bluuebunny/crossref_metadata_2025_split huggingface dataset:
Exploring data merging methods for a distributed processing system
Authors: Konopka Piotr , Haller Barthélémy von 
Published: February 2023

Abstract
The ALICE experiment at the CERN LHC (Large Hadron Collider) is undertaking a major upgrade during the LHC Long Shutdown 2 in 2019-2021, which includes a new computing system called O2 (Online-Offline). The raw data input from the ALICE detectors will increase a hundredfold, up to 3.5 TB/s. By reconstructing the data online, it will be possible to compress the data stream down to 100 GB/s before storing it permanently.
The O2 software is a message-passing system. It will run on approximately 500 computing nodes performing reconstruction, compression, calibration and quality control of the received data stream. As a direct consequence of having a distributed computing system, locally generated data might be incomplete and could require merging to obtain complete results.
This paper presents the O2 Mergers, the software designed to match and combine partial data into complete objects synchronously to data taking. Based on a detailed study and results of extensive benchmarks, a qualitative and quantitative comparison of different merging strategies considered to reach the final design and implementation of the software is discussed.
The new GeoModel suite, a lightweight detector description and visualization toolkit for HEP
Authors: Bandieramonte M , Bianchi R M , Boudreau J , Dell’Acqua A , Tsulaia V 
Published: February 2023

Abstract
The GeoModel toolkit is an open-source suite of standalone tools that provides the user with lightweight tools to describe, visualize, test, and debug detector descriptions and geometries for HEP standalone studies and experiments. GeoModel has been designed with independence and responsiveness in mind and offers a development environment free of other large HEP tools and frameworks, and with a very quick development cycle. With very few and lightweight dependencies, GeoModel is easy to install on all systems, in a modular way; and pre-compiled binaries are provided for the major platforms, for a quick and easy installation. Coded entirely in C++, GeoModel offers the user tools to describe geometries inside C++ code or in external XML files, create persistent representation with a low disk footprint and interactively visualize and inspect the geometry in a 3D view. It also offers a plugin mechanism and an optional Geant4 application to simulate the described geometry in a standalone environment. GeoModel has been developed as part of the software for the ATLAS experiment at the LHC, and evolved towards an experiment-independent toolkit. In this contribution, we describe all the available tools, with a focus on the latest additions, which provide users with more visualization, debug, and simulation tools.
Software Training in High Energy Physics
Authors: Villanueva Michel H. , Malik Sudhir , Evans Meirin Oan 
Published: February 2023

Abstract
Among the upgrades in current high energy physics (HEP) experiments and the new facilities coming online, solving software challenges has become integral to the success of the collaborations. The demand for human resources skilled in both HEP and software domains is increasing. With a highly distributed environment in human resources, the sustainability of the HEP ecosystem requires a continuous effort in the equipment of physicists with the required abilities in software development. In this paper, the collective software training program in HEP and its activities led by the HEP Software Foundation (HSF) and the Institute for Research and Innovation in Software in HEP (IRIS-HEP) are presented. Experiment-agnostic, open, and accessible modules for training have been developed, focusing on common software material with ranges from core software skills needed by everyone to advanced training required to produce high-quality sustainable software. A basic software curriculum was built, and an introductory software training event has been prepared to serve HEP entrants. This program serves individuals with transferable skills that are becoming increasingly important to careers in the realm of software and computing, whether inside or outside HEP.
An analysis of the omega meson conversion decay using neural network-based technique with the CMD-3 experiment
Authors: Kutsenko B D 
Published: February 2023

Abstract
The study of the conversion decay of the omega meson into π
0
e
+
e
− state was performed with the CMD-3 detector at the VEPP-2000 electron-positron collider in Novosibirsk. The main physical background to the process under study is radiative decay ω → π
0
γ, where monochromatic photon converts to e
+
e
− on the material in front of the detector. The deep neural network was used to suppress these background events. The neural network was trained based on Monte Carlo simulation. To control the systematic uncertainty of this approach the events of the process of inelastic scattering e
+
e
− → e
+
e
−
γ (eeγ) and events of annihilation into two photons (γγ) were used since they are similar to signal and background events under study. The cross-sections of these processes are well calculated based on quantum electrodynamics. The difference in the calculated and measured ratio of the number of events Neeγ
/Nγγ
was used to determine a systematic uncertainty. Using integrated luminosity about 10 pb
-1 collected at the c.m. energy range from 775 MeV to 800 MeV the visible cross-section of the process ω → π
0
e
+
e
− was measured. The preliminary result for the branching ratio Br(ω → π
0
e
+
e
−) was obtained.
Denoising Convolutional Networks to Accelerate Detector Simulation
Authors: Banerjee S , Cruz Rodriguez B , Franklin L , Guerrero De La Cruz H , Leininger T , Norberg S , Pedro K , Rosado Trinidad A , Ye Y 
Published: February 2023

Abstract
The high accuracy of detector simulation is crucial for modern particle physics experiments. However, this accuracy comes with a high computational cost, which will be exacerbated by the large datasets and complex detector upgrades associated with next-generation facilities such as the High Luminosity LHC. We explore the viability of regression-based machine learning (ML) approaches using convolutional neural networks (CNNs) to “denoise” faster, lower-quality detector simulations, augmenting them to produce a higher-quality final result with a reduced computational burden. The denoising CNN works in concert with classical detector simulation software rather than replacing it entirely, increasing its reliability compared to other ML approaches to simulation. We obtain promising results from a prototype based on photon showers in the CMS electromagnetic calorimeter. Future directions are also discussed.
Fully Geant4 compatible package for the simulation of Dark Matter in fixed target experiments
Authors: Kirsanov Mikhail 
Published: February 2023

Abstract
A package for the simulation of Dark Matter (DM) particles is presented. It is intended for fixed target experiments. We chose the Geant4 toolkit framework as a standard in order to be compatible with particle tracking programs used in many High Energy Physics (HEP) experiments. One part of the package is a collection of codes that return the cross sections of DM particles’ production in such processes as electron and muon bremsstrahlung off nuclei, annihilation in flight and Primakoff processes. In most cases they are calculated at runtime. In the case of bremsstrahlung processes the total cross sections are either calculated numerically at exact tree level (ETL) or calculated at runtime using approximations validated by comparing with results of the ETL calculations. Other parts contain codes for the simulation of the above mentioned processes and DM particle classes.
Inference of astrophysical parameters with a conditional invertible neural network
Authors: Bister T , Erdmann M , Köthe U , Schulte J 
Published: February 2023

Abstract
Conditional Invertible Neural Networks (cINNs) provide a new technique for the inference of free model parameters by enabling the creation of posterior distributions. With these distributions, the mean parameter values, their uncertainties and the correlations between the parameters can be estimated. In this contribution, we summarize the functionality of cINNs, which are based on normalizing flows, and present the application of this new method to a scenario from astroparticle physics. We show that it is possible to constrain properties of the currently unknown sources of ultra-high-energy cosmic rays and compare the posterior distributions obtained with the network to those acquired using the classic Markov Chain Monte Carlo method.
Machine learning predictor for ‘measurement-to-track’ association for the ATLAS Inner Detector trigger
Authors: Lad Nisha 
Published: February 2023

Abstract
The track finding algorithm adopted for the LHC Run-2 data taking period is based on combinatorial track following, where the number of seeds scales non-linearly with the number of hits. The corresponding CPU time increase, close to cubical, creates huge and ever-increasing demand for computing power. This is particularly problematic for the silicon tracking detectors, where the hit occupancy is the largest. Therefore, it is essential that resource use is reduced, whilst maintaining the ability to reconstruct tracks with minimal loss in efficiency. This paper briefly summarises the work that has been done to optimise the HLT ID track seeding software for ATLAS Run-3 and beyond, in order to reduce the number of fake seeds constructed. An ML-based algorithm has been developed to predict if a pair of hits belong to the same track given input hit features, focusing on cluster width and inverse track inclination. The implementation of the trained predictor in the form of Look-Up Tables is presented, along with performance results in terms of tracking efficiency and speed-up factor using simulated data.
RDataFrame enhancements for HEP analyses
Authors: Guiraud E , Blomer J , Hageboeck S , Naumann A , Padulano V E , Tejedor E , Wunsch S 
Published: February 2023

Abstract
In recent years, RDataFrame, ROOT’s high-level interface for data analysis and processing, has seen widespread adoption on the part of HEP physicists. Much of this success is due to RDataFrame’s ergonomic programming model that enables the implementation of common analysis tasks more easily than previous APIs, without compromising on application performance. Nonetheless, RDataFrame’s interfaces have been further improved by the recent addition of several major HEP-oriented features: in this contribution we will introduce for instance a dedicated syntax to define systematic variations, per-data-sample call-backs useful to define quantities that vary on a per-sample basis, simplifications of collection operations and the injection of just-in-time-compiled Python functions in the optimized C++ event loop.
Reconstruction of Large Radius Tracks with the Exa.TrkX pipeline
Authors: Wang Chun-Yi , Ju Xiangyang , Hsu Shih-Chieh , Murnane Daniel , Calafiura Paolo , Farrell Steven , Spiropulu Maria , Vlimant Jean-Roch , Aurisano Adam , Hewes Jeremy , Cerati Giuseppe , Gray Lindsey , Klijnsma Thomas , Kowalkowski Jim , Atkinson Markus , Neubauer Mark , DeZoort Gage , Thais Savannah , Ballow Alexandra , Lazar Alina , Caillou Sylvain , Rougier Charline , Stark Jan , Vallier Alexis , Sardain Jad 
Published: February 2023

Abstract
Particle tracking is a challenging pattern recognition task at the Large Hadron Collider (LHC) and the High Luminosity-LHC. Conventional algorithms, such as those based on the Kalman Filter, achieve excellent performance in reconstructing the prompt tracks from the collision points. However, they require dedicated configuration and additional computing time to efficiently reconstruct the large radius tracks created away from the collision points. We developed an end-to-end machine learning-based track finding algorithm for the HL-LHC, the Exa.TrkX pipeline. The pipeline is designed so as to be agnostic about global track positions. In this work, we study the performance of the Exa.TrkX pipeline for finding large radius tracks. Trained with all tracks in the event, the pipeline simultaneously reconstructs prompt tracks and large radius tracks with high efficiencies. This new capability offered by the Exa.TrkX pipeline may enable us to search for new physics in real time.
Towards Reliable Neural Generative Modeling of Detectors
Authors: Anderlini L , Barbetti M , Derkach D , Kazeev N , Maevskiy A , Mokhnenko S 
Published: February 2023

Abstract
The increasing luminosities of future data taking at Large Hadron Collider and next generation collider experiments require an unprecedented amount of simulated events to be produced. Such large scale productions demand a significant amount of valuable computing resources. This brings a demand to use new approaches to event generation and simulation of detector responses. In this paper, we discuss the application of generative adversarial networks (GANs) to the simulation of the LHCb experiment events. We emphasize main pitfalls in the application of GANs and study the systematic effects in detail. The presented results are based on the Geant4 simulation of the LHCb Cherenkov detector.
Numerical Regularization for 4-loop Self-Energy Feynman Diagrams
Authors: de Doncker E , Yuasa F , Ishikawa T 
Published: February 2023

Abstract
In recent work we computed 4-loop integrals for self-energy diagrams with 11 massive internal lines. Presently we perform numerical integration and regularization for diagrams with 8 to 11 lines, while considering massive and massless cases. For dimensional regularization, a sequence of integrals is computed depending on a parameter (ε) that is incorporated via the space-time dimension, and approaches zero. We consider diagrams where the leading term in the expansion is in 1/ε
2 or in 1/ε or finite. The numerical integration methods include non-adaptive – double exponential, and Quasi-Monte Carlo methods with composite lattice rules implemented in CUDA C for acceleration on GPUs. The leading term coefficients in the integral expansion are obtained via linear or nonlinear extrapolation as ε tends to zero.
ReneSANCe event generator for high-precision e
+
e
− physics
Authors: Sadykov R , Arbuzov A , Bondarenko S , Dydyshka Y , Kalinovskaya L , Rumyantsev L , Yermolchyk V 
Published: February 2023

Abstract
We present a new version of the Monte Carlo event generator ReneSANCe. The generator takes into account complete one-loop electroweak (EW) corrections, higher order QED corrections in the leading logarithm (LL) approximation and some higher order EW corrections to processes at e
+
e
− colliders with finite particle masses and arbitrary polarizations of initial and final particles. ReneSANCe can produce events in the full phase space and this option is available for a wide range of center-of-mass energies.
SMEFT truncation effects in Higgs boson pair production at NLO QCD
Authors: Heinrich Gudrun , Lang Jannis 
Published: February 2023

Abstract
We present results for Higgs boson pair production in gluon fusion at next-to-leading order in QCD, including effects of anomalous couplings within Standard Model Effective Field Theory (SMEFT). In particular, we investigate truncation effects of the SMEFT series, comparing different ways to treat powers of dimension-six operators and double operator insertions.
Formation of probability and current waves at the scattering of a Gaussian wave packet by a double quantum well
Authors: Peisakhovich Yu G , Shtygashev A A 
Published: February 2023

Abstract
Annotation. A numerical-analytical simulation of scattering by a three-barrier heterostructure of an electronic Gaussian wave packet, the spectral width of which is on the order of the distance between the levels of the doublet of quasi-stationary states, is carried out. This article proves that as a result of scattering, damped waves of electron charge and current densities are formed outside the double well, their characteristics are determined by the structure of the initial wave packet and the poles of the scattering amplitudes. The frequency of these waves is equal to the difference frequency of the doublet, the wavenumber is the difference between the wave numbers of free motion of electrons with resonant energies, and the speed of their propagation is the ratio of these quantities. The system can go into the regime of repetition or amplification of the emission of electron waves if a periodic resonant pumping of the doublet population is provided by scattering of a series of coherent wave packets.
Wave functions of the Hydrogen atom in the momentum representation
Authors: Kirchbach M , Vallejo J A 
Published: February 2023

Abstract
We construct the integral transform passing from the space representation to the radial momentum representation for the Hydrogen atom. The resulting wave functions are explicitly given in terms of complex finite expansions of Gegenbauer functions of the first and second kind, or in terms of (elementary) trigonometric functions. We show their symmetry under the

S
O

(
4
)

group, and their equivalence with those of Lombardi and Ogilvie (2020 Chem. Phys.
538 110886).
Dispensing of quantum information beyond no-broadcasting theorem—is it possible to broadcast anything genuinely quantum?
Authors: Heinosaari Teiko , Jenčová Anna , Plávala Martin 
Published: February 2023

Abstract
No-broadcasting theorem is one of the most fundamental results in quantum information theory; it guarantees that the simplest attacks on any quantum protocol, based on eavesdropping and copying of quantum information, are impossible. Due to the fundamental importance of the no-broadcasting theorem, it is essential to understand the exact boundaries of this limitation. We generalize the standard definition of broadcasting by restricting the set of states which we want to broadcast and restricting the sets of measurements which we use to test the broadcasting. We show that in some of the investigated cases broadcasting is equivalent to commutativity, while in other cases commutativity is not necessary.
Enhancement of the Performance and Accuracy of Soil Moisture Data Transmission in IOT
Authors: Khattar Sonam , Verma Tushar 
Published: February 2023

Abstract
The need for soil moisture data transmission in IoT is growing thus there is a need to improve the accuracy and performance of such IoT systems. The focus of this research is on soil moisture that is detected by IoT sensors. IoT sensor data is collected and transmitted to the farmer or user. Using deep learning, actuators can take be trained to take remedial action in case of need, such as the sprinkling of water in the agricultural environment. The main focus of research is to measure data and enhance accuracy and performance. Sensors are used to monitor and record every aspect of the environment in their respective settings. The most commonly utilized sensors include proximity sensors, temperature sensors, smoke sensors, etc. Since human population, industry, and energy consumption are all expected to rise in the next decades, environmental monitoring tools and applications must be upgraded to be more accurate and efficient. The detection and measurement of soil moisture is the initial step for smart agriculture. With the use of agricultural control centers, farmers may identify crops in need of treatment and figure out the best quantity of water, fertilizer, and pesticides to administer based on sensor data and imagery input. For farmers, this ensures the soil gets the proper quantity of chemicals for optimal health, while also cutting expenses and minimizing environmental effects, all while reducing waste. The objective of the research work is to improve the accuracy and performance when soil moisture data is transmitted over IoT devices.
Sustainable Utilization of Calcium Carbide Residue along with Polypropylene Fiber Waste Materials for Accessing Swelling and Strength Characteristics of Black Cotton Soil
Authors: Hadi Afzali Mohammad , sharma Abhishek 
Published: February 2023

Abstract
Due to industrialization and modernization, huge amount of waste generated by many industries causes a huge threat to the human health, environment and aqua life due to disposal problems. The present study focuses on utilizing industrial wastes to strengthen geotechnical properties of black cotton soil. The toxic compounds included in calcium carbide residue (CCR) created by the acetylene gas companies and polypropylene fiber made by the textile mills are both highly polluting. These materials can be utilized in soil stabilization alone and in combination in various proportions to obtain the finest subgrade material. The use of such industrial waste for subgrade soil stabilization is a cost-effective way of building pavements. This study examines the effects of addition of different percentages of CCR and fiber contents on Atterberg’s limits, compaction characteristics, unconfined compressive strength and California bearing ratio.
Stabilization of CSEBs with the addition of industrial and agricultural wastes
Authors: Rajput Aayushee , Sharma Tarun 
Published: February 2023

Abstract
In today’s world, most of the developing countries are trying to build houses with more durability and with low cost. Compressed Stabilized Earth Blocks are the best option for building cost-effective and sustainable houses. In red bricks, cement is used as a binder but in CSEBs, there is no requirement of cement. A large amount of pollution is generated during the manufacturing of cement as well as in the construction of red burnt bricks. To overcome this situation, CSEBs are used. Waste materials like fly ash, bagasse ash, rice straw etc, can be used for the construction of compressed earth blocks. The use of industrial and agricultural wastes as binders and fibres in place of cement for the construction of CSEBs can reduce the pollution caused by cement production as well as will lessen the construction cost. In this study, agricultural waste like rice straw and industrial wastes like bagasse ash and marble dust are used for the preparation of CSEBs. Rice straw acts as a fibre and marble dust acts as a binder for CSEBs. This makes these blocks economical as well as eco-friendly. This study has been done to show that CSEBs are better in strength, durability and are cost-effective than the red burnt bricks and can replace these bricks in coming future. A total of 27 combinations of percentage were taken into consideration for this study and for each combination 3 specimens were constructed. These specimens were cured in open atmosphere under the shed. And a small quantity of water was sprinkled over the specimens every day. Tests like OMC, MDD and tensile strength test were performed on these blocks after 28 days. It was noticed that tensile strength increases with rise in marble dust content. Overall, it was observed that CSEBs are better than the cement blocks as well as red burnt bricks.