Large Parameter Space Research Articles

Internal structuring of gallium arsenide using short laser pulses.

Laser writing inside semiconductors attracts attention as a possible route for three-dimensional integration in advanced micro technologies. In this context, gallium arsenide (GaAs) is a material for which the best conditions for laser internal modification (LIM) have not been established yet. We address this question by using laser pulses at a fixed wavelength of 1550-nm. A large parameter space is investigated including the response to the applied pulse energy, pulse duration (from femtosecond to nanosecond) and the focusing conditions. We report that well-defined and reproducible internal modifications are achievable with tightly focused nanosecond pulses. The measured writing thresholds are systematically compared to those obtained in silicon (Si), a more extensively studied material. In comparison to Si, we also observe that GaAs is more prone to filamentation effects affecting the modification responses. The reported specific observations for LIM of GaAs should facilitate the future process developments for applications in electronics or photonics.

Robust parameter estimation from pulsar timing data

ABSTRACTRecently, global pulsar timing arrays have released results from searching for a nano-Hertz gravitational wave background signal. Although there has not been any definite evidence of the presence of such a signal in residuals of pulsar timing data yet, with more and improved data in future, a statistically significant detection is expected to be made. Stochastic algorithms are used to sample a very large parameter space to infer results from data. In this paper, we attempt to rule out effects arising from the stochasticity of the sampler in the inference process. We compare different configurations of nested samplers and the more commonly used markov chain monte carlo method to sample the pulsar timing array parameter space and account for times taken by the different samplers on same data. Although we obtain consistent results on parameters from different sampling algorithms, we propose two different samplers for robustness checks on data in the future to account for cross-checks between sampling methods as well as realistic run-times.

A stochastic variance-reduced coordinate descent algorithm for learning sparse Bayesian network from discrete high-dimensional data

This paper addresses the problem of learning a sparse structure Bayesian network from high-dimensional discrete data. Compared to continuous Bayesian networks, learning a discrete Bayesian network is a challenging problem due to the large parameter space. Although many approaches have been developed for learning continuous Bayesian networks, few approaches have been proposed for the discrete ones. In this paper, we address learning Bayesian networks as an optimization problem and propose a score function which guarantees the learnt structure to be a sparse directed acyclic graph. Besides, we implement a block-wised stochastic coordinate descent algorithm to optimize the score function. Specifically, we use a variance reducing method in our optimization algorithm to make the algorithm work efficiently for high-dimensional data. The proposed approach is applied to synthetic data from well-known benchmark networks. The quality, scalability, and robustness of the constructed network are measured. Compared to some competitive approaches, the results reveal that our algorithm outperforms some of the well-known proposed methods.

1D Sine-Map-Coupling-Logistic-Map for 3D model encryption

With the rise of technologies of VR technology, AR technology, and 3D printing, the application of 3D models has become more and more extensive. The data of the 3D model is the floating point and has a unique storage format, and the traditional 2D image encryption algorithms are unsuitable for 3D models. Therefore, based on 1D Sine-Map-Coupling-Logistic-Map (1D-SMCLM), a 3D model encryption algorithm is designed in this paper. The 1D-SMCLM is a new chaotic system with large parameter space and good chaotic characteristics. The keystream generated by the 1D-SMCLM has good randomness and is very suitable for cryptographic systems. In the new encryption algorithm (SMCLM-3ME), the vertices of the 3D models are divided into integer and decimal vertices. The integer part is encrypted by the strategy of simultaneous scrambling and diffusion. The 3D ciphertext model is obtained by combining the integer and fractional parts. Experimental results show that the SMCLM-IE exhibits excellent performance.

A Novel Hyperchaotic 2D-SFCF with Simple Structure and Its Application in Image Encryption.

In this paper, a novel image encryption algorithm is proposed based on hyperchaotic two-dimensional sin-fractional-cos-fractional (2D-SFCF), called sin-fractional-cos-fractional image-encryption (SFCF-IE). The 2D-SFCF is constructed from two one-dimensional cosine fractional (1-DCFs), and it has a more complex chaotic behavior with a larger parameter space than one-dimensional chaotic systems. Compared with the two-dimensional (2D) chaotic system, the 2D-SFCF has a simple structure, and the parameter space in the chaotic state is continuous, which is beneficial to generating the keystream in the cryptosystem. Therefore, in the novel image encryption algorithm, we use the 2D-SFCF to generate the keystream of the cryptosystem. The encryption algorithm is a process of scrambling and diffusion. Different from common diffusion methods, the diffusion starting position of the SFCF-IE is randomly generated, enhancing the algorithm’s security. Simulation experiments show that the image encrypted by this algorithm has better distribution characteristics and can resist common attack methods.

A mechanobiological computer optimization framework to design scaffolds to enhance bone regeneration.

The treatment of large bone defects is a clinical challenge. 3D printed scaffolds are a promising treatment option for such critical-size defects. However, the design of scaffolds to treat such defects is challenging due to the large number of variables impacting bone regeneration; material stiffness, architecture or equivalent scaffold stiffness—due it specific architecture—have all been demonstrated to impact cell behavior and regeneration outcome. Computer design optimization is a powerful tool to find optimal design solutions within a large parameter space for given anatomical constraints. Following this approach, scaffold structures have been optimized to avoid mechanical failure while providing beneficial mechanical stimulation for bone formation within the scaffold pores immediately after implantation. However, due to the dynamics of the bone regeneration process, the mechanical conditions do change from immediately after surgery throughout healing, thus influencing the regeneration process. Therefore, we propose a computer framework to optimize scaffold designs that allows to promote the final bone regeneration outcome. The framework combines a previously developed and validated mechanobiological bone regeneration computer model, a surrogate model for bone healing outcome and an optimization algorithm to optimize scaffold design based on the level of regenerated bone volume. The capability of the framework is verified by optimization of a cylindrical scaffold for the treatment of a critical-size tibia defect, using a clinically relevant large animal model. The combined framework allowed to predict the long-term healing outcome. Such novel approach opens up new opportunities for sustainable strategies in scaffold designs of bone regeneration.

CMB constraints on monodromy inflation at strong coupling

We carry out a thorough numerical examination of field theory monodromy inflation at strong coupling. We perform an MCMC analysis using a Gaussian likelihood, fitting multiparameter models using CMB constraints on the spectral index and the tensor to scalar ratio. We show that models with uniquely positive Wilson coefficients are ruled out. If there are coefficients that can take on both signs, there can be a cancellation of terms that flattens the potentials and allows one to satisfy current data, and forecasts with strong constraints on the tensor to scalar ratio. Models of field theory monodromy are naturally enhanced to include a mechanism for canceling off radiative corrections to vacuum energy, via vacuum energy sequestering (VES). Although they include a much larger parameter space, we find that a similar numerical examination yields no significant change in the Bayesian evidence for VES enhanced models, with naturalness considerations making them more attractive from a theoretical perspective.

Optimization of Acoustic Channels to Minimize Scattered Pressure Fields

Acoustic and elastic metamaterials have shown potential for many different applications in wave manipulation and structural isolation. Minimization of a scattered wave field has been identified as an important use for such materials, and a wide variety of designs have been proposed for this purpose. Several of these designs face practical challenges (e.g. pentamode designs, which are difficult to manufacture), and many others involve numerous tunable parameters and a large design parameter space (e.g. distributions of resonators), making global search-based optimization less tractable and raising the need for gradient-based methods. In this work, gradient-based optimization governed by partial differential equations (PDEs) is used to optimize acoustic channels in a coiled-space metastructure layer surrounding a rigid acoustic scatterer. The optimization technique is chosen to more feasibly search the design parameter space, and the acoustic channel design is chosen to exploit sound speed optimization as a proxy for channel length optimization to give practically achievable structures. Results are presented for both a single plane wave direction and multiple plane wave directions, as well as for both a single frequency and multiple frequencies. The equivalence of sound speed optimization and channel length optimization is demonstrated for a feasible design minimizing the scattered pressure field. Designs with various numbers of optimized channels are also considered.

Limit cycles analysis and control of evolutionary game dynamics with environmental feedback

Recently, an evolutionary game dynamics model taking into account the environmental feedback has been proposed to describe the co-evolution of strategic actions of a population of individuals and the state of the surrounding environment; correspondingly a range of interesting dynamic behaviors have been reported. In this paper, we provide new theoretical insight into such behaviors and discuss control options. Instead of the standard replicator dynamics, we use a more realistic and comprehensive model of replicator–mutator dynamics, to describe the strategic evolution of the population. After integrating the environment feedback, we study the effect of mutations on the resulting closed-loop system dynamics. We prove the conditions for two types of bifurcations, Hopf bifurcation and Heteroclinic bifurcation, both of which result in stable limit cycles. These limit cycles have not been identified in existing works, and we further prove that such limit cycles are in fact persistent in a large parameter space and are almost globally stable. In the end, an intuitive control policy based on incentives is applied, and the effectiveness of this control policy is examined by analysis and simulations.

Multiscale architecture: Mechanics of composite cytoskeletal networks.

Different types of biological cells respond differently to mechanical stresses, and these responses are mainly governed by the cytoskeleton. The main components of this biopolymer network are actin filaments, microtubules, and intermediate filaments, whose mechanical and dynamic properties are highly distinct, thus opening up a large mechanical parameter space. Aside from experiments on whole, living cells, "bottom-up" approaches, utilizing purified, reconstituted protein systems, tremendously help to shed light on the complex mechanics of cytoskeletal networks. Such experiments are relevant in at least three aspects: (i) from a fundamental point of view, cytoskeletal networks provide a perfect model system for polymer physics; (ii) in materials science and "synthetic cell" approaches, one goal is to fully understand properties of cellular materials and reconstitute them in synthetic systems; (iii) many diseases are associated with cell mechanics, so a thorough understanding of the underlying phenomena may help solving pressing biomedical questions. In this review, we discuss the work on networks consisting of one, two, or all three types of filaments, entangled or cross-linked, and consider active elements such as molecular motors and dynamically growing filaments. Interestingly, tuning the interactions among the different filament types results in emergent network properties. We discuss current experimental challenges, such as the comparability of different studies, and recent methodological advances concerning the quantification of attractive forces between filaments and their influence on network mechanics.

Effect of Boundary Scattering on Magneto-Transport Performance in BN-Encapsulated Graphene

For conductors in the ballistic regime, electron-boundary scattering at the sample edge plays a dominant role in determining the transport performance, giving rise to many intriguing phenomena like low-field negative magnetoresistance effect. We systematically investigate the magneto-transport behaviors of BN-encapsulated graphene devices with narrow channel width W, wherein the bulk mean free path L mfp can be very large and highly tunable. By comparing the magnetoresistance features and the amplitude of L mfp in a large parameter space of temperature and carrier density, we reveal that the boundary-scattering-dominated negative magnetoresistance effect can still survive even when the ballistic ratio (L mfp/W) is as low as 0.15. This striking value is much smaller than the expected value for achieving (quasi-) ballistic transport regime (L mfp/W ≥ 1), and can be attributed to the ultra-low specularity of the sample edge of our graphene devices. These findings enrich our understanding of the effects of boundary scattering on channel transport, which is of vital importance for future designs of two-dimensional electronic devices with limited lateral sizes.

EMPRESS. V. Metallicity Diagnostics of Galaxies over 12 + log(O/H) ≃ 6.9–8.9 Established by a Local Galaxy Census: Preparing for JWST Spectroscopy

We present optical-line gas metallicity diagnostics established by the combination of local SDSS galaxies and the largest compilation of extremely metal-poor galaxies (EMPGs) including new EMPGs identified by the Subaru EMPRESS survey. A total of 103 EMPGs are included, covering a large parameter space of magnitude (M i = −19 to −7) and Hβ equivalent width (10–600 Å), i.e., wide ranges of stellar mass and star formation rate. Using reliable metallicity measurements from the direct method for these galaxies, we derive the relationships between strong optical-line ratios and gas-phase metallicity over the range of ≃ 6.9–8.9, corresponding to 0.02–2 solar metallicity Z ⊙. We confirm that the R23 index, ([O iii]+[O ii])/Hβ, is the most accurate metallicity indicator with a metallicity uncertainty of 0.14 dex over the range among various popular metallicity indicators. The other metallicity indicators show large scatters in the metal-poor range (≲0.1 Z ⊙). It is explained by our CLOUDY photoionization modeling that, unlike the R23 index, the other metallicity indicators do not use a sum of singly and doubly ionized lines and cannot trace both low- and high-ionization gas. We find that the accuracy of the metallicity indicators is significantly improved if one uses Hβ equivalent width measurements that tightly correlate with ionization states. In this work, we also present the relation of physical properties with the UV-continuum slope β and ionization production rate ξ ion derived with GALEX data for the EMPGs and provide local anchors of galaxy properties together with the optical-line metallicity indicators that are available in the form of a machine-readable table and useful for forthcoming JWST spectroscopic studies.

Predicting infrasound transmission loss using deep learning

SUMMARY Modelling the spatial distribution of infrasound attenuation (or transmission loss, TL) is key to understanding and interpreting microbarometer data and observations. Such predictions enable the reliable assessment of infrasound source characteristics such as ground pressure levels associated with earthquakes, man-made or volcanic explosion properties, and ocean-generated microbarom wavefields. However, the computational cost inherent in full-waveform modelling tools, such as parabolic equation (PE) codes, often prevents the exploration of a large parameter space, that is variations in wind models, source frequency and source location, when deriving reliable estimates of source or atmospheric properties—in particular for real-time and near-real-time applications. Therefore, many studies rely on analytical regression-based heuristic TL equations that neglect complex vertical wind variations and the range-dependent variation in the atmospheric properties. This introduces significant uncertainties in the predicted TL. In the current contribution, we propose a deep learning approach trained on a large set of simulated wavefields generated using PE simulations and realistic atmospheric winds to predict infrasound ground-level amplitudes up to 1000 km from a ground-based source. Realistic range dependent atmospheric winds are constructed by combining ERA5, NRLMSISE-00 and HWM-14 atmospheric models, and small-scale gravity-wave perturbations computed using the Gardner model. Given a set of wind profiles as input, our new modelling framework provides a fast (0.05 s runtime) and reliable (∼5 dB error on average, compared to PE simulations) estimate of the infrasound TL.

Training a Neural-Network-Based Surrogate Model for Aerodynamic Optimisation Using a Gaussian Process

A two-step idea is presented for efficiently training a neural network (NN) surrogate of a parametric, high-fidelity, high-dimensional computational fluid dynamics model (HDM). It consists first in using a Gaussian process, an acquisition function, the HDM, and a scalar quantity obtained by post-processing the HDM to adaptively sample the parameter space. Next, a NN is trained for a quantity of interest using the HDM solutions computed at the sampled parameter points. This active learning approach is illustrated with numerical experiments for the prediction of the lift-over-drag ratio of a cambered NACA airfoil and the pressure coefficient of a flying wing aircraft mAEWing2 in large parameter spaces of flight conditions and shape design variables. The results demonstrate the superior efficiency and accuracy delivered by the proposed training over alternatives based on uniform and random samplings. The resulting surrogate models are suitable for time-critical applications such as design optimization and uncertainty quantification.

First Constraining Upper Limits on Gravitational-wave Emission from NS 1987A in SNR 1987A

We report on a search for continuous gravitational waves (GWs) from NS 1987A, the neutron star born in SN 1987A. The search covered a frequency band of 75–275 Hz, included a wide range of spin-down parameters for the first time, and coherently integrated 12.8 days of LIGO data below 125 Hz and 8.7 days of LIGO data above 125 Hz from the second Advanced LIGO–Virgo observing run. We found no astrophysical signal. We set upper limits on GW emission as tight as an intrinsic strain of 2 × 10−25 at 90% confidence. The large spin-down parameter space makes this search the first astrophysically consistent one for continuous GWs from NS 1987A. Our upper limits are the first consistent ones to beat an analog of the spin-down limit based on the age of the neutron star and hence are the first GW observations to put new constraints on NS 1987A.

Optical steganography with sign-based keys and video as vessel medium

We propose a compressive steganography technique with double keys based on ghost imaging. Unlike conventional steganography schemes using ghost imaging, the proposed technique could utilize a wide variety of public videos as vessel media of steganography. Furthermore, double keys are introduced in steganography to increase the difficulty of cracking greatly. In demonstration, the reconstructed results are quite sensitive to the variation of vessel medium or keys. Moreover, for image steganography, the typical compression ratio of the proposed technique is measured to be 15.3%. In a word, the proposed technique offers a wide choice of vessel media, large parameter space for steganography keys and fine compression performance, and therefore provides a more flexible, robust and efficient way for information security applications.

Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics

The simulation of fluid dynamics, typically by numerically solving partial differential equations, is an essential tool in many areas of science and engineering. However, the high computational cost can limit application in practice and may prohibit exploring large parameter spaces. Recent deep-learning approaches have demonstrated the potential to yield surrogate models for the simulation of fluid dynamics. While such models exhibit lower accuracy in comparison, their low runtime makes them appealing for design-space exploration. We introduce two novel graph neural network (GNN) models, multi-scale (MuS)-GNN and rotation-equivariant (RE) MuS-GNN, for extrapolating the time evolution of the fluid flow. In both models, previous states are processed through multiple coarsening of the graph, which enables faster information propagation through the network and improves the capture and forecast of the system state, particularly in problems encompassing phenomena spanning a range of length scales. Additionally, REMuS-GNN is architecturally equivariant to rotations, which allows the network to learn the underlying physics more efficiently, leading to improved accuracy and generalization. We analyze these models using two canonical fluid models: advection and incompressible fluid dynamics. Our results show that the proposed GNN models can generalize from uniform advection fields to high-gradient fields on complex domains. The multi-scale graph architecture allows for inference of incompressible Navier–Stokes solutions, within a range of Reynolds numbers and design parameters, more effectively than a baseline single-scale GNN. Simulations obtained with MuS-GNN and REMuS-GNN are between two and four orders of magnitude faster than the numerical solutions on which they were trained.

Light Dark Matter Detection with Hydrogen-Rich Targets and Low-$$T_c$$ TES Detectors

Direct detection of nuclear scatterings of sub-GeV dark matter (DM) particles favors low-Z nuclei. Hydrogen nucleus, which has a single proton, provides the best kinematic match. The characteristic nuclear recoil energy is boosted by a factor of a few tens from those for larger nuclei used in traditional Weakly Interacting Massive Particles searches. Furthermore, hydrogen is optimal for detecting spin-dependent nuclear scatterings of sub-GeV DM, where large parameter space still remains unconstrained yet. In this paper, we first introduce several hydrogen-rich targets, which emit two classes of signals under kinetic excitations. One class of the signals is infrared photons, which are from fundamental vibrational and rotational modes of molecules and at several characteristic wavelengths. Another is acoustic phonons and optical phonons that decay into acoustic phonons. We then discuss the technical status and future researches of low-\(T_c\) transition-edge sensor (TES) detectors, which measure the infrared photons and acoustic phonons with desirable sensitivities. Utilization of hydrogen-rich targets and ultra-sensitive low-\(T_c\) TES detectors for light DM detection requires both theoretical modeling and experimental prototyping.

A fast, semi-analytical model for the Venusian binary cloud system

ABSTRACT The Venusian clouds originate from the binary condensation of H2SO4 and H2O. The two components strongly interact with each other via chemistry and cloud formation. Previous works adopted sophisticated microphysical approaches to understand the clouds. Here, we show that the observed vapour and cloud distributions on Venus can be well explained by a semi-analytical model. Our model assumes local thermodynamical equilibrium for water vapour but not for sulphuric acid vapour, and includes the feedback of cloud condensation and acidity to vapour distributions. The model predicts strong supersaturation of the H2SO4 vapour above 60 km, consistent with our recent cloud condensation model. The semi-analytical model is 100 times faster than the condensation model and 1000 times faster than the microphysical models. This allows us to quickly explore a large parameter space of the sulphuric acid gas-cloud system. We found that the cloud mass loading in the upper clouds has an opposite response of that in the lower clouds to the vapour mixing ratios in the lower atmosphere. The transport of water vapour influences the cloud acidity in all cloud layers, while the transport of sulphuric acid vapour only dominates in the lower clouds. This cloud model is fast enough to be coupled with the climate models and chemistry models to understand the cloudy atmospheres of Venus and Venus-like extra-solar planets.

Muonphilic dark matter explanation of gamma-ray galactic center excess: a comprehensive analysis

The Galactic center gamma-ray excess (GCE) is a long-standing unsolved problem. One of candidate solutions, the dark matter (DM) annihilation, has been recently tested with other astrophysical observations, such as AMS-02 electron-positron spectra, Fermi Dwarf spheroidal galaxies gamma-ray data, and so on. By assuming that the DM particles annihilate purely into a normal charged fermion pair, Di Mauro and Winkle (2021) claimed that only a muon-pair is compatible with the null detection of all the corresponding astrophysical measurements and can explain GCE simultaneously. On the other hand, a muonphilic DM model may also lead to a signal in the recent Fermilab muon g − 2 measurement or be constrained by the latest PandaX-4T limit. In this work, we comprehensively study interactions between DM and muon, including various combinations of DM and mediator spins. In agreement with GCE (not only 2μ but also 4μ final states), we test these interactions against all the thermal DM constraints. Our results show that only the parameter space near the resonance region of mediator can explain GCE and relic density simultaneously, and larger parameter spaces are still allowed if other poorly-known systematic uncertainties are included. Regardless of the DM spin, only the interactions with the spin-0 mediator can explain the recent muon g − 2 excess on top of GCE, relic density, and other DM and mediator constraints.