Search

Cosmological gravitational-wave backgrounds are an exciting science target for next-generation ground-based detectors, as they encode invaluable information about the primordial Universe. However, any such background is expected to be obscured by the astrophysical foreground from compact-binary coalescences. We propose a novel framework to detect a cosmological gravitational-wave background in the presence of binary black holes and binary neutron star signals with next-generation ground-based detectors, including Cosmic Explorer and the Einstein Telescope. Our procedure involves first removing all the individually resolved binary black hole signals by notching them out in the time-frequency domain. Then, we perform joint Bayesian inference on the individually resolved binary neutron star signals, the unresolved binary neutron star foreground, and the cosmological background. For a flat cosmological background, we find that we can claim detection at $5\,\sigma$ level when $\Omega_\mathrm{ref}\geqslant 2.7\times 10^{-12}/\sqrt{T_\mathrm{obs}/\mathrm{yr}}$, where $T_\mathrm{obs}$ is the observation time (in years), which is within a factor of $\lesssim2$ from the sensitivity reached in absence of these astrophysical foregrounds., Comment: Main text: 7 pages, 3 figures, 1 table. Supplemental material: 12 pages, 9 figures, 1 table

A crucial aspect in phase-field modeling, based on the variational formulation of brittle fracture, is the accurate representation of how the fracture surface energy is dissipated during the fracture process in the energy competition within a minimization problem. In general, the family of AT1 functionals showcases a well-defined elastic limit and narrow transition regions before crack onset, as opposed to AT2 models. On the other hand, high-order functionals provide similar accuracy as low-order ones but allow for larger mesh sizes in their discretization, remarkably reducing the computational cost. In this work, we aim to combine both these advantages and propose a novel AT1 fourth-order phase-field model for brittle fracture within an isogeometric framework, which provides a straightforward discretization of the high-order term in the crack surface density functional. For the introduced AT1 functional, we first prove a {\Gamma}-convergence result (in both the continuum and discretized isogeometric setting) based on a careful study of the optimal transition profile, which ultimately provides the explicit correction factor for the toughness and the exact size of the transition region. Fracture irreversibility is modeled by monotonicity of the damage variable and is conveniently enforced using the Projected Successive Over-Relaxation algorithm. Our numerical results indicate that the proposed fourth-order AT1 model is more accurate than the considered lower-order AT1 and AT2 models; this allows to employ larger mesh sizes, entailing a lower computational cost., Comment: 35 pages, 15 images

Next-generation ground-based gravitational wave observatories will observe mergers of intermediate-mass black holes (IMBHs) out to high redshift. Such IMBHs can form through runaway tidal encounters in the cores of dense stellar clusters. In this paper, we ask if the gravitational wave observation of a single merger event between two IMBHs, occurring in the aftermath of the coalescence of the clusters in which they formed, can be used to infer the properties of their host clusters, such as mass, redshift, and half-mass radius. We implement an astrophysically motivated analytic model for cluster evolution and IMBH growth, and we perform IMBH binary parameter estimation using a network of three next-generation detectors. We find that inferring the structural properties of clusters in this way is challenging due to model degeneracy. However, the posteriors on the cluster formation redshifts have relatively narrow peaks, and it may still be possible to infer the cluster formation history by measuring a whole population of IMBH binary merger events., Comment: 29 pages, 16 figures

Layer pruning offers a promising alternative to standard structured pruning, effectively reducing computational costs, latency, and memory footprint. While notable layer-pruning approaches aim to detect unimportant layers for removal, they often rely on single criteria that may not fully capture the complex, underlying properties of layers. We propose a novel approach that combines multiple similarity metrics into a single expressive measure of low-importance layers, called the Consensus criterion. Our technique delivers a triple-win solution: low accuracy drop, high-performance improvement, and increased robustness to adversarial attacks. With up to 78.80% FLOPs reduction and performance on par with state-of-the-art methods across different benchmarks, our approach reduces energy consumption and carbon emissions by up to 66.99% and 68.75%, respectively. Additionally, it avoids shortcut learning and improves robustness by up to 4 percentage points under various adversarial attacks. Overall, the Consensus criterion demonstrates its effectiveness in creating robust, efficient, and environmentally friendly pruned models.

In a context where the Brazilian judiciary system, the largest in the world, faces a crisis due to the slow processing of millions of cases, it becomes imperative to develop efficient methods for analyzing legal texts. We introduce uBERT, a hybrid model that combines Transformer and Recurrent Neural Network architectures to effectively handle long legal texts. Our approach processes the full text regardless of its length while maintaining reasonable computational overhead. Our experiments demonstrate that uBERT achieves superior performance compared to BERT+LSTM when overlapping input is used and is significantly faster than ULMFiT for processing long legal documents., Comment: Presented at 15th Symposium in Information and Human Language Technology (STIL) @ BRACIS'24

The spin of intermediate-mass black holes (IMBHs) growing through repeated black hole mergers in stellar clusters statistically asymptotes to zero. Putative observations of IMBHs with dimensionless spin parameter $\chi\gtrsim 0.6$ would require a phase of coherent gas accretion to spin up the black hole. We estimate the amount of gas necessary to produce a given IMBH spin. If the observed IMBH mass and spin are $M\gtrsim 1000~M_\odot$ and $\chi\gtrsim 0.6$, respectively, the IMBH must have coherently accreted at least $\sim 100~M_\odot$ of gas. In this scenario, as long as the spin is not maximal, the IMBH can only accrete at most half of its mass. Our estimates can constrain the relative contribution of accretion and mergers to the growth of IMBHs in dense stellar environments., Comment: 10 pages, 7 figures, matches published version

Determining stress and strain in a component of a fusion power plant involves defining boundary conditions for the mechanical equilibrium equations, implying the availability of a full reactor model for defining those conditions. To address this fundamental challenge of reactor design, a finite element method (FEM) model for the Mega-Ampere Spherical Tokamak Upgrade (MAST-U) fusion tokamak, operating at the Culham Campus of UKAEA, has been developed and applied to assess mechanical deformations, strain, and stress in the full tokamak structure, taken as a proxy for a fusion power plant. The model, handling 127 million finite elements using about 800 processors in parallel, illustrates the level of fidelity of structural simulations of a complex nuclear device made possible by the modern supercomputing systems. The model predicts gravitational and atmospheric pressure-induced deformations in broad agreement with observations, and enables computing the spectrum of acoustic vibrations of a tokamak, arising from mechanical disturbances like an earthquake or a plasma disruption. We introduce the notion of the density of stress to characterise the distribution of stress in the entire solid body of the tokamak, and to predict the magnitude and locations of stress concentrations. The model enables defining computational requirements for simulating a whole operating fusion power plant, and provides a digital foundation for the assessment of reactor performance as well as for specifying the relevant materials testing programme.

Radiation creep and swelling are irreversible deformation phenomena occurring in materials irradiated even at low temperatures. On the microscopic scale, energetic particles initiate collision cascades, generating and eliminating defects that then interact and coalesce in the presence of internal and external stress. We investigate how copper and tungsten swell and deform under various applied stress states in the low- and high-energy irradiation limits. Simulations show that the two metals respond in a qualitatively similar manner, in a remarkable deviation from the fundamentally different low-temperature plastic behaviour of bcc and fcc. The deviatoric part of plastic strain is particularly sensitive to applied stress, leading to anisotropic dimensional changes. At the same time, the volume change, vacancy content and dislocation density are almost insensitive to the applied stress. Low- as opposed to high-energy irradiation gives rise to greater swelling, faster creep, and higher defect content for the same dose. Simulations show that even at low temperatures, where thermal creep is absent, irradiation results in a stress-dependent irreversible anisotropic deformation of considerable magnitude, with the orientation aligned with the orientation of applied stress. To model the high dose microstructures, we develop an algorithm that at the cost of about 25% overestimation of the defect content is up to ten times faster than collision cascade simulations. The direct time integration of equations of motion of atoms in cascades is replaced by the minimisation of energy of molten spherical regions; multiple insertion of molten zones and the subsequent relaxation steps simulate the increasing radiation exposure.

We present a fully explicit dynamic formulation for geometrically exact shear-deformable beams. The starting point of this work is an existing isogeometric collocation (IGA-C) formulation which is explicit in the strict sense of the time integration algorithm, but still requires a system matrix inversion due to the use of a consistent mass matrix. Moreover, in that work, the efficiency was also limited by an iterative solution scheme needed due to the presence of a nonlinear term in the time-discretized rotational balance equation. In the present paper, we address these limitations and propose a novel fully explicit formulation able to preserve high-order accuracy in space. This is done by extending a predictor--multicorrector approach, originally proposed for standard elastodynamics, to the case of the rotational dynamics of geometrically exact beams. The procedure relies on decoupling the Neumann boundary conditions and on a rearrangement and rescaling of the mass matrix. We demonstrate that an additional gain in terms of computational cost is obtained by properly removing the angular velocity-dependent nonlinear term in the rotational balance equation without any significant loss in terms of accuracy. The high-order spatial accuracy and the improved efficiency of the proposed formulation compared to the existing one are demonstrated through some numerical experiments covering different combinations of boundary conditions.

Stochastic gravitational-wave backgrounds can be of either cosmological or astrophysical origin. The detection of an astrophysical stochastic gravitational-wave background with ground-based interferometers is expected in the near future. Perhaps even more excitingly, the detection of stochastic backgrounds of cosmological origin by future ground-based interferometers could reveal invaluable information about the early Universe. From this perspective, the astrophysical background is a {\it foreground} that can prevent the extraction of this information from the data. In this paper, we revisit a time-frequency domain notching procedure previously proposed to remove the astrophysical foreground in the context of next-generation ground-based detectors, but we consider the more realistic scenario where we remove individually detectable signals by taking into account the uncertainty in the estimation of their parameters. We find that time-frequency domain masks can still efficiently remove the astrophysical foreground and suppress it to about $5\%$ of its original level. Further removal of the foreground formed by unresolvable events (in particular, unresolvable binary neutron stars), which is about $10$ times larger than the residual foreground from realistic notching, would require detector sensitivity improvements. Therefore, the main limitation in the search for a cosmological background is the unresolvable foreground itself, and not the residual of the notching procedure., Comment: 13 pages, 6 figures

We propose an isogeometric approach to model the deformation of active thin films using layered, nonlinear, Kirchhoff Love shells. Isogeometric Collocation and Galerkin formulations are employed to discretize the electrophysiological and mechanical sub-problems, respectively, with the possibility to adopt different element and time-step sizes. Numerical tests illustrate the capabilities of the active stress based approach to effectively simulate the contraction of thin films in both quasi-static and dynamic conditions.

Astrophysical scenarios for the formation and evolution of intermediate-mass black holes (IMBHs) in the mass range $10^2 M_\odot \lesssim M \lesssim 10^6 M_\odot$ remain uncertain, but future ground-based gravitational-wave (GW) interferometers will probe the lower end of the IMBH mass range. We study the detectability of IMBH binary mergers and the measurability of their parameters with next-generation ground-based detector networks consisting of various combinations of Cosmic Explorer (CE) and Einstein Telescope (ET) interferometers. We find that, for binaries with component masses $m_{1,2}\sim 1000\,M_\odot$, an optimal 3-detector network can constrain the masses with errors $\lesssim 0.1\%$ ($\lesssim 1\%$) at $z=0.5$ ($z=2$), and the source redshift can be measured with percent-level accuracy or better at $z\lesssim 2$. The redshift of lighter binaries ($m_{1,2}\lesssim 300\,M_\odot$) can still be measured with $O(10)\%$ accuracy even at $z=10$. Binaries with $z\lesssim 0.5$ can be localized within $1\,\rm{deg}^2$ for $m_{1,2}\lesssim 1000\,M_\odot$, and within $0.1\,\rm{deg}^2$ for comparable mass systems. The sky localization is good enough that it may be possible to cross-correlate GW searches with galaxy catalogs and to search for electromagnetic counterparts to IMBH mergers. We also point out that the low-frequency sensitivity of the detectors is crucial for IMBH detection and parameter estimation. It will be interesting to use our results in conjunction with population synthesis codes to constrain astrophysical IMBH formation models., Comment: 18 pages, 11 figures. All data is available at https://zenodo.org/records/13829385

Deep neural networks have been the predominant paradigm in machine learning for solving cognitive tasks. Such models, however, are restricted by a high computational overhead, limiting their applicability and hindering advancements in the field. Extensive research demonstrated that pruning structures from these models is a straightforward approach to reducing network complexity. In this direction, most efforts focus on removing weights or filters. Studies have also been devoted to layer pruning as it promotes superior computational gains. However, layer pruning often hurts the network predictive ability (i.e., accuracy) at high compression rates. This work introduces an effective layer-pruning strategy that meets all underlying properties pursued by pruning methods. Our method estimates the relative importance of a layer using the Centered Kernel Alignment (CKA) metric, employed to measure the similarity between the representations of the unpruned model and a candidate layer for pruning. We confirm the effectiveness of our method on standard architectures and benchmarks, in which it outperforms existing layer-pruning strategies and other state-of-the-art pruning techniques. Particularly, we remove more than 75% of computation while improving predictive ability. At higher compression regimes, our method exhibits negligible accuracy drop, while other methods notably deteriorate model accuracy. Apart from these benefits, our pruned models exhibit robustness to adversarial and out-of-distribution samples., Comment: Published at International Conference on Pattern Recognition (ICPR), 2024. Oral presentation

General relativity (GR) has proven to be a highly successful theory of gravity since its inception. The theory has thrivingly passed numerous experimental tests, predominantly in weak gravity, low relative speeds, and linear regimes, but also in the strong-field and very low-speed regimes with binary pulsars. Observable gravitational waves (GWs) originate from regions of spacetime where gravity is extremely strong, making them a unique tool for testing GR, in previously inaccessible regions of large curvature, relativistic speeds, and strong gravity. Since their first detection, GWs have been extensively used to test GR, but no deviations have been found so far. Given GR's tremendous success in explaining current astronomical observations and laboratory experiments, accepting any deviation from it requires a very high level of statistical confidence and consistency of the deviation across GW sources. In this paper, we compile a comprehensive list of potential causes that can lead to a false identification of a GR violation in standard tests of GR on data from current and future ground-based GW detectors. These causes include detector noise, signal overlaps, gaps in the data, detector calibration, source model inaccuracy, missing physics in the source and in the underlying environment model, source misidentification, and mismodeling of the astrophysical population. We also provide a rough estimate of when each of these causes will become important for tests of GR for different detector sensitivities. We argue that each of these causes should be thoroughly investigated, quantified, and ruled out before claiming a GR violation in GW observations., Comment: Review article; 1 figure; 1 table; Matches published version

Next-generation gravitational wave detectors such as the Einstein Telescope and Cosmic Explorer will have increased sensitivity and observing volumes, enabling unprecedented precision in parameter estimation. However, this enhanced precision could also reveal systematic biases arising from waveform modeling, which may impact astrophysical inference. We investigate the extent of these biases over a year-long observing run with $10^5$ simulated binary black hole sources using the linear signal approximation. To establish a conservative estimate, we sample binaries from a smoothed truncated power-law population model and compute systematic parameter biases between the IMRPhenomXAS and IMRPhenomD waveform models. For sources with signal-to-noise ratios above 100, we estimate statistically significant parameter biases in $\sim 3\%-20\%$ of the events, depending on the parameter. We find that the average mismatch between waveform models required to achieve a bias of $\leq 1\sigma$ for $99\%$ of detections with signal-to-noise ratios $\geq 100$ should be $\mathcal{O}(10^{-5})$, or at least one order of magnitude better than current levels of waveform accuracy., Comment: 15 pages, 11 figures, 1 table. Matches the version published in Phys. Rev. D 109, 104043

The occurrence of high stress concentrations in reactor components is a still intractable phenomenon encountered in fusion reactor design. We observe and quantitatively model a non-linear high-dose radiation mediated microstructure evolution effect that facilitates fast stress relaxation in the most challenging low-temperature limit. In situ observations of a tensioned tungsten wire exposed to a high-energy ion beam show that internal stress of up to 2 GPa relaxes within minutes, with the extent and time-scale of relaxation accurately predicted by a parameter-free multiscale model informed by atomistic simulations. As opposed to conventional notions of radiation creep, the effect arises from the self-organisation of nanoscale crystal defects, athermally coalescing into extended polarized dislocation networks that compensate and alleviate the external stress., Comment: 10 pages, 5 figures

A major challenge in Natural Language Processing is obtaining annotated data for supervised learning. An option is the use of crowdsourcing platforms for data annotation. However, crowdsourcing introduces issues related to the annotator's experience, consistency, and biases. An alternative is to use zero-shot methods, which in turn have limitations compared to their few-shot or fully supervised counterparts. Recent advancements driven by large language models show potential, but struggle to adapt to specialized domains with severely limited data. The most common approaches therefore involve the human itself randomly annotating a set of datapoints to build initial datasets. But randomly sampling data to be annotated is often inefficient as it ignores the characteristics of the data and the specific needs of the model. The situation worsens when working with imbalanced datasets, as random sampling tends to heavily bias towards the majority classes, leading to excessive annotated data. To address these issues, this paper contributes an automatic and informed data selection architecture to build a small dataset for few-shot learning. Our proposal minimizes the quantity and maximizes diversity of data selected for human annotation, while improving model performance., Comment: Accepted at PROPOR 2024 - The 16th International Conference on Computational Processing of Portuguese

Stellar evolution predicts the existence of a mass gap for black hole remnants produced by pair-instability supernova dynamics, whose lower and upper edges are very uncertain. We study the possibility of constraining the location of the upper end of the pair-instability mass gap, which is believed to appear around ${m_\text{min}} \sim130M_\odot$, using gravitational wave observations of compact binary mergers with next-generation ground-based detectors. While high metallicity may not allow for the formation of first-generation black holes on the "far side" beyond the gap, metal-poor environments containing Population III stars could lead to such heavy black hole mergers. We show that, even in the presence of contamination from other merger channels, next-generation detectors will measure the location of the upper end of the mass gap with a relative precision close to $\Delta {m_\text{min}}/{m_\text{min}} \simeq 4\% (N_\text{det}/100 )^{-1/2}$ at 90% C.L., where $N_\text{det} $ is the number of detected mergers with both members beyond the gap. These future observations could reduce current uncertainties in nuclear and astrophysical processes controlling the location of the gap., Comment: 12 pages, 6 figures

This is a collection of papers presented by the JEM-EUSO Collaboration at the 38th International Cosmic Ray Conference (Nagoya, Japan, July 26-August 3, 2023)

Black hole spectroscopy is a clean and powerful tool to test gravity in the strong-field regime and to probe the nature of compact objects. Next-generation ground-based detectors, such as the Einstein Telescope and Cosmic Explorer, will observe thousands of binary black hole mergers with large signal-to-noise ratios, allowing for accurate measurements of the remnant black hole quasinormal mode frequencies and damping times. In previous work we developed an observable-based parametrization of the quasinormal mode spectrum of spinning black holes beyond general relativity (ParSpec). In this paper we use this parametrization to ask: can next-generation detectors detect or constrain deviations from the Kerr spectrum by stacking multiple observations of binary mergers from astrophysically motivated populations? We focus on two families of tests: (i) agnostic (null) tests, and (ii) theory-based tests, which make use of quasinormal frequency calculations in specific modified theories of gravity. We consider in particular two quadratic gravity theories (Einstein-scalar-Gauss-Bonnet and dynamical Chern-Simons gravity) and various effective field theory-based extensions of general relativity. We find that robust inference of hypothetical corrections to general relativity requires pushing the slow-rotation expansion to high orders. Even when high-order expansions are available, ringdown observations alone may not be sufficient to measure deviations from the Kerr spectrum for theories with dimensionful coupling constants. This is because the constraints are dominated by "light" black hole remnants, and only few of them have sufficiently high signal-to-noise ratio in the ringdown. Black hole spectroscopy with next-generation detectors may be able to set tight constraints on theories with dimensionless coupling, as long as we assume prior knowledge of the mass and spin of the remnant black hole., Comment: references updated

JEM--EUSO is an international program for the development of space-based Ultra-High Energy Cosmic Ray observatories. The program consists of a series of missions which are either under development or in the data analysis phase. All instruments are based on a wide-field-of-view telescope, which operates in the near-UV range, designed to detect the fluorescence light emitted by extensive air showers in the atmosphere. We describe the simulation software ESAFin the framework of the JEM--EUSO program and explain the physical assumptions used. We present here the implementation of the JEM--EUSO, POEMMA, K--EUSO, TUS, Mini--EUSO, EUSO--SPB1 and EUSO--TA configurations in ESAF. For the first time ESAF simulation outputs are compared with experimental data.

We present an efficient approach to quantify the uncertainties associated with the numerical simulations of the laser-based powder bed fusion of metals processes. Our study focuses on a thermomechanical model of an Inconel 625 cantilever beam, based on the AMBench2018-01 benchmark proposed by the National Institute of Standards and Technology (NIST). The proposed approach consists of a forward uncertainty quantification analysis of the residual strains of the cantilever beam given the uncertainty in some of the parameters of the numerical simulation, namely the powder convection coefficient and the activation temperature. The uncertainty on such parameters is modelled by a data-informed probability density function obtained by a Bayesian inversion procedure, based on the displacement experimental data provided by NIST. To overcome the computational challenges of both the Bayesian inversion and the forward uncertainty quantification analysis we employ a multi-fidelity surrogate modelling technique, specifically the multi-index stochastic collocation method. The proposed approach allows us to achieve a 33\% reduction in the uncertainties on the prediction of residual strains compared with what we would get basing the forward UQ analysis on a-priori ranges for the uncertain parameters, and in particular the mode of the probability density function of such quantities (i.e., its ``most likely value'', roughly speaking) results to be in good agreement with the experimental data provided by NIST, even though only displacement data were used for the Bayesian inversion procedure., Comment: 28 pages, 14 figures

Mini-EUSO is a wide Field-of-View (FoV, 44$^{\circ}$) telescope currently in operation from a nadia-facing UV-transparent window in the Russian Zvezda module on the International Space Station (ISS). It is the first detector of the JEM-EUSO program deployed on the ISS, launched in August 2019. The main goal of Mini-EUSO is to measure the UV emissions from the ground and atmosphere, using an orbital platform. Mini-EUSO is mainly sensitive in the 290-430 nm bandwidth. Light is focused by a system of two Fresnel lenses of 25 cm diameter each on the Photo- Detector-Module (PDM), which consists of an array of 36 Multi-Anode Photomultiplier Tubes (MAPMTs), for a total of 2304 pixels working in photon counting mode, in three different time resolutions of 2.5 ${\mu}$s, 320 ${\mu}$s, 40.96 ms operation in parallel. In the longest time scale, the data is continuously acquired to monitor the UV emission of the Earth. It is best suited for the observation of ground sources and therefore has been used for the observational campaigns of the Mini-EUSO. In this contribution, we present the assembled UV flasher, the operation of the field campaign and the analysis of the obtained data. The result is compared with the overall efficiency computed from the expectations which takes into account the atmospheric attenuation and the parameterization of different effects such as the optics efficiency, the MAPMT detection efficiency, BG3 filter transmittance and the transparency of the ISS window.

Active surveillance (AS) is a suitable management option for newly-diagnosed prostate cancer (PCa), which usually presents low to intermediate clinical risk. Patients enrolled in AS have their tumor closely monitored via longitudinal multiparametric magnetic resonance imaging (mpMRI), serum prostate-specific antigen tests, and biopsies. Hence, the patient is prescribed treatment when these tests identify progression to higher-risk PCa. However, current AS protocols rely on detecting tumor progression through direct observation according to standardized monitoring strategies. This approach limits the design of patient-specific AS plans and may lead to the late detection and treatment of tumor progression. Here, we propose to address these issues by leveraging personalized computational predictions of PCa growth. Our forecasts are obtained with a spatiotemporal biomechanistic model informed by patient-specific longitudinal mpMRI data. Our results show that our predictive technology can represent and forecast the global tumor burden for individual patients, achieving concordance correlation coefficients ranging from 0.93 to 0.99 across our cohort (n=7). Additionally, we identify a model-based biomarker of higher-risk PCa: the mean proliferation activity of the tumor (p=0.041). Using logistic regression, we construct a PCa risk classifier based on this biomarker that achieves an area under the receiver operating characteristic curve of 0.83. We further show that coupling our tumor forecasts with this PCa risk classifier enables the early identification of PCa progression to higher-risk disease by more than one year. Thus, we posit that our predictive technology constitutes a promising clinical decision-making tool to design personalized AS plans for PCa patients.

Next-generation ground-based gravitational-wave observatories such as the Einstein Telescope and Cosmic Explorer will detect $O(10^{5}-10^{6})$ signals from compact binary coalescences every year, the exact number depending on uncertainties in the binary merger rate. Several overlapping signals will be present in band at any given time, generating a confusion noise background. We study how this confusion noise affects constraints on possible deviations from general relativity induced by modified gravity and environmental effects. Confusion noise impacts only the signals that last longer in band. Even for a "golden" GW170817-like signal, the constraints broaden by a factor in the range $[10\%,40\%]$ $([70\%,110\%])$ for the fiducial (highest) value of the local binary neutron star merger rate. Our ability to test general relativity or constrain environmental effects will be limited by systematic errors, and not by confusion noise., Comment: 11 pages, 5 figures

This work presents an efficient quadrature rule for shell analysis fully integrated in CAD by means of Isogeometric Analysis (IGA). General CAD-models may consist of trimmed parts such as holes, intersections, cut-offs etc. Therefore, IGA should be able to deal with these models in order to fulfil its promise of closing the gap between design and analysis. Trimming operations violate the tensor-product structure of the used Non-Uniform Rational B-spline (NURBS) basis functions and of typical quadrature rules. Existing efficient patch-wise quadrature rules consider actual knot vectors and are determined in 1D. They are extended to further dimensions by means of a tensor-product. Therefore, they are not directly applicable to trimmed structures. The herein proposed method extends patch-wise quadrature rules to trimmed surfaces. Thereby, the number of quadrature points can be signifficantly reduced. Geometrically linear and non-linear benchmarks of plane, plate and shell structures are investigated. The results are compared to a standard trimming procedure and a good performance is observed.

We present an adaptive scheme for isogeometric phase-field modeling, to perform suitably graded hierarchical refinement and coarsening on both single- and multi-patch geometries by considering truncated hierarchical spline constructions which ensures $C^1$ continuity between patches. We apply the proposed algorithms to the Cahn-Hilliard equation, describing the time-evolving phase separation processes of immiscible fluids. We first verify the accuracy of the hierarchical spline scheme by comparing two classical indicators usually considered in phase-field modeling, for then demonstrating the effectiveness of the grading strategy in terms of accuracy per degree of freedom. A selection of numerical examples confirms the performance of the proposed scheme to simulate standard modes of phase separation using adaptive isogeometric analysis with smooth THB-spline constructions.

We implement a Physics-Informed Neural Network (PINN) for solving the two-dimensional Burgers equations. This type of model can be trained with no previous knowledge of the solution; instead, it relies on evaluating the governing equations of the system in points of the physical domain. It is also possible to use points with a known solution during training. In this paper, we compare PINNs trained with different amounts of governing equation evaluation points and known solution points. Comparing models that were trained purely with known solution points to those that have also used the governing equations, we observe an improvement in the overall observance of the underlying physics in the latter. We also investigate how changing the number of each type of point affects the resulting models differently. Finally, we argue that the addition of the governing equations during training may provide a way to improve the overall performance of the model without relying on additional data, which is especially important for situations where the number of known solution points is limited., Comment: This preprint has not undergone peer review or any post-submission improvements or corrections. The Version of Record of this contribution is published in the Lecture Notes in Computer Science book series (LNAI,volume 13654), and is available online at https://doi.org/10.1007/978-3-031-21689-3_28

Mini-EUSO (Multiwavelength Imaging New Instrument for the Extreme Universe Space Observatory) is a telescope observing the Earth from the International Space Station since 2019. The instrument employs a Fresnel-lens optical system and a focal surface composed of 36 multi-anode photomultiplier tubes, 64 channels each, for a total of 2304 channels with single photon counting sensitivity. Mini-EUSO also contains two ancillary cameras to complement measurements in the near infrared and visible ranges. The scientific objectives of the mission range from the search for extensive air showers generated by Ultra-High Energy Cosmic Rays (UHECRs) with energies above 10$^{21}$ eV, the search for nuclearites and Strange Quark Matter (SQM), up to the study of atmospheric phenomena such as Transient Luminous Events (TLEs), meteors and meteoroids. Mini-EUSO can map the night-time Earth in the near UV range (between 290-430 nm) with a spatial resolution of about 6.3 km (full field of view of 44{\deg}) and a maximum temporal resolution of 2.5 $\mu$s, observing our planet through a nadir-facing UV-transparent window in the Russian Zvezda module. The detector saves triggered transient phenomena with a sampling rate of 2.5 $\mu$s and 320 $\mu$s, as well as continuous acquisition at 40.96 ms scale. In this paper we discuss the detector response and the flat-fielding and calibration procedures. Using the 40.96 ms data, we present $\simeq$6.3 km resolution night-time Earth maps in the UV band, and report on various emissions of anthropogenic and natural origin. We measure ionospheric airglow emissions of dark moonless nights over the sea and ground, studying the effect of clouds, moonlight, and artificial (towns, boats) lights. In addition to paving the way forward for the study of long-term variations of natural and artificial light, we also estimate the observation live-time of future UHECR detectors., Comment: 49 pages, 27 figures, 1 table, published in Remote Sensing of Environment

Financial domain tasks, such as trading in market exchanges, are challenging and have long attracted researchers. The recent achievements and the consequent notoriety of Reinforcement Learning (RL) have also increased its adoption in trading tasks. RL uses a framework with well-established formal concepts, which raises its attractiveness in learning profitable trading strategies. However, RL use without due attention in the financial area can prevent new researchers from following standards or failing to adopt relevant conceptual guidelines. In this work, we embrace the seminal RL technical fundamentals, concepts, and recommendations to perform a unified, theoretically-grounded examination and comparison of previous research that could serve as a structuring guide for the field of study. A selection of twenty-nine articles was reviewed under our classification that considers RL's most common formulations and design patterns from a large volume of available studies. This classification allowed for precise inspection of the most relevant aspects regarding data input, preprocessing, state and action composition, adopted RL techniques, evaluation setups, and overall results. Our analysis approach organized around fundamental RL concepts allowed for a clear identification of current system design best practices, gaps that require further investigation, and promising research opportunities. Finally, this review attempts to promote the development of this field of study by facilitating researchers' commitment to standards adherence and helping them to avoid straying away from the RL constructs' firm ground., Comment: 38 pages

Groundwater flow modeling is commonly used to calculate groundwater heads, estimate groundwater flow paths and travel times, and provide insights into solute transport processes within an aquifer. However, the values of input parameters that drive groundwater flow models are often highly uncertain due to subsurface heterogeneity and geologic complexity in combination with lack of measurements/unreliable measurements. This uncertainty affects the accuracy and reliability of model outputs. Therefore, parameters' uncertainty must be quantified before adopting the model as an engineering tool. In this study, we model the uncertain parameters as random variables and use a Bayesian inversion approach to obtain a posterior,data-informed, probability density function (pdf) for them: in particular, the likelihood function we consider takes into account both well measurements and our prior knowledge about the extent of the springs in the domain under study. To keep the modelistic and computational complexities under control, we assume Gaussianity of the posterior pdf of the parameters. To corroborate this assumption, we run an identifiability analysis of the model: we apply the inversion procedure to several sets of synthetic data polluted by increasing levels of noise, and we determine at which levels of noise we can effectively recover the "true value" of the parameters. We then move to real well data (coming from the Ticino River basin, in northern Italy, and spanning a month in summer 2014), and use the posterior pdf of the parameters as a starting point to perform an Uncertainty Quantification analysis on groundwater travel-time distributions., Comment: First submission

The trigger logic of the Tracking Ultraviolet Setup (TUS) and Multiwavelength Imaging New Instrument for the Extreme Universe Space Observatory (Mini-EUSO) space-based projects of the Joint Experiment Missions - EUSO (JEM-EUSO) program is summarized. The performance results on the search for ultra-high energy cosmic rays are presented.

The effects of neutron irradiation on materials are often interpreted in terms of atomic recoils, initiated by neutron impacts and producing crystal lattice defects. In addition, there is a remarkable two-step process, strongly pronounced in the medium-weight and heavy elements. This process involves the generation of energetic {\gamma} photons in nonelastic collisions of neutrons with atomic nuclei, achieved via capture and inelastic reactions. Subsequently, high-energy electrons are excited through the scattering of {\gamma} photons by the atomic electrons. We derive and validate equations enabling a fast and robust evaluation of photon and electron fluxes produced by the neutrons in the bulk of materials. The two-step n-{\gamma}-e scattering creates a nonequilibrium dynamically fluctuating steady-state population of high-energy electrons, with the spectra of photon and electron energies extending well into the mega-electron-volt range. This stimulates vacancy diffusion through electron-triggered atomic recoils, primarily involving vacancy-impurity dissociation, even if thermal activation is ineffective. Tungsten converts the energy of fusion or fission neutrons into a flux of {\gamma} radiation at the conversion efficiency approaching 99%, with implications for structural materials, superconductors, and insulators, as well as phenomena like corrosion, and helium and hydrogen isotope retention.

The present paper aims at applying uncertainty quantification methodologies to process simulations of powder bed fusion of metal. In particular, for a part-scale thermomechanical model of an Inconel 625 super-alloy beam, we study the uncertainties of three process parameters, namely the activation temperature, the powder convection coefficient and the gas convection coefficient. First, we perform a variance-based global sensitivity analysis to study how each uncertain parameter contributes to the variability of the beam displacements. The results allow us to conclude that the gas convection coefficient has little impact and can therefore be fixed to a constant value for subsequent studies. Then, we conduct an inverse uncertainty quantification analysis, based on a Bayesian approach on synthetic displacements data, to quantify the uncertainties of the two remaining parameters, namely the activation temperature and the powder convection coefficient. Finally, we use the results of the inverse uncertainty quantification analysis to perform a data-informed forward uncertainty quantification analysis of the residual strains. Crucially, we make use of surrogate models based on sparse grids to keep to a minimum the computational burden of every step of the uncertainty quantification analysis. The proposed uncertainty quantification workflow allows us to substantially ease the typical trial-and-error approach used to calibrate power bed fusion part-scale models, and to greatly reduce uncertainties on the numerical prediction of the residual strains. In particular, we demonstrate the possibility of using displacement measurements to obtain a data-informed probability density function of the residual strains, a quantity much more complex to measure than displacements.

Parkinson's disease (PD) is the second most common neurodegenerative disorder worldwide, yet there is no disease-modifying therapy up to this date. The biological complexity underlying PD hampers the investigation of the principal contributors to its pathogenesis. In this context, mechanistic models grounded in molecular-level knowledge provide virtual labs to uncover the primary events triggering PD onset and progression and suggest promising therapeutic targets. Multiple modeling efforts in PD research have focused on the pathological role of $\alpha$-synuclein ($\alpha$syn), a presynaptic protein that emerges from the intricate molecular network as a crucial driver of neurodegeneration. Here, we collect the advances in mathematical modeling of $\alpha$syn homeostasis, focusing on aggregation and degradation pathways, and discussing potential modeling improvements and possible implications in PD therapeutic strategy design.

Next-generation terrestrial gravitational-wave observatories will detect $\mathcal{O}(10^{5})$ signals from compact binary coalescences every year. These signals can last for several hours in the detectors' sensitivity band and they will be affected by multiple unresolved sources contributing to a confusion-noise background in the data. Using an information-matrix formalism, we estimate the impact of the confusion noise power spectral density in broadening the parameter estimates of a GW170817-like event. If our estimate of the confusion noise power spectral density is neglected, we find that masses, spins, and distance are biased in about half of our simulations under realistic circumstances. The sky localization, while still precise, can be biased in up to $80\%$ of our simulations, potentially posing a problem in follow-up searches for electromagnetic counterparts., Comment: 6 pages, 2 figures

Stochastic gravitational-wave backgrounds (SGWBs) derive from the superposition of numerous individually unresolved gravitational-wave (GW) signals. Detecting SGWBs provides us with invaluable information about astrophysics, cosmology, and fundamental physics. In this paper, we study SGWBs from binary black-hole (BBH) and binary neutron-star (BNS) coalescences in a network of next-generation ground-based GW observatories (Cosmic Explorer and Einstein Telescope) and determine how well they can be measured; this then limits how well we can observe other subdominant astrophysical and cosmological SGWBs. We simulate all-Universe populations of BBHs and BNSs and calculate the corresponding SGWBs, which consist of a superposition of (i) undetected signals, and (ii) the residual background from imperfect removal of resolved sources. The sum of the two components sets the sensitivity for observing other SGWBs. Our results show that, even with next-generation observatories, the residual background is large and limits the sensitivity to other SGWBs. The main contributions to the residual background arise from uncertainties in inferring the coalescence phase and luminosity distance of the detected signals. Alternative approaches to signal subtraction would need to be explored to minimize the BBH and BNS foreground in order to observe SGWBs from other subdominant astrophysical and cosmological sources., Comment: 19 pages, 10 figures, matches the published version

The stochastic gravitational-wave backgrounds (SGWBs) for current detectors are dominated by binary black-hole (BBH) and binary neutron-star (BNS) coalescences. The sensitivity of current networks of gravitational-wave (GW) detectors allows only a small fraction of BBHs and BNSs to be resolved and subtracted, but previous work indicated that the situation should significantly improve with next-generation (XG) observatories. We revisit these conclusions by taking into account waveform-modeling uncertainties, updated astrophysical models, and (crucially) the full set of parameters that must be estimated to remove the resolved sources. Compared to previous studies, we find that the residual background from BBHs and BNSs is large even with XG detector networks. New data analysis methods will thus be required to observe the SGWB from cosmic supernovae or contributions from early-Universe phenomena like cosmic strings, stiff post-inflation fluids, or axion inflation., Comment: 6 pages, 2 figures