Hasil untuk "Physics"

L. Reinders, H. Rubinstein, S. Yazaki

A. Flew, N. R. Hanson

J. C. Phillips, W. Harrison

F. Gall

A. Schwarz

K. K. Cetina

J. Grasselli

J. Binney, N. Dowrick, A. Fisher et al.

B. Zeng, Xie Chen, Duan-Lu Zhou et al.

This is the draft version of a textbook, which aims to introduce the quantum information science viewpoints on condensed matter physics to graduate students in physics (or interested researchers). We keep the writing in a self-consistent way, requiring minimum background in quantum information science. Basic knowledge in undergraduate quantum physics and condensed matter physics is assumed. We start slowly from the basic ideas in quantum information theory, but wish to eventually bring the readers to the frontiers of research in condensed matter physics, including topological phases of matter, tensor networks, and symmetry-protected topological phases.

Hind Al Ali, N. Arkani-Hamed, Ian Banta et al.

We lay out a comprehensive physics case for a future high-energy muon collider, exploring a range of collision energies (from 1 to 100 TeV) and luminosities. We highlight the advantages of such a collider over proposed alternatives. We show how one can leverage both the point-like nature of the muons themselves as well as the cloud of electroweak radiation that surrounds the beam to blur the dichotomy between energy and precision in the search for new physics. The physics case is buttressed by a range of studies with applications to electroweak symmetry breaking, dark matter, and the naturalness of the weak scale. Furthermore, we make sharp connections with complementary experiments that are probing new physics effects using electric dipole moments, flavor violation, and gravitational waves. An extensive appendix provides cross section predictions as a function of the center-of-mass energy for many canonical simplified models.

R. Roth

Silvia Dedu, Florentin Șerban

Traditional mean–variance portfolio optimization proves inadequate for cryptocurrency markets, where extreme volatility, fat-tailed return distributions, and unstable correlation structures undermine the validity of variance as a comprehensive risk measure. To address these limitations, this paper proposes a unified entropy-based portfolio optimization framework grounded in the Maximum Entropy Principle (MaxEnt). Within this setting, Shannon entropy, Tsallis entropy, and Weighted Shannon Entropy (WSE) are formally derived as particular specifications of a common constrained optimization problem solved via the method of Lagrange multipliers, ensuring analytical coherence and mathematical transparency. Moreover, the proposed MaxEnt formulation provides an information-theoretic interpretation of portfolio diversification as an inference problem under uncertainty, where optimal allocations correspond to the least informative distributions consistent with prescribed moment constraints. In this perspective, entropy acts as a structural regularizer that governs the geometry of diversification rather than as a direct proxy for risk. This interpretation strengthens the conceptual link between entropy, uncertainty quantification, and decision-making in complex financial systems, offering a robust and distribution-free alternative to classical variance-based portfolio optimization. The proposed framework is empirically illustrated using a portfolio composed of major cryptocurrencies—Bitcoin (BTC), Ethereum (ETH), Solana (SOL), and Binance Coin (BNB)—based on weekly return data. The results reveal systematic differences in the diversification behavior induced by each entropy measure: Shannon entropy favors near-uniform allocations, Tsallis entropy imposes stronger penalties on concentration and enhances robustness to tail risk, while WSE enables the incorporation of asset-specific informational weights reflecting heterogeneous market characteristics. From a theoretical perspective, the paper contributes a coherent MaxEnt formulation that unifies several entropy measures within a single information-theoretic optimization framework, clarifying the role of entropy as a structural regularizer of diversification. From an applied standpoint, the results indicate that entropy-based criteria yield stable and interpretable allocations across turbulent market regimes, offering a flexible alternative to classical risk-based portfolio construction. The framework naturally extends to dynamic multi-period settings and alternative entropy formulations, providing a foundation for future research on robust portfolio optimization under uncertainty.

M. Kormos, M. Collura, G. Takács et al.

Confinement plays an important role in many-body physics from high energy to condensed matter. New results show that it strongly affects the non-equilibrium dynamics after a quantum quench with possible implications from ultracold atoms to QCD. Quarks cannot be observed as free particles in nature because they are confined into baryons and mesons, as a result of the fact that the strong interaction between them increases with their separation. However, it is less known that this phenomenon also occurs in condensed matter and statistical physics as experimentally proved in several quasi-1D compounds1,2. Most of the theoretical and experimental studies so far concentrated on understanding the consequences of confinement for the equilibrium physics of both high-energy and condensed matter systems. Here, instead we show that confinement has dramatic consequences for the non-equilibrium dynamics following a quantum quench and that these effects could be exploited as a quantitative probe of confinement.

F. Wilczek

M. Polyakov, P. Schweitzer

The physics related to the form factors of the energy–momentum tensor spans a wide spectrum of problems, and includes gravitational physics, hard-exclusive reactions, hadronic decays of heavy quarkonia, and the physics of exotic hadrons described as hadroquarkonia. It also provides access to the “last global unknown property:” the D-term. We review the physics associated with the form factors of the energy–momentum tensor and the D-term, their interpretations in terms of mechanical properties, their applications, and the current experimental status.

O. Aharony, J. Marsano, Shiraz Minwalla et al.

Thomas C. Pagano, Elizabeth E. Ebert, Mohammadreza Khanarmuei

ABSTRACT Forecast verification is an essential function of National Meteorological and Hydrological Services (NMHSs), underpinning their ability to deliver accurate, reliable, and actionable weather, climate, and water‐related information. As NMHSs face increasing demands for transparency, accountability, and continuous improvement, they require robust systems to assess and enhance the quality of their forecasts. This article presents a holistic forecast verification capability development framework, built from over a decade of focused effort at the Australian Bureau of Meteorology. The framework integrates best practices in governance, data management, verification metrics, and communication. It acknowledges the importance of user‐centered approaches and highlights areas where verification practices can align with user needs. To support NMHSs in adopting this framework, the article introduces two practical tools: a Verification Planning Template for establishing new verification activities and systems and a Gap Analysis and Maturity Assessment (GAMA) tool for benchmarking and advancing existing practices. These tools provide structured guidance for planning, evaluating, and improving verification within a NMHS, with the ultimate goal of delivering higher quality forecasts that meet diverse stakeholder needs. The Bureau's progress in implementing this framework demonstrates significant benefits, including improved forecast quality, enhanced coordination across verification efforts, and greater trust among users. However, challenges such as data availability, system integration, and resourcing remain pervasive, both within the Bureau and globally. The tools and insights shared in this article offer a pathway for NMHSs to overcome these obstacles, enabling them to better respond to evolving user expectations and operational demands. This work highlights the value of fostering a strong verification culture, supported by collaboration and knowledge sharing across the international meteorological community. By applying the principles and tools presented here, and customizing them to their circumstances, NMHSs can advance toward resilient, evidence‐based verification practices and capabilities that enhance forecast reliability and stakeholder confidence worldwide.

S J Watts, L Crow

In recent years generative artificial intelligence has been used to create data to support scientific analysis. For example, generative adversarial networks (GANs) have been trained using Monte Carlo simulated input and then used to generate data for the same problem. This has the advantage that a GAN creates data in a significantly reduced computing time. $N$ training events for a GAN can result in $NG$ generated events with the gain factor $G$ being greater than one. This appears to violate the principle that one cannot get information for free. This is not the only way to amplify data so this process will be referred to as data amplification which is studied using information theoretic concepts. It is shown that a gain greater than one is possible whilst keeping the information content of the data unchanged. This leads to a mathematical bound, $2\log (\text{Generated}\ \text{Events}) \unicode{x2A7E} {\text{3log(Training Events)}}$ , which only depends on the number of generated and training events. This study determined the conditions for both the underlying and reconstructed probability distributions to ensure this bound. In particular, the resolution of variables in amplified data is not improved by the process but the increase in sample size can still improve statistical significance. The bound was confirmed using computer simulation and analysis of GAN generated data from the literature.

Noé Lugaz

Abstract Are we moving into a new reality where the next human stepping onto a different world will utter “That's one small step for me, a giant leap for my country”? Is further tightening Heliophysics and space weather research to military endeavors the solution to the decrease in federal funding for Heliophysics in the US and the worldwide increase in military budget? I invite researchers to take the time to contemplate those issues and to continue pushing for an ethical, peaceful, cooperative, and curiosity‐driven space science and space weather research.