Regions Of Space Research Articles

The population of Galactic supernova remnants in the TeV range

Supernova remnants (SNRs) are likely to be significant sources of cosmic rays up to the knee of the local cosmic-ray (CR) spectrum. They produce gamma rays in the very-high-energy (VHE) ($E>0.1$ TeV) range mainly via two mechanisms: hadronic interactions of accelerated protons with the interstellar medium and leptonic interactions of accelerated electrons with soft photons. Observations with current instruments have lead to the detection of about a dozen SNRs emitting VHE gamma rays and future instruments should significantly increase this number. Yet, the details of particle acceleration at SNRs and of the mechanisms producing VHE gamma-rays at SNRs remain poorly understood. We aim to study the population of SNRs detected in the TeV range and its properties and confront it to simulated samples in order to address fundamental questions concerning particle acceleration at SNR shocks. Such questions concern the spectrum of accelerated particles, the efficiency of particle acceleration, and the gamma-ray emission being dominated by hadronic or leptonic interactions. By means of Monte Carlo methods, we simulated the population of SNRs in the gamma--ray domain and confronted our simulations to the catalogue of sources from the High Energy Stereoscopic System (H.E.S.S.) Galactic Plane Survey (HGPS). We systematically explored the parameter space defined in our model, including for example, the slope of accelerated particles alpha , the electron-to-proton ratio $K_ ep $, and the efficiency of particle acceleration $ $. In particular, we found possible sets of parameters for which $ 90$<!PCT!> of Monte Carlo realisations are found to be in agreement with the HGPS. These parameters are typically found at $ 4.2 ep $, and $0.03 0.1 $ . We are able to strongly argue against some regions of the parameter space describing the population of Galactic SNRs in the TeV range, such as $ 4.35$, or ep Our model is so far able to explain the SNR population of the HGPS. Our approach, when confronted with the results of future systematic surveys, such as the Cherenkov Telescope Array Observatory, will help remove degeneracy from the solutions and to better understand particle acceleration at SNR shocks in the Galaxy.

Combining evolutionary strategies and novelty detection to go beyond the alignment limit of the Z3 3HDM

We present a novel artificial intelligence approach for beyond the Standard Model parameter space scans by augmenting an evolutionary strategy with novelty detection. Our approach leverages the power of evolutionary strategies, previously shown to quickly converge to the valid regions of the parameter space, with a novelty reward to continue exploration once converged. Taking the Z3 3HDM as our physics case, we show how our methodology allows us to quickly explore highly constrained multidimensional parameter spaces, providing up to eight orders of magnitude higher sampling efficiency when compared with pure random sampling and up to four orders of magnitude when compared to random sampling around the alignment limit. In turn, this enables us to explore regions of the parameter space that have been hitherto overlooked, leading to the possibility of novel phenomenological realizations of the Z3 three Higgs doublet model that had not been considered before. Published by the American Physical Society 2024

The new physics case for beam-dump experiments with accelerated muon beams

As the field examines a future muon collider as a possible successor to the LHC, we must consider how to fully utilize not only the high-energy particle collisions, but also any lower-energy staging facilities necessary in the R&D process. An economical and efficient possibility is to use the accelerated muon beam from either the full experiment or from cooling and acceleration tests in beam-dump experiments. Beam-dump experiments are complementary to the main collider as they achieve sensitivity to very small couplings with minimal instrumentation. We demonstrate the utility of muon beam-dump experiments for new physics searches at energies from 10 GeV to 5 TeV. We find that, even at low energies like those accessible at staging or demonstrator facilities, it is possible to probe new regions of parameter space for a variety of generic BSM models, including muonphilic, leptophilic, Lμ − Lτ, and dark photon scenarios. Such experiments could therefore provide opportunities for discovery of new physics well before the completion of the full multi-TeV collider.

Probabilistic graph networks for learning physics simulations

Inductive biases play a critical role in enabling Graph Networks (GN) to learn particle and mesh-based physics simulations. In this paper, we propose two generalizable inductive biases that minimize rollout error and energy accumulation. GNs conditioned on the input states and relying on the Mean Squared Error (MSE) loss function implicitly assume Gaussian-distributed output errors. Consequently, GNs may either assign probability densities to infeasible regions in the state space of the deterministic physics problem or fail to assign densities to feasible regions. Instead, we advocate for maximizing the likelihood of the actual target distribution, challenging the underlying assumptions of MSE-based regression models using our proposed conditional normalizing flows (cNF) decoder. We discover that this inductive bias enables GNs to significantly improve their next state prediction accuracy. Existing sequential GNs encode temporal dependencies by autoregressively processing the latent representations of the input data. In our work, we find that inducing the Arrow-of-Time inductive bias through an auto-regressive encoding step before autoregressively processing the resulting latent vectors enables GNs to better minimize rollout error. We critically analyze the impact of existing inductive biases on rollout error and energy accumulation and discover that the choice of biases encoded in a GN, rather than the number of inductive biases, has a substantial impact on forward simulation prediction.

Islam and the Transmission of Cultural Identity in Four European Countries

Abstract Studies exploring the integration of European immigrants tend to find cultural gaps between Muslim children and their peers. While some scholars argue that parent-to-child transmission is a key mechanism underlying this pattern, others privilege extrafamilial explanations by pointing to differences in cultural values within Muslim households. In the present study, I argue that these mixed results stem from a tendency in the literature to analyze distinct components of personal culture in isolation from cognate dimensions. To address this shortcoming, I use multigroup latent class models to explore how a wide range of attitudes (tapping ethnocultural identity, gender norms, sexual liberalism, and perspectives on integration) are clustered together in disparate regions of the belief space, marking distinct cultural identities. Then, I fit a series of logistic regressions to map how these cultural identities are distributed among immigrant-origin samples in four European countries and transmitted across generational lines. Ultimately, I arrive at the following conclusion: while Muslim youth stand out from their peers vis-à-vis their cultural identity profiles, there is little evidence to suggest that this pattern is decisively shaped by parent-to-child transmission.

Sequential Monte Carlo with Adaptive Lookahead Support for Improved Importance Sampling

The sequential Monte Carlo, also called the Bayesian particle filter, approximates a posterior probability density function of a latent target state from noisy sensor measurements using a set of Monte Carlo samples. These samples are predicted using an importance density function and then updated using the Bayes’s rule. The updated samples and their corresponding weights provide an estimate of the latent state. The said filtering process is iterated over time for tracking dynamic target states. It is critical to have enough particles in regions of the target state space that contribute to the posterior. The auxiliary and the improved auxiliary particle filters accomplish this by a process that mimics drawing from an importance density that leverages the incoming observation into the sampling step. However these filters are known to fail when the sensor measurements are highly informative and the diffusion over the state transition is large. This paper presents an improvement to the auxiliary particle filter by taking two support points that act as limits in a univariate state space within which particles are samples. The choice of the limits is adaptive. The proposed method is successfully tested using a nonlinear model using simulations.

Wave Function and Information

Two distinct measures of information, connected respectively to the amplitude and phase of the wave function of a particle, are proposed. There are relations between the time derivatives of these two measures and their gradients on the configuration space, which are equivalent to the wave equation. The information related to the amplitude measures the strength of the potential coupling of the particle (which is itself aspatial) with each volume of its configuration space, i.e., its tendency to participate in an interaction localized in a region of ordinary physical space corresponding to that volume. The information connected to the phase is that required to obtain the time evolution of the particle as a persistent entity starting from a random succession of bits. It can be considered as the information provided by conservation principles. The meaning of the so-called “quantum potential” in this context is briefly discussed.

Combining resonant and tail-based anomaly detection

In many well-motivated models of the electroweak scale, cascade decays of new particles can result in highly boosted hadronic resonances (e.g., Z/W/h). This can make these models rich and promising targets for recently developed resonant anomaly detection methods powered by modern machine learning. We demonstrate this using the state-of-the-art classifying anomalies through outer density estimation () method applied to supersymmetry scenarios with gluino pair production. We show that , despite being model agnostic, is nevertheless competitive with dedicated cut-based searches, while simultaneously covering a much wider region of parameter space. The gluino events also populate the tails of the missing energy and HT distributions, making this a novel combination of resonant and tail-based anomaly detection. Published by the American Physical Society 2024

Uncertainty Quantification for Scale-Space Blob Detection

AbstractWe consider the problem of blob detection for uncertain images, such as images that have to be inferred from noisy measurements. Extending recent work motivated by astronomical applications, we propose an approach that represents the uncertainty in the position and size of a blob by a region in a three-dimensional scale space. Motivated by classic tube methods such as the taut-string algorithm, these regions are obtained from level sets of the minimizer of a total variation functional within a high-dimensional tube. The resulting non-smooth optimization problem is challenging to solve, and we compare various numerical approaches for its solution and relate them to the literature on constrained total variation denoising. Finally, the proposed methodology is illustrated on numerical experiments for deconvolution and models related to astrophysics, where it is demonstrated that it allows to represent the uncertainty in the detected blobs in a precise and physically interpretable way.

A cosmological tachyon collider: enhancing the long-short scale coupling

The squeezed limit of the primordial curvature bispectrum is an extremely sensitive probe of new physics and encodes information about additional fields active during inflation such as their masses and spins. In the conventional setup, additional fields are stable with a positive mass squared, and hence induce a decreasing signal in the squeezed limit, making a detection challenging.Here we consider a scalar field that is temporarily unstable by virtue of a transient tachyonic mass, and we construct models in which it is embedded consistently within inflation. Assuming IR-finite couplings between the tachyon and the inflaton, we find an exchange bispectrum with an enhanced long-short scale coupling that grows in the squeezed limit parametrically faster than local non-Gaussianity. Our approximately scale-invariant signal can be thought of as a cosmological tachyon collider.In a sizeable region of parameter space, the leading constraint on our signal comes from the cross correlation of μ-type spectral distortions and temperature anisotropies of the microwave background, whereas temperature and polarization bispectra are less sensitive probes. By including anisotropic spectral distortions in the analysis, future experiments such as CMB-S4 will further reduce the allowed parameter space.

Constructive approach and randomization of a two-parameter chaos system for securing data

Secure communication techniques are important due to the increase in the number of technology users across the world. Likewise, a more random encryption algorithm suitable to secure data from unauthorised users is highly expected. This paper proposes a two-parameter nonlinear chaos map that is sensitive to the trio seed (s0, \alpha, \lambda) and has better information encryption. We introduce the parameter \alpha to linearise the conventional chaos system, which in turn brings a delay in the cryptosystems. The delay is a phenomenon that changes the chaotic features of a system. A small delay in the system leads to more aperiodicity and the unpredictability of the chaotic attractions. We normalise the new chaos map and use the Lipschitz and pseudo-contractive operators to obtain its irregularity region in Hilbert spaces. We also analyse the chaos map in terms of trajectory, Lyapunov exponent, complexity, and information entropy. Results obtained show that the new chaos map has a wide chaotic range and better statistical properties. It also maintains low complexity due to its linearity and produces more key spaces than most existing chaotic maps.

Reactive Gait Composition With Stability: Dynamic Walking Amidst Static and Moving Obstacles

Abstract This paper presents a modular approach to motion planning with provable stability guarantees for robots that move through changing environments via periodic locomotion behaviors. We focus on dynamic walkers as a paradigm for such systems, although the tools developed in this paper can be used to support general compositional approaches to robot motion planning with dynamic movement primitives (DMPs). By formulating the planning process as a switching system with multiple equilibria (SSME), we prove that the system's evolution remains within explicitly characterized trapping regions in the state space under suitable constraints on the frequency of switching among the DMPs. These conditions encapsulate the low-level stability limitations in a form that can be easily communicated to the planner. Furthermore, we show how the available primitives can be safely composed online in a receding horizon manner to enable the robot to react to moving obstacles. The proposed framework can be applied in a wide class of 3D bipedal walking models, and offers a modular approach for integrating readily available low-level locomotion control and high-level planning methods.

Exploring nonstandard Hbb¯ interactions at future electron-proton colliders

In this paper, we use the charged-current Higgs boson production process at future electron-proton colliders, e−p→Hjνe, with the subsequent decay of the Higgs boson into a bb¯ pair, to probe the Standard Model effective field theory with dimension-six operators involving the Higgs boson and the bottom quark. The study is performed for two proposed future high-energy electron-proton colliders, the Large Hadron Electron Collider (LHeC) and the Future Circular Collider (FCC-he) at the center-of-mass energies of 1.3 TeV and 3.46 TeV, respectively. Constraints on the CP-even and CP-odd Hbb¯ couplings are derived by analyzing the simulated signal and background samples. A realistic detector simulation is performed and a multivariate technique using the gradient boosted decision trees algorithm is employed to discriminate the signal from background. Expected limits are obtained at 95% confidence level for the LHeC and FCC-he assuming the integrated luminosities of 1, 2 and 10 ab−1. We find that using 1 ab−1 of data, the CP-even and CP-odd Hbb¯ couplings can be constrained with accuracies of the order of 10−3 and 10−2, respectively, and a significant region of the unprobed parameter space becomes accessible. Published by the American Physical Society 2024

Closer look at postdecay tt¯ entanglement

Top pair production is ideally suited to observe postdecay entanglement, thus providing a novel test of quantum mechanics. We provide top polarized decay amplitudes that can be used to compute semianalytical predictions and, in particular, to better understand the postdecay entanglement arises. We obtain predictions for the LHC, identifying general phase space regions where experimental measurements of tW entanglement are feasible. We also give predictions for polarized e+e− collisions, focusing on the possibility that the postdecay tW entanglement is larger than the tt¯ one. Published by the American Physical Society 2024

Direct stellarator coil design using global optimization: application to a comprehensive exploration of quasi-axisymmetric devices

Many stellarator coil design problems are plagued by multiple minima, where the locally optimal coil sets can sometimes vary substantially in performance. As a result, solving a coil design problem a single time with a local optimization algorithm is usually insufficient and better optima likely do exist. To address this problem, we propose a global optimization algorithm for the design of stellarator coils and outline how to apply box constraints to the physical positions of the coils. The algorithm has a global exploration phase that searches for interesting regions of design space and is followed by three local optimization algorithms that search in these interesting regions (a ‘global-to-local’ approach). The first local algorithm (phase I), following the globalization phase, is based on near-axis expansions and finds stellarator coils that optimize for quasisymmetry in the neighbourhood of a magnetic axis. The second local algorithm (phase II) takes these coil sets and optimizes them for nested flux surfaces and quasisymmetry on a toroidal volume. The final local algorithm (phase III) polishes these configurations for an accurate approximation of quasisymmetry. Using our global algorithm, we study the trade-off between coil length, aspect ratio, rotational transform and quality of quasi-axisymmetry. The database of stellarators, which comprises approximately 200 000 coil sets, is available online and is called QUASR, for ‘quasi-symmetric stellarator repository’.

Simple analysis of complex system safety based on Finite State Machine Network and phase space theory

Safety analysis of complex systems, such as aero-engines, has always been a challenging problem. In particular, the risk propagation caused by system functional interactions has become increasingly prominent, leading to the difficulty of traditional safety analysis methods. Existing methods are mostly based on manual experience and deduction with difficulty to completely analyze the whole system safety regions. In this paper, a model-based framework is proposed by combining Finite State Machine Network (FSMN) and phase space theory to perform safety analysis of complex systems from function-logic perspective. We first construct the system model and system state-transition network based on FSMN. Then combining with phase space theory, we identify system safety regions of the entire operating space from macro perspective, and investigate the system risk propagation from micro perspective. An aero-engine system is taken as an example to illustrate the proposed method. Our results show that this method has the ability to effectively identify the potential risk factors and explore emergent unknown risks, which can provide guidance for safety testing.

The inverted pendulum as a classical analog of the EFT paradigm

The inverted pendulum is a mechanical system with a rapidly oscillating pivot point. Using techniques similar in spirit to the methodology of effective field theories, we derive an effective Lagrangian that allows for the systematic computation of corrections to the so-called Kapitza equation. The derivation of the effective potential of the system requires non-trivial matching conditions, which need to be determined order by order in the power-counting of the problem. The convergence behavior of the series is investigated on the basis of high-order results obtained by this method. The results from this analysis can be used to determine the regions of parameter space, in which the inverted position of the pendulum is stable or unstable to high precision.

Infrared Spectrum of the Pyrene Anion in the CH Stretching Region.

In this work, we report the infrared spectrum of the pyrene anion, measured using messenger tagging with up to three Ar atoms. We assign the spectrum using density functional theory and vibrational perturbation theory. We discuss our results in the context of computed and experimental spectra from the literature as well as recent observations from astronomical sources, addressing the question of whether polycyclic aromatic hydrocarbon anions could contribute to the strong infrared emission bands at 3.29 μm from carbon-rich regions of space.

Magnon-mediated topological superconductivity in a quantum wire

Many emergent phases of matter stem from the intertwined dynamics of quasiparticles. Here we show that a topological superconducting phase emerges as the result of interactions between electrons and magnons in a quantum wire and a helical magnet. The magnon-mediated interaction favors triplet superconductivity over a large magnetic phase space region, and stabilizes topological superconductivity over an extended region of chemical potentials. The superconducting gap depends exponentially on the spin-electron coupling, allowing it to be enhanced through material engineering techniques. Published by the American Physical Society 2024

Deep Reinforcement Learning-Based 3D Trajectory Planning for Cellular Connected UAV

To address the issue of limited application scenarios associated with connectivity assurance based on two-dimensional (2D) trajectory planning, this paper proposes an improved deep reinforcement learning (DRL) -based three-dimensional (3D) trajectory planning method for cellular unmanned aerial vehicles (UAVs) communication. By considering the 3D space environment and integrating factors such as UAV mission completion time and connectivity, we develop an objective function for path optimization and utilize the advanced dueling double deep Q network (D3QN) to optimize it. Additionally, we introduce the prioritized experience replay (PER) mechanism to enhance learning efficiency and expedite convergence. In order to further aid in trajectory planning, our method incorporates a simultaneous navigation and radio mapping (SNARM) framework that generates simulated 3D radio maps and simulates flight processes by utilizing measurement signals from the UAV during flight, thereby reducing actual flight costs. The simulation results demonstrate that the proposed approach effectively enable UAVs to avoid weak coverage regions in space, thereby reducing the weighted sum of flight time and expected interruption time.