A5080057248- Astronomical Observations and Instrumentation
- Galaxies: Formation, Evolution, Phenomena
- Gamma-ray bursts and supernovae
- Astronomy and Astrophysical Research
- Stellar, planetary, and galactic studies
- Advanced Vision and Imaging
- Gaussian Processes and Bayesian Inference
- Remote Sensing in Agriculture
- Spacecraft and Cryogenic Technologies
- Astrophysics and Star Formation Studies
- Paleontology and Stratigraphy of Fossils
- 3D Modeling in Geospatial Applications
- Data Visualization and Analytics
- Advanced Algebra and Logic
- Radio Astronomy Observations and Technology
- Statistical and numerical algorithms
- Video Surveillance and Tracking Methods
- Geophysics and Gravity Measurements
- Blind Source Separation Techniques
- Control Systems in Engineering
- Robotic Mechanisms and Dynamics
- Computability, Logic, AI Algorithms
- Geology and Paleoclimatology Research
- Impact of Light on Environment and Health
- Gas Dynamics and Kinetic Theory
South African Radio Astronomy Observatory
2023-2024
Rhodes University
2024
Cardiff University
2019-2023
In this paper we study the molecular gas content of a representative sample 67 most massive early-type galaxies in local universe, drawn uniformly from MASSIVE survey. We present new IRAM-30m telescope observations 30 these galaxies, allowing us to probe entire fixed molecular-to-stellar mass fraction 0.1%. The total detection rate is 25$^{+5.9}_{-4.4}$%, and by combining ATLAS$^{\rm 3D}$ surveys find joint 22.4$^{+2.4}_{-2.1}$%. This seems be independent galaxy mass, size, position on...
ABSTRACT We present detailed morphology measurements for 8.67 million galaxies in the DESI Legacy Imaging Surveys (DECaLS, MzLS, and BASS, plus DES). These are automated made by deep learning models trained on Galaxy Zoo volunteer votes. Our typically predict fraction of volunteers selecting each answer to within 5–10 per cent every GZ question. The newly collected votes DESI-LS DR8 images as well historical from DECaLS. also release Extending our outside previously released DECaLS/SDSS...
Medium-timescale (minutes to hours) radio transients are a relatively unexplored population. The wide field-of-view and high instantaneous sensitivity of instruments such as MeerKAT provides an opportunity probe this class sources, using image-plane detection techniques. We aim systematically mine archival synthesis imaging data in order search for medium-timescale variables that not detected by conventional long-track image deploy prototype blind transient variable pipeline named TRON. This...
Medium-timescale (minutes to hours) radio transients are a relatively unexplored population. The wide field-of-view and high instantaneous sensitivity of instruments such as MeerKAT provides an opportunity probe this class sources, using image-plane detection techniques. previous letter in series describes our project associated TRON pipeline designed mine archival data for transient variable sources. In letter, we report on new transient, flare, with Gaia DR3 6865945581361480448, G type...
Abstract Giant star-forming clumps (GSFCs) are areas of intensive star-formation that commonly observed in high-redshift (z ≳ 1) galaxies but their formation and role galaxy evolution remain unclear. Observations low-redshift clumpy analogues rare the availability wide-field survey data makes detection large samples much more feasible. Deep Learning (DL), particular Convolutional Neural Networks (CNNs), have been successfully applied to image classification tasks astrophysical analysis....
ABSTRACT Despite the evidence that supermassive black holes (SMBHs) co-evolve with their host galaxy, and most of growth these SMBHs occurs via merger-free processes, underlying mechanisms which drive this secular co-evolution are poorly understood. We investigate role both strong weak large-scale galactic bars play in mediating relationship. Using 48 871 disc galaxies a volume-limited sample from Galaxy Zoo DESI, we analyse active nucleus (AGN) fraction strongly barred, weakly unbarred up...
Next generation interferometers, such as the Square Kilometre Array, are set to obtain vast quantities of information about kinematics cold gas in galaxies. Given volume data produced by facilities astronomers will need fast, reliable, tools informatively filter and classify incoming real time. In this paper, we use machine learning techniques with a hydrodynamical simulation training predict kinematic behaviour galaxies test these models on both simulated interferometric data. Using power...
Despite the evidence that supermassive black holes (SMBHs) co-evolve with their host galaxy, and most of growth these SMBHs occurs via merger-free processes, underlying mechanisms which drive this secular co-evolution are poorly understood. We investigate role both strong weak large-scale galactic bars play in mediating relationship. Using 72,940 disc galaxies a volume-limited sample from Galaxy Zoo DESI, we analyse active nucleus (AGN) fraction strongly barred, weakly unbarred up to z = 0.1...
Abstract The last decade has witnessed a rapid growth of the field exoplanet discovery and characterization. However, several big challenges remain, many which could be addressed using machine learning methodology. For instance, most prolific method for detecting exoplanets inferring their characteristics, transit photometry, is very sensitive to presence stellar spots. current practice in literature identifying effects spots visually correcting them manually or discarding affected data....
In the upcoming decades large facilities, such as SKA, will provide resolved observations of kinematics millions galaxies. order to assist in timely exploitation these vast datasets we explore use a self-supervised, physics aware neural network capable Bayesian kinematic modelling We demonstrate network's ability model cold gas galaxies with an emphasis on recovering physical parameters and accompanying errors. The is able recover rotation curves, inclinations disc scale lengths for both CO...
The last decade has witnessed a rapid growth of the field exoplanet discovery and characterisation. However, several big challenges remain, many which could be addressed using machine learning methodology. For instance, most prolific method for detecting exoplanets inferring their characteristics, transit photometry, is very sensitive to presence stellar spots. current practice in literature identify effects spots visually correct them manually or discard affected data. This paper explores...
Giant Star-forming Clumps (GSFCs) are areas of intensive star-formation that commonly observed in high-redshift (z>1) galaxies but their formation and role galaxy evolution remain unclear. High-resolution observations low-redshift clumpy analogues rare restricted to a limited set the increasing availability wide-field survey data makes detection large samples increasingly feasible. Deep Learning, particular CNNs, have been successfully applied image classification tasks astrophysical...
We present detailed morphology measurements for 8.67 million galaxies in the DESI Legacy Imaging Surveys (DECaLS, MzLS, and BASS, plus DES). These are automated made by deep learning models trained on Galaxy Zoo volunteer votes. Our typically predict fraction of volunteers selecting each answer to within 5-10\% every GZ question. The newly-collected votes DESI-LS DR8 images as well historical from DECaLS. also release Extending our outside previously-released DECaLS/SDSS intersection...