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Fast radio bursts (FRBs) are millisecond-duration radio waves from the Universe. Even though more than 50 physical models have been proposed, the origin and physical mechanism of FRB emissions are still unknown. The classification of FRBs is one of the primary approaches to understanding their mechanisms, but previous studies classified conventionally using only a few observational parameters, such as fluence and duration, which might be incomplete. To overcome this problem, we use an unsupervised machine-learning model, the Uniform Manifold Approximation and Projection (UMAP) to handle seven parameters simultaneously, including amplitude, linear temporal drift, time duration, central frequency, bandwidth, scaled energy, and fluence. We test the method for homogeneous 977 sub-bursts of FRB 20121102A detected by the Arecibo telescope. Our machine-learning analysis identified five distinct clusters, suggesting the possible existence of multiple different physical mechanisms responsible for the observed FRBs from the FRB 20121102A source. The geometry of the emission region and the propagation effect of FRB signals could also make such distinct clusters. This research will be a benchmark for future FRB classifications when dedicated radio telescopes such as the Square Kilometer Array (SKA) or Bustling Universe Radio Survey Telescope in Taiwan (BURSTT) discover more FRBs than before., Comment: Accepted for publication for PASA

Fast Radio Bursts (FRBs) are mysterious bursts in the millisecond timescale at radio wavelengths. Currently, there is little understanding about the classification of repeating FRBs, based on difference in physics, which is of great importance in understanding their origin. Recent works from the literature focus on using specific parameters to classify FRBs to draw inferences on the possible physical mechanisms or properties of these FRB subtypes. In this study, we use publicly available 1652 repeating FRBs from FRB121102 detected with the Five-hundred-meter Aperture Spherical Telescope (FAST), and studied them with an unsupervised machine learning model. By fine-tuning the hyperparameters of the model, we found that there is an indication for four clusters from the bursts of FRB121102 instead of the two clusters ("Classical" and "Atypical") suggested in the literature. Wherein, the "Atypical" cluster can be further classified into three sub-clusters with distinct characteristics. Our findings show that the clustering result we obtained is more comprehensive not only because our study produced results which are consistent with those in the literature but also because our work uses more physical parameters to create these clusters. Overall, our methods and analyses produced a more holistic approach in clustering the repeating FRBs of FRB121102., Comment: 24 pages, 14 figures, accepted for publication in MNRAS. For summary video, please see https://www.youtube.com/watch?v=wYx6t2G__84&list=PLOpYDs2PkYlYIiKDjDz6r6aKXcXdJZXYb&index=13&ab_channel=NCHUAstronomy

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 physical properties among these bursts, suggesting heterogeneous mechanisms of FRBs. In this paper, we conduct a categorisation on the extremely frequently repeating FRB 20201124A with the assistance of machine learning, as such techniques have the potential to use subtle differences and correlations that humans are unaware of to better classify bursts. The research is carried out by applying the unsupervised Uniform Manifold Approximation and Projection (UMAP) model on the FRB 20201124A data provided by Five-hundred-meter Aperture Spherical radio Telescope (FAST). The algorithm eventually categorises the bursts into three clusters. In addition to the two categories in previous work based on waiting time, a new way for categorisation has been found. The three clusters are either high energy, high frequency, or low frequency, reflecting the distribution of FRB energy and frequency. Importantly, a similar machine learning result is found in another frequently repeating FRB20121102A, implying a common mechanism among this kind of FRB. This work is one of the first steps towards the systematical categorisation of the extremely frequently repeating FRBs., Comment: 8 pages, 9 figures, accepted for publication in MNRAS. For summary video, please see https://www.youtube.com/watch?v=1PPCMKcrIf4&list=PLOpYDs2PkYlYIiKDjDz6r6aKXcXdJZXYb&index=14&ab_channel=NCHUAstronomy

A correlation between the intrinsic energy and the burst duration of non-repeating fast radio bursts (FRBs) has been reported. If it exists, the correlation can be used to estimate intrinsic energy from the duration, and thus can provide us with a new distance measure for cosmology. However, the correlation suffered from small number statistics (68 FRBs) and was not free from contamination by latent repeating populations, which might not have such a correlation. How to separate/exclude the repeating bursts from the mixture of all different types of FRBs is essential to see this property. Using a much larger sample from the new FRB catalogue (containing 536 FRBs) recently released by the CHIME/FRB project, combined with a new classification method developed based on unsupervised machine learning, we carried out further scrutiny of the relation. We found that there is a weak correlation between the intrinsic energy and duration for non-repeating FRBs at z < 0.3 with Kendall's tau correlation coefficient of 0.239 and significance of 0.001 (statistically significant), whose slope looks similar to that of gamma-ray bursts. This correlation becomes weaker and insignificant at higher redshifts (z > 0.3), possibly due to the lack of the faint FRBs at high-z and/or the redshift evolution of the correlation. The scattering time in the CHIME/FRB catalogue shows an intriguing trend: it varies along the line obtained from linear fit on the energy versus duration plane between these two parameters. A possible cosmological application of the relation must wait for faint FRBs at high-z., Comment: 9 pages, 7 figures, accepted for publication in MNRAS

Fast radio bursts (FRBs) are mysterious millisecond pulses in radio, most of which originate from distant galaxies. Revealing the origin of FRBs is becoming central in astronomy. The redshift evolution of the FRB energy function, i.e., the number density of FRB sources as a function of energy, provides important implications for the FRB progenitors. Here we show the energy functions of FRBs selected from the recently released Canadian Hydrogen Intensity Mapping Experiment (CHIME) catalogue using the $V_{\rm max}$ method. The $V_{\rm max}$ method allows us to measure the redshift evolution of the energy functions as it is without any prior assumption on the evolution. We use a homogeneous sample of 164 non-repeating FRB sources, which are about one order of magnitude larger than previously investigated samples. The energy functions of non-repeating FRBs show Schechter function-like shapes at $z\lesssim1$. The energy functions and volumetric rates of non-repeating FRBs decrease towards higher redshifts similar to the cosmic stellar-mass density evolution: there is no significant difference between the non-repeating FRB rate and cosmic stellar-mass density evolution with a 1\% significance threshold, whereas the cosmic star-formation rate scenario is rejected with a more than 99\% confidence level. Our results indicate that the event rate of non-repeating FRBs is likely controlled by old populations rather than young populations which are traced by the cosmic star-formation rate density. This suggests old populations such as old neutron stars and black holes as more likely progenitors of non-repeating FRBs., Comment: Accepted for publication in MNRAS. A summary video is available at https://youtu.be/qbUctrFHLAs

Einstein's General Relativity (GR) is the basis of modern astronomy and astrophysics. Testing the validity of basic assumptions of GR is important. In this work, we test a possible violation of the Weak Equivalence Principle (WEP), i.e., there might be a time-lag between photons of different frequencies caused by the effect of gravitational fields if the speeds of photons are slightly different at different frequencies. We use Fast Radio Bursts (FRBs) , which are astronomical transients with millisecond timescales detected in the radio frequency range. Being at cosmological distances, accumulated time delay of FRBs can be caused by the plasma in between an FRB source and an observer, and by gravitational fields in the path of the signal. We segregate the delay due to dispersion and gravitational field using the post-Newtonian formalism (PPN) parameter $\Delta \gamma$, which defines the space-curvature due to gravity by a unit test mass. We did not detect any time-delay from FRBs but obtained tight constraints on the upper limit of $\Delta \gamma$. For FRB20181117C with $z = 1.83 \pm 0.28$ and $\nu_{obs}$ = $676.5\,{\rm MHz}$, the best possible constraint is obtained at log($\Delta \gamma$) = $-21.58 ^{+0.10}_{-0.12}$ and log($\Delta \gamma$/$r_{\rm E}$) = $-21.75 ^{+0.10}_{-0.14}$, respectively, where $r_{\rm E}$ is the energy ratio of two photons of the same FRB signal. This constraint is about one order of magnitude better than the previous constraint obtained with FRBs, and five orders tighter than any constraint obtained using other cosmological sources., Comment: Accepted for publication in Monthly Notices of the Royal Astronomical Society

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 observed to repeat, and others have not. Due to limited observing periods and telescope sensitivities, some bursts may be misclassified as non-repeaters. Therefore, it is important to clearly distinguish FRBs into repeaters and non-repeaters, to better understand their origins. In this work, we classify repeaters and non-repeaters using unsupervised machine learning, without relying on expensive monitoring observations. We present a repeating FRB recognition method based on the Uniform Manifold Approximation and Projection (UMAP). The main goals of this work are to: (i) show that the unsupervised UMAP can classify repeating FRB population without any prior knowledge about their repetition, (ii) evaluate the assumption that non-repeating FRBs are contaminated by repeating FRBs, and (iii) recognise the FRB repeater candidates without monitoring observations and release a corresponding catalogue. We apply our method to the Canadian Hydrogen Intensity Mapping Experiment Fast Radio Burst (CHIME/FRB) database. We found that the unsupervised UMAP classification provides a repeating FRB completeness of 95 per cent and identifies 188 FRB repeater source candidates from 474 non-repeater sources. This work paves the way to a new classification of repeaters and non-repeaters based on a single epoch observation of FRBs., Comment: 10 pages, 9 figures, accepted for publication in MNRAS. For summary video, please see https://youtu.be/fWfvfFPDhcQ