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The physical properties of fast radio burst (FRB) host galaxies provide important clues towards the nature of FRB sources. The 16 FRB hosts identified thus far span three orders of magnitude in mass and specific star formation rate, implicating a ubiquitously occurring progenitor object. FRBs localized with ∼arcsecond accuracy also enable effective searches for associated multiwavelength and multi-time-scale counterparts, such as the persistent radio source associated with FRB 20121102A. Here we present a localization of the repeating source FRB 20201124A, and its association with a host galaxy (SDSS J050803.48+260338.0, z = 0.098) and persistent radio source. The galaxy is massive (${\sim}3\times 10^{10}\, \text{M}_{\odot }$), star-forming (few solar masses per year), and dusty. Very Large Array and Very Long Baseline Array observations of the persistent radio source measure a luminosity of 1.2 × 1029 erg s−1 Hz−1, and show that is extended on scales ≳50 mas. We associate this radio emission with the ongoing star formation activity in SDSS J050803.48+260338.0. Deeper, high-resolution optical observations are required to better utilize the milliarcsecond-scale localization of FRB 20201124A and determine the origin of the large dispersion measure (150–220 pc cm−3) contributed by the host. SDSS J050803.48+260338.0 is an order of magnitude more massive than any galaxy or stellar system previously associated with a repeating FRB source, but is comparable to the hosts of so far non-repeating FRBs, further building the link between the two apparent populations.

 

Abstract The dispersive sweep of fast radio bursts (FRBs) has been used to probe the ionized baryon content of the intergalactic medium 1 , which is assumed to dominate the total extragalactic dispersion. Although the host-galaxy contributions to the dispersion measure appear to be small for most FRBs 2 , in at least one case there is evidence for an extreme magneto-ionic local environment 3,4 and a compact persistent radio source 5 . Here we report the detection and localization of the repeating FRB 20190520B, which is co-located with a compact, persistent radio source and associated with a dwarf host galaxy of high specific-star-formation rate at a redshift of 0.241 ± 0.001. The estimated host-galaxy dispersion measure of approximately $${903}_{-111}^{+72}$$ 903 − 111 + 72 parsecs per cubic centimetre, which is nearly an order of magnitude higher than the average of FRB host galaxies 2,6 , far exceeds the dispersion-measure contribution of the intergalactic medium. Caution is thus warranted in inferring redshifts for FRBs without accurate host-galaxy identifications. 

Abstract We present the localization and host galaxies of one repeating and two apparently nonrepeating fast radio bursts (FRBs). FRB 20180301A was detected and localized with the Karl G. Jansky Very Large Array to a star-forming galaxy at z = 0.3304. FRB20191228A and FRB20200906A were detected and localized by the Australian Square Kilometre Array Pathfinder to host galaxies at z = 0.2430 and z = 0.3688, respectively. We combine these with 13 other well-localized FRBs in the literature, and analyze the host galaxy properties. We find no significant differences in the host properties of repeating and apparently nonrepeating FRBs. FRB hosts are moderately star forming, with masses slightly offset from the star-forming main sequence. Star formation and low-ionization nuclear emission-line region emission are major sources of ionization in FRB host galaxies, with the former dominant in repeating FRB hosts. FRB hosts do not track stellar mass and star formation as seen in field galaxies (more than 95% confidence). FRBs are rare in massive red galaxies, suggesting that progenitor formation channels are not solely dominated by delayed channels which lag star formation by gigayears. The global properties of FRB hosts are indistinguishable from core-collapse supernovae and short gamma-ray bursts hosts, and the spatial offset (from galaxy centers) of FRBs is mostly inconsistent with that of the Galactic neutron star population (95% confidence). The spatial offsets of FRBs (normalized to the galaxy effective radius) also differ from those of globular clusters in late- and early-type galaxies with 95% confidence. 

Abstract We present an analysis of a densely repeating sample of bursts from the first repeating fast radio burst, FRB 121102. We reanalyzed the data used by Gourdji et al. and detected 93 additional bursts using our single-pulse search pipeline. In total, we detected 133 bursts in three hours of data at a center frequency of 1.4 GHz using the Arecibo telescope, and develop robust modeling strategies to constrain the spectro-temporal properties of all of the bursts in the sample. Most of the burst profiles show a scattering tail, and burst spectra are well modeled by a Gaussian with a median width of 230 MHz. We find a lack of emission below 1300 MHz, consistent with previous studies of FRB 121102. We also find that the peak of the log-normal distribution of wait times decreases from 207 to 75 s using our larger sample of bursts, as compared to that of Gourdji et al. Our observations do not favor either Poissonian or Weibull distributions for the burst rate distribution. We searched for periodicity in the bursts using multiple techniques, but did not detect any significant period. The cumulative burst energy distribution exhibits a broken power-law shape, with the lower- and higher-energy slopes of −0.4 ± 0.1 and −1.8 ± 0.2, with the break at (2.3 ± 0.2) × 10 37 erg. We provide our burst fitting routines as a Python package burstfit 4 4 https://github.com/thepetabyteproject/burstfit that can be used to model the spectrogram of any complex fast radio burst or pulsar pulse using robust fitting techniques. All of the other analysis scripts and results are publicly available. 5 5 https://github.com/thepetabyteproject/FRB121102 

ABSTRACT The origin of fast radio bursts (FRBs) still remains a mystery, even with the increased number of discoveries in the last 3 yr. Growing evidence suggests that some FRBs may originate from magnetars. Large, single-dish telescopes such as Arecibo Observatory (AO) and Green Bank Telescope (GBT) have the sensitivity to detect FRB 121102-like bursts at gigaparsec distances. Here, we present searches using AO and GBT that aimed to find potential radio bursts at 11 sites of past gamma-ray bursts that show evidence for the birth of a magnetar. We also performed a search towards GW170817, which has a merger remnant whose nature remains uncertain. We place $10\sigma$ fluence upper limits of ≈0.036 Jy ms at 1.4 GHz and ≈0.063 Jy ms at 4.5 GHz for the AO data and fluence upper limits of ≈0.085 Jy ms at 1.4 GHz and ≈0.098 Jy ms at 1.9 GHz for the GBT data, for a maximum pulse width of ≈42 ms. The AO observations had sufficient sensitivity to detect any FRB of similar luminosity to the one recently detected from the Galactic magnetar SGR 1935+2154. Assuming a Schechter function for the luminosity function of FRBs, we find that our non-detections favour a steep power-law index (α ≲ −1.1) and a large cut-off luminosity (L0 ≳ 1041 erg s−1). 

ABSTRACT 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 real-time triggers for prompt follow-ups with other instruments. In this paper, we have used the technique of Transfer Learning to train the state-of-the-art deep neural networks for classification of FRB and Radio Frequency Interference (RFI) candidates. These are convolutional neural networks which work on radio frequency-time and dispersion measure-time images as the inputs. We trained these networks using simulated FRBs and real RFI candidates from telescopes at the Green Bank Observatory. We present 11 deep learning models, each with an accuracy and recall above 99.5 per cent on our test data set comprising of real RFI and pulsar candidates. As we demonstrate, these algorithms are telescope and frequency agnostic and are able to detect all FRBs with signal-to-noise ratios above 10 in ASKAP and Parkes data. We also provide an open-source python package fetch (Fast Extragalactic Transient Candidate Hunter) for classification of candidates, using our models. Using fetch, these models can be deployed along with any commensal search pipeline for real-time candidate classification.