in the world. This revolutionary facility is producing over a petabyte of data a year, much of which will never be inspected by eye. Machine learning and data based approaches to exploiting this vast data archive are clearly required, For instance developing robust unsupervised algorithms for automated source/line detection, and innovative calibration algorithms based on machine learning.
People |
ORCID iD |
| Timothy Davis (Primary Supervisor) | |
| James Dawson (Student) |
Publications
Dawson J
(2019)
Using machine learning to study the kinematics of cold gas in galaxies
in Monthly Notices of the Royal Astronomical Society
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| ST/P006779/1 | 01/10/2017 | 30/09/2024 | |||
| 1945968 | Studentship | ST/P006779/1 | 01/10/2017 | 30/09/2021 | James Dawson |
| Title | Optimisation and release of KinMS Python tool |
| Description | I am a leader in the recent boost in developing KinMSpy, a Python tool for astronomers modelling the kinematics of molecular gas in astronomy. Along with a colleague we have improved the speed of this code and provided stable documentation as well as a PyPi package release. |
| Type Of Material | Improvements to research infrastructure |
| Year Produced | 2020 |
| Provided To Others? | Yes |
| Impact | Several requests have begun arriving for using the tool and there are ongoing debugging requests. |
| URL | https://github.com/TimothyADavis/KinMSpy/tree/dev |
| Title | Machine learning model for position angle and circularity predictions of CO gas in galaxies |
| Description | This model allows users to analyse their galaxy observations from interferometers such as ALMA, VLA, SKA, etc. They can then retrieve information on the level of ordered rotation of the gas in those galaxies and their position angles for further modelling. |
| Type Of Material | Computer model/algorithm |
| Year Produced | 2019 |
| Provided To Others? | Yes |
| Impact | Several conference talks have been accepted on the topic and this work forms the groundwork for my next publication using more advanced machine learning methods. |
| URL | https://github.com/SpaceMeerkat/CAE |
| Description | Using machine learning to study the kinematics of cold gas in galaxies |
| Organisation | RWTH Aachen University |
| Country | Germany |
| Sector | Academic/University |
| PI Contribution | Principle investigator on the project leading to 2+ papers on using machine learning to prepare for the SKA. |
| Collaborator Contribution | PyTorch guidance and paper reviews by Justus Schock. |
| Impact | MNRAS paper accepted and published under the title "Using machine learning to study the kinematics of cold gas in galaxies". |
| Start Year | 2019 |
| Title | KinMSpy active development |
| Description | The KinMS (KINematic Molecular Simulation) package can be used to simulate observations of arbitary molecular/atomic cold gas distributions. The routines are written with flexibility in mind, and have been used in various different applications, including investigating the kinematics of molecular gas in early-type galaxies (Davis et al, MNRAS, Volume 429, Issue 1, p.534-555, 2013), and determining supermassive black-hole masses from CO interfermetric observations (Davis et al., Nature, 2013). They are also useful for creating input datacubes for further simulation in e.g. CASA's sim_observe tool. |
| Type Of Technology | Software |
| Year Produced | 2020 |
| Open Source License? | Yes |
| Impact | Several requests to use the tool and regular bug updates. |
| URL | https://github.com/TimothyADavis/KinMSpy/tree/dev |
| Description | Taking part in the ARIEL machine learning challenge 2019 and presenting at ECML PKDD |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Study participants or study members |
| Results and Impact | In 2019 I won ARIEL machine learning challenge. This involved removing sunspot noise from exoplanet transit light-curves and using neural networks to predict their corresponding radii. In 2020 I presented this work at ECML PKDD. Around 50-100 people attended the conference and I am currently engaged in writing a joint paper on the results. |
| Year(s) Of Engagement Activity | 2019 |
| URL | https://ariel-datachallenge.azurewebsites.net/ML |