Search

The LIGO, Virgo and KAGRA gravitational wave observatories are currently undertaking their O4 observing run offering the opportunity to discover new electromagnetic counterparts to gravitational wave events. We examine the capability of the Neil Gehrels Swift Observatory (Swift) to respond to these triggers, primarily binary neutron star mergers, with both the UV/Optical Telescope (UVOT) and the X-ray Telescope (XRT). We simulate Swift's response to a trigger under different strategies using model skymaps, convolving these with the 2MPZ catalogue to produce an ordered list of observing fields, deriving the time taken for Swift to reach the correct field and simulating the instrumental responses to modelled kilonovae and short gamma-ray burst afterglows. We find that UVOT using the $u$ filter with an exposure time of order 120 s is optimal for most follow-up observations and that we are likely to detect counterparts in $\sim6$% of all binary neutron star triggers. We find that the gravitational wave 90% error area and measured distance to the trigger allow us to select optimal triggers to follow-up. Focussing on sources less than 300 Mpc away or 500 Mpc if the error area is less than a few hundred square degrees, distances greater than previously assumed, offer the best opportunity for discovery by Swift with $\sim5 - 30$% of triggers having detection probabilities $\geq 0.5$. At even greater distances, we can further optimise our follow-up by adopting a longer 250 s or 500 s exposure time., Comment: 16 pages, 13 figures. Submitted to MNRAS

We report the discovery of a peculiar X-ray transient, EP240408a, by Einstein Probe (EP) and follow-up studies made with EP, Swift, NICER, GROND, ATCA and other ground-based multi-wavelength telescopes. The new transient was first detected with Wide-field X-ray Telescope (WXT) on board EP on April 8th, 2024, manifested in an intense yet brief X-ray flare lasting for 12 seconds. The flare reached a peak flux of 3.9x10^(-9) erg/cm2/s in 0.5-4 keV, about 300 times brighter than the underlying X-ray emission detected throughout the observation. Rapid and more precise follow-up observations by EP/FXT, Swift and NICER confirmed the finding of this new transient. Its X-ray spectrum is non-thermal in 0.5-10 keV, with a power-law photon index varying within 1.8-2.5. The X-ray light curve shows a plateau lasting for about 4 days, followed by a steep decay till becoming undetectable about 10 days after the initial detection. Based on its temporal property and constraints from previous EP observations, an unusual timescale in the range of 7-23 days is found for EP240408a, which is intermediate between the commonly found fast and long-term transients. No counterparts have been found in optical and near-infrared, with the earliest observation at 17 hours after the initial X-ray detection, suggestive of intrinsically weak emission in these bands. We demonstrate that the remarkable properties of EP240408a are inconsistent with any of the transient types known so far, by comparison with, in particular, jetted tidal disruption events, gamma-ray bursts, X-ray binaries and fast blue optical transients. The nature of EP240408a thus remains an enigma. We suggest that EP240408a may represent a new type of transients with intermediate timescales of the order of about 10 days. The detection and follow-ups of more of such objects are essential for revealing their origin., Comment: 25 pages, 11 figures

Subpolar frontal zones are characterized by energetic storms, intense seasonal cycles, and close connectivity with surrounding continental shelf topography. At the same time, predicting the ocean state depends on appropriate partition of resolved and parameterized dynamics, the latter of which requires understanding the dynamical processes generating diffusivity throughout the water column. While submesoscale frontal instabilities are shown to produce turbulent kinetic energy (TKE) and mixing in the surface boundary layer (SBL) of the global ocean, their development in complex dynamical regimes (e.g., elevated preexisting turbulence, large ageostrophic shear, or in proximity to topographic boundaries) is less understood. This study investigates the development of submesoscale instabilities, i.e. symmetric instability (SI) and centrifugal instability (CI), near topographic boundaries using a hindcast model of the Drake Passage and Scotia Sea region. The model suggests subsurface SI and CI are widespread along the northern continental margins of the Antarctic Circumpolar Current (ACC) due to topographic shearing of the anticyclonic side of Polar Front jets. Forced instabilities may facilitate persistent mixing along Namuncur\'a - Burwood Bank, as well as in other southern (northern) hemisphere currents with low potential vorticity and a seamount or sloping topography on the left- (right-) downstream side.

This study reports a comprehensive environmental scan of the generative AI (GenAI) infrastructure in the national network for clinical and translational science across 36 institutions supported by the Clinical and Translational Science Award (CTSA) Program led by the National Center for Advancing Translational Sciences (NCATS) of the National Institutes of Health (NIH) at the United States. With the rapid advancement of GenAI technologies, including large language models (LLMs), healthcare institutions face unprecedented opportunities and challenges. This research explores the current status of GenAI integration, focusing on stakeholder roles, governance structures, and ethical considerations by administering a survey among leaders of health institutions (i.e., representing academic medical centers and health systems) to assess the institutional readiness and approach towards GenAI adoption. Key findings indicate a diverse range of institutional strategies, with most organizations in the experimental phase of GenAI deployment. The study highlights significant variations in governance models, with a strong preference for centralized decision-making but notable gaps in workforce training and ethical oversight. Moreover, the results underscore the need for a more coordinated approach to GenAI governance, emphasizing collaboration among senior leaders, clinicians, information technology staff, and researchers. Our analysis also reveals concerns regarding GenAI bias, data security, and stakeholder trust, which must be addressed to ensure the ethical and effective implementation of GenAI technologies. This study offers valuable insights into the challenges and opportunities of GenAI integration in healthcare, providing a roadmap for institutions aiming to leverage GenAI for improved quality of care and operational efficiency.

The energy surplus resulting from radiative forcing causes warming of the Earth system. This initial warming drives a myriad of changes including in sea surface temperatures (SSTs), leading to different radiative feedbacks. The relationship between the radiative feedbacks and the pattern of SST changes is referred to as the "pattern effect". The current approach to study the pattern effect relies on diagnosing the response of atmosphere-only models to perturbations in the SST boundary condition. Here, we argue that the fluctuation-dissipation relation (FDR), together with coarse-graining procedures, is a computationally cheap and theoretically grounded alternative to model experiments. We introduce a protocol to study the pattern effect and present its application in a state-of-the-art coupled climate model. By focusing on the coupled dynamics, we unveil the role of the slow ocean component in setting the pattern effect. We present a new "sensitivity map", representing a first, qualitative prediction of the response of the average top-of-the-atmosphere (TOA) radiative flux to perturbations in the SST field. We find negative sensitivity throughout the tropics, in contrast to the current understanding of a positive-negative dipole of sensitivity in the tropical Pacific. Considering only the shortest time scales, the response is dominated by the fast atmospheric variability and we recover results in qualitative agreement with the literature. Therefore, the difference between our results and previous studies, largely comes from including the atmosphere-ocean coupling. The framework offers a conceptually novel perspective on the pattern effect: feedbacks in the coupled system are encoded in a temporally and spatially dependent response operator, rather than time-independent maps as for previous studies.

As the complexities of Dynamic Data Driven Applications Systems increase, preserving their resilience becomes more challenging. For instance, maintaining power grid resilience is becoming increasingly complicated due to the growing number of stochastic variables (such as renewable outputs) and extreme weather events that add uncertainty to the grid. Current optimization methods have struggled to accommodate this rise in complexity. This has fueled the growing interest in data-driven methods used to operate the grid, leading to more vulnerability to cyberattacks. One such disruption that is commonly discussed is the adversarial disruption, where the intruder attempts to add a small perturbation to input data in order to "manipulate" the system operation. During the last few years, work on adversarial training and disruptions on the power system has gained popularity. In this paper, we will first review these applications, specifically on the most common types of adversarial disruptions: evasion and poisoning disruptions. Through this review, we highlight the gap between poisoning and evasion research when applied to the power grid. This is due to the underlying assumption that model training is secure, leading to evasion disruptions being the primary type of studied disruption. Finally, we will examine the impacts of data poisoning interventions and showcase how they can endanger power grid resilience., Comment: 9 pages, 2 figures, DDDAS-2024 Conference

The Einstein Probe (EP) achieved its first detection and localization of a bright X-ray flare, EP240219a, on February 19, 2024, during its commissioning phase. Subsequent targeted searches triggered by the EP240219a alert identified a faint, untriggered gamma-ray burst (GRB) in the archived data of Fermi/GBM, Swift/BAT, Insight-HXMT/HE and INTEGRAL/SPI-ACS. The EP/WXT light curve reveals a long duration of approximately 160 seconds with a slow decay, whereas the Fermi/GBM light curve shows a total duration of approximately 70 seconds. The peak in the Fermi/GBM light curve occurs slightly later with respect to the peak seen in the EP/WXT light curve. Our spectral analysis shows that a single cutoff power-law model effectively describes the joint EP/WXT-Fermi/GBM spectra in general, indicating coherent broad emission typical of GRBs. The model yielded a photon index of $\sim -1.70 \pm 0.05$ and a peak energy of $\sim 257 \pm 134$ keV. After detection of GRB 240219A, long-term observations identified several candidates in optical and radio wavelengths, none of which was confirmed as the afterglow counterpart during subsequent optical and near-infrared follow-ups. The analysis of GRB 240219A classifies it as an X-ray rich GRB with a high peak energy, presenting both challenges and opportunities for studying the physical origins of X-ray flashes (XRFs), X-ray rich GRBs (XRRs), and classical GRBs (C-GRBs). Furthermore, linking the cutoff power-law component to non-thermal synchrotron radiation suggests that the burst is driven by a Poynting flux-dominated outflow., Comment: 14 pages, 8 figures, 3 tables

Symmetric instability (SI) is a frontal instability arising from the interaction of rotation with lateral and vertical shear of a frontal jet and is a generalization of shear, centrifugal, and gravitational instabilities. While the onset of SI has been studied in numerous observations and models, intuition about its growth in physical ocean comes primarily from constant-viscosity linear instability analysis and large eddy simulation (LES). A forward cascade arising from SI in the real ocean, where numerous fine-to-microscale processes interact with growing SI velocity cells, is less understood. While many instances of symmetrically unstable flow have been observed, observations of enhanced turbulent kinetic energy (TKE) dissipation ($\epsilon$) at these sites are less common. We use numerical instability analysis of an idealized geostrophic jet to show that viscous-diffusive effects of preexisting turbulence from other turbulent processes (e.g., competing instabilities, internal wave processes, or boundary layer processes) can suppress the growth of SI in the real ocean. For example, a moderate level of ambient turbulence, represented by uniform diffusivity and viscosity of $\kappa =\nu = 10^{-4} m^2/s$, restricts the wavelength range of SI's fastest-growing mode from $\mathcal{O}(10-100)$m to $\mathcal{O}(100)$m and elongates its e-folding timescale by $\mathcal{O}(1-10)$ hrs; suggesting the net viscous-diffusive effects of preexisting turbulence can damp the growth of SI. Viscous damping is one possible explanation for the rarity of SI structures in the real ocean, and our results motivate the inclusion of dependence on previous-timestep $\epsilon$ or $\kappa$ when parameterizing SI in regional models.

The increasing availability of biomedical data is helping to design more robust deep learning (DL) algorithms to analyze biomedical samples. Currently, one of the main limitations to train DL algorithms to perform a specific task is the need for medical experts to label data. Automatic methods to label data exist, however automatic labels can be noisy and it is not completely clear when automatic labels can be adopted to train DL models. This paper aims to investigate under which circumstances automatic labels can be adopted to train a DL model on the classification of Whole Slide Images (WSI). The analysis involves multiple architectures, such as Convolutional Neural Networks (CNN) and Vision Transformer (ViT), and over 10000 WSIs, collected from three use cases: celiac disease, lung cancer and colon cancer, which one including respectively binary, multiclass and multilabel data. The results allow identifying 10% as the percentage of noisy labels that lead to train competitive models for the classification of WSIs. Therefore, an algorithm generating automatic labels needs to fit this criterion to be adopted. The application of the Semantic Knowledge Extractor Tool (SKET) algorithm to generate automatic labels leads to performance comparable to the one obtained with manual labels, since it generates a percentage of noisy labels between 2-5%. Automatic labels are as effective as manual ones, reaching solid performance comparable to the one obtained training models with manual labels., Comment: pre-print of the journal paper

As generative AI continues to evolve, educators face the challenge of preparing students for a future where AI-assisted work is integral to professional success. This paper introduces ChatISA, an in-house, multi-model chatbot designed to support students in an Information Systems and Analytics department. ChatISA comprises four primary modules-Coding Companion, Project Coach, Exam Ally, and Interview Mentor-each tailored to enhance different aspects of the educational experience. Through iterative development, student feedback, and leveraging open-source frameworks, we created a robust tool that addresses coding inquiries, project management, exam preparation, and interview readiness. The implementation of ChatISA revealed significant insights and challenges, including the necessity of ethical guidelines and balancing AI usage with maintaining student agency. Our findings underscore the importance of adaptive pedagogy and proactive engagement with AI tools to maximize their educational benefits. To support broader adoption and innovation, all code for ChatISA is made publicly available on GitHub, enabling other institutions to customize and integrate similar AI-driven educational tools within their curricula., Comment: 18 pages

Time-domain astrophysics continues to grow rapidly, with the inception of new surveys drastically increasing data volumes. Democratised, distributed approaches to training sets for machine learning classifiers are crucial to make the most of this torrent of discovery -- with citizen science approaches proving effective at meeting these requirements. In this paper, we describe the creation of and the initial results from the $\textit{Kilonova Seekers}$ citizen science project, built to find transient phenomena from the GOTO telescopes in near real-time. $\textit{Kilonova Seekers}$ launched in July 2023 and received over 600,000 classifications from approximately 2,000 volunteers over the course of the LIGO-Virgo-KAGRA O4a observing run. During this time, the project has yielded 20 discoveries, generated a `gold-standard' training set of 17,682 detections for augmenting deep-learned classifiers, and measured the performance and biases of Zooniverse volunteers on real-bogus classification. This project will continue throughout the lifetime of GOTO, pushing candidates at ever-greater cadence, and directly facilitate the next-generation classification algorithms currently in development., Comment: 20 pages, 15 figures. Accepted in MNRAS

Power grid expansion planning requires making large investment decisions in the present that will impact the future cost and reliability of a system exposed to wide-ranging uncertainties. Extreme temperatures can pose significant challenges to providing power by increasing demand and decreasing supply and have contributed to recent major power outages. We propose to address a modeling challenge of such high-impact, low-frequency events with a bi-objective stochastic integer optimization model that finds solutions with different trade-offs between efficiency in normal conditions and risk to extreme events. We propose a conditional sampling approach paired with a risk measure to address the inherent challenge in approximating the risk of low-frequency events within a sampling based approach. We present a model for spatially correlated, county-specific temperatures and a method to generate both unconditional and conditionally extreme temperature samples from this model efficiently. These models are investigated within an extensive case study with realistic data that demonstrates the effectiveness of the bi-objective approach and the conditional sampling technique. We find that spatial correlations in the temperature samples are essential to finding good solutions and that modeling generator temperature dependence is an important consideration for finding efficient, low-risk solutions., Comment: 43 pages, 10 figures

Quadratic unconstrained binary optimization (QUBO) tasks are very important in chemistry, finance, job scheduling, and so on, which can be represented using graph structures, with the variables as nodes and the interaction between them as edges. Variational quantum algorithms, especially the Quantum Approximate Optimization Algorithm (QAOA) and its variants, present a promising way, potentially exceeding the capabilities of classical algorithms, for addressing QUBO tasks. However, the possibility of using message-passing machines, inspired by classical graph neural networks, to enhance the power and performance of these quantum algorithms for QUBO tasks was not investigated. This study introduces a novel variational quantum graph optimization algorithm that integrates the message-passing mechanism, which demonstrates significant improvements in performance for solving QUBO problems in terms of resource efficiency and solution precision, compared to QAOA, its variants, and other quantum graph neural networks. Furthermore, in terms of scalability on QUBO tasks, our algorithm shows superior performance compared to QAOA, presenting a substantial advancement in the field of quantum approximate optimization., Comment: 11pages,5figures

Gamma-ray bursts (GRBs) are traditionally classified as either short GRBs with durations $\lesssim 2$ s that are powered by compact object mergers, or long GRBs with durations $\gtrsim 2$ s that powered by the deaths of massive stars. Recent results, however, have challenged this dichotomy and suggest that there exists a population of merger-driven long bursts. One such example, GRB 191019A, has a $t_{90} \approx 64$ s but many of its other properties -- including its host galaxy, afterglow luminosity and lack of associated supernova -- are more consistent with a short GRB. Here we propose an alternative interpretation: that GRB 191019A (which is located in the nucleus of its host) is an atypical jetted tidal disruption event (TDE). In particular, we suggest the short timescale and rapid decline, not expected for standard TDEs, are the result of an "ultra-deep" encounter, in which the star came well within the tidal radius of the black hole and promptly self-intersected, circularised, accreted, and launched a relativistic outflow. This model reproduces the timescale and luminosity through a prompt super-Eddington accretion phase and accounts for the lack of late optical emission. This would make GRB 191019A only the fifth jetted TDE and the first discovered ultra-deep TDE. The ultra-deep TDE model can be distinguished from merger-driven long GRBs via the soft X-ray flash that results from prompt self-intersection of the debris stream; the detection of this flash will be possible with wide-field and soft-X-ray satellites such as $\textit{Einstein Probe}$ or $\textit{SVOM}$., Comment: 10 pages, 2 figures and 1 table. Accepted to ApJL

Carbon credits purchased in the voluntary carbon market allow unavoidable emissions, such as from international flights for essential travel, to be offset by an equivalent climate benefit, such as avoiding emissions from tropical deforestation. However, many concerns regarding the credibility of these offsetting claims have been raised. Moreover, the credit market is manual, therefore inefficient and unscalable, and non-fungible, therefore illiquid. To address these issues, we propose an efficient digital methodology that combines remote sensing data, modern econometric techniques, and on-chain certification and trading to create a new digital carbon asset (the PACT stablecoin) against which carbon offsetting claims can be transparently verified. PACT stablecoins are produced as outputs from a reproducible computational pipeline for estimating the climate benefits of carbon offset projects that not only quantifies the CO2 emissions involved, but also allows for similar credits to be pooled based on their co-benefits such as biodiversity and jurisdictional attributes, increasing liquidity through fungibility within pools. We implement and evaluate the PACT carbon stablecoin on the Tezos blockchain, which is designed to facilitate low-cost transactions while minimizing environmental impact. Our implementation includes a contract for a registry for tracking issuance, ownership, and retirement of credits, and a custodian contract to bridge on-chain and off-chain transactions. Our work brings scale and trust to the voluntary carbon market by providing a transparent, scalable, and efficient framework for high integrity carbon credit transactions., Comment: 10 pages. Extended version, March 2024. A shortened version is to be published at the 6th IEEE International Conference on Blockchain and Cryptocurrency (ICBC 2024)

We present the first release of the Gravitational Wave AfterglowPy Analysis (GWAPA) webtool. GWAPA is designed to provide the community with an interactive tool for rapid analysis of gravitational wave afterglow counterparts and can be extended to the general case of gamma-ray burst afterglows seen at different angles. It is based on the afterglowpy package and allows users to upload observational data and vary afterglow parameters to infer the properties of the explosion. Multiple jet structures, including top hat, Gaussian and power laws, in addition to a spherical outflow model are implemented. A Python script for MCMC fitting is also available to download, with initial guesses taken from GWAPA., Comment: Published in RNAAS. GWAPA will be available at https://gwapa.web.roma2.infn.it/ once essential server repairs are complete

The ANA Avatar XPRIZE was a four-year competition to develop a robotic "avatar" system to allow a human operator to sense, communicate, and act in a remote environment as though physically present. The competition featured a unique requirement that judges would operate the avatars after less than one hour of training on the human-machine interfaces, and avatar systems were judged on both objective and subjective scoring metrics. This paper presents a unified summary and analysis of the competition from technical, judging, and organizational perspectives. We study the use of telerobotics technologies and innovations pursued by the competing teams in their avatar systems, and correlate the use of these technologies with judges' task performance and subjective survey ratings. It also summarizes perspectives from team leads, judges, and organizers about the competition's execution and impact to inform the future development of telerobotics and telepresence., Comment: 26 pages, preprint of article appearing in International Journal of Social Robotics

We introduce a novel approach for estimating the spectrum of quantum many-body Hamiltonians, and more generally, of Hermitian operators, using quantum time evolution. In our approach we are evolving a maximally mixed state under the Hamiltonian of interest and collecting specific time-series measurements to estimate its spectrum. We demonstrate the advantage of our technique over currently used classical statistical sampling methods. We showcase our approach by experimentally estimating the spectral decomposition of a 2-qubit Heisenberg Hamiltonian on an IBM Quantum backend. For this purpose, we develop a hardware-efficient decomposition that controls $n$-qubit Pauli rotations against the physically closest qubit alongside expressing two-qubit rotations in terms of the native entangling interaction. This substantially reduced the accumulation of errors from noisy two-qubit operations in time evolution simulation protocols. We conclude by discussing the potential impact of our work and the future directions of research it opens., Comment: 9 pages, 6 figures

Electric vehicles (EVs) are becoming more popular within the United States, making up an increasingly large portion of the US's electricity consumption. Hence, there is much attention has been directed on how to manage EVs within the power sector. A well-investigated strategy for managing the increase in electricity demand from EV charging is aggregation, which allows for an intermediary to manage electricity flow between EV owners and their utilities. When implemented effectively, EV aggregation provides key benefits to power grids by relieving electrical loads.. These benefits are aggregation's ability to shift EV loads to peak shave, which often leads to lower emissions, electricity generation prices, and consumer costs depending on the penetration levels of non-dispatchable electricity sources. This review seeks to appropriately highlight the broad vulnerabilities of EV aggregation alongside its benefits, namely those regarding battery degradation, rebound peaks, and cybersecurity. The holistic overview of EV aggregation provides comparisons that balance expectations with realistic performance.

Magnetic insulators such as yttrium iron garnets (YIGs) are of paramount importance for spin-wave or magnonic devices as their ultralow damping enables ultralow power dissipation that is free of Joule heating, exotic magnon quantum state, and coherent coupling to other wave excitations. Magnetic insulator heterostructures bestow superior structural and magnetic properties and house immense design space thanks to the strong and engineerable exchange interaction between individual layers. To fully unleash their potential, realizing low damping and strong exchange coupling simultaneously is critical, which often requires high quality interface. Here, we show that such a demand is realized in an all-insulator thulium iron garnet (TmIG)/YIG bilayer system. The ultralow dissipation rates in both YIG and TmIG, along with their significant spin-spin interaction at the interface, enable strong and coherent magnon-magnon coupling with a benchmarking cooperativity value larger than the conventional ferromagnetic metal-based heterostructures. The coupling strength can be tuned by varying the magnetic insulator layer thickness and magnon modes, which is consistent with analytical calculations and micromagnetic simulations. Our results demonstrate TmIG/YIG as a novel platform for investigating hybrid magnonic phenomena and open opportunities in magnon devices comprising all-insulator heterostructures., Comment: 45 pages, 18 figures, and 2 tables

In recent years, searches of archival X-ray data have revealed galaxies exhibiting nuclear quasi-periodic eruptions with periods of several hours. These are reminiscent of the tidal disruption of a star by a supermassive black hole, and the repeated, partial stripping of a white dwarf in an eccentric orbit around a ~10^5 solar mass black hole provides an attractive model. A separate class of periodic nuclear transients, with significantly longer timescales, have recently been discovered optically, and may arise from the partial stripping of a main-sequence star by a ~10^7 solar mass black hole. No clear connection between these classes has been made. We present the discovery of an X-ray nuclear transient which shows quasi-periodic outbursts with a period of weeks. We discuss possible origins for the emission, and propose that this system bridges the two existing classes outlined above. This discovery was made possible by the rapid identification, dissemination and follow up of an X-ray transient found by the new live \swift-XRT transient detector, demonstrating the importance of low-latency, sensitive searches for X-ray transients., Comment: To be published in Nature Astronomy at 1600 BST on September 7th. This version for arXiv includes the main article, Methods and Supplementary Information combined into a single file

The composition of relativistic gamma-ray burst (GRB) jets and their emission mechanisms are still debated, and they could be matter or magnetically dominated. One way to distinguish these mechanisms arises because a Poynting flux dominated jet may produce low-frequency radio emission during the energetic prompt phase, through magnetic reconnection at the shock front. We present a search for radio emission coincident with three GRB X-ray flares with the LOw Frequency ARray (LOFAR), in a rapid response mode follow-up of long GRB 210112A (at z~2) with a 2 hour duration, where our observations began 511 seconds after the initial swift-BAT trigger. Using timesliced imaging at 120-168 MHz, we obtain upper limits at 3 sigma confidence of 42 mJy averaging over 320 second snapshot images, and 87 mJy averaging over 60 second snapshot images. LOFAR's fast response time means that all three potential radio counterparts to X-ray flares are observable after accounting for dispersion at the estimated source redshift. Furthermore, the radio pulse in the magnetic wind model was expected to be detectable at our observing frequency and flux density limits which allows us to disfavour a region of parameter space for this GRB. However, we note that stricter constraints on redshift and the fraction of energy in the magnetic field are required to further test jet characteristics across the GRB population., Comment: 13 pages, 7 figures, accepted for publication in MNRAS

The LOFAR Two-Metre Sky Survey (LoTSS) is an invaluable new tool for investigating the properties of sources at low frequencies and has helped to open up the study of galaxy populations in this regime. In this work, we perform a search for host galaxies of gamma-ray bursts (GRBs). We use the relative density of sources in Data Release 2 of LoTSS to define the probability of a chance alignment, $P_{\rm chance}$, and find 18 sources corresponding to 17 GRBs which meet a $P_{\rm chance}$<1% criterion. We examine the nature and properties of these radio sources using both LOFAR data and broadband information, including their radio spectral index, star formation rate estimates and any contributions from active galactic nucleus emission. Assuming the radio emission is dominated by star formation, we find that our sources show high star formation rates ($10^1$-$10^3$ $M_{\odot}$ yr$^{-1}$) compared with both a field galaxy sample and a sample of core-collapse supernova hosts, and the majority of putative hosts are consistent with ultraluminous infrared galaxy (ULIRG) classifications. As a result of our analyses, we define a final sample of eight likely GRB host candidates in the LoTSS DR2 survey., Comment: 15 pages, 9 figures and 6 tables. Accepted by MNRAS