Search Results (566)

We formulate within Supergravity a model of induced-gravity inflation, excellently consistent with ACT DR6, inspired by the Palatini gravity. The inflaton belongs in the decomposition of a conjugate pair of Higgs superfields which lead to the spontaneous breaking of a $U{\left(1\right)}_{B-L}$ symmetry at a scale close to the range (0.145–8.35)×10${}^{16}\mathrm{GeV}$. The inflaton field is canonically normalized thanks to one real and shift-symmetric contribution into the Kähler potential. It also includes two separate holomorphic and antiholomorphic logarithmic terms, the argument of which can be interpreted as the coupling of the inflaton to the Ricci scalar. The attainment of inflation allows for subplanckian inflaton values and energy scales below the cut-off scale of the corresponding effective theory. Embedding the model in a $B-L$ extension of the MSSM we show how the $\mu$ parameter can be generated and non-thermal leptogenesis can be successfully realized. An outcome of our scheme is split SUSY with gravitino mass in the range (40–60) $\mathrm{PeV}$, which is consistent with the results of LHC on the Higgs boson mass. Full article

We investigate finite-size effects in the T–$\mu$ phase diagram of magnetized quark matter within the framework of a nonlocal extension of the Polyakov–Nambu–Jona-Lasinio (PNJL) model. Finite-size corrections are incorporated through the multiple reflection expansion (MRE) formalism, which describes a spherical quark droplet of radius R and modifies the density of states by including surface and curvature contributions. We consider two-flavor quark matter at finite temperature and chemical potential in the presence of a uniform magnetic field with strengths ranging from $eB=0$ to 1 ${\mathrm{GeV}}^2$, and droplet radii from $R=3$ fm to the bulk limit. The nonlocal PNJL (nlPNJL) model naturally reproduces both magnetic catalysis at low temperatures and inverse magnetic catalysis near the chiral transition, in agreement with lattice QCD results. We analyze the chiral condensate, the traced Polyakov loop, the normalized quark condensate, and the corresponding susceptibilities. We find that finite-size effects do not modify the overall structure of the phase diagram, and that the coincidence of the chiral restoration and deconfinement transitions persists for all magnetic field strengths and system sizes explored, within the present implementation in which finite-size corrections are restricted to the fermionic sector. However, the critical end point (CEP) is notably shifted as a function of both magnetic field strength and system size: It moves toward higher chemical potentials and lower temperatures as system size decreases, an effect that is significantly amplified by strong magnetic fields. Our results have potential implications for the physics of phase conversion in compact stars and for the interpretation of relativistic heavy-ion collision experiments. Full article

Nearby ($z<0.1$) TeV-detected, high-energy-peaked BL Lacertae objects (HBLs) are among the most prominent extragalactic sources of the highest-energy photons, sometimes detected at energies of ∼10 TeV or beyond. These objects show a strong and complex flux variability, with strong flares and exceptional outbursts, as well as very rapid and large-amplitude TeV-band variations on timescales down to a few minutes during such instances. The higher-energy component of broadband spectral energy distribution (SED) is stretched over the MeV–TeV domain and, generally peaking beyond 100 GeV, has a controversial origin, and different emission scenarios (one- or multi-zone synchrotron self-Compton, hadronic cascades, etc.) are proposed. This paper presents a review of the TeV-band timing and spectral results obtained in the framework of different observational campaigns for nearby HBLs, their implications for different emission scenarios, and basic results from the corresponding SED modelings. Finally, the prospect of filling the observational gaps above some threshold energy by means of the planned projects for the dedicated $\gamma$-ray observations and, consequently, solving the different persisting problems related to the innermost structure, particle acceleration, and emission mechanisms are also presented. Full article

We study the projected sensitivities of the Jiangmen Underground Neutrino Observatory (JUNO) and the Deep Underground Neutrino Experiment (DUNE) to cosmic-ray-produced dark mesons in a confining dark sector with a leptophobic vector portal. Using the same atmospheric dark meson flux framework as in our previous JUNO study, which includes proton bremsstrahlung, Standard Model meson decays, and Drell–Yan production followed by dark hadronization described by a modified Quark Combination Model, we perform a controlled comparison between JUNO and DUNE within a common source-side setup. Our results indicate that JUNO achieves stronger overall sensitivity across most of the parameter space, primarily because its inclusive event-level visible-energy criterion efficiently retains soft elastic recoils. In contrast, DUNE demonstrates systematically larger visible effective cross sections in the deep-inelastic scattering (DIS) channel, where energetic final states readily exceed its particle-level hadronic thresholds. Moreover, kinematic hardening of elastic recoils at heavier mediator masses ($m_{Z^{\prime }}\gtrsim 1$ GeV) and higher incident energies ($E_{K_D}\gtrsim 1$ GeV) further enhances DUNE’s elastic acceptance. Nevertheless, over most of the benchmark parameter space considered here, JUNO yields a larger total signal rate because the incident dark meson flux peaks sharply at low energies, favoring the soft elastic regime. Consequently, this interplay between flux distribution and detector thresholds causes the sensitivity gap between JUNO and DUNE to narrow significantly in the heavy-mediator regime. Full article

Accurate extreme rainfall representation is critical for resilient hydrological design and sustainable water management in tropical regions. This study evaluates the GPM IMERG product across three diverse watersheds in East Java (Welang, Kedak, and Grindulu) using Extreme Value Theory (EVT). By employing Generalized Extreme Value (GEV) and Peaks Over Threshold (POT) methods, the research assesses the reliability of satellite estimates in characterizing the extreme events that safeguard community security and infrastructure longevity. Results indicate that while GPM IMERG excels at monthly scales, it lacks the daily precision required for effective flash flood mitigation, particularly in small basins. Crucially, GEV analysis reveals a structural mismatch: ground observations exhibit heavy-tailed (Fréchet) distributions, while GPM IMERG follows bounded (Weibull) distributions. Consequently, the satellite product underestimates high-magnitude events at long return periods, the exact events that define the design limits of adaptive hydraulic structures. Complementary POT analysis identifies scale-dependent biases across catchments. These findings suggest that while GPM IMERG is robust for regional monitoring, it requires distribution-specific bias correction to support disaster-resilient engineering. Addressing these gaps is essential for achieving climate-responsive sustainable development in data-scarce regions. Full article

Wind energy is a cornerstone of the global transition to renewable and sustainable energy systems. However, the same meteorological processes that generate this clean energy can also produce extreme wind events that threaten the structural integrity and operational reliability of wind turbines and power grids. Therefore, accurately predicting extreme wind speeds is a critical link between promoting clean energy and ensuring infrastructure resilience. Traditional models often struggle to capture the multimodal characteristics of extreme wind speeds under complex meteorological conditions due to fixed distribution assumptions or unstable training of mixture models, leading to estimation biases that undermine planning reliability and may result in catastrophic turbine failures or overly conservative designs. To address these issues—particularly weight imbalance and overfitting–this study proposes an enhanced regularized extreme value mixture model (ERDC-EVMM). This method integrates elastic network regularization and Kullback–Leibler divergence constraints within a Mixture of Experts framework, and employs K-means initialization and momentum-based training to enhance convergence stability. Validated using daily extreme wind speed sequences from coastal and inland wind farms, the model outperforms standard GEV and mixture models in terms of goodness-of-fit, percentile accuracy, and return period estimates, while achieving a convergence speed that is more than 30% faster (82 iterations). By balancing accuracy and training stability, the ERDC-EVMM model provides a reliable statistical tool for extreme wind speed forecasting, supporting the safe expansion of wind energy infrastructure and the design of climate-resilient communities. Full article

In this work, we present a detailed comparison of the SuSAv2 (SuperScaling Approach version 2) and RDWIA (Relativistic Distorted-Wave Impulse Approximation) models with measurements of charged-current neutrino-induced single-pion production from different experiments (T2K, MINERvA and MiniBooNE), studying the differences between the two theoretical descriptions. The neutrino energy range in these experiments spans from hundreds of MeV to roughly 20 GeV, and the nuclear targets are mainly composed of ¹²C. The SuSAv2 model uses the single-nucleon inelastic structure functions from the ANL-Osaka DCC model, which allows for a separation of pion production channels, distinguishing between the ${\pi }^{+}$, ${\pi }^{-}$ and ${\pi }^0$ final states. In the RDWIA approach, the Hybrid model developed by the Ghent group is used for the description of the boson–pion–nucleon vertex. Full article

We propose a sequential design method aiming at the estimation of an extreme quantile based on a sample of binary data corresponding to peaks over a given threshold. This study is motivated by an industrial challenge in material reliability and consists of estimating a failure quantile from trials whose outcomes are reduced to indicators of whether the specimen has failed at the tested stress levels. The proposed approach relies on a splitting strategy that decomposes the target extreme probability into a product of higher-order conditional probabilities, enabling a progressive exploration of the tail of the distribution through sampling under truncated laws. We consider GEV and Weibull models for the underlying distribution, and the sequential estimation of their parameters is carried out using an enhanced maximum likelihood procedure specifically adapted to binary data, addressing the substantial uncertainty inherent to such limited information. Full article

Intensity–Duration–Frequency (IDF) curves are the cornerstone of hydraulic infrastructure design, yet standard methodologies often fail to account for the complex dependence structure of rainfall characteristics and the non-stationary effects of climate change. This study develops a robust Regional Copula Framework for the Kastoria Lake basin, Greece, utilizing sub-hourly rainfall records from four meteorological stations (2007–2024). We employ a forensic data quality control process to pool 277 independent storm events. Unlike traditional approaches, our analysis demonstrates that the Generalized Extreme Value (GEV) distribution (ξ = 0.348) significantly outperforms the standard Lognormal distribution in modeling heavy-tailed rainfall intensities. The dependence between storm duration and intensity was found to be consistently negative (τ = −0.35), a structure best captured by the Rotated Gumbel (90°) copula, which physically reflects the region’s convective storm dynamics. Trend analysis revealed a statistically significant decrease in peak intensity (τ = −0.14) coupled with an increase in storm duration (τ = 0.22), a hydro-climatic shift that contrasts with increasing intensity trends reported in the wider Balkan region. These findings suggest a regime transition from flash-flood dominance to volume-critical events, necessitating updated design criteria that integrate both multivariate dependence and local climatic non-stationarity. Full article

We investigate whether short-range nucleon–nucleon correlations (NN-SRC) and cluster configurations in nuclei can be explored by studying spectator neutrons produced in high-energy nucleus–nucleus collisions. In particular, we propose to measure the multiplicity distributions of forward spectator neutrons in symmetric ¹²C–¹²C and ⁴⁰Ca–⁴⁰Ca collisions at $\sqrt{s_{\mathrm{NN}}}=11$ GeV with the Spin Physics Detector (SPD) at the NICA facility. To assess this method, we simulate the production of spectator nucleons in these reactions using the Abrasion–Ablation Monte Carlo for Colliders model with MST clustering (AAMCC-MST). Short-range nucleon–nucleon correlations inside ¹²C and ⁴⁰Ca are implemented via a Monte Carlo rejection sampling procedure. Our results indicate that spectator production exhibits only a weak dependence on the specific features of NN-SRC. We also observe that including $\alpha$-cluster configurations in ¹²C leads to a reduction of the average multiplicity of spectator neutrons as a function of collision centrality. Full article

Rainfall is a significant input for several engineering designs such as hydraulic structures, culverts, bridges and ducts, rainfall water sewer, and highway drainage system. The detailed statistical analysis of extreme daily rainfall of each arid environment’s region is essential to estimate the relevant input value for designing and analyzing engineering structures and agricultural planning. This paper aims to assess the best-fitting distribution to estimate the design of rainfall depth (XT) and maximum rainfall values for different return periods (2, 10, 25, 50, 100, and 150). This study used extreme daily rainfall historical data collected in period of 1970–2020, collected from four rainfall gauge stations nearby the Wadi Al-Aqiq that are selected for analysis; they are Al Faqir (J109), Umm Al Birak (J112), Madinah Munawara (M001), and Bir Al Mashi (M103). The methodology approved in this paper examined four frequency distributions, namely: GEV (Generalised Extreme Value), Gumbel, Weibull, and Pearson type III to identify the most suitable and extreme storm design depth corresponding to different return periods. The results demonstrate that GEV and Pearson Type 3 produce higher extremes values, while the Weibull method is commonly suggested in the HYFRAN-PLUS MODEL (DSS) for criterion suitability. The findings for the 100-year storm design demonstrate that extreme values generated by the Hyfran-Plus model are higher than the decision support system (DSS). All (DSS) comparative values are less than the maximum historical data from 1970–2020, except the Al Faqir station (DSS), which has a value of 79.6 mm that exceeds the historical maximum of 71 mm. This study will provide advantageous information about the study area for water resources planners, farmers, and urban engineers to assess water availability and create storage. Full article

Extreme sea levels along the Mexican coasts pose an increasing risk to coastal infrastructure and communities, particularly under the combined influence of tropical cyclones and ongoing sea-level rise. This study analyzes tide-gauge records from the Mexican Pacific and Gulf of Mexico–Caribbean coasts to characterize the statistical behavior and seasonal modulation of extreme sea-level residuals. Astronomical tides were removed through harmonic analysis to isolate the meteorological residual associated with storm-driven processes. Extreme events were evaluated using complementary extreme-value frameworks, including Generalized Extreme Value (GEV) distributions applied to monthly maxima and a Peaks-Over-Threshold (POT) approach applied to the continuous residual series with temporal declustering and Generalized Pareto Distribution (GPD) fitting. While both approaches consistently capture regional patterns, the POT–GPD framework is adopted as the primary basis for return-level estimation due to its explicit representation of event-scale extremes. The results reveal marked regional variability. Pacific stations exhibit bounded or near-Gumbel behavior (ξ ≈ −0.30 to −0.02) and a strong seasonal concentration of extremes during the tropical cyclone season. In contrast, Gulf of Mexico–Caribbean stations display higher absolute extremes and a broader seasonal footprint, with Veracruz showing a tendency toward heavier-tailed behavior (ξ ≈ 0.13). Return levels for a 25-year return period range from approximately 0.85–0.95 m in the Pacific to about 1.7 m in Veracruz. Longer return periods (e.g., 100 years) exceed 2.2 m in Veracruz but are associated with substantial uncertainty due to record-length limitations. The analysis of ENSO variability indicates that ENSO acts primarily as a secondary modulator of background sea-level variability rather than a deterministic driver of extreme events, with the largest anomalies typically associated with tropical cyclone activity. Overall, the results demonstrate that extreme sea levels along the Mexican coasts are governed by region-specific forcing and tail behavior requiring localized extreme-value modeling strategies. The proposed framework provides a robust and reproducible baseline for coastal hazard assessment and supports the integration of sea-level rise into future risk and design analyses. Full article

River water quality assessment has traditionally been conducted using univariate or threshold-based approaches; however, the exploration of extremes assessment under bivariate water quality variables has been limited by many studies. Understanding the compound extremes of low river discharge (Q) and elevated river water temperatures (RWTs) resulting from climatic variability is essential for effective water quality management and protection of the river. This study investigates the joint behaviour of RWTs and Q in six Indian rivers: Kaveri, Mahi, Sabarmati, Vardha, Bhadra, and Yamuna. The Weibull-3P and Generalised Extreme Value (GEV-3P) distributions best fit for Q and RWTs, respectively. The adequacy of eighteen different parametric copula classes was evaluated. The Gaussian copula provided the best fit for the Vardha River, the Frank copula for Bhadra, and the BB8 copula for the Yamuna River. The evaluation of joint return periods (RPs) and conditional distributions has identified notable spatial variability in compound hydrological and thermal extreme hazards. The semi-arid Vardha River showed the shortest RPs for simultaneous low Q and high RWTs, indicating a greater likelihood of combined extremes. Conversely, the monsoon-fed Bhadra River displayed moderate hazard levels, while the Himalayan-fed Yamuna River had the longest joint RPs and the lowest conditional probabilities. This suggests that simultaneous extreme drought and heat events are less likely in the Yamuna basin, although significant risks remain for less severe thresholds. Full article

Experimentally, the $1^{+}$ state $X(3872)$ was first discovered, and subsequently, its partner state $Y(4260)$, with the same quark content $(c\overline{q}q\overline{c})$ and quantum number $1^{-}$ was also observed. Inspired by this pattern, we systematically investigate the newly discovered $1^{+}$ state $T_{cc}$ and its possible $1^{-}$ partner, the $T_{D{D}_1}$ system with the same quark content $(c\overline{q}c\overline{q})$. Within the framework of the chiral quark model, we perform a comprehensive study of the bound and resonance states of $T_{D{D}_1}$ using the Gaussian expansion method (GEM). Two quark configurations, the molecular structure and the diquark structure, are considered in our calculations. Our results indicate the existence of a shallow bound state dominated by the $D{D}_1^{*}$ channel, which is analogous to the experimentally observed $T_{cc}$, as well as two compact resonant states with narrow widths around 4.5 GeV. To avoid the influence of model parameters on the results, we additionally fitted a new set of parameters and obtained consistent conclusions. According to our calculation results, although the color-octet and diquark configurations have relatively high energies, the channel-coupling effects induced by them play a crucial role in the formation of these stable states. We strongly encourage experimental efforts to search for the stable states predicted in the $T_{D{D}_1}$ system. Full article

The increasing frequency of disasters caused by landslides, mainly due to climate change leading to more intense extreme events, requires reliable predictive models for risk mitigation. Italy, in particular, is a country at high risk of landslides, but the lack of an updated catalogue of landslide activation dates poses a significant challenge for defining reliable activation thresholds. This study develops a methodology for mapping landslide susceptibility based on events in a pilot area of central Italy, integrating a database of landslides with known activation dates with predisposing and triggering parameters. Two statistical techniques were compared to assess their predictive performance in discriminating landslide from non-landslide conditions during extreme precipitation events. A comparison between binary logistic regression (BLR) and decision trees (QUEST) revealed the clear superiority of the BLR model, which achieved excellent predictive accuracy (AUC = 0.913). The model identified clay-rich lithology, gentle slopes (0–16°) and maximum daily precipitation as the most significant controlling factors. This result led to the generation of three derivative products: a susceptibility map, a hazard map for an extreme precipitation scenario with a 100-year return period, and a spatially distributed map of activation thresholds. This threshold map quantifies the intensity of precipitation required to exceed a critical probability of landslide initiation (p > 0.7) at any point in the territory. The susceptibility map highlights critical areas within the study area, while the hazard map also includes the return period of the event. The threshold map is a direct and operational tool for early warning systems, transforming a statistical model into a guide for real-time risk management. The study area serves as a pilot area that could allow this methodology to be replicated. With the integration of real-time meteorological data, it could function as a real-time warning system. The proposed framework therefore provides a directly actionable tool for civil protection agencies, land-use planning authorities, and emergency managers, enabling location-specific rainfall alert thresholds to be issued rather than a single regional value, with the potential to reduce both false alarms and missed warnings. Full article