Search

We develop benchmarks for resonant di-scalar production in the generic complex singlet scalar extension of the Standard Model (SM), which contains two new scalars. These benchmarks maximize di-scalar resonant production: $pp\rightarrow h_2 \rightarrow h_1 h_1/h_1h_3/h_3h_3$, where $h_1$ is the observed SM-like Higgs boson and $h_{2,3}$ are new scalars. The decays $h_2\rightarrow h_1h_3$ and $h_2\rightarrow h_3h_3$ may be the only way to discover $h_3$, leading to a discovery of two new scalars at once. Current LHC and projected future collider (HL-LHC, FCC-ee, ILC500) constraints are used to produce benchmarks at the HL-LHC for $h_2$ masses between 250 GeV and 1 TeV and a future $pp$ collider for $h_2$ masses between 250 GeV and 12 TeV. We update the current LHC bounds on the singlet-Higgs boson mixing angle. As the mass of $h_2$ increases, certain limiting behaviors of the maximum rates are uncovered due to theoretical constraints on the parameters. These limits, which can be derived analytically, are ${\rm BR}(h_2\rightarrow h_1h_1)\rightarrow 0.25$, ${\rm BR}(h_2\rightarrow h_3h_3)\rightarrow 0.5$, and ${\rm BR}(h_2\rightarrow h_1h_3) \rightarrow 0$. It can also be shown that the maximum rates of $pp\rightarrow h_2\rightarrow h_1h_1/h_3h_3$ approach the same value. Hence, all three $h_2\rightarrow h_ih_j$ decays are promising discovery modes for $h_2$ masses below $\mathcal{O}(1\,{\rm TeV})$, while above $\mathcal{O}(1\,{\rm TeV})$ the decays $h_2\rightarrow h_1h_1/h_3h_3$ are more encouraging. Masses for $h_3$ are chosen to produce a large range of signatures including multi-b, multi-vector boson, and multi-$h_1$ production. The behavior of the maximum rates imply that in the multi-TeV region this model may be discovered in the Higgs quartet production mode before Higgs triple production is observed. The maximum di- and four Higgs production rates are similar in the multi-TeV range., Comment: v2: matches version accepted by PRD, typos fixed, references added, discussion expanded, results unchanged, 28 pgs+21 pgs of appendices and references, 9 figures; v1: 27 pages+20 pages of appendices and references, 9 figures

We present detailed forecasts for the constraints on primordial magnetic fields (PMFs) that will be obtained with the LiteBIRD satellite. The constraints are driven by the effects of PMFs on the CMB anisotropies: the gravitational effects of magnetically-induced perturbations; the effects on the thermal and ionization history of the Universe; the Faraday rotation imprint on the CMB polarization; and the non-Gaussianities induced in polarization anisotropies. LiteBIRD represents a sensitive probe for PMFs and by exploiting all the physical effects, it will be able to improve the current limit coming from Planck. In particular, thanks to its accurate $B$-mode polarization measurement, LiteBIRD will improve the constraints on infrared configurations for the gravitational effect, giving $B_{\rm 1\,Mpc}^{n_{\rm B} =-2.9} < 0.8$ nG at 95% C.L., potentially opening the possibility to detect nanogauss fields with high significance. We also observe a significant improvement in the limits when marginalized over the spectral index, $B_{1\,{\rm Mpc}}^{\rm marg}< 2.2$ nG at 95% C.L. From the thermal history effect, which relies mainly on $E$-mode polarization data, we obtain a significant improvement for all PMF configurations, with the marginalized case, $\sqrt{\langle B^2\rangle}^{\rm marg}<0.50$ nG at 95% C.L. Faraday rotation constraints will take advantage of the wide frequency coverage of LiteBIRD and the high sensitivity in $B$ modes, improving the limits by orders of magnitude with respect to current results, $B_{1\,{\rm Mpc}}^{n_{\rm B} =-2.9} < 3.2$ nG at 95% C.L. Finally, non-Gaussianities of the $B$-mode polarization can probe PMFs at the level of 1 nG, again significantly improving the current bounds from Planck. Altogether our forecasts represent a broad collection of complementary probes, providing conservative limits on PMF characteristics that will be achieved with LiteBIRD., Comment: 51 pages, 24 figures, abstract shortened

Frequent disruptions like highway constructions are common now-a-days, often impacting environmental justice communities (communities with low socio-economic status with disproportionately high and adverse human health and environmental effects) that live nearby. Based on our interactions via focus groups with the members of four environmental justice communities impacted by a major highway construction, a common concern is a sense of uncertainty about project activities and loss of social connectedness, leading to increased stress, depression, anxiety and diminished well-being. This paper addresses this concern by developing a localized social media system called PureConnect with a goal to raise the level of awareness about the project and increase social connectedness among the community members. PureConnect has been designed using active engagement with four environmental justice communities affected by a major highway construction. It has been deployed in the real world among the members of the four environmental justice communities, and a detailed analysis of the data collected from this deployment as well as surveys show that PureConnect is potentially useful in improving community members' well-being and the members appreciate the functionalities it provides., Comment: Submitted in COMPSAC 2024

Narrow absorption features in nearby supernova (SN) spectra are a powerful diagnostic of the slow-moving material in the line of sight: they are extensively used to infer dust extinction from the host galaxies, and they can also serve in the detection of circumstellar material originating from the SN progenitor and present in the vicinity of the explosion. Despite their wide use, very few studies have examined the biases of the methods to characterize narrow lines, and not many statistical analyses exist. This is the first paper of a series in which we present a statistical analysis of narrow lines of SN spectra of various resolutions. We develop a robust automated methodology to measure the equivalent width (EW) and velocity of narrow absorption lines from intervening material in the line of sight of SNe, including Na I D , Ca II H&K, K i and diffuse interstellar bands (DIBs). We carefully study systematic biases in heterogeneous spectra from the literature by simulating different signal-to-noise, spectral resolution, slit size and orientation and present the real capabilities and limitations of using low- and mid-resolution spectra to study these lines. In particular, we find that the measurement of the equivalent width of the narrow lines in low-resolution spectra is highly affected by the evolving broad P-Cygni profiles of the SN ejecta, both for core-collapse and type Ia SNe, inducing a conspicuous apparent evolution. We present thus an easy way to detect and exclude those cases to obtain more robust and reliable measurements. Finally, after considering all possible effects, we analyse the temporal evolution of the narrow features in a large sample of nearby SNe to detect any possible variation in their EWs over time. We find no time evolution of the narrow line features in our large sample for all SN types, Comment: Accepted in A&A

Following the Phase-II upgrade during Long Shutdown (LS3), the LHC aims to reach a peak instantaneous luminosity of $7.5\times 10^{34}$cm$^{-2}$s$^{-1}$, which corresponds to an average of around 200 inelastic proton-proton collisions per beam-crossing (every 25 ns). To cope with these conditions, the ATLAS Inner Detector will be replaced by a new all-silicon system -- the Inner Tracker (ITk). The ITk will be operational for more than ten years, during which time ATLAS is expected to record approximately 4000 fb$^{-1}$ of data. The ITk's pixel sub-system is based on hybrid pixel modules with new silicon sensors and readout chips. These studies focus on testbeam campaigns undertaken to study the spatial resolution and efficiencies of hybrid pixel detector modules based on the first large-structure prototype front-end readout chip -- the RD53A -- using planar silicon sensors. These devices have been irradiated to replicate the effect of the high radiation environment present during operation in the ATLAS detector. Results for devices using sensors with different punch-through bias structures and using different readout chips are summarised. Those with sensors incorporating a punch-through bias structure are found to exhibit systematically lower efficiency than those without, as a result of local areas of relative inefficiency around the punch-through dots. Despite this, all devices measured are found to satisfy the requirement of 97% efficiency at $V_\mathrm{bias}=400$ V after being irradiated to end-of-life fluence., Comment: 26 pages, 18 figures, prepared for submission to JINST. There have been some minor language changes for clarity and consistency. A more appropriate diagram for the punchthrough biasing structures discussed has replaced the previous one. Some plots have been enlarged for readability. There was a small bug in Figure 10b, which has been amended. Results and conclusions are unaffected

We report on a measurement of astrophysical tau neutrinos with 9.7 years of IceCube data. Using convolutional neural networks trained on images derived from simulated events, seven candidate $\nu_\tau$ events were found with visible energies ranging from roughly 20 TeV to 1 PeV and a median expected parent $\nu_\tau$ energy of about 200 TeV. Considering backgrounds from astrophysical and atmospheric neutrinos, and muons from $\pi^\pm/K^\pm$ decays in atmospheric air showers, we obtain a total estimated background of about 0.5 events, dominated by non-$\nu_\tau$ astrophysical neutrinos. Thus, we rule out the absence of astrophysical $\nu_\tau$ at the $5\sigma$ level. The measured astrophysical $\nu_\tau$ flux is consistent with expectations based on previously published IceCube astrophysical neutrino flux measurements and neutrino oscillations., Comment: Accepted for publication in Physical Review Letters. This version includes full author list metadata

The IceCube Neutrino Observatory relies on an array of photomultiplier tubes to detect Cherenkov light produced by charged particles in the South Pole ice. IceCube data analyses depend on an in-depth characterization of the glacial ice, and on novel approaches in event reconstruction that utilize fast approximations of photoelectron yields. Here, a more accurate model is derived for event reconstruction that better captures our current knowledge of ice optical properties. When evaluated on a Monte Carlo simulation set, the median angular resolution for in-ice particle showers improves by over a factor of three compared to a reconstruction based on a simplified model of the ice. The most substantial improvement is obtained when including effects of birefringence due to the polycrystalline structure of the ice. When evaluated on data classified as particle showers in the high-energy starting events sample, a significantly improved description of the events is observed., Comment: 28 pages, 18 figures, 1 table, submitted to JINST, updated to account for comments received

Writing declarative models has numerous benefits, ranging from automated reasoning and correction of design-level properties before systems are built to automated testing and debugging of their implementations after they are built. Unfortunately, the model itself needs to be correct to gain these benefits. Alloy is a commonly used modeling language that has several existing efforts to repair faulty models automatically. Currently, these efforts are search-based methods that use an Abstract Syntax Tree (AST) representation of the model and do not scale. One issue is that ASTs themselves suffer from exponential growth in their data size due to the limitation that ASTs will often have identical nodes separately listed in the tree. To address this issue, we introduce a novel code representation schema, Complex Structurally Balanced Abstract Semantic Graph (CSBASG), which represents code as a complex-weighted directed graph that lists a semantic element as a node in the graph and ensures its structural balance for almost finitely enumerable code segments. We evaluate the efficiency of our CSBASG representation for Alloy models in terms of it's compactness compared to ASTs, and we explore if a CSBASG can ease the process of comparing two Alloy predicates. Moreover, with this representation in place, we identify several future applications of CSBASG, including Alloy code generation and automated repair., Comment: 12 pages

Current and future Type Ia Supernova (SN Ia) surveys will need to adopt new approaches to classifying SNe and obtaining their redshifts without spectra if they wish to reach their full potential. We present here a novel approach that uses only photometry to identify SNe Ia in the 5-year Dark Energy Survey (DES) dataset using the SuperNNova classifier. Our approach, which does not rely on any information from the SN host-galaxy, recovers SNe Ia that might otherwise be lost due to a lack of an identifiable host. We select 2,298 high-quality SNe Ia from the DES 5-year dataset an almost complete sample of detected SNe Ia. More than 700 of these have no spectroscopic host redshift and are potentially new SNIa compared to the DES-SN5YR cosmology analysis. To analyse these SNe Ia, we derive their redshifts and properties using only their light-curves with a modified version of the SALT2 light-curve fitter. Compared to other DES SN Ia samples with spectroscopic redshifts, our new sample has in average higher redshift, bluer and broader light-curves, and fainter host-galaxies. Future surveys such as LSST will also face an additional challenge, the scarcity of spectroscopic resources for follow-up. When applying our novel method to DES data, we reduce the need for follow-up by a factor of four and three for host-galaxy and live SN respectively compared to earlier approaches. Our novel method thus leads to better optimisation of spectroscopic resources for follow-up., Comment: Accepted MNRAS

A measurement of the diffuse astrophysical neutrino spectrum is presented using IceCube data collected from 2011-2022 (10.3 years). We developed novel detection techniques to search for events with a contained vertex and exiting track induced by muon neutrinos undergoing a charged-current interaction. Searching for these starting track events allows us to not only more effectively reject atmospheric muons but also atmospheric neutrino backgrounds in the southern sky, opening a new window to the sub-100 TeV astrophysical neutrino sky. The event selection is constructed using a dynamic starting track veto and machine learning algorithms. We use this data to measure the astrophysical diffuse flux as a single power law flux (SPL) with a best-fit spectral index of $\gamma = 2.58 ^{+0.10}_{-0.09}$ and per-flavor normalization of $\phi^{\mathrm{Astro}}_{\mathrm{per-flavor}} = 1.68 ^{+0.19}_{-0.22} \times 10^{-18} \times \mathrm{GeV}^{-1} \mathrm{cm}^{-2} \mathrm{s}^{-1} \mathrm{sr}^{-1}$ (at 100 TeV). The sensitive energy range for this dataset is 3 - 550 TeV under the SPL assumption. This data was also used to measure the flux under a broken power law, however we did not find any evidence of a low energy cutoff., Comment: 27 pages, 28 figures

Atomistic simulations often rely on interatomic potentials to access greater time- and length- scales than those accessible to first principles methods such as density functional theory (DFT). However, since a parameterised potential typically cannot reproduce the true potential energy surface of a given system, we should expect a decrease in accuracy and increase in error in quantities of interest calculated from simulations. Quantifying the uncertainty on the outputs of atomistic simulations is thus an important, necessary step so that there is confidence in results and available metrics to explore improvements in said simulations. Here, we address this research question by forming ensembles of Atomic Cluster Expansion (ACE) potentials, and using Conformal Prediction with DFT training data to provide meaningful, calibrated error bars on several quantities of interest for silicon: the bulk modulus, elastic constants, relaxed vacancy formation energy, and the vacancy migration barrier. We evaluate the effects on uncertainty bounds using a range of different potentials and training sets., Comment: 15 pages, 8 figures

Planned disruptions such as highway constructions are commonplace nowadays and the communities living near these disruptions generally tend to be environmental justice communities -- low socioeconomic status with disproportionately high and adverse human health and environmental effects. A major concern is that such activities negatively impact people's well-being by disrupting their daily commutes via frequent road closures and increased dust and air pollution. This paper addresses this concern by developing a personalized navigation service called PureNav to mitigate the negative impacts of disruptions in daily commutes on people's well-being. PureNav has been designed using active engagement with four environmental justice communities affected by major highway construction. It has been deployed in the real world among the members of the four communities, and a detailed analysis of the data collected from this deployment as well as surveys show that PureNav is potentially useful in improving people's well-being. The paper describes the design, implementation, and evaluation of PureNav, and offers suggestions for further improving its efficacy., Comment: Accepted for publication in the proceedings of the 2023 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)

Determining when people are struggling from video enables a finer-grained understanding of actions and opens opportunities for building intelligent support visual interfaces. In this paper, we present a new dataset with three assembly activities and corresponding performance baselines for the determination of struggle from video. Three real-world problem-solving activities including assembling plumbing pipes (Pipes-Struggle), pitching camping tents (Tent-Struggle) and solving the Tower of Hanoi puzzle (Tower-Struggle) are introduced. Video segments were scored w.r.t. the level of struggle as perceived by annotators using a forced choice 4-point scale. Each video segment was annotated by a single expert annotator in addition to crowd-sourced annotations. The dataset is the first struggle annotation dataset and contains 5.1 hours of video and 725,100 frames from 73 participants in total. We evaluate three decision-making tasks: struggle classification, struggle level regression, and struggle label distribution learning. We provide baseline results for each of the tasks utilising several mainstream deep neural networks, along with an ablation study and visualisation of results. Our work is motivated toward assistive systems that analyze struggle, support users during manual activities and encourage learning, as well as other video understanding competencies.

We explore Cluster Editing and its generalization Correlation Clustering with a new operation called permissive vertex splitting which addresses finding overlapping clusters in the face of uncertain information. We determine that both problems are NP-hard, yet they exhibit significant differences in parameterized complexity and approximability. For Cluster Editing with Permissive Vertex Splitting, we show a polynomial kernel when parameterized by the solution size and develop a polynomial-time algorithm with approximation factor 7. In the case of Correlation Clustering, we establish para-NP-hardness when parameterized by solution size and demonstrate that computing an $n^{1-\epsilon}$-approximation is NP-hard for any constant $\epsilon > 0$. Additionally, we extend the established link between Correlation Clustering and Multicut to the setting with permissive vertex splitting., Comment: Version 2 includes minor changes incorporating reviewer feedback. Short version appeared at SWAT 2024

An array of twelve circular-orifice synthetic jet actuators (SJAs) was used to provide the unsteady forcing required for flow separation control over a National Advisory Committee for Aeronautics (NACA) 0025 airfoil at a chord-based Reynolds number of 100000 and an angle of attack of 10{\deg}. Two distinct high- and low-forcing frequencies corresponding to the shear layer and wake instabilities were used at an identical blowing strength for flow control. Particle image velocimetry (PIV) was used to measure the velocity fields at the centerline of the airfoil. The results showed the presence of a turbulent shear layer stretching from the edge of the reattached boundary layer to the irrotational flow with an invariant mean spanwise vorticity in the wall-normal direction. It was revealed that the coherent structures for the high-frequency controlled case are advected along the boundary of the rigid-body rotation shear layer and the irrotational flow, whereas for the low-frequency actuation, some structures directly pass through the rigid-body rotation region, disrupting the wall-normal balance of vorticity. Analytical expressions were derived for the variation of the mean spanwise vorticity in the rigid-body rotation region and the curvature-multiplied mean angular momentum in the irrotational flow region based on order-of-magnitude analysis and semi-empirical grounds. The resulting patterns showed an excellent agreement with the measured experimental data.

Writing declarative models has numerous benefits, ranging from automated reasoning and correction of design-level properties before systems are built, to automated testing and debugging of their implementations after they are built. Alloy is a declarative modeling language that is well-suited for verifying system designs. A key strength of Alloy is its scenario-finding toolset, the Analyzer, which allows users to explore all valid scenarios that adhere to the model's constraints up to a user-provided scope. However, even with visualized scenarios, it is difficult to write correct Alloy models. To address this, a growing body of work explores different techniques for debugging Alloy models. In order to develop and evaluate these techniques in an effective manor, this paper presents an empirical study of over 97,000 models written by novice users trying to learn Alloy. We investigate how users write both correct and incorrect models in order to produce a comprehensive benchmark for future use as well as a series of observations to guide debugging and educational efforts for Alloy model development.

Due to their short timescale, stellar flares are a challenging target for the most modern synoptic sky surveys. The upcoming Vera C. Rubin Legacy Survey of Space and Time (LSST), a project designed to collect more data than any precursor survey, is unlikely to detect flares with more than one data point in its main survey. We developed a methodology to enable LSST studies of stellar flares, with a focus on flare temperature and temperature evolution, which remain poorly constrained compared to flare morphology. By leveraging the sensitivity expected from the Rubin system, Differential Chromatic Refraction can be used to constrain flare temperature from a single-epoch detection, which will enable statistical studies of flare temperatures and constrain models of the physical processes behind flare emission using the unprecedentedly high volume of data produced by Rubin over the 10-year LSST. We model the refraction effect as a function of the atmospheric column density, photometric filter, and temperature of the flare, and show that flare temperatures at or above ~4,000K can be constrained by a single g-band observation at airmass X > 1.2, given the minimum specified requirement on single-visit relative astrometric accuracy of LSST, and that a surprisingly large number of LSST observations is in fact likely be conducted at X > 1.2, in spite of image quality requirements pushing the survey to preferentially low X. Having failed to measure flare DCR in LSST precursor surveys, we make recommendations on survey design and data products that enable these studies in LSST and other future surveys., Comment: 18 pages, 16 figures, 1 table. Submitted to the Astrophysical Journal Supplement Series

We present a physics-based model that accurately predicts the performance of Medtronic's implantable medical device battery lithium/carbon monofluoride (CF$_x$) - silver vanadium oxide (SVO) under both low-rate background monitoring and high-rate pulsing currents. The distinct properties of multiple active materials are reflected by parameterizing their thermodynamics, kinetics, and mass transport properties separately. Diffusion limitations of Li$^+$ in SVO are used to explain cell voltage transient behavior during pulse and post-pulse relaxation. We also introduce change in cathode electronic conductivity, Li metal anode surface morphology, and film resistance buildup to capture evolution of cell internal resistance throughout multi-year electrical tests. We share our insights on how the Li$^+$ redistribution process between active materials can restore pulse capability of the hybrid electrode, allow CF$_x$ to indirectly contribute to capacity release during pulsing, and affect the operation protocols and design principles of batteries with other hybrid electrodes. We also discuss additional complexities in porous electrode model parameterization and electrochemical characterization techniques due to parallel reactions and solid diffusion pathways across active materials. We hope our models implemented in the Hybrid Multiphase Porous Electrode Theory (Hybrid-MPET) framework can complement future experimental research and accelerate development of multi-active material electrodes with targeted performance., Comment: For code and sample usage, please visit: https://github.com/HarryQL/Hybrid-MPET/tree/medtronic_pulse

In many Reinforcement Learning (RL) papers, learning curves are useful indicators to measure the effectiveness of RL algorithms. However, the complete raw data of the learning curves are rarely available. As a result, it is usually necessary to reproduce the experiments from scratch, which can be time-consuming and error-prone. We present Open RL Benchmark, a set of fully tracked RL experiments, including not only the usual data such as episodic return, but also all algorithm-specific and system metrics. Open RL Benchmark is community-driven: anyone can download, use, and contribute to the data. At the time of writing, more than 25,000 runs have been tracked, for a cumulative duration of more than 8 years. Open RL Benchmark covers a wide range of RL libraries and reference implementations. Special care is taken to ensure that each experiment is precisely reproducible by providing not only the full parameters, but also the versions of the dependencies used to generate it. In addition, Open RL Benchmark comes with a command-line interface (CLI) for easy fetching and generating figures to present the results. In this document, we include two case studies to demonstrate the usefulness of Open RL Benchmark in practice. To the best of our knowledge, Open RL Benchmark is the first RL benchmark of its kind, and the authors hope that it will improve and facilitate the work of researchers in the field., Comment: Under review

Touchscreen-based electronic devices such as smart phones and smart tablets are widely used in our daily life. While the security of electronic devices have been heavily investigated recently, the resilience of touchscreens against various attacks has yet to be thoroughly investigated. In this paper, for the first time, we show that touchscreen-based electronic devices are vulnerable to intentional electromagnetic interference (IEMI) attacks in a systematic way and how to conduct this attack in a practical way. Our contribution lies in not just demonstrating the attack, but also analyzing and quantifying the underlying mechanism allowing the novel IEMI attack on touchscreens in detail. We show how to calculate both the minimum amount of electric field and signal frequency required to induce touchscreen ghost touches. We further analyze our IEMI attack on real touchscreens with different magnitudes, frequencies, duration, and multitouch patterns. The mechanism of controlling the touchscreen-enabled electronic devices with IEMI signals is also elaborated. We design and evaluate an out-of-sight touchscreen locator and touch injection feedback mechanism to assist a practical IEMI attack. Our attack works directly on the touchscreen circuit regardless of the touchscreen scanning mechanism or operating system. Our attack can inject short-tap, long-press, and omni-directional gestures on touchscreens from a distance larger than the average thickness of common tabletops. Compared with the state-of-the-art touchscreen attack, ours can accurately inject different types of touch events without the need for sensing signal synchronization, which makes our attack more robust and practical. In addition, rather than showing a simple proof-of-concept attack, we present and demonstrate the first ready-to-use IEMI based touchscreen attack vector with end-to-end attack scenarios., Comment: This paper has been accepted by 2022 IEEE Symposium on Security and Privacy (SP) and won distinguished paper award

We construct a strong baseline method for building damage detection by starting with the highly-engineered winning solution of the xView2 competition, and gradually stripping away components. This way, we obtain a much simpler method, while retaining adequate performance. We expect the simplified solution to be more widely and easily applicable. This expectation is based on the reduced complexity, as well as the fact that we choose hyperparameters based on simple heuristics, that transfer to other datasets. We then re-arrange the xView2 dataset splits such that the test locations are not seen during training, contrary to the competition setup. In this setting, we find that both the complex and the simplified model fail to generalize to unseen locations. Analyzing the dataset indicates that this failure to generalize is not only a model-based problem, but that the difficulty might also be influenced by the unequal class distributions between events. Code, including the baseline model, is available under https://github.com/PaulBorneP/Xview2_Strong_Baseline, Comment: 11 pages, 5 figures

Gravitational-wave astronomy has developed enormously over the last decade with the first detections across different frequency bands, but has yet to access $0.1-10$ $\mathrm{Hz}$ gravitational waves. Gravitational waves in this band are emitted by some of the most enigmatic sources, including intermediate-mass binary black hole mergers, early inspiralling compact binaries, and possibly cosmic inflation. To tap this exciting band, we propose the construction of a detector based on pulsar timing principles, the Artificial Precision Timing Array (APTA). We envision APTA as a solar system array of artificial "pulsars"$-$precision-clock-carrying satellites that emit pulsing electromagnetic signals towards Earth or other centrum. In this fundamental study, we estimate the clock precision needed for APTA to successfully detect gravitational waves. Our results suggest that a clock relative uncertainty of $10^{-17}$, which is currently attainable, would be sufficient for APTA to surpass LISA's sensitivity in the decihertz band and observe $10^3-10^4$ $\mathrm{M}_\odot$ black hole mergers. Future atomic clock technology realistically expected in the next decade would enable the detection of an increasingly diverse set of astrophysical sources, including stellar-mass compact binaries that merge in the LIGO-Virgo-KAGRA band, extreme-mass-ratio inspirals, and Type Ia supernovae. This work opens up a new area of research into designing and constructing artificial gravitational-wave detectors relying on the successful principles of pulsar timing., Comment: 10 pages, 3 figures

Name that Neutrino is a citizen science project where volunteers aid in classification of events for the IceCube Neutrino Observatory, an immense particle detector at the geographic South Pole. From March 2023 to September 2023, volunteers did classifications of videos produced from simulated data of both neutrino signal and background interactions. Name that Neutrino obtained more than 128,000 classifications by over 1,800 registered volunteers that were compared to results obtained by a deep neural network machine-learning algorithm. Possible improvements for both Name that Neutrino and the deep neural network are discussed.

We demonstrate a real-time, converged 5G-PON through the Cooperative Transport Interface, synchronising 5G and PON-DBA upstream schedulers. This innovative approach, implemented using 5G and PON open network implementations, significantly enhances network resource allocation, reducing latency.

Hot Jupiters were many of the first exoplanets discovered in the 1990s, but in the decades since their discovery, the mysteries surrounding their origins remain. Here, we present nine new hot Jupiters (TOI-1855 b, TOI-2107 b, TOI-2368 b, TOI-3321 b, TOI-3894 b, TOI-3919 b, TOI-4153 b, TOI-5232 b, and TOI-5301 b) discovered by NASA's TESS mission and confirmed using ground-based imaging and spectroscopy. These discoveries are the first in a series of papers named the Migration and Evolution of giant ExoPlanets (MEEP) survey and are part of an ongoing effort to build a complete sample of hot Jupiters orbiting FGK stars, with a limiting Gaia $G$-band magnitude of 12.5. This effort aims to use homogeneous detection and analysis techniques to generate a set of precisely measured stellar and planetary properties that is ripe for statistical analysis. The nine planets presented in this work occupy a range of masses (0.55 Jupiter masses (M$_{\rm{J}}$) $<$ M$_{\rm{P}}$ $<$ 3.88 M$_{\rm{J}}$) and sizes (0.967 Jupiter radii (R$_{\rm{J}}$) $<$ R$_{\rm{P}}$ $<$ 1.438 R$_{\rm{J}}$) and orbit stars that range in temperature from 5360 K $<$ Teff $<$ 6860 K with Gaia $G$-band magnitudes ranging from 11.1 to 12.7. Two of the planets in our sample have detectable orbital eccentricity: TOI-3919 b ($e = 0.259^{+0.033}_{-0.036}$) and TOI-5301 b ($e = 0.33^{+0.11}_{-0.10}$). These eccentric planets join a growing sample of eccentric hot Jupiters that are consistent with high-eccentricity tidal migration, one of the three most prominent theories explaining hot Jupiter formation and evolution., Comment: 35 pages, 7 tables, and 14 figures. Submitted to AAS Journals on 2023 Dec 28

We propose a pipeline for identifying important entities from intelligence reports that constructs a knowledge graph, where nodes correspond to entities of fine-grained types (e.g. traffickers) extracted from the text and edges correspond to extracted relations between entities (e.g. cartel membership). The important entities in intelligence reports then map to central nodes in the knowledge graph. We introduce a novel method that extracts fine-grained entities in a few-shot setting (few labeled examples), given limited resources available to label the frequently changing entity types that intelligence analysts are interested in. It outperforms other state-of-the-art methods. Next, we identify challenges facing previous evaluations of zero-shot (no labeled examples) methods for extracting relations, affecting the step of populating edges. Finally, we explore the utility of the pipeline: given the goal of identifying important entities, we evaluate the impact of relation extraction errors on the identification of central nodes in several real and synthetic networks. The impact of these errors varies significantly by graph topology, suggesting that confidence in measurements based on automatically extracted relations should depend on observed network features., Comment: 14 pages, 6 figures, to appear at CompleNet 2024

This research work aims to develop an analytical approach for optimizing team formation and predicting team performance in a competitive environment based on data on the competitors' skills prior to the team formation. There are several approaches in scientific literature to optimize and predict a team's performance. However, most studies employ fine-grained skill statistics of the individual members or constraints such as teams with a set group of members. Currently, no research tackles the highly constrained domain of the FIRST Robotics Competition. This research effort aims to fill this gap by providing an analytical method for optimizing and predicting team performance in a competitive environment while allowing these constraints and only using metrics on previous team performance, not on each individual member's performance. We apply our method to the drafting process of the FIRST Robotics competition, a domain in which the skills change year-over-year, team members change throughout the season, each match only has a superficial set of statistics, and alliance formation is key to competitive success. First, we develop a method that could extrapolate individual members' performance based on overall team performance. An alliance optimization algorithm is developed to optimize team formation and a deep neural network model is trained to predict the winning team, both using highly post-processed real-world data. Our method is able to successfully extract individual members' metrics from overall team statistics, form competitive teams, and predict the winning team with 84.08% accuracy.

We present the full Hubble diagram of photometrically-classified Type Ia supernovae (SNe Ia) from the Dark Energy Survey supernova program (DES-SN). DES-SN discovered more than 20,000 SN candidates and obtained spectroscopic redshifts of 7,000 host galaxies. Based on the light-curve quality, we select 1635 photometrically-identified SNe Ia with spectroscopic redshift 0.10$< z <$1.13, which is the largest sample of supernovae from any single survey and increases the number of known $z>0.5$ supernovae by a factor of five. In a companion paper, we present cosmological results of the DES-SN sample combined with 194 spectroscopically-classified SNe Ia at low redshift as an anchor for cosmological fits. Here we present extensive modeling of this combined sample and validate the entire analysis pipeline used to derive distances. We show that the statistical and systematic uncertainties on cosmological parameters are $\sigma_{\Omega_M,{\rm stat+sys}}^{\Lambda{\rm CDM}}=$0.017 in a flat $\Lambda$CDM model, and $(\sigma_{\Omega_M},\sigma_w)_{\rm stat+sys}^{w{\rm CDM}}=$(0.082, 0.152) in a flat $w$CDM model. Combining the DES SN data with the highly complementary CMB measurements by Planck Collaboration (2020) reduces uncertainties on cosmological parameters by a factor of 4. In all cases, statistical uncertainties dominate over systematics. We show that uncertainties due to photometric classification make up less than 10% of the total systematic uncertainty budget. This result sets the stage for the next generation of SN cosmology surveys such as the Vera C. Rubin Observatory's Legacy Survey of Space and Time., Comment: 39 pages, 19 figures; Submitted to ApJ; companion paper Dark Energy Collaboration et al. on consecutive arxiv number 2401.02929

We present cosmological constraints from the sample of Type Ia supernovae (SN Ia) discovered during the full five years of the Dark Energy Survey (DES) Supernova Program. In contrast to most previous cosmological samples, in which SN are classified based on their spectra, we classify the DES SNe using a machine learning algorithm applied to their light curves in four photometric bands. Spectroscopic redshifts are acquired from a dedicated follow-up survey of the host galaxies. After accounting for the likelihood of each SN being a SN Ia, we find 1635 DES SNe in the redshift range $0.100.5$ SNe compared to the previous leading compilation of Pantheon+, and results in the tightest cosmological constraints achieved by any SN data set to date. To derive cosmological constraints we combine the DES supernova data with a high-quality external low-redshift sample consisting of 194 SNe Ia spanning $0.025