Article
Astronomy & Astrophysics
Joseph Walker, Frank Krauss
Summary: We present theoretical results for the sensitivity of charm Yukawa coupling measurements in future high-luminosity LHC runs in three channels. Simple kinematic and jet feature cuts are applied to reduce backgrounds and false positives, and neural network data structures are utilized for analysis.
Article
Physics, Particles & Fields
Ulrich Haisch, Giacomo Polesello
Summary: This study proposes novel LHC searches for leptoquarks (LQs) using production methods beyond strong interaction, and provides detailed evaluations of achievable sensitivities for corresponding LQ signatures. Realistic analysis strategies are developed for resonant signals involving bottom quark and tau lepton, top quark and missing transverse energy, and light-flavour jets plus missing transverse energy, allowing for stringent constraints on the masses and couplings of third-generation singlet vector LQs. The proposed search channels have the potential to probe interesting parts of the LQ parameter space addressing B-physics anomalies at future LHC runs.
JOURNAL OF HIGH ENERGY PHYSICS
(2021)
Article
Physics, Multidisciplinary
S. A. Antipov, D. Amorim, N. Biancacci, X. Buffat, E. Metral, N. Mounet, A. Oeftiger, D. Valuch
Summary: Landau damping is crucial for maintaining collective beam stability in particle accelerators, with precise knowledge of its strength being essential for accurate predictions on beam stability in high-energy colliders. An experimental procedure using active transverse feedback has been demonstrated to quantify the strength of Landau damping and the limits of beam stability, with stability diagrams for different Landau octupole strengths being measured at the Large Hadron Collider as a proof-of-principle test. This procedure could potentially provide an accurate way of measuring stability diagrams throughout the machine cycle in the future.
PHYSICAL REVIEW LETTERS
(2021)
Article
Astronomy & Astrophysics
Bradley Garland, Sebastian Jager, Charanjit K. Khosa, Sandra Kvedaraite
Summary: This study investigates the sensitivity of future proton-proton colliders to a contact interaction indicated by rare B-decay anomalies. The results show that the future FCC-hh collider can exclude certain scales and discover higher Lambda values with appropriate luminosity.
Article
Multidisciplinary Sciences
Anthony Alexiades Armenakas, Oliver K. Baker
Summary: This study utilizes quantum search method to perform high energy physics event selection on the unsorted ATLAS Open Data database, introducing a new approach using quantum computing to rapidly identify rare events, showcasing the potential application of quantum computing in the HEP field.
SCIENTIFIC REPORTS
(2021)
Article
Physics, Particles & Fields
Felipe F. Freitas, Joao Goncalves, Antonio P. Morais, Roman Pasechnik
Summary: This work investigates TeV-scale vector-like fermion signatures at the LHC based on a trinification gauge group, using Deep Learning and evolutionary algorithms to optimize neural networks for estimating various VLQ masses. By combining detector images and tabular data, VLQs specific to the model are successfully excluded up to a mass of 800 GeV in the high-luminosity and Run-III phases of the LHC program.
EUROPEAN PHYSICAL JOURNAL C
(2022)
Article
Computer Science, Artificial Intelligence
Qi Zhang, Jinghua Li, Yanfeng Sun, Shaofan Wang, Junbin Gao, Baocai Yin
Summary: This paper proposes a novel graph neural network called AFGNN, which can capture all frequency information on large-scale graphs. AFGNN consists of two stages: the first stage extracts comprehensive frequency information using low-, middle-, and high-pass graph filters, while the second stage generates customized graph filters for each node using node-level attention-based feature combination. Experimental results demonstrate the superiority of AFGNN over other scalable GNNs and spectral GNNs.
Article
Physics, Particles & Fields
Debabrata Bhowmik, Jayita Lahiri, Satyaki Bhattacharya, Biswarup Mukhopadhyaya, Ritesh K. Singh
Summary: In this study, we investigate the potential of using the channel monoHiggs + missing transverse energy (MET) to discover signals of dark matter at the high-luminosity Large Hadron Collider (LHC). We consider a Higgs-portal scenario, where an extension of the Standard Model is introduced with a real scalar gauge-singlet serving as a dark matter candidate. We ensure the viability of this scenario by postulating the existence of dimension-6 operators that cancel certain amplitudes for the elastic scattering of dark matter. We analyze the mono-Higgs signal in detail, utilizing the accompanying MET in order to suppress background noise. We study signals for the Higgs decaying into both diphoton and b (b) over bar channels, optimizing event selection criteria through a cut-based simulation. Furthermore, we demonstrate how statistical significance can be improved using boosted decision trees and artificial neural networks.
EUROPEAN PHYSICAL JOURNAL C
(2022)
Article
Physics, Particles & Fields
Junxing Pan, Jung-Hsin Chen, Xiao-Gang He, Gang Li, Jhih-Ying Su
Summary: The study focused on the search for a triply charged Higgs boson from a complex Higgs quadruplet, with detailed collider analyses conducted at a 100 TeV pp collider. The research found that about 100 fb-1 of data would be needed for a 5 sigma discovery, and sensitivity to triply charged Higgs bosons below 1 TeV was also evaluated at the LHC. The results showed that the sensitivity varied based on factors such as v Delta and Delta m values.
EUROPEAN PHYSICAL JOURNAL C
(2021)
Article
Computer Science, Theory & Methods
Likhitha Mankali, Lilas Alrahis, Satwik Patnaik, Johann Knechtel, Ozgur Sinanoglu
Summary: Hardware obfuscation is an important solution for protecting against IP piracy and reverse engineering of ICs. Current evaluation methods focus on small scales of obfuscation and specific obfuscation schemes, which suggests the need for a holistic assessment framework. In this study, the researchers propose Titan, a comprehensive framework for large-scale gate and interconnect obfuscation, using a graph neural network-based attack framework. The evaluation of Titan shows significant information leakage and superior performance compared to state-of-the-art attacks.
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
(2023)
Article
Computer Science, Artificial Intelligence
Weishuai Che, Zhaowei Liu, Yingjie Wang, Jinglei Liu
Summary: The development of the Internet and big data has led to the importance of graphs as a data representation structure. However, as data size increases, graph embedding faces challenges in computational complexity and memory requirements. To address this, this paper proposes a multilevel embedding refinement framework (MERIT) based on large-scale graphs, using spectral distance-constrained graph coarsening algorithms and an improved graph convolutional neural network model. Experimental results show the effectiveness of MERIT, with an average AUROC score 8% higher than other baseline methods.
COMPLEX & INTELLIGENT SYSTEMS
(2023)
Article
Physics, Nuclear
Changfeng Li, Deeptak Biswas, Nihar Ranjan Sahoo
Summary: The higher-order cumulants of net-proton number, net-charge, and net-strangeness multiplicity distributions are widely studied in heavy-ion collisions to search for the quantum-chromodynamics critical point and extract the chemical freezeout parameters. The event-by-event fluctuations of the net-strangeness multiplicity distributions are crucial in determining the chemical freeze-out parameter in the strangeness sector. The study focuses on the net-K, net-A, and net-(K + A) multiplicity distributions and their different order of cumulants, including resonance decay contributions.
Article
Astronomy & Astrophysics
Eduardo da Silva Almeida, Alexandre Alves, Oscar J. P. Eboli, F. S. Queiroz
Summary: We investigate the production of color-adjoint leptons (leptogluons) using lepton parton density function of the proton at the LHC. We show that this mechanism can extend the LHC's ability to search for leptogluons with masses up to about 3.5 TeV and compositeness scales of O(1) TeV. Differentiating leptogluons from scalar and vector leptoquarks is also possible in this channel with a data sample of around 100 signal events. The resonant channel can be combined with other leptogluon production processes to enhance exclusion limits and discovery prospects at the LHC.
Article
Multidisciplinary Sciences
Mathieu Couttenier, Sebastien Di Rollo, Louise Inguere, Mathis Mohand, Lukas Schmidt
Summary: Artisanal and small-scale mines (Asm) are a common presence in Africa and other regions, but lack of reliable geospatial information makes monitoring and regulation challenging. This study presents a strategy for mapping ASM locations using satellite data and a convolutional neural network, successfully detecting ASM activities over a vast area.
Article
Physics, Nuclear
Salah-Eddine Dahbi, Joshua Choma, Gaogalalwe Mokgatitswane, Xifeng Ruan, Benjamin Lieberman, Bruce Mellado, Turgay Celik
Summary: The article introduces a method of using weak supervision machine learning techniques to search for resonances, which can be used to search for resonances with little or no prior knowledge. By combining weak supervision with deep neural network algorithms, it is possible to search for new resonances at the LHC.
INTERNATIONAL JOURNAL OF MODERN PHYSICS A
(2022)