The IceCube Neutrino Observatory publishes "alert events", i.e. detections of high-energy neutrinos with a moderate-to-high probability of being of astrophysical origin. While some events are produced in the atmosphere, a fraction of alert events should point back to their astrophysical sources. We aim to identify multiple alert events possibly related to a single astrophysical counterpart by searching for spatial and temporal clusterings in 13 years of alert data. We identify spatial clusters ("multiplets") by checking for events overlapping within their uncertainty regions. In order to reduce chance coincidences and to improve the signal purity of our sample, we apply different thresholds. We investigate the weighted mean position of these multiplets for an over-fluctuation of gamma-ray counterparts. As a final step, we apply expectation maximization to search for temporal clusters around the identified weighted mean positions. We find no statistically significant clustering of alert events around a specific origin direction or in time. This could be because the selections are still dominated by atmospheric background. Another possibility is that we are not yet sensitive enough and only detect single events from sources. In this case, we need more data in order to observe a clustering of events around their origin.
The sustained gamma ray emission (SGRE) from the Sun is one of the fascinating high energy phenomena closely related to the acceleration of protons to energies >300 MeV. Here we report on the solar cycle variation of SGRE events based on observations from Fermi's Large Area Telescope (LAT). This report covers solar cycles (SCs) 24 and 25 during which Fermi has been operating. Since SGRE events are closely related to solar energetic particle (SEP) events and interplanetary type II radio bursts caused by fast and wide coronal mass ejections (CMEs), we consider these phenomena as well. Many studies have shown that SC 25 is similar or slightly stronger than SC 24. The number of SEP events, GLE events, IP type II bursts, and fast and wide CMEs confirm this conclusion. However, the number of SGRE events observed by Fermi LAT has diminished significantly in SC 25 relative to SC 24. One of the issues has been the reduced coverage of the Sun since 2018 due to a mechanical problem with a solar array of the Fermi mission. By identifying the Fermi LAT gaps and the number of energetic events (fast and wide CMEs, interplanetary type II bursts) we conclude that about three times more SGRE events must have occurred than the 15 events observed by Fermi.
With the advancement of third-generation gravitational wave detectors, the identification of strongly lensed gravitational wave (GW) events is expected to play an increasingly vital role in cosmology and fundamental physics. However, traditional Bayesian inference methods suffer from combinatorial computational overhead as the number of events grows, making real-time analysis infeasible. To address this, we propose a deep learning model named Squeeze-and-Excitation Multilayer Perceptron Data-efficient Image Transformer (SEMD), based on Vision Transformers, which classifies strongly lensed GW events by modeling morphological similarity between time-frequency spectrogram pairs. By integrating Squeeze-and-Excitation attention mechanisms and multilayer perceptrons , SEMD achieves strong feature extraction and discrimination. Trained and evaluated on simulated datasets using Advanced LIGO and Einstein Telescope noise, the model demonstrates robustness and generalization across different detector sensitivities and physical conditions, highlighting the promise of deep learning for rapid identification of strongly lensed GW signals.
Document-level event extraction aims to extract structured event information from unstructured text. However, a single document often contains limited event information and the roles of different event arguments may be biased due to the influence of the information source. This paper addresses the limitations of traditional document-level event extraction by proposing the task of cross-document event extraction (CDEE) to integrate event information from multiple documents and provide a comprehensive perspective on events. We construct a novel cross-document event extraction dataset, namely CLES, which contains 20,059 documents and 37,688 mention-level events, where over 70% of them are cross-document. To build a benchmark, we propose a CDEE pipeline that includes 5 steps, namely event extraction, coreference resolution, entity normalization, role normalization and entity-role resolution. Our CDEE pipeline achieves about 72% F1 in end-to-end cross-document event extraction, suggesting the challenge of this task. Our work builds a new line of information extraction research and will attract new research attention.
Christian N. Setzer, Hiranya V. Peiris, Oleg Korobkin
et al.
The 2017 detection of a kilonova coincident with gravitational-wave emission has identified neutron star mergers as the major source of the heaviest elements, and dramatically constrained alternative theories of gravity. Observing a population of such sources has the potential to transform cosmology, nuclear physics, and astrophysics. However, with only one confident multi-messenger detection currently available, modelling the diversity of signals expected from such a population requires improved theoretical understanding. In particular, models which are quick to evaluate, and are calibrated with more detailed multi-physics simulations, are needed to design observational strategies for kilonovae detection, and to obtain rapid-response interpretations of new observations. We use grey-opacity models to construct populations of kilonovae, spanning ejecta parameters predicted by numerical simulations. Our modelling focuses on wavelengths relevant for upcoming optical surveys, such as the Rubin Observatory Legacy Survey of Space and Time (LSST). In these simulations, we implement heating rates that are based on nuclear reaction network calculations. We create a Gaussian-process emulator for kilonova grey opacities, calibrated with detailed radiative transfer simulations. Using recent fits to numerical relativity simulations, we predict how the ejecta parameters from BNS mergers shape the population of kilonovae, accounting for the viewing-angle dependence. Our simulated population of binary neutron star (BNS) mergers produce peak i-band absolute magnitudes $-20 \leq M_i \leq -11$. A comparison with detailed radiative transfer calculations indicates that further improvements are needed to accurately reproduce spectral shapes over the full light curve evolution.
Ewoud Wempe, Léon V. E. Koopmans, A. Renske A. C. Wierda
et al.
To unlock the full spectrum of astrophysical and cosmological applications of gravitational-wave detections, it is essential to localise the associated black-hole mergers to high precision inside their host galaxies. One possible method to achieve this is to compare the properties of multiple detections of gravitationally-lensed binary black-hole merger events with the properties of strong gravitational lens systems located in the joint sky localisation of the gravitational-wave detections. In this work, we simulate the population of binary black-hole mergers lensed by galaxy-scale lenses and detectable by LIGO-Virgo-Kagra in the coming decade and the population of galaxy-scale strong lenses that will be detected by Euclid. We use these simulations to investigate the prospects for localising strongly lensed binary black-hole mergers inside the lensed galaxies of 'Euclid-like' galaxy-scale strong lenses. We find that for 20-50% of strongly lensed gravitational wave events the lens system is detectable with Euclid, if the event falls in its survey footprint. Of these, we expect to correctly identify the strongly-lensed host galaxy as likely (with posterior probability) host galaxy - based on Bayesian evidence ranking of candidate hosts - for 34.6-21.9% of quadruply-lensed gravitational-wave events when given an a-priori 1-5 deg^2 gravitational-wave-only sky localisation. For triply and doubly lensed gravitational-wave events, this becomes 29.8-14.9% and 16.4-6.6% respectively. If successfully identified, however, the localisation can be better than a fraction of the host-galaxy size, i.e. of order milli-arcseconds. A first detection in the coming decade, however, probably requires dedicated deep and high-resolution follow-ups and continued upgrades in the current and planned gravitational-wave detectors.
Evangelia Spiliopoulou, Salvador Medina Maza, Eduard Hovy
et al.
Social media has become an important tool to share information about crisis events such as natural disasters and mass attacks. Detecting actionable posts that contain useful information requires rapid analysis of huge volume of data in real-time. This poses a complex problem due to the large amount of posts that do not contain any actionable information. Furthermore, the classification of information in real-time systems requires training on out-of-domain data, as we do not have any data from a new emerging crisis. Prior work focuses on models pre-trained on similar event types. However, those models capture unnecessary event-specific biases, like the location of the event, which affect the generalizability and performance of the classifiers on new unseen data from an emerging new event. In our work, we train an adversarial neural model to remove latent event-specific biases and improve the performance on tweet importance classification.
Event cameras are novel sensors that report brightness changes in the form of asynchronous "events" instead of intensity frames. They have significant advantages over conventional cameras: high temporal resolution, high dynamic range, and no motion blur. Since the output of event cameras is fundamentally different from conventional cameras, it is commonly accepted that they require the development of specialized algorithms to accommodate the particular nature of events. In this work, we take a different view and propose to apply existing, mature computer vision techniques to videos reconstructed from event data. We propose a novel recurrent network to reconstruct videos from a stream of events, and train it on a large amount of simulated event data. Our experiments show that our approach surpasses state-of-the-art reconstruction methods by a large margin (> 20%) in terms of image quality. We further apply off-the-shelf computer vision algorithms to videos reconstructed from event data on tasks such as object classification and visual-inertial odometry, and show that this strategy consistently outperforms algorithms that were specifically designed for event data. We believe that our approach opens the door to bringing the outstanding properties of event cameras to an entirely new range of tasks. A video of the experiments is available at https://youtu.be/IdYrC4cUO0I
We analyze the position of the two populations of blue stragglers in the globular cluster M30 in the Hertzsprung-Russell diagram. Both populations of blue stragglers are brighter than the cluster's turn-off, but one population (the blue blue-stragglers) align along the zero-age main-sequence whereas the (red) population is elevated in brightness (or colour) by $\sim 0.75$ mag. Based on stellar evolution and merger simulations we argue that the red population, which composes about 40\% of the blue stragglers in M 30, is formed at a constant rate of $\sim 2.8$ blue stragglers per Gyr over the last $\sim 10$ Gyr. The blue population is formed in a burst that started $\sim 3.2$ Gyr ago at a peak rate of $30$ blue stragglers per Gyr$^{-1}$ with an e-folding time scale of $0.93$ Gyr. We speculate that the burst resulted from the core collapse of the cluster at an age of about 9.8 Gyr, whereas the constantly formed population is the result of mass transfer and mergers through binary evolution. In that case about half the binaries in the cluster effectively result in a blue straggler.
Agnieszka Król, Audrey Mauguen, Yassin Mazroui
et al.
Extensions in the field of joint modeling of correlated data and dynamic predictions improve the development of prognosis research. The R package frailtypack provides estimations of various joint models for longitudinal data and survival events. In particular, it fits models for recurrent events and a terminal event (frailtyPenal), models for two survival outcomes for clustered data (frailtyPenal), models for two types of recurrent events and a terminal event (multivPenal), models for a longitudinal biomarker and a terminal event (longiPenal) and models for a longitudinal biomarker, recurrent events and a terminal event (trivPenal). The estimators are obtained using a standard and penalized maximum likelihood approach, each model function allows to evaluate goodness-of-fit analyses and plots of baseline hazard functions. Finally, the package provides individual dynamic predictions of the terminal event and evaluation of predictive accuracy. This paper presents theoretical models with estimation techniques, applies the methods for predictions and illustrates frailtypack functions details with examples.
Gautam S. Thakur, Kevin A. Sparks, Robert N. Stewart
et al.
For past several decades, research efforts in population modelling has proven its efficacy in understanding the basic information about residential and commercial areas, as well as for the purposes of planning, development and improvement of the community as an eco-system. More or less, such efforts assume static nature of population distribution, in turn limited by the current ability to capture the dynamics of population change at a finer resolution of space and time. Fast forward today, more and more people are becoming mobile, traveling across borders impacting the nuts and bolts of our urban fabric. Unfortunately, our current efforts are being surpassed by the need to capture such transient population. It is becoming imperative to identify and define them, as well as measure their dynamics and interconnectedness. In this work, we intend to research urban population mobility patterns, gauge their transient nature, and extend our knowledge of their visited locations. We plan to achieve this by designing and developing novel methods and using VGI data that models and characterizes transient population dynamics.
Stochasticity can play an important role in the dynamics of biologically relevant populations. These span a broad range of scales: from intra-cellular populations of molecules to population of cells and then to groups of plants, animals and people. Large deviations in stochastic population dynamics - such as those determining population extinction, fixation or switching between different states - are presently in a focus of attention of statistical physicists. We review recent progress in applying different variants of dissipative WKB approximation (after Wentzel, Kramers and Brillouin) to this class of problems. The WKB approximation allows one to evaluate the mean time and/or probability of population extinction, fixation and switches resulting from either intrinsic (demographic) noise, or a combination of the demographic noise and environmental variations, deterministic or random. We mostly cover well-mixed populations, single and multiple, but also briefly consider populations on heterogeneous networks and spatial populations. The spatial setting also allows one to study large fluctuations of the speed of biological invasions. Finally, we briefly discuss possible directions of future work.
Jedidiah McClurg, Hossein Hojjat, Nate Foster
et al.
Software-defined networking (SDN) programs must simultaneously describe static forwarding behavior and dynamic updates in response to events. Event-driven updates are critical to get right, but difficult to implement correctly due to the high degree of concurrency in networks. Existing SDN platforms offer weak guarantees that can break application invariants, leading to problems such as dropped packets, degraded performance, security violations, etc. This paper introduces EVENT-DRIVEN CONSISTENT UPDATES that are guaranteed to preserve well-defined behaviors when transitioning between configurations in response to events. We propose NETWORK EVENT STRUCTURES (NESs) to model constraints on updates, such as which events can be enabled simultaneously and causal dependencies between events. We define an extension of the NetKAT language with mutable state, give semantics to stateful programs using NESs, and discuss provably-correct strategies for implementing NESs in SDNs. Finally, we evaluate our approach empirically, demonstrating that it gives well-defined consistency guarantees while avoiding expensive synchronization and packet buffering.
We present a multi-passband photometric study of the Blue Straggler Star (BSS) population in the Galactic globular cluster (GC) NGC\,1261, using available space- and ground-based survey data.~The inner BSS population is found to have two distinct sequences in the color-magnitude diagram, similar to double BSS sequences detected in other GCs. These well defined sequences are presumably linked to single short-lived events such as core collapse, which are expected to boost the formation of BSSs.~In agreement with this, we find a BSS sequence in NGC\,1261 which can be well reproduced individually by a theoretical model prediction of a 2 Gyr old population of stellar collision products, which are expected to form in the denser inner regions during short-lived core contraction phases.~Additionally, we report the occurrence of a group of BSSs with unusually blue colours in the CMD, which are consistent with a corresponding model of a 200 Myr old population of stellar collision products.~The properties of the NGC\,1261 BSS populations, including their spatial distributions, suggest an advanced dynamical evolutionary state of the cluster, but the core of this GC does not show the classical signatures of core-collapse.~We argue these apparent contradictions provide evidence for a post-core-collapse bounce state seen in dynamical simulations of old GCs.
Richard A. Neher, Boris I. Shraiman, Daniel S. Fisher
Adaptation often involves the acquisition of a large number of genomic changes which arise as mutations in single individuals. In asexual populations, combinations of mutations can fix only when they arise in the same lineage, but for populations in which genetic information is exchanged, beneficial mutations can arise in different individuals and be combined later. In large populations, when the product of the population size N and the total beneficial mutation rate U_b is large, many new beneficial alleles can be segregating in the population simultaneously. We calculate the rate of adaptation, v, in several models of such sexual populations and show that v is linear in NU_b only in sufficiently small populations. In large populations, v increases much more slowly as log NU_b. The prefactor of this logarithm, however, increases as the square of the recombination rate. This acceleration of adaptation by recombination implies a strong evolutionary advantage of sex.
Charged particles and photons have been measured in central Pb+Pb collisions at 158 A GeV in a common ($η$ - $φ$)-phase space region in the WA98 experiment at the CERN SPS. The measured distributions have been analyzed %using the sliding window method in order to quantify the frequency with which phase space regions of varying sizes have either small or large neutral pion fraction. The measured results are compared with VENUS model simulated events and with mixed events. Events with both large and small charged-neutral fluctuations are observed to occur more frequently than expected statistically, as deduced from mixed events, or as predicted by model simulations, with the difference becoming more prominent with decreasing size of the $Δη$ - $Δφ$ region.
Jet cross sections can be in principle compared to simple pQCD calculations, based on the hypothesis of factorization. But often it is useful or even necessary to not only compute the production rate of the very high pt jets, but in addition the "rest of the event". The proposed talk is based on recent work, where we try to construct an event generator fully compatible with pQCD which allows to compute complete events, consisting of high pt jets plus all the other low pt particles produced at the same time. Whereas in "generators of inclusive spectra" like Pythia one may easily trigger on high pt phenomena, this is not so obvious for "generators of physical events", where in principle one has to generate a very large number of events in order to finally obtain rare events (like those with a very high pt jet). We recently developped an independnat block method which allow us ta have a direct access to dedicated variables 1. We will present latest results concerning this approach.
The stellar populations of galaxies contain a wealth of detailed information. From the youngest, most massive stars, to almost invisible remnants, the history of star formation is encoded in the stars that make up a galaxy. Extracting some, or all, of this informationhas long been a goal of stellar population studies. This was achieved in the last couple of decades and it is now a routine task, which forms a crucial ingredient in much of observational galaxy evolution, from our Galaxy out to the most distant systems found. In many of these domains we are now limited not by sample size, but by systematic uncertainties and this will increasingly be the case in the future. The aim of this review is to outline the challenges faced by stellar population studies in the coming decade within the context of upcoming observational facilities. I will highlight the need to better understand the near-IR spectral range and outline the difficulties presented by less well understood phases of stellar evolution such as thermally pulsing AGB stars, horizontal branch stars and the very first stars. The influence of rotation and binarity on stellar population modeling is also briefly discussed.