The Cms, LHCb Collaborations V. Khachatryan, A. Sirunyan
et al.
The standard model of particle physics describes the fundamental particles and their interactions via the strong, electromagnetic and weak forces. It provides precise predictions for measurable quantities that can be tested experimentally. The probabilities, or branching fractions, of the strange B meson () and the B0 meson decaying into two oppositely charged muons (μ+ and μ−) are especially interesting because of their sensitivity to theories that extend the standard model. The standard model predicts that the and decays are very rare, with about four of the former occurring for every billion mesons produced, and one of the latter occurring for every ten billion B0 mesons. A difference in the observed branching fractions with respect to the predictions of the standard model would provide a direction in which the standard model should be extended. Before the Large Hadron Collider (LHC) at CERN started operating, no evidence for either decay mode had been found. Upper limits on the branching fractions were an order of magnitude above the standard model predictions. The CMS (Compact Muon Solenoid) and LHCb (Large Hadron Collider beauty) collaborations have performed a joint analysis of the data from proton–proton collisions that they collected in 2011 at a centre-of-mass energy of seven teraelectronvolts and in 2012 at eight teraelectronvolts. Here we report the first observation of the µ+µ− decay, with a statistical significance exceeding six standard deviations, and the best measurement so far of its branching fraction. Furthermore, we obtained evidence for the µ+µ− decay with a statistical significance of three standard deviations. Both measurements are statistically compatible with standard model predictions and allow stringent constraints to be placed on theories beyond the standard model. The LHC experiments will resume taking data in 2015, recording proton–proton collisions at a centre-of-mass energy of 13 teraelectronvolts, which will approximately double the production rates of and B0 mesons and lead to further improvements in the precision of these crucial tests of the standard model.
Abstract Understanding protein conformational dynamics is critical for elucidating disease mechanisms and facilitating structure-based drug design. While AlphaFold2-based methods have advanced static structure prediction, modeling multiple conformation states with both accuracy and efficiency remains challenging. We present AlphaFlex, an innovative framework that integrates flexible residue prediction with a directed masking strategy for multiple sequence alignments. By selectively relaxing co-evolutionary constraints in dynamic regions, AlphaFlex enables accurate prediction of biologically relevant states while preserving structural fidelity. Benchmarking on 69 apo-holo pairs from the CoDNaS dataset, including enzymes, binding proteins, and chaperones, reveals AlphaFlex’s superior performance in both flexible residue identification and multiple conformation prediction. Comparative analysis against AF-Cluster, AF2-conformations, and AlphaFlow demonstrates AlphaFlex’s significant advantage, achieving the highest success rate (42%) in accurately predicting apo/holo structures (TM-score >0.95 for both states) while consistently outperforming these methods in reproducing both apo and holo conformations. Notably, AlphaFlex maintains robust performance for membrane proteins, resolving inward- and outward-facing conformations with equal fidelity. Case studies further reveal its unique ability to sample physically plausible intermediate states along transition pathways. These results establish AlphaFlex as a pivotal computational tool for mapping protein conformational landscapes, with promising potential to accelerate structure-based therapeutic development.
The distribution of particles inside hadronic jets produced in the decay of boosted W and Z bosons can be used to discriminate such jets from the continuum background. Given that a jet has been identified as likely resulting from the hadronic decay of a boosted W or Z boson, this paper presents a technique for further differentiating Z bosons from W bosons. The variables used are jet mass, jet charge, and a b-tagging discriminant. A likelihood tagger is constructed from these variables and tested in the simulation of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$W'\rightarrow WZ$$\end{document}W′→WZ for bosons in the transverse momentum range 200 GeV \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$<p_\text {T}<$$\end{document}<pT< 400 GeV in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sqrt{s}=8$$\end{document}s=8 TeV pp collisions with the ATLAS detector at the LHC. For Z-boson tagging efficiencies of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\epsilon _Z=90$$\end{document}ϵZ=90, 50, and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$10\,\%$$\end{document}10%, one can achieve \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$W^+$$\end{document}W+-boson tagging rejection factors (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$1/\epsilon _{W^+}$$\end{document}1/ϵW+) of 1.7, 8.3 and 1000, respectively. It is not possible to measure these efficiencies in the data due to the lack of a pure sample of high \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p_\text {T}$$\end{document}pT, hadronically decaying Z bosons. However, the modelling of the tagger inputs for boosted W bosons is studied in data using a \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$t\bar{t}$$\end{document}tt¯-enriched sample of events in 20.3 fb\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${}^{-1}$$\end{document}-1 of data at \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sqrt{s}=8$$\end{document}s=8 TeV. The inputs are well modelled within uncertainties, which builds confidence in the expected tagger performance.
Abstract Friction stir processing (FSP) is applied to modify the surface microstructure of cast AZ91 magnesium alloy. The electrochemical and hydrogen evolution measurements reveal that the corrosion rate of processed alloy in 3.5 wt% NaCl solution is significantly decreased. This is mainly attributed to the alteration of corrosion process induced by modification on the morphology and distribution of β-Mg17Al12 phase via FSP. It is originally reported that the formation of a compact and continuous β phase layer on the FSPed surface owing to the segregation of fine β phase effectively enhances the stability and passivity of corrosion product film.
In this work, we consider an extension of symmetric teleparallel gravity, namely, f(Q) gravity, where the fundamental block to describe spacetime is the nonmetricity, Q. Within this formulation of gravitation, we perform an observational analysis of several modified f(Q) models using the redshift approach, where the f(Q) Lagrangian is reformulated as an explicit function of the redshift, f(z). Various different polynomial parametrizations of f(z) are proposed, including new terms which would allow for deviations from the Λ Cold Dark Matter model. Given a variety of observational probes, such as the expansion rate data from early type galaxies, type Ia supernovae, quasars, gamma ray bursts, baryon acoustic oscillations data, and cosmic microwave background distance priors, we have checked the validity of these models at the background level in order to verify if this new formalism provides us with plausible alternative models to explain the late time acceleration of the Universe. Indeed, this novel approach provides a different perspective on the formulation of observationally reliable alternative models of gravity.
Abstract This study proposes a cooperative multi-agent system for managing the energy of a stand-alone microgrid. The multi-agent system learns to control the components of the microgrid so as this to achieve its purposes and operate effectively, by means of a distributed, collaborative reinforcement learning method in continuous actions-states space. Stand-alone microgrids present challenges regarding guaranteeing electricity supply and increasing the reliability of the system under the uncertainties introduced by the renewable power sources and the stochastic demand of the consumers. In this article we consider a microgrid that consists of power production, power consumption and power storage units: the power production group includes a Photovoltaic source, a fuel cell and a diesel generator; the power consumption group includes an electrolyzer unit, a desalination plant and a variable electrical load that represent the power consumption of a building; the power storage group includes only the Battery bank. We conjecture that a distributed multi-agent system presents specific advantages to control the microgrid components which operate in a continuous states and actions space: For this purpose we propose the use of fuzzy Q-Learning methods for agents representing microgrid components to act as independent learners, while sharing state variables to coordinate their behavior. Experimental results highlight both the effectiveness of individual agents to control system components, as well as the effectiveness of the multi-agent system to guarantee electricity supply and increase the reliability of the microgrid.
Peptides play a pivotal role in a wide range of biological activities through participating in up to 40% protein-protein interactions in cellular processes. They also demonstrate remarkable specificity and efficacy, making them promising candidates for drug development. However, predicting peptide-protein complexes by traditional computational approaches, such as Docking and Molecular Dynamics simulations, still remains a challenge due to high computational cost, flexible nature of peptides, and limited structural information of peptide-protein complexes. In recent years, the surge of available biological data has given rise to the development of an increasing number of machine learning models for predicting peptide-protein interactions. These models offer efficient solutions to address the challenges associated with traditional computational approaches. Furthermore, they offer enhanced accuracy, robustness, and interpretability in their predictive outcomes. This review presents a comprehensive overview of machine learning and deep learning models that have emerged in recent years for the prediction of peptide-protein interactions.
A bstractThe spectra of charged particles produced within the pseudorapidity window |η| 20 GeV, RpA exhibits weak momentum dependence and shows a moderate enhancement above unity.
Yager [1] introduced the concept of q-rung orthopair fuzzy sets (q-ROFSs) in which the sum of the qth exponent of the support for membership and the qth exponent of the support against membership is bounded by one. Thus, the q-ROFSs are an important way to express uncertain information in broader space, and they are superior to the intuitionistic fuzzy sets (IFSs) and the Pythagorean fuzzy sets (PFSs). However, in dealing with many real life situations, it is not appropriate for experts to precisely quantify their judgements with a crisp number due to insufficiency in available information. In such situation it is advisable for decision makers to provide their judgements by the subset of the closed interval [0, 1]. The notion of interval-valued q-rung orthopair fuzzy sets (IVq-ROFSs) is presented in this paper, which allows decision makers to provide their satisfying degrees and non-satisfying degrees to a given set of alternatives by an interval value. Some of its important operations such as: negation, union and intersection are also given. Based on these operations, the aggregation of IVq-ROFSs is also studied.
A search for supersymmetry is presented based on multijet events with large missing transverse momentum produced in proton-proton collisions at a center-of-mass energy of root s = 13 TeV. The data, corresponding to an integrated luminosity of 35.9 fb(-1), were collected with the CMS detector at the CERN LHC in 2016. The analysis utilizes four-dimensional exclusive search regions defined in terms of the number of jets, the number of tagged bottom quark jets, the scalar sum of jet transverse momenta, and the magnitude of the vector sum of jet transverse momenta. No evidence for a significant excess of events is observed relative to the expectation from the standard model. Limits on the cross sections for the pair production of gluinos and squarks are derived in the context of simplified models. Assuming the lightest supersymmetric particle to be a weakly interacting neutralino, 95% confidence level lower limits on the gluino mass as large as 1800 to 1960 GeV are derived, and on the squark mass as large as 960 to 1390 GeV, depending on the production and decay scenario.