Related papers: Using Deep Learning for Robust Classification of F…
This work shows how human physical reasoning can guide machine-driven symbolic regression toward discovering empirical laws from observations. As an example, we derive a simple equation that classifies fast radio bursts (FRBs) into two…
Fast radio bursts (FRBs) are astronomical transients with millisecond timescales. Although most of the FRBs are not observed to repeat, a few of them are detected to repeat more than hundreds of times. There exist a large variety of…
The increasing field of view of radio telescopes and improved data processing capabilities have led to a surge in the detection of Fast Radio Bursts (FRBs). The discovery rate of FRBs is already a few per day and is expected to increase…
In this paper, we use Topological Data Analysis (TDA), a mathematical approach for studying data shape, to analyse Fast Radio Bursts (FRBs). Applying the Mapper algorithm, we visualise the topological structure of a large FRB sample. Our…
The origins of fast radio bursts (FRBs), astronomical transients with millisecond timescales, remain unknown. One of the difficulties stems from the possibility that observed FRBs could be heterogeneous in origin; as some of them have been…
Modern radio telescopes combine thousands of receivers, long-distance networks, large-scale compute hardware, and intricate software. Due to this complexity, failures occur relatively frequently. In this work we propose novel use of…
Fast Radio Bursts (FRBs) are classified into repeaters and non-repeaters, with only a few percent of the observed FRB population from the Canadian Hydrogen Intensity Mapping Experiment (CHIME) confirmed as repeaters. However, this figure…
The detection of fast radio bursts (FRBs) in radio astronomy is a complex task due to the challenges posed by radio frequency interference (RFI) and signal dispersion in the interstellar medium. Traditional search algorithms are often…
We investigate a variant of variational autoencoders where there is a superstructure of discrete latent variables on top of the latent features. In general, our superstructure is a tree structure of multiple super latent variables and it is…
Fast Radio Bursts (FRBs) have emerged as one of the most dynamic areas of research in astronomy and cosmology. Despite increasing number of FRBs have been reported, the exact origin of FRBs remains elusive. Investigating the intrinsic…
The CHIME/FRB collaboration has recently published a catalog containing about half a thousand fast radio bursts (FRBs) including their spectra and several reconstructed properties, like signal widths, amplitudes, etc. We have developed a…
With the upcoming commensal surveys for Fast Radio Bursts (FRBs), and their high candidate rate, usage of machine learning algorithms for candidate classification is a necessity. Such algorithms will also play a pivotal role in sending…
To probe this question, we perform a statistical analysis using the first Canadian Hydrogen Intensity Mapping Experiment Fast Radio Burst (CHIME/FRB) catalog and identify a few discriminant properties between repeating and non-repeating…
By composing graphical models with deep learning architectures, we learn generative models with the strengths of both frameworks. The structured variational autoencoder (SVAE) inherits structure and interpretability from graphical models,…
Time domain radio astronomy observing campaigns frequently generate large volumes of data. Our goal is to develop automated methods that can identify events of interest buried within the larger data stream. The V-FASTR fast transient system…
Variational autoencoders (VAEs) are widely used deep generative models capable of learning unsupervised latent representations of data. Such representations are often difficult to interpret or control. We consider the problem of…
Fast radio bursts (FRBs) are bright, millisecond-duration radio pulses whose origins are unknown. To date, only one (FRB 121102) out of several dozen has been seen to repeat, though the extent to which it is exceptional remains unclear. We…
We model the sample of fast radio bursts (FRB), including the newly discovered CHIME repeaters, using the synchrotron blast wave model of Metzger, Margalit & Sironi (2019). This model postulates that FRBs are precursor radiation from…
Fast Radio Bursts (FRBs) are extremely luminous and brief signals (with duration of milliseconds or even shorter) of extragalactic origin. Despite the fact that hundreds of FRBs have been discovered to date, their nature still remains…
Recent rapid development of deep learning algorithms, which can implicitly capture structures in high-dimensional data, opens a new chapter in astronomical data analysis. We report here a new implementation of deep learning techniques for…