Jameel Alp, Alyssa M. Bren, Tyson Sievers et al.
Hasil untuk "hep-ex"
Menampilkan 20 dari ~115030 hasil · dari CrossRef, arXiv, DOAJ
Bharathi Selvan, Melissa M. Tran, Christine O’Connell et al.
Tushar Khanna, MariaLisa Itzoe, Josh Mukherjee et al.
Adedeji Adenusi, Xucong Meng, Mohammad Bilal et al.
Kazuki Natsui, Seiichi Yoshikawa, Ayano Kagata et al.
Marco Meyer-Conde, Nobuyuki Kanda, Hirotaka Takahashi et al.
High-Energy Physics (HEP) and Gravitational Wave (GW) communities serve different scientific purposes. However, their methodologies might potentially offer mutual enrichment through common software developments. A suite of libraries is currently being prototyped and made available at https://git.ligo.org/kagra/libraries-addons/root, extending at no cost the CERN ROOT data analysis framework toward advanced signal processing. We will also present a performance benchmark comparing the FFTW and KFR library performances.
Rikab Gambhir, Radha Mastandrea, Benjamin Nachman et al.
We present the first study of anti-isolated Upsilon decays to two muons ($Υ\to μ^+ μ^-$) in proton-proton collisions at the Large Hadron Collider. Using a machine learning (ML)-based anomaly detection strategy, we "rediscover" the $Υ$ in 13 TeV CMS Open Data from 2016, despite overwhelming anti-isolated backgrounds. We elevate the signal significance to $6.4 σ$ using these methods, starting from $1.6 σ$ using the dimuon mass spectrum alone. Moreover, we demonstrate improved sensitivity from using an ML-based estimate of the multi-feature likelihood compared to traditional "cut-and-count" methods. Our work demonstrates that it is possible and practical to find real signals in experimental collider data using ML-based anomaly detection, and we distill a readily-accessible benchmark dataset from the CMS Open Data to facilitate future anomaly detection developments.
Diana L. Snyder, Jeffrey A. Alexander, Karthik Ravi et al.
Jan Söderman, Sven Almer
Karen Curtin, Michael J. Madsen, Zhe Yu et al.
Samantha Whitwell, Kyle Kreitman, Claudio Tombazzi
Siqi Miao, Zhiyuan Lu, Mia Liu et al.
This study introduces a novel transformer model optimized for large-scale point cloud processing in scientific domains such as high-energy physics (HEP) and astrophysics. Addressing the limitations of graph neural networks and standard transformers, our model integrates local inductive bias and achieves near-linear complexity with hardware-friendly regular operations. One contribution of this work is the quantitative analysis of the error-complexity tradeoff of various sparsification techniques for building efficient transformers. Our findings highlight the superiority of using locality-sensitive hashing (LSH), especially OR & AND-construction LSH, in kernel approximation for large-scale point cloud data with local inductive bias. Based on this finding, we propose LSH-based Efficient Point Transformer (HEPT), which combines E$^2$LSH with OR & AND constructions and is built upon regular computations. HEPT demonstrates remarkable performance on two critical yet time-consuming HEP tasks, significantly outperforming existing GNNs and transformers in accuracy and computational speed, marking a significant advancement in geometric deep learning and large-scale scientific data processing. Our code is available at https://github.com/Graph-COM/HEPT.
Lei Hao
The tree-level measurement of the CKM $γ$ angle is a crucial test of $CP$ violation in the Standard Model (SM). Discrepancies between direct measurements (tree-level decays) and indirect measurements (loop decays) could indicate physics beyond the SM. Recent measurements using decays such as $B^0 \rightarrow DK^{*0}, B^{\pm} \rightarrow [Dπ^0/γ]_{D^*}h^{\pm}$ (where $D$ decays to $K_{S}^{0} h^+h^-$ with $h=K, π$) and $B^{\pm} \rightarrow [h'^+h'^-π^+π^-]_Dh^{\pm}$ are presented. Additionally, the combination of previous $γ$ measurements at LHCb, excluding the aforementioned results, is also presented. The LHCb result, with a precision of $(63.8^{+3.5}_{-3.7})^{\circ}$ establishes itself as a dominant measurement in this field.
E. Giulio Villani, Dengfeng Zhang, Adnan Malik et al.
Strip and pixels sensors, fabricated on high resistivity silicon substrate, normally of p-type, are used in detectors for High Energy Physics (HEP) typically in a hybrid detector assembly. Furthermore, and owing to their inherent advantages over hybrid sensors, Monolithic Active Pixel Sensors (MAPS) fabricated in CMOS technology have been increasingly implemented in HEP experiments. In all cases, their use in higher radiation areas (HL-LHC and beyond) will require options to improve their radiation hardness and time resolution. These aspects demand a deep understanding of their radiation damage and reliable models to predict their behaviours at high fluences. As a first step, we fabricated several Schottky and n-on-p diodes, to allow a comparison of results and provide a backup solution for test devices, on 6 or 4-inch p-type silicon wafers with 50 μm epitaxial thickness and of doping concentration as they are normally used in HEP detectors and CMOS MAPS devices. In this paper, details of the design and fabrication process, along with test results of the fabricated devices before irradiation, will be provided. Additional test results on irradiated devices will be provided in subsequent publications.
Kimitoshi Kubo, Hiroki Niwa, Kazuteru Komuro
Kazuki Natsui, Masaki Maruyama, Shuji Terai
Roberto Trasolini, Kai Zhu, Natasha Klemm et al.
Ritu R. Singh, Abhilash Perisetti, Kumar Pallav et al.
Wataru Inui, Tomohiro Sugiyama, Takahiro Uotani
W. Sannaa, J. BouSaba, Y. Magnus et al.
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