bluuebunny/crossref_metadata_2025_split huggingface dataset:
Application of the Quantum Kernel Algorithm on the Particle Identification at the BESIII Experiment
Authors: Li Teng , Yao Zhipeng , Huang Xingtao , Zou Jiaheng , Lin Tao , Li Weidong 
Published: February 2023

Abstract
Particle identification is one of most fundamental tools in various particle physics experiments. For the BESIII experiment on the BEPCII, the realization of numerous physical goals heavily relies on advanced particle identification algorithms. In recent years, the emerging of quantum machine learning could potentially arm particle physics experiments with a powerful new toolbox. In this work, targeting at the muon/pion discrimination problem at BESIII, we have developed a quantum SVM classifier for the Noisy Intermediate-Scale Quantum (NISQ) device. By studying and optimizing various encoding circuits, the quantum SVM trained with the BESIII MC data shows comparable discrimination power than other traditional machine learning models. This has demonstrated the potential of using quantum machine learning techniques to form a new approach for particle identification in particle physics experiments. In this talk, we present the application of the quantum SVM for particle identification at BESIII, and demonstrate how to construct and optimize the quantum kernel. Furthermore, we present results obtained from the noisy simulator as well as the small-scale hardware to show the potential advantage of the quantum SVM algorithm.
Autoencoder-extended Conditional Invertible Neural Networks for Unfolding Signal Traces
Authors: Erdmann M , Hafner K , Schulte J , Straub M 
Published: February 2023

Abstract
The reconstruction of cosmic ray-induced air showers from measurements of radio waves constitutes a major challenge. In this work, we focus on recovering the full three-dimensional electromagnetic field from two recorded signal traces of an antenna station covering two horizontal polarization directions. The simulated field is folded by a direction and frequency-dependent characteristic antenna response pattern, resulting in voltage signal traces as a function of time. Both signal traces are contaminated by simulated background noise. We use conditional Invertible Neural Networks (cINNs) to learn posterior distributions, from which the most likely electromagnetic field given a measured signal trace can be inferred. To improve robustness, we extend the method with an autoencoder by reducing the parameter phase space and decoupling the cINN from specific data shapes. Thereby, each signal trace is condensed into a small number of abstract parameters in the latent space on which the cINN operates. The presented method shows promising results and can be transferred to other unfolding problems where the recovery of the pre-measurement state is of interest.
Declarative interfaces for HEP data analysis: FuncADL and ADL/CutLang
Authors: Huh C , Proffitt M , Prosper H B , Sekmen S , Sen B , Unel G , Watts G 
Published: February 2023

Abstract
Analysis description languages are declarative interfaces for HEP data analysis that allow users to avoid writing event loops, simplify code, and enable performance improvements to be decoupled from analysis development. One example is FuncADL, inspired by functional programming and developed using Python as a host language. FuncADL borrows concepts from database query languages to isolate the interface from the underlying physical and logical schemas. The same query can be used to select data from different sources and formats and with different execution mechanisms. FuncADL is one of the tools being developed by IRIS-HEP for highly scalable physics analysis for the LHC and HL-LHC. FuncADL is demonstrated by implementing example analysis tasks designed by HSF and IRIS-HEP. Another language example is ADL, which expresses the physics content of an analysis in a standard and unambiguous way, independent of computing frameworks. In ADL, analyses are described in human-readable text files composed of blocks with a keyword-expression structure. Two infrastructures are available to render ADL executable: CutLang, a runtime interpreter written in C++; and adl2tnm, a transpiler converting ADL into C++ or Python code. ADL/CutLang are already used in several physics studies and educational projects, and are adapted for use with LHC Open Data.
Deferred Optical Photon simulation for the JUNO experiment
Authors: Lin Tao 
Published: February 2023

Abstract
The Jiangmen Underground Neutrino Observatory (JUNO) is designed to determine the neutrino mass ordering and precisely measure oscillation parameters. It is being built in South China at a depth of 700 m underground and comprises a central detector, water Cerenkov detector and top tracker. The central detector is designed to detect anti-neutrinos with an energy resolution of 3% at 1 MeV, using a 20 kt liquid scintillator target with 17,612 20-inch PMTs and 25,600 3-inch PMTs. The scintillator provides a light yield of approximately 10,000 photons per MeV. Monte Carlo simulation is a crucial tool for developing an understanding of detector performance, requiring the production of large samples of background processes with optical photons. Simulation of large numbers of optical photons with Geant4 is computationally challenging for both processing time and memory resources. In order to optimize resource usage, a deferred optical photon simulation workflow is proposed and implemented using Geant4 classes. The key idea is to simulate events initially without optical photons, only performing the optical photon simulation when user specified criteria are met.
In this contribution, the design and the implementation of the deferred optical photon simulation will be presented. Optical simulation comprises generation of photons and propagation through the detector implementing optical physics processes including reflection, refraction, scattering and absorption. Instead of generating the optical photons at each step by Geant4 immediately, the necessary data to generate optical photons at each step are collected, which is termed GenStep. At the end of each event, user specified criteria determines if the optical photon simulation is performed using a class called G4OPSimulator. The class G4OPSimulator implements a customized simulation workflow, based on Geant4 internal classes including G4TrackingManager and G4StackManager. The simulator passes references to the collected GenStep objects to customized Scintillation and Cerenkov processes which generate the optical photons in G4Track objects. As a track could be absorbed and re-emitted, the secondaries will be retrieved from the G4TrackingManager and pushed to the G4StackManager. The performance of the simulator will be presented. The technique of deferred optical photon simulation can be applied to all event types and it is expected to be particularly beneficial with rare processes. Especially the events must be selected during the detector simulation at runtime, instead of the event generation. An application of the technique to the simulation of such events will be shown.
Impact of quantum noise on the training of quantum Generative Adversarial Networks
Authors: Borras Kerstin , Chang Su Yeon , Funcke Lena , Grossi Michele , Hartung Tobias , Jansen Karl , Kruecker Dirk , Kühn Stefan , Rehm Florian , Tüysüz Cenk , Vallecorsa Sofia 
Published: February 2023

Abstract
Current noisy intermediate-scale quantum devices suffer from various sources of intrinsic quantum noise. Overcoming the effects of noise is a major challenge, for which different error mitigation and error correction techniques have been proposed. In this paper, we conduct a first study of the performance of quantum Generative Adversarial Networks (qGANs) in the presence of different types of quantum noise, focusing on a simplified use case in high-energy physics. In particular, we explore the effects of readout and two-qubit gate errors on the qGAN training process. Simulating a noisy quantum device classically with IBM’s Qiskit framework, we examine the threshold of error rates up to which a reliable training is possible. In addition, we investigate the importance of various hyperparameters for the training process in the presence of different error rates, and we explore the impact of readout error mitigation on the results.
The Iterative Clustering framework for the CMS HGCAL Reconstruction
Authors: Pantaleo Felice , Rovere Marco 
Published: February 2023

Abstract
To sustain the harsher conditions of the high-luminosity LHC [1], the CMS Collaboration [2] is designing a novel endcap calorimeter system [3]. The new calorimeter will predominantly use silicon sensors to achieve sufficient radiation tolerance and will maintain highly granular information in the readout to help mitigate the effects of the pile up. In regions characterized by lower radiation levels, small scintillator tiles with individual SiPM on-tile readout are employed. A unique reconstruction framework (TICL: The Iterative CLustering) is being developed within the CMS Software CMSSW to fully exploit the granularity and other significant detector features, such as particle identification and precision timing, with a view to mitigating pile up in the very dense environment of HL-LHC. The TICL framework has been thought of with heterogeneous computing in mind: the algorithms and their data structures are designed to be executed on GPUs. In addition, geometry agnostic data structures have been designed to provide fast navigation and searching capabilities. Seeding capabilities (also exploiting information coming from other detectors), dynamic cluster masking, energy calibration, and particle identification are the main components of the framework. To allow for maximal flexibility, TICL allows the composition of different combinations of modules that can be chained together in an iterative fashion.
Machine learning based background rejection for Baikal-GVD neutrino telescope
Authors: Kalashev O. , Kharuk I. , Rubtsov G. 
Published: February 2023

Abstract
Baikal-GVD is a gigaton-scale underwater neutrino telescope currently under construction in Lake Baikal. Its principal components are optical modules, registering photons propagating through the telescope’s working volume. Part of the activations of the optical modules are due to the natural luminescence of the water, and thus appear as noise in the data. We present a neural network, which efficiently rejects this background and reaches 97% signal purity (precision) and 99% survival efficiency (recall) on the Monte-Carlo data. The neural network has a U-net like architecture based on the temporal structure of optical modules activations.
Multi-Jet Event classification with Convolutional neural network at Large Scale
Authors: Kim Jiwoong , Moon Chang-Seong , Nam Hokyeong , Goh Junghwan , Bae Dongsung , Yoo Changhyun , Kim Sungwon , Kim Tongil , Yoo Hwidong , Hwang Soonwook , Cho Kihyeon , Hahm Jaegyoon , Myung Hunjoo , Kim Minsik , Hong Taeyoung 
Published: February 2023

Abstract
We present an application of Scalable Deep Learning to analyze simulation data of the LHC proton-proton collisions at 13 TeV. We built a Deep Learning model based on the Convolutional Neural Network (CNN) which utilizes detector responses as two-dimensional images reflecting the geometry of the Compact Muon Solenoid (CMS) detector. The model discriminates signal events of the R-parity violating Supersymmetry (RPV SUSY) from the background events with multiple jets due to the inelastic QCD scattering (QCD multi-jets). With the CNN model, we obtained x1.85 efficiency and x1.2 expected significance with respect to the traditional cut-based method. We demonstrated the scalability of the model at a Large Scale with the High-Performance Computing (HPC) resources at the Korea Institute of Science and Technology Information (KISTI) up to 1024 nodes.
NA61/SHINE online noise filtering using machine learning methods
Authors: Kawęcka Anna , Bryliński Wojciech , Omana Kuttan Manjunath , Linnyk Olena , Pawlowski Janik , Schmidt Katarzyna , Słodkowski Marcin , Wyszyński Oskar , Zieliński Jakub 
Published: February 2023

Abstract
The NA61/SHINE is a high-energy physics experiment operating at the SPS accelerator at CERN. The physics program of the experiment was recently extended, requiring a significant upgrade of the detector setup. The main goal of the upgrade is to increase the event flow rate from 80Hz to 1kHz by exchanging the read-out electronics of the NA61/SHINE main tracking detectors (Time-Projection-Chambers - TPCs). As the amount of collected data will increase significantly, a tool for online noise filtering is needed. The standard method is based on the reconstruction of tracks and removal of clusters which do not belong to any particle trajectory. However, this method takes a substantial amount of time and resources. A novel approach based on machine learning methods is presented in this proceedings.
Neural Network-Based Primary Vertex Reconstruction with FPGAs for the Upgrade of the CMS Level-1 Trigger System
Authors: Brown C. , Bundock A. , Komm M. , Loncar V. , Pierini M. , Radburn-Smith B. , Shtipliyski A. , Summers S. , Dancu J.-S. , Tapper A. 
Published: February 2023

Abstract
The CMS experiment will be upgraded to maintain physics sensitivity and exploit the improved performance of the High Luminosity LHC. Part of this upgrade will see the first level (Level-1) trigger use charged particle tracks reconstructed within the full outer silicon tracker volume as an input for the first time and new algorithms are being designed to make use of these tracks. One such algorithm is primary vertex finding which is used to identify the hard scatter in an event and separate the primary interaction from additional simultaneous interactions. This work presents a novel approach to regress the primary vertex position and to reject tracks from additional soft interactions, which uses an end-to-end neural network. This neural network possesses simultaneous knowledge of all stages in the reconstruction chain, which allows for end-to-end optimisation. The improved performance of this network versus a baseline approach in the primary vertex regression and track-to-vertex classification is shown. A quantised and pruned version of the neural network is deployed on an FPGA to match the stringent timing and computing requirements of the Level-1 Trigger.
Quantum machine learning in the latent space of high energy physics events
Authors: Anna Woźniak Kinga , Belis Vasilis , Pierini Maurizio , Vallecorsa Sofia , Dissertori Günther , Barkoutsos Panagiotis , Tavernelli Ivano 
Published: February 2023

Abstract
We investigate supervised and unsupervised quantum machine learning algorithms in the context of typical data analyses at the LHC. To accommodate the constraints on the problem size, dictated by limitations on the quantum hardware, we concatenate the quantum algorithms to the encoder of a classical convolutional autoencoder, used for dimensionality reduction. We present results for a quantum classifier and a quantum anomaly detection algorithm, comparing performance to corresponding classical algorithms.
Study of dynamic geometry in simulation and reconstruction of liquid-based detector
Authors: Li Jingshu , Zhang Shu , Zhang Yumei , You Zhengyun 
Published: February 2023

Abstract
The non-uniformity of liquid in a detector is difficult to be described with the traditional detector description method used in Geant4. To solve the simulation and reconstruction problem with dynamic and non-uniform liquid-based detector, a method of combining GDML and tessellated 3D modeling is put forward to export the detector geometry information from computational fluid dynamics simulation software and import it into the detector simulation software. This method can be used to study the effects of non-uniformity on detector performance. It can be further used to solve other dynamic geometry-related problems in particle and nuclear physics experiments.
Symmetry aware generation of two-staged particle decays in high-energy physics
Authors: Eich Niclas , Erdmann Martin , Fischer Benjamin , Zimmermann Niklas 
Published: February 2023

Abstract
We present a specialised layer for generative modeling of LHC events with generative adversarial networks. We use Lorentz boosts, rotations, momentum and energy conservation to build a network cell generating a 2-body particle decay. This cell is stacked consecutively in order to model two staged decays, respecting the symmetries across the decay chain. We allow for modifications of the resulting four-vectors in order to model higher order and detector effects. We give an evaluation of the generator quality in a Higgs decay into two Z bosons, further decaying into a muon pair each.
Vectorised Neutrino Reconstruction by Computing Graphs
Authors: Noll Dennis , Erdmann Martin , Fackeldey Peter , Fischer Benjamin 
Published: February 2023

Abstract
Many particle physics analyses are adopting the concept of vectorised computing, often making them increasingly performant and resource-efficient. While a variety of computing steps can be vectorised directly, some calculations are challenging to implement. One of these is the analytical neutrino reconstruction which involves fitting that naturally varies between events. We show a vectorised implementation of the analytical neutrino reconstruction using a graph computing model. It uses established deep learning software libraries and is natively portable to local and external hardware accelerators such as GPUs. Using the example of ttH events with a semi-leptonic final state, we present performance studies for our implementation.
A vendor-agnostic, single code-based GPU tracking for the Inner Tracking System of the ALICE experiment
Authors: Concas M 
Published: February 2023

Abstract
During the LHC Run 3 the ALICE online computing farm will process up to 50 times more Pb-Pb events per second than in Run 2. The implied computing resource scaling requires a shift in the approach that comprises the extensive usage of Graphics Processing Units (GPU) for the processing. We will give an overview of the state of the art for the data reconstruction on GPUs in ALICE, with additional focus on the Inner Tracking System detector. A detailed teardown of adopted techniques, implemented algorithms and approaches and performance report will be shown. Additionally, we will show how we support different GPUs brands (NVIDIA and AMD) with a single code-base using an automatic code translation and generation for different target architectures. Strengths and possible weaknesses of this approach will be discussed. Finally, an overview of the next steps towards an even more comprehensive usage of GPUs in ALICE software will be illustrated.
A factorisation-aware matrix element emulator
Authors: Truong H 
Published: February 2023

Abstract
In this contribution I present a neural network based model to emulate matrix elements. This model improves on existing methods by taking advantage of the known factorisation properties of matrix elements to drastically improve per-point accuracy. By building in the factorisation properties into the model we can control the behaviour of simulated matrix elements when extrapolating into more singular regions than the ones used for training the neural network. I apply this model to the case of leading-order jet production in e
+
e
−collisions with up to five jets where this model can reproduce the matrix elements with errors below the one-percent level on the phase-space covered during fitting and testing, with a robust extrapolation to the parts of the phase-space where the matrix elements are more singular than seen at the fitting stage. Finally I discuss the usage and performance of the model on a GPU system.
Loop integral evaluation and asymptotic expansion with pySecDec
Authors: Magerya Vitaly 
Published: February 2023

Abstract
The evaluation of higher-loop Feynman integrals is at the core of the quest to reduce the uncertainty of theoretical predictions and match experimental data from the LHC and future colliders. pySecDec is a program to evaluate such integrals numerically based on the sector decomposition approach; its new release version 1.5 introduces features significantly improving its performance: automatic adaptive evaluation of weighted sums of integrals (e.g. amplitudes) and asymptotic expansion in kinematic ratios. Here we briefly review both, illustrating the expected performance benefits.
Development of hard X-ray spectrometer with full digital data acquisition for runaway electron studies at HL-2M
Authors: Cheng Shikui , Zhang Jie , Zhang Yipo , He Li , Xu Hongbing , Zhu Yuxuan , Zhan Xuwen , Shi Zhongbing , Zhong Wulv 
Published: February 2023

Abstract
The development of hard X-ray (HXR) spectrometers with full digital data acquisition for runaway electron studies on the HL-2M tokamak is presented. Consisting of large-sized NaI:Tl detectors and high-performance multichannel analyzers, this system can realize the measurement of HXR with energies ranging from 0.5 to 10 MeV, and spectrum temporal resolution of 1 ms. An average energy resolution of 56 keV at 662 keV has been achieved at counts rates of about 100 kcps. The spectra data is acquired by a digital-based data acquisition system with fast digitization and software signal processing technology. The energy calibrations are performed by using several radioisotope gamma-ray sources, and the detection efficiencies are simulated by MCNP code. First experimental results from HL-2M Ohmic discharges are presented in this paper.
Persistent anticyclonic conditions and climate change exacerbated the exceptional 2022 European-Mediterranean drought
Authors: Faranda Davide , Pascale Salvatore , Bulut Burak 
Published: February 2023

Abstract
A prolonged drought affected Western Europe and the Mediterranean region in 2022 producing large socio-ecological impacts. The role of anthropogenic climate change (ACC) in exacerbating this drought has been often invoked in the public debate, but the link between atmospheric circulation and ACC has not received much attention so far. Here we address this question by applying the method of circulation analogs, which allows us to identify atmospheric patterns in the period 1836-2021 very similar to those occurred in 2022. By comparing the circulation analogs when global warming was absent (1836-1915) with those occurred recently (1942-2021), and by excluding interannual and interdecadal variability as possible drivers, we identify the contribution of ACC. The 2022 drought was associated with a persistent anticyclonic anomaly over Western Europe. Circulation analogs of this atmospheric pattern in 1941-2021 feature 500 hPa geopotential height anomalies larger in both extent and magnitude, and higher temperatures at the surface, relative to those in 1836-1915. Both factors exacerbated the drought, by increasing the area affected and enhancing soil drying through evapotranspiration. While the occurrence of the atmospheric circulation associated with the 2022 drought has not become more frequent in recent decades, the influence of the Atlantic Multidecadal oscillation cannot be ruled-out.
The hot deformation behaviors and constitutive modeling of Hastelloy C276
Authors: Wu Jia-Fu , Jiang Yu-Qiang , Deng Wei , Yao Gu-Wen 
Published: February 2023

Abstract
Hastelloy C276 is widely used in the new generation of nuclear power plants, and hot deformation is the optimum way to form the C276 part. In this investigation, the hot deformation and constitutive modeling of Hastelloy C276 alloy are researched, and the processing maps are drawn. The results show that strain rate and hot deformation temperature have remarkable impacts on the deformation behaviors of the Hastelloy C276 alloy. The yield behavior and the flow stress are predicted based on the Arrhenius constitutive equation, and the correlation coefficients are 0.9613 and 0.9837, indicating the high prediction ability of the established constitutive equation. Rising the deformation temperature can decrease the unstable deformation area, and the studied alloy can be deformed at low strain rates. With the increased strain rate, flow localization occurs, which is not suitable for the hot deformation.