Parameter Space Research Articles

Direct detection of light dark matter charged under a Lμ−Lτ symmetry

A possible extension of the Standard Model able to explain the recent measurement of the anomalous magnetic moment of the muon consists in adding a gauged U(1)Lμ−Lτ symmetry. If the dark matter particle is charged under this symmetry, then the kinetic mixing between the new gauge boson and the photon induces dark matter-electron interactions. We derive direct detection constraints on light dark matter charged under a U(1)Lμ−Lτ symmetry with electron recoil experiments and explore prospects with XLZD and OSCURA to close in the parameter space able to explain simultaneously the recent measurement on the anomalous magnetic moment of the muon and the observed relic density of dark matter. We further discuss the spin-dependent scattering contribution arising in this model, which was ignored previously in the literature. Published by the American Physical Society 2024

Rapid Prototyping for Accelerated Establishment of Film Processing‐Performance Relationships in Silicon Phthalocyanine OFETs

AbstractUnderstanding the complex relationships underlying the performance of organic electronic devices, such as organic field‐effect transistors (OFETs), requires researchers to navigate a multi‐dimensional parameter space that includes material design, solution formulation, fabrication parameters, and device geometry. Herein, a recently developed materials acceleration platform is demonstrated, named the RoboMapper, to perform direct on‐chip fabrication of OFETs by ultrasonic meniscus printing using silicon phthalocyanine (SiPc) derivatives as the semiconductor. OFETs using bis(tri‐n‐butylsilyl oxide) SiPc ((3BS)2‐SiPc) exhibited the best device performance characterized by the highest electron field‐effect mobility (µe). Through optical microscopy and grazing‐incidence wide‐angle X‐ray scattering (GIWAXS), the favorable performance of (3BS)2‐SiPc is attributed to the specific film morphology and molecular packing achieved with optimal print conditions. Investigating the impact of deposition parameters reveals the crucial role of solvent evaporation rate and print speed in achieving high‐quality film formation. Overall, optimal fabrication conditions for (3BS)2‐SiPc devices include slow print speeds and fast evaporating solutions achieved by using a mixture of co‐solvents and an elevated substrate temperature. The results of this work reveal distinct relationships between deposition conditions, film properties, and device performance for each SiPc derivative and emphasize the necessity of high throughput experimentation to comprehensively understand process‐performance relationships in organic semiconductors.

Electromagnetic scattering by curved surfaces and calculation of radiation force: Lattice Boltzmann simulations

This study aims to investigate the effectiveness of the lattice Boltzmann method (LBM) in studying the scattering of electromagnetic waves by curved and complex surfaces. The computation of Maxwell’s equations is done by solving for a pair of distribution functions, which evolve based on a two-step process of collision and streaming. LBM bypasses the need for expansion via vector spherical harmonics and thus is amenable to scatterers with complex geometries. We have employed LBM to compute the scattering width and radiation force for perfect electrically conducting and dielectric cylinders of circular and elliptical cross sections. Both smooth and corrugated surfaces are studied, and the results are compared against known analytical and numerical solutions from other methods. To ensure the broad applicability of the method, we have explored a wide range of parameter space—the dielectric constant and particle size to the wavelength ratio spanning Rayleigh, Mie, and geometrical optics regimes. Our simulations have successfully reproduced well-known analytical and numerical solutions, confirming the accuracy and reliability of the LBM for scattering calculations by complex-shaped objects.

SN1987A constraints to BSM models with extra neutral bosons near the trapping regime: U(1)Lμ−Lτ model as an illustrative example

New physics beyond the Standard Model (BSM) with an extra neutral boson can be constrained from the observation of SN1987A, since productions of this neutral boson in a supernova (SN) could accelerate the SN cooling and potentially lead to a period of the neutrino burst incompatible with the observation. The constraint to the model is formulated by the condition LNB≤3×1052 erg/s according to Raffelt with LNB the luminosity of BSM neutral boson. Computing the above luminosity in the large coupling case, the so-called trapping regime, is nontrivial since the luminosity is a competition between the large production rate and the efficient absorption or decay rate of the neutral boson. We illustrate such a subtlety using U(1)Lμ−Lτ model as an example where the Z′ luminosity, LZ′, from the neutrinosphere is calculated. We calculate Z′ production, absorption, and decay rates through pair-coalescence, semi-Compton, loop-bremsstrahlung from proton-neutron scattering, and their inverse processes in a benchmark SN simulation with muons. We point out that, as the coupling constant gZ′ increases, LZ′ shall be approaching to a constant plateau value for a given mZ′ instead of monotonically decreasing down to zero as obtained in the previous literature. We demonstrate that this plateau phenomenon can be understood by physical arguments and justified by numerical calculations. With a different result on LZ′ from the previous one, we discuss impacts on the constraints to U(1)Lμ−Lτ parameter space by SN1987A. The implication of our result to the similar constraint on a generic BSM model with an extra neutral boson is also discussed. Published by the American Physical Society 2024

Gravitationally misaligned ultralight dark matter and implications for neutron stars

We examine the possibility that dark matter (DM) may be an ultralight scalar that was misaligned via nonminimal coupling to gravity, in the early Universe. For a certain regime of scalar masses, gravitational effects in neutron stars could place interesting bounds on the viable parameter space of the model, even in the absence of nongravitational interactions between DM and ordinary matter. Published by the American Physical Society 2024

The role of data embedding in equivariant quantum convolutional neural networks

AbstractGeometric deep learning refers to the scenario in which the symmetries of a dataset are used to constrain the parameter space of a neural network and thus, improve their trainability and generalization. Recently, this idea has been incorporated into the field of quantum machine learning, which has given rise to equivariant quantum neural networks (EQNNs). In this work, we investigate the role of classical-to-quantum embedding on the performance of equivariant quantum convolutional neural networks (EQCNNs) for the classification of images. We discuss the connection between the data embedding method and the resulting representation of a symmetry group and analyze how changing representation affects the expressibility of an EQCNN. We numerically compare the classification accuracy of EQCNNs with three different basis-permuted amplitude embeddings to the one obtained from a non-equivariant quantum convolutional neural network (QCNN). Our results show a clear dependence of classification accuracy on the underlying embedding, especially for initial training iterations. The improvement in classification accuracy of EQCNN over non-equivariant QCNN may be present or absent depending on the particular embedding and dataset used. The noisy simulation using simple noise models shows that certain EQCNNs are more robust to noise than non-equivariant QCNNs. It is expected that the results of this work can be useful to the community for a better understanding of the importance of data embedding choice in the context of geometric quantum machine learning.

Fast parameter optimization for high-fidelity crystal plasticity simulation using active learning

Crystal plasticity (CP) simulation is a powerful tool for studying and understanding the mechanical behavior of materials. A critical aspect of this method is the accurate determination of CP parameters, which ensures that the constitutive model accurately represents the real deformation behavior of a material, especially in high-fidelity simulations. However, identifying these parameters poses a significant challenge due to the high computational cost and the difficulty of finding optimal solutions within a vast and complex parameter space. To address these challenges, we propose a fast search strategy that leverages active learning (AL) and experimental data to accelerate the optimization of CP parameters. Using the Al-Cu eutectic materials as a case study, we introduced a quantitative index, C ecp, to measure the consistency between simulated and experimental stress-strain curves. We demonstrated that Gaussian process regression (GPR) serves as the most appropriate surrogate model for relating CP parameters and C ecp based on our dataset. After only six iterations guided by AL, the optimal CP parameters were successfully identified, resulting in a high-fidelity CP model for analyzing the mechanical behavior of Al-Cu eutectic materials. The machine learning-enhanced strategy is far superior to traditional methods in terms of both efficiency and accuracy. It advances our understanding of the macro-micro relationships of materials and accelerates the material design process.

Probing stochastic ultralight dark matter with space-based gravitational-wave interferometers

Ultralight particles are theoretically well-motivated dark matter candidates. In the vicinity of the Solar System, these ultralight particles can be described as a superposition of plane waves, resulting in a stochastic field with sizable amplitude fluctuations on scales determined by the velocity dispersion of dark matter. In this work, we systematically investigate the sensitivity of space-based gravitational-wave interferometers to the stochastic ultralight dark matter field within the frequentist framework. We derive the projected sensitivity of a single detector using the time-delay interferometry. Our results show that space-based gravitational-wave interferometers have the potential to probe unconstrained regions in parameter space and improve the current limit on coupling strengths. Furthermore, we explore the sensitivity of a detector network and investigate the optimal configuration for ultralight dark matter detection. We introduce the overlap reduction function for ultralight dark matter, which quantifies the degree of correlation between the signals observed by different detectors. We find that the configuration, where the signals observed by two detectors are uncorrelated, is the optimal choice for ultralight dark matter detection due to a smaller chance of missing signals. This contrasts with the detection of stochastic gravitational-wave background, where the correlated configuration is preferred. Our results may provide useful insights for potential joint observations involving space-based gravitational-wave detectors like LISA and Taiji, as well as other ultralight dark matter detection networks operating in the coherence limit. Published by the American Physical Society 2024

Asymmetric limit cycles within Lorenz chaos induce anomalous mobility for a memory-driven active particle

On applying a small bias force, nonequilibrium systems may respond in paradoxical ways such as with giant negative mobility (GNM)—a large net drift opposite to the applied bias, or giant positive mobility (GPM)—an anomalously large drift in the same direction as the applied bias. Such behaviors have been extensively studied in idealized models of externally driven passive inertial particles. Here, we consider a minimal model of a memory-driven active particle inspired from experiments with walking and superwalking droplets, whose equation of motion maps to the celebrated Lorenz system. By adding a small bias force to this Lorenz model for the active particle, we uncover a dynamical mechanism for simultaneous emergence of GNM and GPM in the parameter space. Within the chaotic sea of the parameter space, a symmetric pair of coexisting asymmetric limit cycles separate and migrate under applied bias force, resulting in anomalous transport behaviors that are sensitive to the active particle's memory. Our work highlights a general dynamical mechanism for the emergence of anomalous transport behaviors for active particles described by low-dimensional nonlinear models. Published by the American Physical Society 2024

Dark sector tunneling field potentials for a dark big bang

All of the significant evidence for dark matter observed thus far has been through its gravitational interactions. After 40 years of direct detection experiments, the parameter space for weakly interacting massive particles (WIMPs) as dark matter candidates is rapidly approaching the neutrino floor. Moreover, there have been no signs of WIMPs in any particle production experiments so far. In this light, we consider a dark sector that is strongly decoupled from the visible sector, interacting exclusively through gravity. In this model, proposed by Freese and Winkler [Dark matter and gravitational waves from a dark big bang, ], dark matter can be produced through a first-order phase transition in the dark sector dubbed “the dark big bang.” We fully determine the allowed region of parameter space for the tunneling potential that leads to the realization of a dark big bang and is consistent with all experimental bounds available. Published by the American Physical Society 2024

Unfolding Electrolyzer Characteristics to Reveal Solar-to-Chemical Efficiency Potential: Rapid Analysis Method Bridging Electrochemistry and Photovoltaics.

Development of photovoltaic-electrochemical (PV-EC) systems for energy storage and industry decarbonization requires multidisciplinary collaborative efforts of different research groups from both photovoltaic and electrochemical research communities. Consequently, the evaluation of the solar-to-chemical or solar-to-fuel efficiency of a new electrolyzer (EC) as a part of a PV-EC system is a time-consuming task that is challenging in a routine optimization loop. To address this issue, a new rapid assessment method is proposed. This method employs power balance requirements to unfold the input EC characteristics into the parameter space of PV-EC systems. The system parameters, composed with the EC output characteristics, yield the solar-to-chemical efficiency attainable by the electrolyzer in combination with any PV device under any irradiance at any relative PV-to-EC scaling and any mode of power coupling. This comprehensive overview is achieved via a mathematically simple conversion of the EC characteristics in any spreadsheet software. The method, designed to streamline the development and minimize the efforts of both the photovoltaic and electrochemical communities, is demonstrated via the analysis of CO2-reduction electrolyzer characteristics and verified with dedicated PV-EC experiments.

Integrating deep learning and discrete cosine transform for surface waves full waveform inversion

Summary Accurate estimations of near-surface S-wave velocity (Vs) models hold particular significance in geological and engineering investigations. On the one hand, the popular Multichannel Analysis of Surface Waves (MASW) is limited to the 1D and the plane wave assumptions. On the other hand, the more advanced and computationally expensive full-waveform inversion (FWI) approach is often solved within a deterministic framework that hampers an accurate uncertainty assessment and makes the final predictions heavily reliant on the starting model. Here we combine deep learning with Discrete Cosine Transforms (DCT) to solve the FWI of surface waves and to efficiently estimate the inversion uncertainties. Our neural network approach effectively learns the inverse non-linear mapping between DCT-compressed seismograms and DCT-compressed S-velocity models. The incorporation of DCT into the deep learning framework provides several advantages: it notably reduces parameter space dimensionality and alleviates the ill-conditioning of the problem. Additionally, it decreases the complexity of the network architecture and the computational cost for the training phase compared to training in the full domain. A Monte Carlo simulation is also used to propagate the uncertainties from the data to the model space. We first test the implemented inversion method on synthetic data to showcase the generalization capabilities of the trained network and to explore the implications of incorrect noise assumptions in the recorded seismograms and inaccurate wavelet estimations. Further, we demonstrate the applicability of the implemented method to field data. In this case, available borehole information is used to validate our predictions. In both the synthetic and field applications, the predictions provided by the proposed method are compared with those of a deterministic FWI and the outcomes of a network trained in the full data and model spaces. Our experiments confirm that the implemented deep-learning inversion efficiently and successfully solves the FWI problem and yields more accurate and stable results than a network trained without the DCT compression. This opens the possibility to efficiently train a neural network that provides accurate instantaneous predictions of Vs near-surface models and related uncertainties.

Probabilistic projections of the Amery Ice Shelf catchment, Antarctica, under conditions of high ice-shelf basal melt

Abstract. Antarctica's Lambert Glacier drains about one-sixth of the ice from the East Antarctic Ice Sheet and is considered stable due to the strong buttressing provided by the Amery Ice Shelf. While previous projections of the sea-level contribution from this sector of the ice sheet have predicted significant mass loss only with near-complete removal of the ice shelf, the ocean warming necessary for this was deemed unlikely. Recent climate projections through 2300 indicate that sufficient ocean warming is a distinct possibility after 2100. This work explores the impact of parametric uncertainty on projections of the response of the Lambert–Amery system (hereafter “the Amery sector”) to abrupt ocean warming through Bayesian calibration of a perturbed-parameter ice-sheet model ensemble. We address the computational cost of uncertainty quantification for ice-sheet model projections via statistical emulation, which employs surrogate models for fast and inexpensive parameter space exploration while retaining critical features of the high-fidelity simulations. To this end, we build Gaussian process (GP) emulators from simulations of the Amery sector at a medium resolution (4–20 km mesh) using the Model for Prediction Across Scales (MPAS)-Albany Land Ice (MALI) model. We consider six input parameters that control basal friction, ice stiffness, calving, and ice-shelf basal melting. From these, we generate 200 perturbed input parameter initializations using space filling Sobol sampling. For our end-to-end probabilistic modeling workflow, we first train emulators on the simulation ensemble and then calibrate the input parameters using observations of the mass balance, grounding line movement, and calving front movement with priors assigned via expert knowledge. Next, we use MALI to project a subset of simulations to 2300 using ocean and atmosphere forcings from a climate model for both low- and high-greenhouse-gas-emission scenarios. From these simulation outputs, we build multivariate emulators by combining GP regression with principal component dimension reduction to emulate multivariate sea-level contribution time series data from the MALI simulations. We then use these emulators to propagate uncertainty from model input parameters to predictions of glacier mass loss through 2300, demonstrating that the calibrated posterior distributions have both greater mass loss and reduced variance compared to the uncalibrated prior distributions. Parametric uncertainty is large enough through about 2130 that the two projections under different emission scenarios are indistinguishable from one another. However, after rapid ocean warming in the first half of the 22nd century, the projections become statistically distinct within decades. Overall, this study demonstrates an efficient Bayesian calibration and uncertainty propagation workflow for ice-sheet model projections and identifies the potential for large sea-level rise contributions from the Amery sector of the Antarctic Ice Sheet after 2100 under high-greenhouse-gas-emission scenarios.

Discontinuity-Induced Dynamics in the Conductance-Based Adaptive Exponential Integrate-and-Fire Model.

In this article, we present a computational study of the Conductance-Based Adaptive Exponential (CAdEx) integrate-and-fire neuronal model, focusing on its multiple timescale nature, and on how it shapes its main dynamical regimes. In particular, we show that the spiking and so-called delayed bursting regimes of the model are triggered by discontinuity-induced bifurcations that are directly related to the multiple-timescale aspect of the model, and are mediated by canard solutions. By means of a numerical bifurcation analysis of the model, using the software package COCO, we can precisely describe the mechanisms behind these dynamical scenarios. Spike-increment transitions are revealed. These transitions are accompanied by a fold and a period-doubling bifurcation, and are organised in parameter space along an isola periodic solutions with resets. Finally, we also unveil the presence of a homoclinic bifurcation terminating a canard explosion which, together with the presence of resets, organises the delayed bursting regime of the model.

On the non-dissipative orbital evolution of a binary system comprising non-compact components with misaligned spin and orbital angular momenta

Abstract In this Paper we determine the non-dissipative tidal evolution of a close binary system with an arbitrary eccentricity in which the spin angular momenta of both components is misaligned with the orbital angular momentum. We focus on the situation where the orbital angular momentum dominates the spin angular momenta and so remains at small inclination to the conserved total angular momentum. Torques arising from rotational distortion and tidal distortion taking account of Coriolis forces are included. This extends the previous work of Ivanov & Papaloizou relaxing the limitation resulting from the assumption that one of the components is compact and has zero spin angular momentum. Unlike the above study, the evolution of spin-orbit inclination angles is driven by both types of torque. We develop a simple analytic theory describing the evolution of orbital angles and compare it with direct numerical simulations. We find that the tidal torque prevails near ’critical curves’ in parameter space where the time-averaged apsidal precession rate is close to zero. In the limit of small spin, these curves exist only for systems that have at least one component with retrograde rotation. As in our previous work, we find solutions close to these curves for which the apsidal angle librates. As noted there, this could result in oscillation between prograde and retrograde states. We consider the application of our approach to systems with parameters similar to those of the misaligned binary DI Herc.

Predictions for the abundance and clustering of Hα emitting galaxies

Abstract We predict the surface density and clustering bias of Hα emitting galaxies for the Euclid and Nancy Grace Roman Space Telescope redshift surveys using a new calibration of the GALFORM galaxy formation model. We generate 3000 GALFORM models to train an ensemble of deep learning algorithms to create an emulator. We then use this emulator in a Markov Chain Monte Carlo (MCMC) parameter search of an eleven-dimensional parameter space, to find a best-fitting model to a calibration dataset that includes local luminosity function data, and, for the first time, higher redshift data, namely the number counts of Hα emitters. We discover tensions when exploring fits for the observational data when applying a heuristic weighting scheme in the MCMC framework. We find improved fits to the Hα number counts while maintaining appropriate predictions for the local universe luminosity function. For a flux limited Euclid-like survey to a depth of 2 × 10−16 erg−1 s−1 cm−2 for sources in the redshift range 0.9 < z < 1.8, we estimate 2962-4331 Hα emission-line sources deg−2. For a Nancy Grace Roman survey, with a flux limit of 1 × 10−16 erg−1 s−1 cm−2 and a redshift range 1.0 < z < 2.0, we predict 6786-10322 Hα emission-line sources deg−2.

Generating X-Ray Transit Profiles with Batman

We present an adaptation of the exoplanet transit model code batman, in order to permit the generation of X-ray transits. Our underlying extended coronal model assumes an isothermal plasma that is radially symmetric. While this ignores the effect of bright, active regions, observations of transits in X-rays will require averaging across multiple epochs of data for the foreseeable future, significantly reducing the importance of more complex modeling. Our publicly available code successfully generates the predicted W-shaped transit profile in X-rays due to the optically thin nature of the emission, which concentrates the expected observational emission around the limb of the photospheric stellar disk. We provide some examples based on the best known X-ray transit target, HD 189733 b, and examine the effect of varying the planet size, coronal temperature, and impact parameter on the resulting transit profile. We also derived scaling relationships for how the overall transit detectability is affected by changing these parameters. Over most of the parameter space, we find that the detectability scales linearly with the cross-sectional area of the planet in X-rays. The relationship with increasing coronal temperature is less fixed, but averages out to a power law with slope −1/4 except when the impact parameter is high. Indeed, varying impact parameter has little effect on detectability at all until it approaches unity.

Millisecond pulsars phenomenology under the light of graph theory

We compute and apply the minimum spanning tree (MST) of the binary millisecond pulsar population, and discuss aspects of the known phenomenology of these systems in this context. We find that the MST effectively separates different classes of spider pulsars ---eclipsing radio pulsars in tight binary systems with a companion of either sim 0.1–0.8 M$_ (redbacks) or $ M$_ in mass (black widows)--- into distinct branches. The MST also separates black widows (BWs) in globular clusters from those found in the field and groups other pulsar classes of interest, including transitional millisecond pulsars (tMSPs). Using the MST and a defined ranking for similarity, we identify possible candidates likely to belong to these pulsar classes. In particular, based on this approach, we propose the BW classification of J1300+1240, J1630+3550, J1317-0157, J1221-0633, J1627+3219, J1737-0314A, and J1701-3006F, discuss that of J1908+2105, and analyze J1723-2837, J1431-4715, and J1902-5105 as possible transitional systems. We introduce an algorithm that quickly locates where new pulsars fall within the MST and use this to examine the positions of the tMSP IGR J18245-2452 (PSR J1824-2452I), the tMSP candidate 3FGL J1544.6-1125, and the accreting millisecond X-ray pulsar SAX J1808.4-3658. Assessing the positions of these sources in the MST ---assuming a range for their unknown variables (e.g., the spin period derivative of PSR J1824-2452I)---, we can effectively narrow down the parameter space necessary for searching for and determining key pulsar parameters through targeted observations.

Top quark production through flavor violating neutral currents within the 2HDM type-III

In this paper, we explore the parameter space for processes with fermionic flavor violation within the Two Higgs Doublets Model (2HDM) type III, processes of Flavor Changing Neutral Currents (FCNCs) are present at Leading Order (LO) as dynamics beyond the Standard Model (SM) are included. We analyze the possible Yukawa couplings beyond the SM involving the quark top, as the experimental signatures would be a clear signal to conclude about the model. The processes we analyzed are same sign top pair production at the [Formula: see text] collisions, and single top quark production in [Formula: see text] collisions, through Deep Inelastic Scattering (DIS). To examine the scenarios that could exhibit such exotic events, we perform a scan in the parameter space to determine the observation possibilities for FCNC. We show the results considering the model parameters, coming from modified Yukawa interactions, [Formula: see text] of the order one. In addition, we explore the order of magnitude of possible single top production via [Formula: see text], at the proposed muon–proton collider. We found scattering values for these exotic processes of up to [Formula: see text].

Complex scalar dark matter in a new gauged U(1) symmetry with kinetic and direct mixings

We propose a scalar dark matter model featuring a hidden gauge symmetry, denoted as U(1)X, with two complex scalars, Φ and S. In this framework, Φ spontaneously breaks the U(1)X gauge symmetry while S serves as a viable dark matter candidate. Particularly, the kinetic and direct mixings between the U(1)X and U(1)Y gauge groups provide a portal between dark matter and the Standard Model particles. These mixings offer a plausible explanation for the W boson mass anomaly observed by the Collider Detector at Fermilab Collaboration. We study the comprehensive phenomenological constraints of this model from colliders and dark matter detection experiments, including Z′ searches at the LHC, the 125 GeV Higgs boson measurements, the relic density of dark matter, and the indirect detection of dark matter annihilation. By randomly scanning the parameter space, we find that the regions where mZ′≳4750 GeV and mZ′≲4750 GeV for gx close to 1 remain viable and can be tested by future experiments. Published by the American Physical Society 2024