We summarize the objectives and results of the ``international scoping study of a future neutrino factory and superbeam facility'' (ISS) physics working group. Furthermore, we discuss how the ISS study should develop into a neutrino factory design study (IDS-NF) from the point of view of physics and performance evaluation.
We present the results of a National Science Foundation (NSF) Project Scoping Workshop, the purpose of which was to assess the current status of calculations for the nuclear matrix elements governing neutrinoless double-beta decay and determine if more work on them is required. After reviewing important recent progress in the application of effective field theory, lattice quantum chromodynamics, and ab initio nuclear-structure theory to double-beta decay, we discuss the state of the art in nuclear-physics uncertainty quantification and then construct a road map for work in all these areas to f…
This topical group focuses on searches for signals from physics Beyond the Standard Model (BSM), both from a theoretical and experimental perspective, including neutrino-related BSM and searches for BSM in other sectors using neutrino facilities.
We present a summary of heavy-flavour physics at the the time of the DIS2019 conference. This summary is based upon the relevant plenary and parallel talks presented at the workshop. The summary is divided into four broad areas: (1) spectroscopy, (2) the production of b and c quarks in collisions, (3) top quark production and properties, and (4) b and c quark decay.
New Physics Working Group Summary of LCWS2000 at FNAL
本文对2000年10月24-28日召开的LCWS2000@FNAL会议P5-WG（New/Alternative Physics Working Group）进行了总结。总共有13个讲座，7个关于新粒子/相互作用的对撞机信号的讲座和6个关于额外维度物理的讲座。对于那些新的/替代的物理学/想法，我们在参与者中进行了非常活跃和热烈的讨论。
An introduction to gauge theories and group theory in particle physics
School of Physics, Sun Yat-Sen University（中山大学物理学院）；Institute of High Energy Physics, Chinese Academy of Sciences（中国科学院高能物理研究所）；CAS Center for Excellence in Particle Physics（中国科学院粒子物理中心）；School of Science, Sun Yat-Sen University（中山大学理学院）；Institute of Theoretical Physics, Chinese Academy of Sciences（中国科学院理论物理研究所）；School of Physical Sciences, University of Chinese Academy of Sciences（中国科学院大学物理科学学院）；Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences（中国科学院大学杭州高等研究院）
General analyses of $B$-physics processes beyond the Standard Model require accounting for operator mixing in the renormalization-group evolution from the matching scale down to the typical scale of $B$ physics. For this purpose the anomalous dimensions of the full set of local dimension-six operators beyond the Standard Model are needed. We present here for the first time a complete and non-redundant set of dimension-six operators relevant for $B$-meson mixing and decay, together with the complete one-loop anomalous dimensions in QCD and QED. These results are an important step towards the au…
Universität Hamburg（汉堡大学）；Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）；Rutgers University（罗格斯大学）
The identification of anomalous overdensities in data - group or collective anomaly detection - is a rich problem with a large number of real world applications. However, it has received relatively little attention in the broader ML community, as compared to point anomalies or other types of single instance outliers. One reason for this is the lack of powerful benchmark datasets. In this paper, we first explain how, after the Nobel-prize winning discovery of the Higgs boson, unsupervised group anomaly detection has become a new frontier of fundamental physics (where the motivation is to find n…
University of Chicago（芝加哥大学）；Atomwise（Atomwise公司）；Enrico Fermi Institute（恩里科·费米研究所）；Flatiron Institute（熨斗研究所）
We present a neural network architecture that is fully equivariant with respect to transformations under the Lorentz group, a fundamental symmetry of space and time in physics. The architecture is based on the theory of the finite-dimensional representations of the Lorentz group and the equivariant nonlinearity involves the tensor product. For classification tasks in particle physics, we demonstrate that such an equivariant architecture leads to drastically simpler models that have relatively few learnable parameters and are much more physically interpretable than leading approaches that use C…
