Hasil untuk "Physics"

E. Kou, P. Urquijo, W. Altmannshofer et al.

We present the physics program of the Belle II experiment, located on the intensity frontier SuperKEKB e+e- collider. Belle II collected its first collisions in 2018, and is expected to operate for the next decade. It is anticipated to collect 50/ab of collision data over its lifetime. This book is the outcome of a joint effort of Belle II collaborators and theorists through the Belle II theory interface platform (B2TiP), an effort that commenced in 2014. The aim of B2TiP was to elucidate the potential impacts of the Belle II program, which includes a wide scope of physics topics: B physics, charm, tau, quarkonium, electroweak precision measurements and dark sector searches. It is composed of nine working groups (WGs), which are coordinated by teams of theorist and experimentalists conveners: Semileptonic and leptonic B decays, Radiative and Electroweak penguins, phi_1 and phi_2 (time-dependent CP violation) measurements, phi_3 measurements, Charmless hadronic B decay, Charm, Quarkonium(like), tau and low-multiplicity processes, new physics and global fit analyses. This book highlights "golden- and silver-channels", i.e. those that would have the highest potential impact in the field. Theorists scrutinised the role of those measurements and estimated the respective theoretical uncertainties, achievable now as well as prospects for the future. Experimentalists investigated the expected improvements with the large dataset expected from Belle II, taking into account improved performance from the upgraded detector.

P. Gibbon

These notes are intended to provide a brief primer in plasma physics, introducing common definitions, basic properties, and typical processes found in plasmas. These concepts are inherent in contemporary plasma-based accelerator schemes, and thus provide a foundation for the more advanced expositions that follow in this volume. No prior knowledge of plasma physics is required, but the reader is assumed to be familiar with basic electrodynamics and fluid mechanics.

B. Draine

A. Falcon

M. Meneghini, C. de Santi, I. Abid et al.

R. Mittal, R. Ni, J. Seo

Flow physics plays a key role in nearly every facet of the COVID-19 pandemic. This includes the generation and aerosolization of virus-laden respiratory droplets from a host, its airborne dispersion and deposition on surfaces, as well as the subsequent inhalation of these bioaerosols by unsuspecting recipients. Fluid dynamics is also key to preventative measures such as the use of face masks, hand washing, ventilation of indoor environments and even social distancing. This article summarizes what we know and, more importantly, what we need to learn about the science underlying these issues so that we are better prepared to tackle the next outbreak of COVID-19 or a similar disease.

N. G. Kampen, William P. Reinhardt

V. S. Vladimirov

U. F. Kocks, H. Mecking

A. Gibson, T. Sekiguchi, K. Lackner et al.

A. diSessa

M. Shifman, A. Vainshtein, V. Zakharov

N. Krall, A. Trivelpiece

J. Beacham, C. Burrage, D. Curtin et al.

The Physics Beyond Colliders initiative is an exploratory study aimed at exploiting the full scientific potential of the CERN's accelerator complex and scientific infrastructures through projects complementary to the LHC and other possible future colliders. These projects will target fundamental physics questions in modern particle physics. This document presents the status of the proposals presented in the framework of the Beyond the Standard Model physics working group, and explore their physics reach and the impact that CERN could have in the next 10-20 years on the international landscape.

M. A. Sprague, S. Ananthan, G. Vijayakumar et al.

Ruiyang Zhang, Yang Liu, Hao Sun

This paper introduces an innovative physics-informed deep learning framework for metamodeling of nonlinear structural systems with scarce data. The basic concept is to incorporate physics knowledge (e.g., laws of physics, scientific principles) into deep long short-term memory (LSTM) networks, which boosts the learning within a feasible solution space. The physics constraints are embedded in the loss function to enforce the model training which can accurately capture latent system nonlinearity even with very limited available training datasets. Specifically for dynamic structures, physical laws of equation of motion, state dependency and hysteretic constitutive relationship are considered to construct the physics loss. In particular, two physics-informed multi-LSTM network architectures are proposed for structural metamodeling. The satisfactory performance of the proposed framework is successfully demonstrated through two illustrative examples (e.g., nonlinear structures subjected to ground motion excitation). It turns out that the embedded physics can alleviate overfitting issues, reduce the need of big training datasets, and improve the robustness of the trained model for more reliable prediction. As a result, the physics-informed deep learning paradigm outperforms classical non-physics-guided data-driven neural networks.

M. Liberman

F. Sahli Costabal, Yibo Yang, P. Perdikaris et al.

A critical procedure in diagnosing atrial fibrillation is the creation of electro-anatomic activation maps. Current methods generate these mappings from interpolation using a few sparse data points recorded inside the atria; they neither include prior knowledge of the underlying physics nor uncertainty of these recordings. Here we propose a physics-informed neural network for cardiac activation mapping that accounts for the underlying wave propagation dynamics and we quantify the epistemic uncertainty associated with these predictions. These uncertainty estimates not only allow us to quantify the predictive error of the neural network, but also help to reduce it by judiciously selecting new informative measurement locations via active learning. We illustrate the potential of our approach using a synthetic benchmark problem and a personalized electrophysiology model of the left atrium. We show that our new method outperforms linear interpolation and Gaussian process regression for the benchmark problem and linear interpolation at clinical densities for the left atrium. In both cases, the active learning algorithm achieves lower error levels than random allocation. Our findings open the door toward physics-based electro-anatomic mapping with the ultimate goals to reduce procedural time and improve diagnostic predictability for patients affected by atrial fibrillation. Open source code is available at https://github.com/fsahli/EikonalNet.

J. Willard, X. Jia, Shaoming Xu et al.

Juno collaboration Angel Abusleme, T. Adam, Shakeel Ahmad et al.

The Jiangmen Underground Neutrino Observatory (JUNO) is a 20 kton liquid scintillator detector in a laboratory at 700-m underground. An excellent energy resolution and a large fiducial volume offer exciting opportunities for addressing many important topics in neutrino and astroparticle physics. With six years of data, the neutrino mass ordering can be determined at a 3-4σ significance and the neutrino oscillation parameters sin2 θ12, ∆m21, and |∆m32| can be measured to a precision of 0.6% or better, by detecting reactor antineutrinos from the Taishan and Yangjiang nuclear power plants. With ten years of data, neutrinos from all past core-collapse supernovae could be observed at a 3σ significance; a lower limit of the proton lifetime, 8.34× 1033 years (90% C.L.), can be set by searching for p → ν̄K+; detection of solar neutrinos would shed new light on the solar metallicity problem and examine the vacuum-matter transition region. A typical core-collapse supernova at a distance of 10 kpc would lead to ∼ 5000 inverse-beta-decay events and ∼ 2000 (300) all-flavor neutrino-proton (electron) elastic scattering events in JUNO. Geoneutrinos can be detected with a rate of ∼ 400 events per year. Construction of the detector is very challenging. In this review, we summarize the final design of the JUNO detector and the key R&D achievements, following the Conceptual Design Report in 2015 [2]. All 20-inch PMTs have been procured and tested. The average photon detection efficiency is 28.9% for the 15,000 MCP PMTs and 28.1% for the 5,000 dynode PMTs, higher than the JUNO requirement of 27%. Together with the > 20 m attenuation length of the liquid scintillator achieved in a 20-ton pilot purification test and the > 96% transparency of the acrylic panel, we expect a yield of 1345 photoelectrons per MeV and an effective relative energy resolution of 3.02%/ √ E(MeV) in simulations [3]. To maintain the high performance, the underwater electronics is designed to have a loss rate < 0.5% in six years. With degassing membranes and a micro-bubble system, the radon concentration in the 35 kton water pool could be lowered to < 10 mBq/m3. Acrylic panels of radiopurity < 0.5 ppt U/Th for the 35.4-m diameter liquid scintillator vessel are produced with a dedicated production line. The 20 kton liquid scintillator will be purified onsite with Alumina filtration, distillation, water extraction, and gas stripping. Together with other low background handling, singles in the fiducial volume can be controlled to ∼ 10 Hz. The JUNO experiment also features a double calorimeter system with 25,600 3-inch PMTs, a liquid scintillator testing facility OSIRIS, and a near detector TAO.