arXiv Physics @arxiv_physics@qoto.org

We derive a relativistic field equation for the trace of the metric perturbation beyond the weak field approximation to the Einstein field equations. The dynamics is governed by a massive Klein-Gordon equation on curved space-time, where the effective mass of the field is associated with the material and the dark energy content via the cosmological term. We solve the equation in the case of a Schwarzschild black hole and show that it can be cast into an effective Schrödinger form with an effective geometric potential which binds the zero angular momentum states. The non-zero angular momentum states experience a positive potential peak before the event horizon pointing to gravitational waves scattering. Black holes scatter gravitational waves and thus we provide an unambiguous testable prediction of black hole existence. The Newtonian limit for this equation points to the possibility of reflecting gravitational waves at interfaces with sharp density boundary, thus opening up gravitational wave propulsion physics. We discuss this type of propulsion in the light of Newton's third law of Mechanics. Compelling questions such as the existence of quanta of this field which may account for the dark matter content are also addressed.

We deduce the quantum mechanical prediction of $-{\bf a}\cdot{\bf b}$ for the singlet spin state employing local measurement functions following Bell's approach. Our derivation is corroborated through a computational simulation conducted via the Mathematica programming environment.

This work presents a novel approach to distribute orbitals into subspaces within electron-pairing-based natural orbital functionals (NOFs). This approach modifies the coupling between weakly and strongly occupied orbitals by applying an alternating orbital sorting strategy. In contrast to the previous orbital sorting that enforced electron pairing within subspaces of contiguous orbitals, the new approach provides greater flexibility, enabling a calculation scheme where the size of the subspaces can be gradually expanded. As a consequence, one can start using subspaces of only one weakly occupied orbital (perfect pairing) and progressively enlarge their size by incorporating more weakly occupied orbitals (extended pairing) up to the maximum size allowed by the basis set. In this way, the alternate orbital sorting allows solving first a simpler problem with small subspaces and leverage its orbital solution for the more intensive problem with larger subspaces, thereby reducing the overall computational cost and improving convergence, as we observed in the DoNOF program. The efficiency provided by the new sorting approach has been validated through benchmark calculations in H2O, H2O2, and NH3. In particular, we compared three strategies: i) solving directly the calculation with the largest subspaces (one-shot strategy), as was usually done before this work, ii) starting with perfect pairing and stepwise increasing the number of orbitals in the subspaces one by one until reaching the maximum size (incremental strategy), and iii) starting with perfect pairing and transitioning directly to the maximum subspace size (two-step strategy). Our results show that the two-step approach emerges as the most effective strategy, achieving the lowest computational cost while maintaining high accuracy.

Hurricane track forecasting remains a significant challenge due to the complex interactions between the atmosphere, land, and ocean. Although AI-based numerical weather prediction models, such as Google Graphcast operation, have significantly improved hurricane track forecasts, they currently function as atmosphere-only models, omitting critical land and ocean interactions. To investigate the impact of land feedback, we conducted independent simulations using the physics-based Hurricane WRF experimental model to assess how soil moisture variations influence storm trajectories. Our results show that land surface conditions significantly alter storm paths, demonstrating the importance of land-atmosphere coupling in hurricane prediction. Although recent advances have introduced AI-based atmosphere-ocean coupled models, a fully functional AI-driven atmosphere-land-ocean model does not yet exist. Our findings suggest that AI-NWP models could be further improved by incorporating land surface interactions, improving both forecast accuracy and explainability. Developing a fully coupled AI-based weather model would mark a critical step toward more reliable and physically consistent hurricane forecasting, with direct applications for disaster preparedness and risk mitigation.

Decisions on public health interventions to control infectious disease are often informed by computational models. Interpreting the predicted outcomes of a public health decision requires not only high-quality modelling, but also an ethical framework for assessing the benefits and harms associated with different options. The design and specification of ethical frameworks matured independently of computational modelling, so many values recognised as important for ethical decision-making are missing from computational models. We demonstrate a proof-of-concept approach to incorporate multiple public health values into the evaluation of a simple computational model for vaccination against a pathogen such as SARS-CoV-2. By examining a bounded space of alternative prioritisations of values (outcome equity and aggregate benefit) we identify value trade-offs, where the outcomes of optimal strategies differ depending on the ethical framework. This work demonstrates an approach to incorporating diverse values into decision criteria used to evaluate outcomes of models of infectious disease interventions.

In this work, we build a model to combine the mass generated from the Higgs mechanism and that from the dynamical chiral symmetry breaking mechanism. This is motivated by the fermion mass hierarchy that the neutrino mass is smaller than the charged lepton mass and the charged lepton mass is smaller than the quark mass. Since they participate different interactions, it is natural to conjecture that interactions contribute to the fermion mass. This conjecture could be explained via the dynamical chiral symmetry breaking mechanism with assuming the existence of a non-perturbative regime. In addition, this model predicts a different ratio of fermion Yukawa coupling to the Higgs self coupling, which could be verified in the near future.

Ortiz and Ibarra-Castor (2024) have presented a "Generalized redshift formula" taking account of only energy conservation considerations. Contrary to their claim, we emphasize to invoke both energy and momentum considerations in order to deduce all three types of redshift (Doppler, gravitational and cosmological). We formulate our views on the three physical effects in a consistent manner in addition to addressing the lack of relevant references in Ref.(Ortizand Ibarra-Castor, 2024).

We present an active method for robust exoatmospheric positioning using space-based intensity modulated direct detection (IMDD) LiDAR with a spacecraft-based transmitter and receiver and a constellation of orbital reflectors. We discuss the precision of the method, the power requirements, the advantages of such a method over existing GPS/GNSS systems, and potential applications.

A new statistical definition for the mean turbulent boundary layer thickness is introduced, based on the identification of the point where streamwise velocity skewness changes sign in the outermost region of the boundary layer. This definition is motivated by the phenomenology of streamwise velocity fluctuations near the turbulent/non-turbulent interface, whose local characteristics are shown to be universal for turbulent boundary layers under low freestream turbulence conditions (e.g., with or without pressure gradients, surface roughness, etc.). This approach provides a turbulent boundary layer thickness that is consistent with previous definitions, such as those based on Reynolds shear stress or `composite' mean velocity profiles, while being independent of arbitrary thresholds and applicable to past single-point measurements. Two methods are proposed for estimating the turbulent boundary layer thickness using this definition: one based on simple linear interpolation and the other on fitting a generalised Fourier model to the outer skewness profile. The robustness and limitations of these methods are demonstrated through analysis of several published experimental and numerical datasets, which cover a range of canonical and non-canonical turbulent boundary layers. These datasets vary in wall-normal resolution and measurement noise, particularly in the critical turbulent/non-turbulent interface region.

Muon colliders offer a compelling opportunity to explore the TeV scale and conduct precision tests of the Standard Model, all within a relatively compact geographical footprint. This paper introduces a new detector concept, MAIA (Muon Accelerator Instrumented Apparatus), optimized for $\sqrt{s}=10$ TeV $μμ$ collisions. The detector features an all-silicon tracker immersed in a 5T solenoid field. High-granularity silicon-tungsten and iron-scintillator calorimeters surrounding the solenoid capture high-energy electronic and hadronic showers, respectively, and support particle-flow reconstruction. The outermost subsystem comprises an air-gap muon spectrometer, which enables standalone track reconstruction for high-momentum muons. The performance of the MAIA detector is evaluated in terms of differential particle reconstruction efficiencies and resolutions. Beam-induced background (BIB) simulations generated in FLUKA are overlaid with single particle gun samples to assess detector reconstruction capabilities under realistic experimental conditions. Even with BIB, reconstruction efficiencies exceed 95% for energetic tracks, photons, and neutrons in the central region of the detector. This paper outlines promising avenues of future work, including forward region optimization and opportunities for enhanced flavor/boosted object tagging, and addresses the technological assumptions needed to achieve the desired detector performance.

Wakefield wavelengths associated with solid-state plasmas greatly limit the accelerating length. An alternative approach employs 2D carbon-based nanomaterials, like graphene or carbon nanotubes (CNTs), configured into structured targets. These nanostructures are designed with voids or low-density regions to effectively reduce the overall plasma density. This reduction enables the use of longer-wavelength lasers and also extends the plasma wavelength and the acceleration length. In this study, we present, to our knowledge, the first numerical demonstration of electron acceleration via self-injection into a wakefield bubble driven by an infrared laser pulse in structured CNT targets, similar to the behavior observed in gaseous plasmas for LWFA in the nonlinear (or bubble) regime. Using the PIConGPU code, bundles of CNTs are modeled in a 3D geometry as 25 nm-thick carbon tubes with an initial density of $10^{22}$ cm$^{-3}$. The carbon plasma is ionized by a three-cycle, 800 nm wavelength laser pulse with a peak intensity of $10^{21}$ W cm$^{-2}$, achieving an effective plasma density of $10^{20}$ cm$^{-3}$. The same laser also drives the wakefield bubble, responsible for the electron self-injection and acceleration. Simulation results indicate that fs-long electron bunches with hundreds of pC charge can be self-injected and accelerated at gradients exceeding 1~TeV$/$m. Both charge and accelerating gradient figures are unprecedented when compared with LWFA in gaseous plasma.

In this paper an extensive database of SPARC H-modes confinement predictions has been provided, to assess its variability with respect to few input assumptions. The simulations have been performed within the ASTRA framework, using the quasi-linear model TGLF SAT2, including electromagnetic effects, for the core transport, and a neural network trained on EPED simulations to predict the pedestal height and width self-consistently. The database has been developed starting from two SPARC H-mode discharges (12.2 T, i.e. Primary Reference Discharge or PRD, and 8 T, i.e. reduced field) and permuting 4 input parameters (W concentration, DT mixture concentration, temperature ratio at top of pedestal and deviation of pedestal pressure from the EPED prediction), to perform a sensitivity study. For the PRD a scan of auxiliary input power (ion cyclotron heating) has been performed up to 25MW, to keep highly radiative plasmas above the LH power threshold as predicted by Martin and Schmidtmayr power scalings. A scan of pedestal density has then been performed for both PRD and 8T databases. ptop/pEPED and Ti/Te at top of pedestal showed the biggest impact on the fusion gain. Significant variation is observed across the database, highlighting the importance of sensitivity studies. Below a certain W concentration, the 12T database shows that Q > 5 is consistently achieved for full-field H-modes with 11 MW of auxiliary power, and values of Q > 2 are assured when increasing the input power to keep the plasma in H-mode. The 8T database demonstrates that SPARC can access a Q > 1 operational window with low W concentration, making it a potentially interesting scenario for obtaining breakeven conditions.

The most sensitive direct neutrino mass searches today are based on measurement of the endpoint of the beta spectrum of tritium to infer limits on the mass of the unobserved recoiling neutrino. To avoid the smearing associated with the distribution of molecular final states in the T-He molecule, the next generation of these experiments will need to employ atomic (T) rather than molecular (T$_{2}$) tritium sources. Following production, atomic T can be trapped in gravitational and / or magnetic bottles for beta spectrum experiments, if and only if it can first be cooled to millikelvin temperatures. Accomplishing this cooling presents substantial technological challenges. The Project 8 collaboration is developing a technique based on magnetic evaporative cooling along a beamline (MECB) for the purpose of cooling T to feed a magneto-gravitational trap that also serves as a cyclotron radiation emission spectroscope. Initial tests of the approach are planned in a pathfinder apparatus using atomic Li. This paper presents a method for analyzing the dynamics of the MECB technique, and applies these calculations to the design of systems for cooling and slowing of atomic Li and T. A scheme is outlined that could provide a current of T at the millikelvin temperatures required for the Project 8 neutrino mass search.

The development of robust, real-time optical methods for the detection and tracking of particles in complex multiple scattering media is a problem of practical importance in a number of fields, including environmental monitoring, air quality assessment, and homeland security. In this paper we develop a holographic, optical-theorem-based method for the detection of particles embedded in complex environments where wavefronts undergo strong multiple scattering. The proposed methodology is adaptive, to the complex medium, which is integral to the sensing apparatus, and thereby enables constant monitoring, through progressive adaptation. This feature, along with the holographic nature of the developed approach, also renders as a by-product real-time imaging capabilities for the continuous tracking of particles traversing the region under surveillance. In addition, the proposed methodology also enables the development of customized sensors that leverage a controllable complex multiple scattering medium and the derived holographic sensing technology for real-time particle detection and tracking. We demonstrate, with the help of realistic computer simulations, holographic techniques capable of detecting and tracking small particles under such conditions and analyze the role of multiple scattering in enhancing the detection performance. Potential applications include the identification of aerosolized biological substances, which is critical for biosecurity and the rapid detection of hazardous airborne particles in confined or densely populated areas.

The work addresses the issue of precision measurement of the concentration of molecular gas components in the atmosphere and their isotopic composition. The primary focus is investigating the composition of gas mixtures and determining the isotopic ratios of individual gas components using proposed local and remote methods for detecting the content of methane, carbon dioxide, and water vapour molecules based on laser spectroscopy in the infrared range.

Due to limited possibilities of experimental investigations for non-equilibrium gas flows, numerical results are of highest interest. Although the well-established Direct Simulation Monte Carlo (DSMC) method achieves highly accurate solutions, the computational requirements increase excessively for lower Knudsen regimes. Computationally more efficient simulations can be achieved with stochastic continuum-based methods using either the Bhatnagar-Gross-Krook (BGK) or the Fokker-Planck (FP) approximations where, instead of particle collisions, particle relaxation processes are considered. This paper explains the implementation of different stochastic BGK and FP methods in the open-source particle code PICLas for multi-species molecular gas flows. For verification, the results of different test cases are compared.

Climate change significantly impacts public health, driving the emergence and spread of epidemics. Climate health models are essential for assessing and predicting disease outbreaks influenced by climatic variables like temperature and precipitation. For instance, dengue and malaria correlate with temperature changes, while cholera is linked to precipitation anomalies. Advances in AI-enabled weather prediction (AI-NWP) have improved forecasting, but integrating climate models with health systems is hindered by the lack of comprehensive, granular health datasets. This study introduces EpiClim: India's Epidemic-Climate Dataset, the first weekly district-wise dataset for major epidemics in India from 2009 to the present, sourced from the Integrated Disease Surveillance Programme (IDSP). The dataset, covering diseases like dengue, malaria, and acute-diarrheal disease, bridges the gap between climate and health data, enabling the integration of climate forecasts with epidemic prediction models. This work lays the foundation for coupling predictive climate health models with weather and climate models, advancing efforts to mitigate climate-induced public health crises.

The coronavirus pandemic corresponds to a serious global health crisis which not only changed the way people used to live but also how people behaved in their daily lives. Information from social and behavioural sciences can help in modifying human behaviour to comply with the recommendations of health officials, as the pandemic requires large-scale behaviour change and puts significant mental stress on individuals. The aim of this paper is to examine the changes in human behaviour brought about by the COVID-19 pandemic, which has caused a global health crisis and altered the way people live and interact. The collection of data has been done through online mode and the behaviour of the people is observed, and the results were finally analysed using the Analytical Hierarchy Process (AHP) which is a multi-criteria decision-making method to rank the factors that had the greatest impact on the changes in human behaviour. During the study, parameters taken under consideration were the ones which were most likely to affect the human behaviour as an impact of COVID-19 lockdown on health, relationship with family and friends, overall lifestyle, online education and work from home, screen time etc. The paper explains each criterion and how it affected human behaviour the most.

We analytically solve Poisson's equation for the magnetic scalar potential generated by a magnetized prism and determine a closed-form solution for the magnetic scalar potential given only in terms of arctan and natural logarithmic functions. We furthermore show that this can be written as a demagnetization vector, containing all the geometric information of the prism, multiplied with the magnetization, analogous to the way demagnetization tensors are written. We validate the derived analytical expression for the magnetic scalar potential by comparing with a finite element simulation and show that these agree perfectly. We finally extend the concept of the demagnetization tensor, which contains the geometric information for the source generating the potential, to gravitational objects.