Search

We study the properties of long gamma-ray burst (GRB) host galaxies using a statistical modelling framework derived to model damped Lyman-$\alpha$ absorbers (DLAs) in quasar spectra at high redshift. The distribution of NHI for GRB-DLAs is $\sim$10 times higher than what is found for quasar-DLAs at similar impact parameters. We interpret this as a temporal selection effect due to the short-lived GRB progenitor probing its host at the onset of a starburst where the interstellar medium may exhibit multiple overdense regions. Owing to the larger NHI, the dust extinction is larger with 29 per cent of GRB-DLAs exhibiting A(V)>1 mag in agreement with the fraction of 'dark bursts'. Despite the differences in NHI distributions, we find that high-redshift 2 < z < 3 quasar- and GRB-DLAs trace the luminosity function of star-forming host galaxies in the same way. We propose that their differences may arise from the fact that the galaxies are sampled at different times in their star formation histories, and that the absorption sightlines probe the galaxy haloes differently. Quasar-DLAs sample the full H I cross-section, whereas GRB-DLAs sample only regions hosting cold neutral medium. Previous studies have found that GRBs avoid high-metallicity galaxies ($\sim$0.5 $Z_{\odot}$). Since at these redshifts galaxies on average have lower metallicities, our sample is only weakly sensitive to such a threshold. Lastly, we find that the modest detection rate of cold gas (H$_2$ or C I) in GRB spectra can be explained mainly by a low volume filling factor of cold gas clouds and to a lesser degree by destruction from the GRB explosion itself., Comment: 13 pages, accepted for publication by MNRAS

Ultra-long gamma-ray bursts (ULGRBs) are characterized by exceptionally long-duration central engine activities, with characteristic timescales exceeding 1000 seconds. We present ground-based optical afterglow observations of the ultra-long gamma-ray burst GRB 211024B, detected by \textit{Swift}. Its X-ray light curve exhibits a characteristic ``internal plateau" with a shallow decay phase lasting approximately $\sim 15$ ks, followed by a steep decline ($\alpha_{\rm drop}\sim-7.5$). Moreover, the early optical emission predicted by the late r-band optical afterglow is significantly higher than the observed value, indicating an external shock with energy injection. To explain these observations, we propose a magnetar central engine model. The magnetar collapse into a black hole due to spin-down or hyperaccretion, leading to the observed steep break in the X-ray light curve. The afterglow model fitting reveals that the afterglow injection luminosity varies with different assumptions of the circumburst medium density, implying different potential energy sources. For the interstellar medium (ISM) case with a fixed injection end time, the energy may originate from the magnetar's dipole radiation. However, in other scenarios, relativistic jets produced by the magnetar/black hole system could be the primary energy source., Comment: 18 pages, 7 figures, accepted by ApJ

Detections of fast X-ray transients (FXTs) have been accrued over the last few decades. However, their origin has remained mysterious. There is now rapid progress thanks to timely discoveries and localisations with the Einstein Probe mission. Early results indicate that FXTs may frequently, but not always, be associated with gamma-ray bursts (GRBs). Here, we report on the multi-wavelength counterpart of FXT EP240414a, which has no reported gamma-ray counterpart. The transient is located 25.7~kpc in projection from a massive galaxy at $z=0.40$. We perform comprehensive photometric and spectroscopic follow-up. The optical light curve shows at least three distinct emission episodes with timescales of $\sim 1, 4$ and 15 days and peak absolute magnitudes of $M_R \sim -20$, $-21$, and $-19.5$, respectively. The optical spectrum at early times is extremely blue, inconsistent with afterglow emission. It may arise from the interaction of both jet and supernova shock waves with the stellar envelope and a dense circumstellar medium, as has been suggested for some Fast Blue Optical Transients (LFBOTs). At late times, the spectrum evolves to a broad-lined~Type~Ic supernova, similar to those seen in collapsar long-GRBs. This implies that the progenitor of EP240414a is a massive star creating a jet-forming supernova inside a dense envelope, resulting in an X-ray outburst with a luminosity of $\sim 10^{48}$ erg s$^{-1}$, and the complex observed optical/IR light curves. If correct, this argues for a causal link between the progenitors of long-GRBs, FXTs and LFBOTs., Comment: 36 pages, 13 figures, submitted to ApJ

Multi-Agent Path Finding (MAPF) is the problem of effectively finding efficient collision-free paths for a group of agents in a shared workspace. The MAPF community has largely focused on developing high-performance heuristic search methods. Recently, several works have applied various machine learning (ML) techniques to solve MAPF, usually involving sophisticated architectures, reinforcement learning techniques, and set-ups, but none using large amounts of high-quality supervised data. Our initial objective in this work was to show how simple large scale imitation learning of high-quality heuristic search methods can lead to state-of-the-art ML MAPF performance. However, we find that, at least with our model architecture, simple large scale (700k examples with hundreds of agents per example) imitation learning does \textit{not} produce impressive results. Instead, we find that by using prior work that post-processes MAPF model predictions to resolve 1-step collisions (CS-PIBT), we can train a simple ML MAPF model in minutes that dramatically outperforms existing ML MAPF policies. This has serious implications for all future ML MAPF policies (with local communication) which currently struggle to scale. In particular, this finding implies that future learnt policies should (1) always use smart 1-step collision shields (e.g. CS-PIBT), (2) always include the collision shield with greedy actions as a baseline (e.g. PIBT) and (3) motivates future models to focus on longer horizon / more complex planning as 1-step collisions can be efficiently resolved.

We present experiences and lessons learned from increasing data readiness of heterogeneous data for artificial intelligence projects using visual analysis methods. Increasing the data readiness level involves understanding both the data as well as the context in which it is used, which are challenges well suitable to visual analysis. For this purpose, we contribute a mapping between data readiness aspects and visual analysis techniques suitable for different data types. We use the defined mapping to increase data readiness levels in use cases involving time-varying data, including numerical, categorical, and text. In addition to the mapping, we extend the data readiness concept to better take aspects of the task and solution into account and explicitly address distribution shifts during data collection time. We report on our experiences in using the presented visual analysis techniques to aid future artificial intelligence projects in raising the data readiness level.

The Atacama Large Millimeter/submillimeter Array (ALMA) offers new diagnostic capabilities for studying the Sun, providing complementary insights through high spatial and temporal resolution at millimeter wavelengths. ALMA acts as a linear thermometer for atmospheric gas, aiding in understanding the solar atmosphere's structure, dynamics, and energy balance. Given the Sun's complex emission patterns and rapid evolution, high-cadence imaging is essential for solar observations. Snapshot imaging is required, though it limits available visibility data, making full exploitation of ALMA's capabilities non-trivial. Challenges in processing solar ALMA data highlight the need for revising and enhancing the solar observing mode. The ALMA development study High-Cadence Imaging of the Sun demonstrated the potential benefits of high cadence observations through a forward modelling approach. The resulting report provides initial recommendations for improved post-processing solar ALMA data and explores increasing the observing cadence to sub-second intervals to improve image reliability., Comment: ALMA Memo 628 summarising the results of the ESO-funded ALMA development study "High-cadence Imaging of the Sun", concluded in 2023 (74 pages, 53 figures)

Galaxy clusters are the most massive, gravitationally-bound structures in the Universe, emerging through hierarchical structure formation of large-scale dark matter and baryon overdensities. Early galaxy ``proto-clusters'' are believed to be important physical drivers of the overall cosmic star-formation rate density and serve as ``hotspots'' for the reionization of the intergalactic medium. Our understanding of the formation of these structures at the earliest cosmic epochs is, however, limited to sparse observations of their galaxy members, or based on phenomenological models and cosmological simulations. Here we report the detection of a massive neutral, atomic hydrogen (HI) gas reservoir permeating a galaxy proto-cluster at redshift $z=5.4$, observed one billion years after the Big Bang. The presence of this cold gas is revealed by strong damped Lyman-$\alpha$ absorption features observed in several background galaxy spectra taken with JWST/NIRSpec in close on-sky projection. While overall the sightlines probe a large range in HI column densities, $N_{\rm HI} = 10^{21.7}-10^{23.5}$ cm$^{-2}$, they are similar across nearby sightlines, demonstrating that they probe the same dense, neutral gas. This observation of a massive, large-scale overdensity of cold neutral gas challenges current large-scale cosmological simulations and has strong implications for the reionization topology of the Universe., Comment: Submitted

In this work, we introduce a spatio-temporal kernel for Gaussian process (GP) regression-based sound field estimation. Notably, GPs have the attractive property that the sound field is a linear function of the measurements, allowing the field to be estimated efficiently from distributed microphone measurements. However, to ensure analytical tractability, most existing kernels for sound field estimation have been formulated in the frequency domain, formed independently for each frequency. To address the analytical intractability of spatio-temporal kernels, we here propose to instead learn the kernel directly from data by the means of deep kernel learning. Furthermore, to improve the generalization of the deep kernel, we propose a method for regularizing the learning process using the wave equation. The representational advantages of the deep kernel and the improved generalization obtained by using the wave equation regularization are illustrated using numerical simulations., Comment: Accepted for IWAENC 2024

Gamma-ray bursts are the most energetic known explosions. Despite fading rapidly, they allow to measure redshift and important properties of their host-galaxies. We report the photometric and spectroscopic study of GRB 160203A and its host-galaxy. Fine-structure absorption lines, detected in the afterglow at different epochs, allow us to investigate variability due to the strong fading background source. We obtained two optical to near-infrared spectra of the afterglow with X-shooter on ESO/VLT, 18 min and 5.7 hrs after the burst, allowing us to investigate temporal changes of fine-structure absorption lines. We measured HI column density log N(HI/cm-2)=21.75+/-0.10, and several heavy-element ions along the GRB sight-line in the host-galaxy: SiII,AlII,AlIII,CII,NiII,SiIV,CIV,ZnII,FeII, and FeII and SiII fine structure transitions from energetic levels excited by the afterglow, at a redshift z=3.518. We measured [M/H]TOT=-0.78+/-0.13 and [Zn/Fe]FIT=0.69+/-0.15, representing the total(dust-corrected) metallicity and dust depletion, respectively. We detected additional intervening systems along the line of sight at z=1.03,z=1.26,z=1.98,z=1.99,z=2.20 and z=2.83. We could not measure significant variability in the fine-structure lines throughout all the observations and determined an upper limit for the GRB distance from the absorber of d<300 pc, adopting the canonical UV pumping scenario. However, we note that the quality of our data is not sufficient to conclusively rule out collisions as an alternative mechanism. GRB 160203A belongs to a growing sample of GRBs with medium resolution spectroscopy, provided by the Swift/X-shooter legacy program, which enables detailed investigation of the interstellar medium in high-redshift GRB host-galaxies. In particular, this host galaxy shows relatively high metal enrichment and dust depletion already in place when the universe was only 1.8 Gyr old., Comment: 16 pages, 9 figures, 2 appendices, A&A accepted

Long GRB hosts at z<1 are usually low-mass, low metallicity star-forming galaxies. Here we present the until now most detailed, spatially resolved study of the host of GRB 171205A, a grand-design barred spiral galaxy at z=0.036. Our analysis includes MUSE integral field spectroscopy, complemented by high spatial resolution UV/VIS HST imaging and CO(1-0) and HI 21cm data. The GRB is located in a small star-forming region in a spiral arm of the galaxy at a deprojected distance of ~ 8 kpc from the center. The galaxy shows a smooth negative metallicity gradient and the metallicity at the GRB site is half solar, slightly below the mean metallicity at the corresponding distance from the center. Star formation in this galaxy is concentrated in a few HII regions between 5-7 kpc from the center and at the end of the bar, inwards of the GRB region, however, the HII region hosting the GRB is in the top 10% of regions with highest specific star-formation rate. The stellar population at the GRB site has a very young component (< 5 Myr) contributing a significant part of the light. Ionized and molecular gas show only minor deviations at the end of the bar. A parallel study found an asymmetric HI distribution and some additional gas near the position of the GRB, which might explain the star-forming region of the GRB site. Our study shows that long GRBs can occur in many types of star-forming galaxies, however, the actual GRB sites consistently have low metallicity, high star formation and a young population. Furthermore, gas inflow or interactions triggering the star formation producing the GRB progenitor might not be evident in ionized or even molecular gas but only in HI., Comment: 22 pages, 18 figures, Appendix with additional figures, A&A under revision

The nature of the minute-to-hour long Fast X-ray Transients (FXTs) localised by telescopes such as Chandra, Swift, and XMM-Newton remains mysterious, with numerous models suggested for the events. Here, we report multi-wavelength observations of EP240315a, a 1600 s long transient detected by the Einstein Probe, showing it to have a redshift of z=4.859. We measure a low column density of neutral hydrogen, indicating that the event is embedded in a low-density environment, further supported by direct detection of leaking ionising Lyman-continuum. The observed properties are consistent with EP240315a being a long-duration gamma-ray burst, and these observations support an interpretation in which a significant fraction of the FXT population are lower-luminosity examples of similar events. Such transients are detectable at high redshifts by the Einstein Probe and, in the (near) future, out to even larger distances by SVOM, THESEUS, and Athena, providing samples of events into the epoch of reionisation., Comment: 41 pages, 7 figures, submitted

One of the surprising early findings with JWST has been the discovery of a strong "roll-over" or a softening of the absorption edge of Ly$\alpha$ in a large number of galaxies at ($z\gtrsim 6$), in addition to systematic offsets from photometric redshift estimates and fundamental galaxy scaling relations. This has been interpreted as damped Ly$\alpha$ absorption (DLA) wings from high column densities of neutral atomic hydrogen (HI), signifying major gas accretion events in the formation of these galaxies. To explore this new phenomenon systematically, we assemble the JWST/NIRSpec PRImordial gas Mass AssembLy (PRIMAL) legacy survey of 494 galaxies at $z=5.5-13.4$. We characterize this benchmark sample in full and spectroscopically derive the galaxy redshifts, metallicities, star-formation rates, and ultraviolet slopes. We define a new diagnostic, the Ly$\alpha$ damping parameter $D_{\rm Ly\alpha}$ to measure and quantify the Ly$\alpha$ emission strength, HI fraction in the IGM, or local HI column density for each source. The JWST-PRIMAL survey is based on the spectroscopic DAWN JWST Archive (DJA-Spec). All the software, reduced spectra, and spectroscopically derived quantities and catalogs are made publicly available in dedicated repositories. The fraction of strong galaxy DLAs are found to be in the range $65-95\%$ at $z>5.5$. The fraction of strong Ly$\alpha$ emitters (LAEs) is found to increase with decreasing redshift, in qualitative agreement with previous observational results, and are predominantly associated with low-metallicity and UV faint galaxies. By contrast, strong DLAs are observed in galaxies with a variety of intrinsic physical properties. Our results indicate that strong DLAs likely reflect a particular early assembly phase of reionization-era galaxies, at which point they are largely dominated by pristine HI gas accretion. [abridged], Comment: 20 pages, 18 figures, 3 tables. Submitted to A&A. Comments welcome! All data and catalogs are available through the DAWN JWST Archive (DJA): https://dawn-cph.github.io/dja/ and https://github.com/keheintz/jwst-primal

Multi-agent path finding (MAPF) is the problem of finding collision-free paths for a team of agents to reach their goal locations. State-of-the-art classical MAPF solvers typically employ heuristic search to find solutions for hundreds of agents but are typically centralized and can struggle to scale when run with short timeouts. Machine learning (ML) approaches that learn policies for each agent are appealing as these could enable decentralized systems and scale well while maintaining good solution quality. Current ML approaches to MAPF have proposed methods that have started to scratch the surface of this potential. However, state-of-the-art ML approaches produce "local" policies that only plan for a single timestep and have poor success rates and scalability. Our main idea is that we can improve a ML local policy by using heuristic search methods on the output probability distribution to resolve deadlocks and enable full horizon planning. We show several model-agnostic ways to use heuristic search with learnt policies that significantly improve the policies' success rates and scalability. To our best knowledge, we demonstrate the first time ML-based MAPF approaches have scaled to high congestion scenarios (e.g. 20% agent density)., Comment: Accepted in ICAPS 2024