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Abstract In 2007 we were part of a team that discovered the so-called “Lorimer Burst”, the first example of a new class of objects now known as fast radio bursts (FRBs). These enigmatic events are only a few ms in duration and occur at random locations on the sky at a rate of a few thousand per day. Several thousand FRBs are currently known. While it is now well established that they have a cosmological origin, and about 10% of all currently known sources have been seen to exhibit multiple bursts, the origins of these enigmatic sources are currently poorly understood. In this article, we review the discovery of FRBs and present some of the highlights from the vast body of work by an international community. Following a brief overview of the scale of the visible Universe in §1, we describe the key moments in radio astronomy (§2) that led up to the discovery of the Lorimer burst (§3). Early efforts to find more FRBs are described in §4 which led to the discovery of the first repeating source (§5). In §6, as we close out on the second decade of FRBs, we outline some of the many open questions in the field and look ahead to the coming years where many surprises are surely in store. 

Abstract Radio-frequency interference (RFI) is becoming an increasingly significant problem for most radio telescopes. Working with Green Bank Telescope data from PSR J1730+0747 in the form of complex-valued channelized voltages and their respective high-resolution power spectral densities, we evaluate a variety of statistical measures to characterize RFI. As a baseline for performance comparison, we use median absolute deviation (MAD) in complex channelized voltage data and spectral kurtosis (SK) in power spectral density data to characterize and filter out RFI. From a new perspective, we implement the Shapiro–Wilks (SW) test for normality and two information theoretical measures, spectral entropy (SE) and spectral relative entropy (SRE), and apply them to mitigate RFI. The baseline RFI mitigation algorithms are compared against our novel RFI detection algorithms to determine how effective and robust the performance is. Except for MAD, we find significant improvements in signal-to-noise ratio through the application of SE, symmetrical SRE, asymmetrical SRE, SK, and SW. These algorithms also do a good job of characterizing broad-band RFI. Time- and frequency-variable RFI signals are best detected by SK and SW tests. 

Abstract Although neutron star–black hole binaries have been identified through mergers detected in gravitational waves, a pulsar–black hole binary has yet to be detected. While short-period binaries are detectable due to a clear signal in the pulsar’s timing residuals, effects from a long-period binary could be masked by other timing effects, allowing them to go undetected. In particular, a long-period binary measured over a small subset of its orbital period could manifest via time derivatives of the spin frequency incompatible with isolated pulsar properties. We assess the possibility of pulsars having unknown companions in long-period binaries and put constraints on the range of binary properties that may remain undetected in current data, but that may be detectable with further observations. We find that for 35% of canonical pulsars with published higher-order derivatives, the precision of measurements is not enough to confidently reject binarity (period ≳2 kyr), and that a black hole binary companion could not be ruled out for a sample of pulsars without published constraints if the period is >1 kyr. While we find no convincing cases in the literature, we put more stringent limits on orbital period and longitude of periastron for the few pulsars with published higher-order frequency derivatives (n≥ 3). We discuss the detectability of candidates and find that a sample pulsar in a 100 yr orbit could be detectable within 5–10 yr. 

Abstract Pulsar timing array experiments have recently uncovered evidence for a nanohertz gravitational wave background by precisely timing an ensemble of millisecond pulsars. The next significant milestones for these experiments include characterizing the detected background with greater precision, identifying its source(s), and detecting continuous gravitational waves from individual supermassive black hole binaries. To achieve these objectives, generating accurate and precise times of arrival of pulses from pulsar observations is crucial. Incorrect polarization calibration of the observed pulsar profiles may introduce errors in the measured times of arrival. Further, previous studies have demonstrated that robust polarization calibration of pulsar profiles can reduce noise in the pulsar timing data and improve timing solutions. In this paper, we investigate and compare the impact of different polarization calibration methods on pulsar timing precision using three distinct calibration techniques: the Ideal Feed Assumption (IFA), Measurement Equation Modeling (MEM), and Measurement Equation Template Matching (METM). Three NANOGrav pulsars—PSRs J1643−1224, J1744−1134, and J1909−3744—observed with the 800 MHz and 1.5 GHz receivers at the Green Bank Telescope (GBT) are utilized for our analysis. Our findings reveal that all three calibration methods enhance timing precision compared to scenarios where no polarization calibration is performed. Additionally, among the three calibration methods, the IFA approach generally provides the best results for timing analysis of pulsars observed with the GBT receiver system. We attribute the comparatively poorer performance of the MEM and METM methods to potential instabilities in the reference noise diode coupled to the receiver and temporal variations in the profile of the reference pulsar, respectively. 

Abstract Based on the rate of change of its orbital period, PSR J2043+1711 has a substantial peculiar acceleration of 3.5 ± 0.8 mm s^–1yr^–1, which deviates from the acceleration predicted by equilibrium Milky Way (MW) models at a 4σlevel. The magnitude of the peculiar acceleration is too large to be explained by disequilibrium effects of the MW interacting with orbiting dwarf galaxies (∼1 mm s^–1yr^–1), and too small to be caused by period variations due to the pulsar being a redback. We identify and examine two plausible causes for the anomalous acceleration: a stellar flyby, and a long-period orbital companion. We identify a main-sequence star in Gaia DR3 and Pan-STARRS DR2 with the correct mass, distance, and on-sky position to potentially explain the observed peculiar acceleration. However, the star and the pulsar system have substantially different proper motions, indicating that they are not gravitationally bound. However, it is possible that this is an unrelated star that just happens to be located near J2043+1711 along our line of sight (chance probability of 1.6%). Therefore, we also constrain possible orbital parameters for a circumbinary companion in a hierarchical triple system with J2043+1711; the changes in the spindown rate of the pulsar are consistent with an outer object that has an orbital period of 60 kyr, a companion mass of 0.3M_⊙(indicative of a white dwarf or low-mass star), and a semimajor axis of 1900 au. Continued timing and/or future faint optical observations of J2043+1711 may eventually allow us to differentiate between these scenarios. 

Abstract Using neural networks, we integrate the ability to account for Doppler smearing due to a pulsar’s orbital motion with the pulsar population synthesis package psrpoppy to develop accurate modeling of the observed binary pulsar population. As a first application, we show that binary neutron star systems where the two components have highly unequal mass are, on average, easier to detect than systems that are symmetric in mass. We then investigate the population of ultracompact (1.5 minutes ≤ P b ≤ 15 minutes) neutron star–white dwarf (NS–WD) and double neutron star (DNS) systems, which are promising sources for the Laser Interferometer Space Antenna gravitational-wave detector. Given the nondetection of these systems in radio surveys thus far, we estimate a 95% confidence upper limit of ∼1450 and ∼1100 ultracompact NS–WD and DNS systems in the Milky Way that are beaming toward the Earth, respectively. We also show that using survey integration times in the range 20 s–200 s with time-domain resampling will maximize the signal-to-noise ratio as well as the probability of detection of these ultracompact binary systems. Among all the large-scale radio pulsar surveys, those that are currently being carried out using archival data collected with the Arecibo radio telescope have a ∼50%–80% chance of detecting at least one of these systems using current integration integration times and ∼80%–95% using optimal integration times in the next several years. 

Abstract Noise characterization for pulsar-timing applications accounts for interstellar dispersion by assuming a known frequency dependence of the delay it introduces in the times of arrival (TOAs). However, calculations of this delay suffer from misestimations due to other chromatic effects in the observations. The precision in modeling dispersion is dependent on the observed bandwidth. In this work, we calculate the offsets in infinite-frequency TOAs due to misestimations in the modeling of dispersion when using varying bandwidths at the Green Bank Telescope. We use a set of broadband observations of PSR J1643−1224, a pulsar with unusual chromatic timing behavior. We artificially restricted these observations to a narrowband frequency range, then used both the broad- and narrowband data sets to calculate residuals with a timing model that does not account for time variations in the dispersion. By fitting the resulting residuals to a dispersion model and comparing the fits, we quantify the error introduced in the timing parameters due to using a reduced frequency range. Moreover, by calculating the autocovariance function of the parameters, we obtained a characteristic timescale over which the dispersion misestimates are correlated. For PSR J1643−1224, which has one of the highest dispersion measures (DM) in the NANOGrav pulsar timing array, we find that the infinite-frequency TOAs suffer from a systematic offset of ∼22μs due to incomplete frequency sampling, with correlations over about one month. For lower-DM pulsars, the offset is ∼7μs. This error quantification can be used to provide more robust noise modeling in the NANOGrav data, thereby increasing the sensitivity and improving the parameter estimation in gravitational wave searches. 

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 We test the impact of an evolving supermassive black hole mass scaling relation (M_BH–M_bulge) on the predictions for the gravitational-wave background (GWB). The observed GWB amplitude is 2–3 times higher than predicted by astrophysically informed models, which suggests the need to revise the assumptions in those models. We compare a semi-analytic model’s ability to reproduce the observed GWB spectrum with a static versus evolving-amplitudeM_BH–M_bulgerelation. We additionally consider the influence of the choice of galaxy stellar mass function (GSMF) on the modeled GWB spectra. Our models are able to reproduce the GWB amplitude with either a large number density of massive galaxies or a positively evolvingM_BH–M_bulgeamplitude (i.e., theM_BH/M_bulgeratio was higher in the past). If we assume that theM_BH–M_bulgeamplitude does not evolve, our models require a GSMF that implies an undetected population of massive galaxies (M_⋆≥ 10¹¹M_⊙atz> 1). When theM_BH–M_bulgeamplitude is allowed to evolve, we can model the GWB spectrum with all fiducial values and anM_BH–M_bulgeamplitude that evolves asα(z) =α₀(1 +z)^1.04±0.5. 

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.