A5051166700- Astronomy and Astrophysical Research
- Gamma-ray bursts and supernovae
- Galaxies: Formation, Evolution, Phenomena
- Anomaly Detection Techniques and Applications
- Stellar, planetary, and galactic studies
- Radio Astronomy Observations and Technology
- Astronomical Observations and Instrumentation
- Time Series Analysis and Forecasting
- Astrophysics and Cosmic Phenomena
- Gaussian Processes and Bayesian Inference
- Spectroscopy and Chemometric Analyses
- Adaptive optics and wavefront sensing
- Fault Detection and Control Systems
- Network Security and Intrusion Detection
- History and Developments in Astronomy
- Geophysics and Gravity Measurements
- Cosmology and Gravitation Theories
- Computational Physics and Python Applications
- Respiratory viral infections research
- Cognitive Radio Networks and Spectrum Sensing
- Data-Driven Disease Surveillance
- Direction-of-Arrival Estimation Techniques
- Machine Learning and Data Classification
- Pulsars and Gravitational Waves Research
- Multidisciplinary Science and Engineering Research
University of the Western Cape
2020-2025
South African Radio Astronomy Observatory
2015-2025
African Institute for Mathematical Sciences
2014-2023
The University of Western Australia
2018
Netherlands Institute for Radio Astronomy
2018
University College London
2015-2018
SKA Telescope, South Africa
2017-2018
Rutgers, The State University of New Jersey
2018
Leiden University
2018
International Centre for Radio Astronomy Research
2018
Abstract In this work we explore the applicability of unsupervised machine learning algorithms to finding radio transients. Facilities such as Square Kilometre Array (SKA) will provide huge volumes data in which detect rare transients; challenge for astronomers is how find them. We demonstrate effectiveness anomaly detection using 1.3 GHz light curves from SKA precursor MeerKAT. make use three sets descriptive parameters (‘feature sets’) applied two techniques Astronomaly package and analyse...
The Large Synoptic Survey Telescope is designed to provide an unprecedented optical imaging dataset that will support investigations of our Solar System, Galaxy and Universe, across half the sky over ten years repeated observation. However, exactly how LSST observations be taken (the observing strategy or "cadence") not yet finalized. In this dynamically-evolving community white paper, we explore detailed performance anticipated science expected depend on small changes strategy. Using...
Automated photometric supernova classification has become an active area of research in recent years light current and upcoming imaging surveys such as the Dark Energy Survey (DES) Large Synoptic Telescope, given that spectroscopic confirmation type for all supernovae discovered will be impossible. Here, we develop a multi-faceted pipeline, combining existing new approaches. Our pipeline consists two stages: extracting descriptive features from curves using machine learning algorithm....
The unprecedented volume and rate of transient events that will be discovered by the Large Synoptic Survey Telescope (LSST) demands astronomical community update its followup paradigm. Alert-brokers -- automated software system to sift through, characterize, annotate prioritize for critical tools managing alert streams in LSST era. Arizona-NOAO Temporal Analysis Response Events System (ANTARES) is one such broker. In this work, we develop a machine learning pipeline characterize classify...
MeerKAT’s large number (64) of 13.5 m diameter antennas, spanning 8 km with a densely packed 1 core, create powerful instrument for wide-area surveys, high sensitivity over wide range angular scales. The MeerKAT Galaxy Cluster Legacy Survey (MGCLS) is programme long-track L -band (900−1670 MHz) observations 115 galaxy clusters, observed ∼6−10 h each in full polarisation. first legacy product data release (DR1), made available this paper, includes the visibilities, basic image cubes at ∼8″...
Vera C. Rubin Observatory is a ground-based astronomical facility under construction, joint project of the National Science Foundation and U.S. Department Energy, designed to conduct multi-purpose 10-year optical survey southern hemisphere sky: Legacy Survey Space Time. Significant flexibility in strategy remains within constraints imposed by core science goals probing dark energy matter, cataloging Solar System, exploring transient sky, mapping Milky Way. The survey's massive data...
Abstract Next-generation surveys like the Legacy Survey of Space and Time (LSST) on Vera C. Rubin Observatory (Rubin) will generate orders magnitude more discoveries transients variable stars than previous surveys. To prepare for this data deluge, we developed Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC), a competition that aimed to catalyze development robust classifiers under LSST-like conditions nonrepresentative training set large photometric test...
We discuss the ground-breaking science that will be possible with a wide area survey, using MeerKAT telescope, known as MeerKLASS (MeerKAT Large Area Synoptic Survey). The current specifications of make it great fit for applications require large survey speeds but not necessarily high angular resolutions. In particular, cosmology, over $\sim 4,000 \, {\rm deg}^2$ 4,000$ hours potentially provide first ever measurements baryon acoustic oscillations 21cm intensity mapping technique, enough...
Astronomers have typically set out to solve supervised machine learning problems by creating their own representations from scratch. We show that deep models trained answer every Galaxy Zoo DECaLS question learn meaningful semantic of galaxies are useful for new tasks on which the were never trained. exploit these outperform several recent approaches at practical crucial investigating large galaxy samples. The first task is identifying similar morphology a query galaxy. Given single assigned...
ABSTRACT We report the discovery of a unique object in MeerKAT Galaxy Cluster Legacy Survey (MGCLS) using machine learning anomaly detection framework astronomaly. This strange, ring-like source is 30′ from MGCLS field centred on Abell 209, and not readily explained by simple physical models. With an assumed host galaxy at redshift 0.55, luminosity (1025 W Hz−1) comparable to powerful radio galaxies. The consists ring emission 175 kpc across, quadrilateral enhanced brightness regions bearing...
ABSTRACT Modern astronomical surveys are producing data sets of unprecedented size and richness, increasing the potential for high-impact scientific discovery. This possibility, coupled with challenge exploring a large number sources, has led to development novel machine-learning-based anomaly detection approaches, such as astronomaly. For first time, we test scalability astronomaly by applying it almost 4 million images galaxies from Dark Energy Camera Legacy Survey. We use trained deep...
We train a machine learning algorithm to learn cosmological structure formation from N-body simulations. The infers the relationship between initial conditions and final dark matter haloes, without need introduce approximate halo collapse models. gain insights into physics driving by evaluating predictive performance of when provided with different types information about local environment around particles. learns predict whether or not particles will end up in haloes given mass range, based...
Point source detection at low signal-to-noise is challenging for astronomical surveys, particularly in radio interferometry images where the noise correlated. Machine learning a promising solution, allowing development of algorithms tailored to specific telescope arrays and science cases. We present DeepSource - deep solution that uses convolutional neural networks achieve these goals. enhances Signal-to-Noise Ratio (SNR) original map then dynamic blob detect sources. Trained tested on two...
In recent years, machine learning (ML) methods have remarkably improved how cosmologists can interpret data. The next decade will bring new opportunities for data-driven cosmological discovery, but also present challenges adopting ML methodologies and understanding the results. could transform our field, this transformation require astronomy community to both foster promote interdisciplinary research endeavors.
Abstract Modern telescopes generate catalogs of millions objects with the potential for new scientific discoveries, but this is beyond what can be examined visually. Here we introduce ASTRONOMALY: PROTEGE, an extension general-purpose machine-learning-based active anomaly detection framework ASTRONOMALY. PROTEGE designed to provide well-selected recommendations visual inspection, based on a small amount optimized human labeling. The resulting sample contains rare or unusual sources that are...
The Photometric LSST Astronomical Time Series Classification Challenge (PLAsTiCC) is an open data challenge to classify simulated astronomical time-series in preparation for observations from the Large Synoptic Survey Telescope (LSST), which will achieve first light 2019 and commence its 10-year main survey 2022. revolutionize our understanding of changing sky, discovering measuring millions time-varying objects. In this challenge, we pose question: how well can objects sky that vary...
Identification of anomalous light curves within time-domain surveys is often challenging. In addition, with the growing number wide-field and volume data produced exceeding astronomers ability for manual evaluation, outlier anomaly detection becoming vital transient science. We present an unsupervised method discovery using a clustering technique Astronomaly package. As proof concept, we evaluate 85553 minute-cadenced collected over two 1.5 hour periods as part Deeper, Wider, Faster program,...
Abstract The Vera C. Rubin Observatory will increase the number of observed supernovae (SNe) by an order magnitude; however, it is impossible to spectroscopically confirm class for all SNe discovered. Thus, photometric classification crucial, but its accuracy depends on not-yet-finalized observing strategy Observatory’s Legacy Survey Space and Time (LSST). We quantitatively analyze impact LSST using simulated multiband light curves from Photometric Astronomical Time-Series Classification...
New telescopes like the Square Kilometre Array (SKA) will push into a new sensitivity regime and expose systematics, such as direction-dependent effects, that could previously be ignored. Current methods for handling systematics rely on alternating best estimates of instrumental calibration models underlying sky, which can lead to inadequate uncertainty biased results because any correlations between parameters are These deconvolution algorithms produce single image is assumed true...
ABSTRACT Unsupervised learning, a branch of machine learning that can operate on unlabelled data, has proven to be powerful tool for data exploration and discovery in astronomy. As large surveys new telescopes drive rapid increase size richness, these techniques offer the promise discovering classes objects efficient sorting into similar types. However, unsupervised generally require feature extraction derive simple but informative representations images. In this paper, we explore use...
The Vera C. Rubin Observatory's Legacy Survey of Space and Time is forecast to collect a large sample Type Ia supernovae (SNe Ia) that could be instrumental in unveiling the nature Dark Energy. feat, however, requires measuring two components Hubble diagram - distance modulus redshift with high degree accuracy. Distance estimated from SNe parameters extracted light curve fits, where average quality curves primarily driven by survey such as cadence number visits per band. An optimal observing...
ABSTRACT New time-domain surveys, such as the Vera C. Rubin Observatory Legacy Survey of Space and Time, will observe millions transient alerts each night, making standard approaches visually identifying new interesting transients infeasible. We present two novel methods automatically detecting anomalous light curves in real-time. Both are based on simple idea that if from a known population can be accurately modelled, any deviations model predictions likely anomalies. The first modelling...
With the advent of powerful telescopes such as Square Kilometer Array and Vera C. Rubin Observatory, we are entering an era multiwavelength transient astronomy that will lead to a dramatic increase in data volume. Machine learning techniques well suited address this challenge rapidly classify newly detected transients. We present classification algorithm consisting three steps: (1) interpolation augmentation using Gaussian processes; (2) feature extraction wavelets; (3) with random forests....