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We explore constraints on the size of cool gas clouds in the circumgalactic medium (CGM) obtainable from the presence, or lack thereof, of refractive scattering in fast radio bursts (FRBs). Our refractive analysis sets the most conservative bounds on parsec-scale CGM clumpiness as it does not make assumptions about the turbulent density cascade. We find that the bulk of low-redshift cool CGM gas, constrained to have densities of \\(n_{\\rm e} \\lesssim 10^{-2}\\,{\\rm cm^{-3}}\\), likely cannot produce two refractive images and, hence, scattering. It is only for extremely small cloud sizes \\(\\lesssim 0.1\\) pc (about a hundred times smaller than the so-called shattering scale) that such densities could result in detectable scattering. Dense \\(n_{\\rm e} \\gtrsim 0.1\\,{\\rm cm^{-3}}\\) gas with shattering-scale cloud sizes is more likely to inhabit the inner several kiloparsecs of the low-redshift CGM: such clouds would result in multiple refractive images and large scattering times \\(\\gtrsim 1 - 10\\) ms, but a small fraction FRB sightlines are likely to be affected. We argue that such large scattering times from an intervening CGM would be a signature of sub-parsec clouds, even if diffractive scattering from turbulence contributes to the overall scattering. At redshift \\(z\\sim 3\\), we estimate \\(\\sim 0.1\\%\\) of FRBs to intersect massive proto-clusters, which may be the most likely place to see scattering owing to their ubiquitous \\(n_{\\rm e} \\approx 1\\,{\\rm cm^{-3}}\\) cold gas. While much of our discussion assumes a single cloud size, we show similar results hold for a CGM cloud-size distribution motivated by hydrodynamic simulations.

It has been proposed that measurements of scattering times (\\(\\tau\\)) from fast radio bursts (FRB) may be used to infer the FRB host dispersion measure (DM) and its redshift. This approach relies on the existence of a correlation between \\(\\tau\\) and DM within FRB hosts such as that observed for Galactic pulsars. We assess the measurability of a \\(\\tau - \\)DM\\(_{\\rm host}\\) relation through simulated observations of FRBs within the ASKAP/CRAFT survey, taking into account instrumental effects. We show that even when the FRB hosts intrinsically follow the \\(\\tau - \\)DM relation measured for pulsars, this correlation cannot be inferred from FRB observations; this limitation arises mostly from the large variance around the average cosmic DM value given by the Macquart relation, the variance within the \\(\\tau - \\)DM relation itself, and observational biases against large \\(\\tau\\) values. We argue that theoretical relations have little utility as priors on redshift, e.g., for purposes of galaxy identification, and that the recent lack of an observed correlation between scattering and DM in the ASKAP/CRAFT survey is not unexpected, even if our understanding of a \\(\\tau - \\)DM\\(_{\\rm host}\\) relation is correct.

FRBs constitute a unique probe of various astrophysical and cosmological environments via their characteristic dispersion and rotation (RM) measures that encode information about the ionized gas traversed by the FRB sightlines. In this work, we analyse observed RM measured for 14 localized FRBs at \\(0.05 \\lesssim z \\lesssim 0.5\\), to infer total magnetic fields in various galactic environments. Additionally, we calculate \\(f_{\\rm gas}\\) - the average fraction of halo baryons in the ionized CGM. We build a spectroscopic dataset of FRB foreground galaxy halos, acquired with VLT/MUSE and FLIMFLAM survey. We develop a novel Bayesian algorithm and use it to correlate the individual intervening halos with the observed RM. This approach allows us to disentangle the magnetic fields present in various environments traversed by the FRB. Our analysis yields the first direct FRB constraints on the strength of magnetic fields in the ISM and halos of the FRB host galaxies, as well as in halos of foreground galaxies. We find that the average magnetic field in the ISM of FRB hosts is \\(B_{\\rm host}^{\\rm local} = 5.44^{+1.13}_{-0.87}\\mu{\\rm G}\\). Additionally, we place upper limits on average magnetic field in FRB host halos, \\(B_{\\rm host}^{\\rm halo} < 4.81\\mu{\\rm G}\\), and in foreground intervening halos, \\(B_{\\rm f/g}^{\\rm halo} < 4.31\\mu{\\rm G}\\). Moreover, we estimate the average fraction of cosmic baryons inside \\(10 \\lesssim \\log_{10} \\left( M_{\\rm halo} / M_{\\odot}\\right) \\lesssim 13.1\\) halos \\(f_{\\rm gas} = 0.45^{+0.21}_{-0.19}\\). We find that the magnetic fields inferred in this work are in good agreement with previous measurements. In contrast to previous studies that analysed FRB RMs and have not considered contributions from the halos of the foreground and/or FRB host galaxies, we show that they can contribute a non-negligible amount of RM and must be taken into account when analysing future FRB samples.

Accurately localising fast radio bursts (FRBs) is essential for understanding their birth environments and for their use as cosmological probes. Recent advances in radio interferometry, particularly with MeerKAT, have enabled the localisation of individual bursts with arcsecond precision. In this work, we present the localisation of 15 apparently non-repeating FRBs detected with MeerKAT. Two of the FRBs, discovered in 2022, were localised in 8 second images from the projects which MeerTRAP was commensal to, while eight were localised using the transient buffer (TB) pipeline, and another one through SeeKAT, all with arcsecond precision. Four additional FRBs lacked TB triggers and sufficient signal, limiting their localisation only to arcminute precision. For eight of the FRBs in our sample, we identify host galaxies with greater than 90% confidence, and one with 80% confidence, while two FRBs have ambiguous associations. We measured spectroscopic redshifts for six host galaxies, ranging from 0.33 to 0.85, demonstrating MeerKAT's sensitivity to high redshift FRBs. We modelled the spectral energy distributions of host galaxies with sufficient photometric coverage to derive their stellar population and star formation properties. This work represents one of the largest uniform samples of well-localised distant FRBs to date, laying the groundwork for using MeerKAT FRBs as cosmological probes and understand how FRB hosts evolve at high redshift.

When deploying time series forecasting models based on machine learning to real world settings, one often encounter situations where the data distribution drifts. Such drifts expose the forecasting models to out-of-distribution (OOD) data, and machine learning models lack robustness in these settings. Robustness can be improved by using deep generative models or genetic algorithms to augment time series datasets, but these approaches lack interpretability and are computationally expensive. In this work, we develop an interpretable and simple framework for generating time series. Our method combines time-series decompositions with analytic functions, and is able to generate time series with characteristics matching both in- and out-of-distribution data. This approach allows users to generate new time series in an interpretable fashion, which can be used to augment the dataset and improve forecasting robustness. We demonstrate our framework through EXPRTS, a visual analytics tool designed for univariate time series forecasting models and datasets. Different visualizations of the data distribution, forecasting errors and single time series instances enable users to explore time series datasets, apply transformations, and evaluate forecasting model robustness across diverse scenarios. We show how our framework can generate meaningful OOD time series that improve model robustness, and we validate EXPRTS effectiveness and usability through three use-cases and a user study.

We present a deep learning approach to classify fast radio bursts (FRBs) based purely on morphology as encoded on recorded dynamic spectrum from 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 sub-classes, i.e., repeater and non-repeater, based on their various temporal and spectral properties and relation between the sub-pulse structures. Additionally, we also used 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 CHIME repeating and non-repeating 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 non-repeaters. Such inferences may become increasingly significant when trained on larger data sets that will exist in the near future.

A common issue for machine learning models applied to time-series forecasting is the temporal evolution of the data distributions (i.e., concept drift). Because most of the training data does not reflect such changes, the models present poor performance on the new out-of-distribution scenarios and, therefore, the impact of such events cannot be reliably anticipated ahead of time. We present and publicly release CounterfacTS, a tool to probe the robustness of deep learning models in time-series forecasting tasks via counterfactuals. CounterfacTS has a user-friendly interface that allows the user to visualize, compare and quantify time series data and their forecasts, for a number of datasets and deep learning models. Furthermore, the user can apply various transformations to the time series and explore the resulting changes in the forecasts in an interpretable manner. Through example cases, we illustrate how CounterfacTS can be used to i) identify the main features characterizing and differentiating sets of time series, ii) assess how the model performance depends on these characateristics, and iii) guide transformations of the original time series to create counterfactuals with desired properties for training and increasing the forecasting performance in new regions of the data distribution. We discuss the importance of visualizing and considering the location of the data in a projected feature space to transform time-series and create effective counterfactuals for training the models. Overall, CounterfacTS aids at creating counterfactuals to efficiently explore the impact of hypothetical scenarios not covered by the original data in time-series forecasting tasks.

We demonstrate the potential of line-intensity mapping to place constraints on the initial mass function (IMF) of Population III stars via measurements of the mean He II 1640A/H\\(\\alpha\\) line-intensity ratio. We extend the 21cmFAST code with modern high-redshift galaxy-formation and photoionization models, and estimate the line emission from Population II and Population III galaxies at redshifts \\(5 \\le z \\le 20\\). In our models, mean ratio values of He II/H\\(\\alpha \\gtrsim 0.1\\) indicate top-heavy Population III IMFs with stars of several hundred solar masses, reached at \\(z \\gtrsim 10\\) when Population III stars dominate star formation. A next-generation space mission with capabilities moderately superior to those of CDIM will be able to probe this scenario by measuring the He II and H\\(\\alpha\\) fluctuation power spectrum signals and their cross-correlation at high significance up to \\(z\\sim 20\\). Moreover, regardless of the IMF, a ratio value of He II/H\\(\\alpha \\lesssim 0.01\\) indicates low Population III star formation and, therefore, it signals the end of the period dominated by this stellar population. However, a detection of the corresponding He II power spectrum may be only possible for top-heavy Population III IMFs or through cross-correlation with the stronger H\\(\\alpha\\) signal. Finally, ratio values of \\(0.01 \\lesssim\\) He II/H\\(\\alpha\\) \\(\\lesssim 0.1\\) are complex to interpret because they can be driven by several competing effects. We discuss how various measurements at different redshifts and the combination of the line-intensity ratio with other probes can assist in constraining the Population III IMF in this case.

The circumgalactic medium (CGM) surrounding galaxies is believed to be a significant reservoir of baryons, yet its total mass remains poorly constrained. We present a novel approach to probe the CGM of the Andromeda galaxy (M31) using fast radio bursts (FRBs) from the CHIME/FRB Catalog 2. By comparing the dispersion measures (DMs) of 171 FRBs whose sightlines intersect M31's halo (within \\(r_{\\rm vir}= 302\\,\\mathrm{kpc}\\)) to a control sample of 684 FRBs, we estimate the DM contribution from M31's CGM. We find evidence for an excess DM of \\(\\delta\\mathrm{DM} = 5.9\\)--\\(59.6\\,\\mathrm{pc\\,cm^{-3}}\\) in the inner halo (\\(\\approx 0\\)--\\(151\\,\\mathrm{kpc}\\)) and \\(\\delta\\mathrm{DM} = 25.7\\)--\\(64.6\\,\\mathrm{pc\\,cm^{-3}}\\) in the outer halo (\\(\\approx 151\\)--\\(302\\,\\mathrm{kpc}\\)). Using a generalized halo model parameterized by a closure radius \\(r_{\\mathrm{close}}\\), we constrain the baryon distribution and infer a best-fit value for \\(r_{\\mathrm{close}}\\) to be \\(9.2^{+9.9}_{-4.9}\\,r_{\\rm vir}\\) (\\(1\\sigma\\)), corresponding to a total CGM mass of \\(M_{b,\\mathrm{halo}} = 18.6^{+7.9}_{-8.4} \\times 10^{10}\\,M_\\odot\\). Our results suggest that M31 may harbor a substantial fraction of its cosmic baryon budget in diffuse, ionized gas. This work demonstrates the potential of FRBs as a powerful tool for studying the CGM of nearby galaxies, with future larger samples expected to provide tighter constraints on the baryon content of galactic halos.

We report on quasar outflow properties revealed by analyzing more than 60 composite outflow spectra built from \\(\\sim 60\\,000\\) CIV absorption troughs in the SDSS-III/BOSS DR12QBAL catalog. We assess the dependences of the equivalent widths of many outflow metal absorption features on outflow velocity, trough width and position, and quasar magnitude and redshift. The evolution of the equivalent widths of the OVI and NV lines with outflow velocity correlates with that of the mean absorption-line width, the outflow electron density, and the strength of lines arising from collisionally-excited meta-stable states. None of these correlations is found for the other high- or low-ionization species, and different behaviors with trough width are also suggested. We find no dependence on quasar magnitude or redshift in any case. All the observed trends can be reconciled by considering a multiphase stratified outflow structure, where inner regions are colder, denser and host lower-ionization species. Given the prevalence of radiative acceleration in quasar outflows found by Mas-Ribas & Mauland (2019), we suggest that radiation pressure sweeps up and compresses the outflowing gas outwards, creating waves or filaments where the multiphase stratified structure could take form. This scenario is supported by the suggested correlation between electron density and outflow velocity, and the similar behavior observed for the line and line-locking components of the absorption features. We show that this outflow structure is also consistent with other X-ray, radiative transfer, and polarization results, and discuss the implications of our findings for future observational and numerical quasar outflow studies.