David C. Stenning

Inferring the distribution of exoplanetary system architectures from survey data increasingly relies on forward modeling with N -body simulations, but the computational cost of these models makes rigorous statistical inference impractical. We develop a statistical emulator for the distribution of outcomes of planet formation simulations, enabling fast conditional sampling in place of expensive integrations. The emulator targets the joint distribution of detected multiplicity and three observables derived from neighboring planet pairs: orbital period ratios, period-normalized transit duration ratios, and orbital spacing in mutual Hill radii. Our approach combines a calibrated model for multiplicity, flexible regression models for conditional distributions, dependence modeling in a transformed probability space, and methods to capture rare events in the tails. We introduce diagnostics to assess calibration, dependence preservation, and reproduction of rare events. The resulting emulator accurately captures heteroskedasticity, dependence structure, and tail behavior of the simulator outputs while reducing computational cost by several orders of magnitude relative to direct N -body simulation. This framework enables integration of detailed dynamical models into simulation-based inference for exoplanet populations.

This paper introduces an approach to analyze nonhomogeneous Poisson processes (NHPP) observed with noise, focusing on previously unstudied second-order characteristics of the noisy process. Utilizing a hierarchical Bayesian model with noisy data, we estimate hyperparameters governing a physically motivated NHPP intensity. Simulation studies demonstrate the reliability of this methodology in accurately estimating hyperparameters. Leveraging the posterior distribution, we then infer the probability of detecting a certain number of events within a given radius, the k-contact distance. We demonstrate our methodology with an application to observations of fast radio bursts (FRBs) detected by the Canadian Hydrogen Intensity Mapping Experiment’s FRB Project (CHIME/FRB). This approach allows us to identify repeating FRB sources by bounding or directly simulating the probability of observing k physically independent sources within some radius in the detection domain or the probability of coincidence (PC). The new methodology improves the repeater detection PC in 91% of cases when applied to the largest sample of previously classified observations, with a median improvement factor (existing metric over PC from our methodology) of ∼ 4800.

We present a catalog of 4539 fast radio bursts (FRBs) observed with the Canadian Hydrogen Intensity Mapping Experiment telescope between 2018 July 25 and 2023 September 15. These bursts originate from 3641 unique sources, including 981 bursts from 83 known repeating sources. For each FRB, the catalog provides an O ( 1 0 ′ ) estimate of sky location along with corresponding measurements of cumulative exposure time and survey sensitivity over the observing period. It includes a total-intensity dynamic spectrum between 400 and 800 MHz at 0.983 ms resolution. From this spectrum, we constrain a model of the burst morphology and measure key parameters such as arrival time, intrinsic temporal width, dispersion measure, scattering time, and flux density. This second catalog includes all FRBs from the first catalog, with every event reprocessed using a uniform and improved analysis framework. We show that previously published inferences remain valid under the updated measurements. We assess consistency of the detection rate across observational parameters, present initial distributions of burst properties, and outline ongoing and future studies that will use this catalog to investigate the nature of FRBs and their utility as astrophysical and cosmological probes.

We present a deep learning approach to classify fast radio bursts (FRBs) based purely on morphology as encoded on recorded dynamic spectrum from Canadian Hydrogen Intensity Mapping Experiment (CHIME)/FRB Catalog 2. We implemented transfer learning with a pretrained ConvNext architecture, exploiting its powerful feature extraction ability. ConvNext was adapted to classify dedispersed dynamic spectra (which we treat as images) of the FRBs into one of the two subclasses, i.e., repeater and nonrepeater, based on their various temporal and spectral properties and the relation between the subpulse structures. Additionally, we also used a mathematical model representation of the total intensity data to interpret the deep learning model. Upon fine-tuning the pretrained ConvNext on the FRB spectrograms, we were able to achieve high classification metrics while substantially reducing training time and computing power as compared to training a deep learning model from scratch with random weights and biases without any feature extraction ability. Importantly, our results suggest that the morphological differences between repeating and nonrepeating CHIME events persist in Catalog 2 and the deep-learning model leveraged these differences for classification. The fine-tuned deep-learning model can be used for inference, which enables us to predict whether an FRB’s morphology resembles that of repeaters or nonrepeaters. Such inferences may become increasingly significant when trained on larger datasets that will exist in the near future.

Precise localizations of a small number of repeating fast radio bursts (FRBs) using very long baseline interferometry (VLBI) have enabled multiwavelength follow-up observations revealing diverse local environments. However, the 2%–3% of FRB sources that are observed to repeat may not be representative of the full population. Here we use the VLBI capabilities of the full CHIME Outrigger array for the first time to localize a nearby (40 Mpc), bright (kJy), and apparently one-off FRB source, FRB 20250316A, to its environment on 13 pc scales. We use optical and radio observations to place deep constraints on associated transient emission and the properties of its local environment. We place a 5 σ upper limit of L _{9.9 GHz} < 2.1 × 10 ²⁵ erg s ⁻¹ Hz ⁻¹ on spatially coincident radio emission, a factor of 100 lower than any known compact persistent radio source associated with an FRB. Our Keck Cosmic Webb Imager observations allow us to characterize the gas density, metallicity, nature of gas ionization, dust extinction, and star formation rate through emission line fluxes. We leverage the exceptional brightness and proximity of this source to place deep constraints on the repetition of FRB 20250316A and find that it is inconsistent with all well-studied repeaters given the nondetection of bursts at lower spectral energies. We explore the implications of a measured offset of 190 ± 20 pc from the center of the nearest star formation region in the context of progenitor channels. FRB 20250316A marks the beginning of an era of routine localizations for one-off FRBs on tens of milliarcseconds scales, enabling large-scale studies of their local environments.

An important task in the study of fast radio bursts (FRBs) remains the automatic classification of repeating and nonrepeating sources based on their morphological properties. We propose a statistical model that considers a modified logistic regression to classify FRB sources. The classical logistic regression model is modified to accommodate the small proportion of repeaters in the data, a feature that is likely due to the sampling procedure and duration and is not a characteristic of the population of FRB sources. The weighted logistic regression hinges on the choice of a tuning parameter that represents the true proportion τ of repeating FRB sources in the entire population. The proposed method has a sound statistical foundation, direct interpretability, and operates with only five parameters, enabling quicker retraining with added data. Using the CHIME/FRB Collaboration sample of repeating and nonrepeating FRBs and numerical experiments, we achieve a classification accuracy for repeaters of nearly 75% or higher when τ is set in the range of 50%–60%. This implies a tentative high proportion of repeaters, which is surprising, but is also in agreement with recent estimates of τ that are obtained using other methods.

We report 10 fast radio bursts (FRBs) detected in the far sidelobe region (i.e., ≥5° off-meridian) of the Canadian Hydrogen Intensity Mapping Experiment (CHIME) from August 28 2018 to August 31 2021. We localize the bursts by fitting their spectra with a model of the CHIME/FRB synthesized beam response. We find that the far sidelobe events have on average ∼500 times greater fluxes than events detected in CHIME’s main lobe. We show that the sidelobe sample is therefore statistically ∼20 times closer than the main lobe sample. We find promising host galaxy candidates (P _cc < 1%) for two of the FRBs, 20190112B and 20210310B, at distances of 38 and 16 Mpc, respectively. CHIME/FRB did not observe repetition of similar brightness from the uniform sample of 10 sidelobe FRBs in a total exposure time of 35,580 hr. Under the assumption of Poisson-distributed bursts, we infer that the mean repetition interval above the detection threshold of the far sidelobe events is longer than 11,880 hr, which is at least 2380 times larger than the interval from known CHIME/FRB detected repeating sources, with some caveats, notably that very narrowband events could have been missed. Our results from these far sidelobe events suggest one of two scenarios: either (1) all FRBs repeat and the repetition intervals span a wide range, with high-rate repeaters being a rare sub-population, or (2) non-repeating FRBs are a distinct population different from known repeaters.