bluuebunny/crossref_metadata_2025_split huggingface dataset:
A First Application of Collaborative Learning In Particle Physics
Authors: Vergani Stefano , Bagoly Attila 
Published: February 2023

Abstract
Over the last ten years, the popularity of Machine Learning (ML) has grown exponentially in all scientific fields, including particle physics. The industry has also developed new powerful tools that, imported into academia, could revolutionise research. One recent industry development that has not yet come to the attention of the particle physics community is Collaborative Learning (CL), a framework that allows training the same ML model with different datasets. This work explores the potential of CL, testing the library Colearn with neutrino physics simulation. Colearn, developed by the British Cambridge-based firm Fetch.AI, enables decentralised machine learning tasks. Being a blockchain-mediated CL system, it allows multiple stakeholders to build a shared ML model without needing to rely on a central authority. A generic Liquid Argon Time-Projection Chamber (LArTPC) has been simulated and images produced by fictitious neutrino interactions have been used to produce several datasets. These datasets, called learners, participated successfully in training a Deep Learning (DL) Keras model using blockchain technologies in a decentralised way. This test explores the feasibility of training a single ML model using different simulation datasets coming from different research groups. In this work, we also discuss a framework that instead makes different ML models compete against each other on the same dataset. The final goal is then to train the most performant ML model across the entire scientific community for a given experiment, either using all of the datasets available or selecting the model which performs best among every model developed in the community.
GAN with an Auxiliary Regressor for the Fast Simulation of the Electromagnetic Calorimeter Response
Authors: Rogachev Alexander , Ratnikov Fedor 
Published: February 2023

Abstract
High energy physics experiments essentially rely on simulated data for physics analyses. However, running detailed simulation models requires a tremendous amount of computation resources. New approaches to speed up detector simulation are therefore needed. The generation of calorimeter responses is often the most expensive component of the simulation chain for HEP experiments. It was shown that deep learning techniques, especially Generative Adversarial Networks, may be used to reproduce the calorimeter response. However, those applications are challenging, as the generated responses need evaluation not only in terms of image consistency: different physics-based quality metrics should be also taken into consideration.
In our work, we develop a multitask GAN-based framework with the goal to speed up the response generation of the Electromagnetic Calorimeter (ECAL) of the LHCb detector at LHC. We introduce the Auxiliary Regressor as a second task to evaluate a proxy metric of the given input that is used by the Discriminator of the GAN. We show that this approach improves the stability of GAN and the model produces samples with better physics distributions.
Generating muonic force carriers events with classical and quantum neural networks
Authors: Ramazyan T. , Kiss O. , Grossi M. , Kajomovitz E. , Vallecorsa S. 
Published: February 2023

Abstract
Generative models (GM) are promising applications for near-term quantum computers due to the probabilistic nature of quantum mechanics. This work compares a classical conditional generative adversarial network (CGAN) with a quantum circuit Born machine while addressing their strengths and limitations to generate muonic force carriers (MFCs) events. The former uses a neural network as a discriminator to train the generator, while the latter takes advantage of the stochastic nature of measurements in quantum mechanics to generate samples. We consider a muon fixed-target collision between muons produced at the high-energy collisions of the LHC and the detector material of the ForwArd Search ExpeRiment (FASER) or the ATLAS calorimeter. In the ATLAS case, independent muon measurements performed by the inner detector (ID) and muon system (MS) can help observe new force carriers coupled to muons, which are usually not detected. We numerically observed that CGANs could reproduce the complete data set and interpolate to different regimes. Moreover, we show on a simplified problem that Born machines are promising generative models for near-term quantum devices.
Hyperparameter optimization of data-driven AI models on HPC systems
Authors: Wulff Eric , Girone Maria , Pata Joosep 
Published: February 2023

Abstract
In the European Center of Excellence in Exascale Computing ”Research on AI- and Simulation-Based Engineering at Exascale” (CoE RAISE), researchers develop novel, scalable AI technologies towards Exascale. This work exercises High Performance Computing resources to perform large-scale hyperparameter optimization using distributed training on multiple compute nodes. This is part of RAISE’s work on data-driven use cases which leverages AI- and HPC cross-methods developed within the project. In response to the demand for parallelizable and resource efficient hyperparameter optimization methods, advanced hyperparameter search algorithms are benchmarked and compared. The evaluated algorithms, including Random Search, Hyperband and ASHA, are tested and compared in terms of both accuracy and accuracy per compute resources spent. As an example use case, a graph neural network model known as MLPF, developed for Machine Learned Particle-Flow reconstruction, acts as the base model for optimization. Results show that hyperparameter optimization significantly increased the performance of MLPF and that this would not have been possible without access to large-scale High Performance Computing resources. It is also shown that, in the case of MLPF, the ASHA algorithm in combination with Bayesian optimization gives the largest performance increase per compute resources spent out of the investigated algorithms.
Interrelation of equivariant Gaussian processes and convolutional neural networks
Authors: Demichev Andrey , Kryukov Alexander 
Published: February 2023

Abstract
Currently there exists rather promising new trend in machine leaning (ML) based on the relationship between neural networks (NN) and Gaussian processes (GP), including many related subtopics, e.g., signal propagation in NNs, theoretical derivation of learning curve for NNs, QFT methods in ML, etc. An important feature of convolutional neural networks (CNN) is their equivariance (consistency) with respect to the symmetry transformations of the input data. In this work we establish a relationship between the many-channel limit for CNNs equivariant with respect to two-dimensional Euclidean group with vector-valued neuron activations and the corresponding independently introduced equivariant Gaussian processes (GP).
Leveraging HPC resources with distributed RDataFrame
Authors: Padulano V. E. , Kabadzhov I. D. , Saavedra E. T. , Guiraud E. 
Published: February 2023

Abstract
The declarative approach to data analysis provides high-level abstractions for users to operate on their datasets in a much more ergonomic fashion compared to imperative interfaces. ROOT offers such a tool with RDataFrame, which has been tested in production environments and used in real-world analyses with optimal results. Its programming model acts by creating a computation graph with the operations issued by the user and executing it lazily only when the final results are queried. It has always been oriented towards parallelisation, with native support for multi-thread execution on a single machine. Recently, RDataFrame has been extended with a Python layer that is capable of steering and executing the RDataFrame computation graph over a set of distributed resources. In addition, such a layer requires minimal code changes for an RDataFrame application to run distributedly. The new tool effectively allows running a C++ event loop based on RDataFrame while leveraging common industry tools like Dask to schedule the usage of resources. This work presents results and insights gathered through the distributed RDataFrame tool running a physics analysis connecting multiple nodes with a Dask scheduler that requests resources from a Slurm cluster.
neos: End-to-End-Optimised Summary Statistics for High Energy Physics
Authors: Simpson Nathan , Heinrich Lukas 
Published: February 2023

Abstract
The advent of deep learning has yielded powerful tools to automatically compute gradients of computations. This is because training a neural network equates to iteratively updating its parameters using gradient descent to find the minimum of a loss function. Deep learning is then a subset of a broader paradigm; a workflow with free parameters that is end-to-end optimisable, provided one can keep track of the gradients all the way through.
This work introduces neos: an example implementation following this paradigm of a fully differentiable high-energy physics workflow, capable of optimising a learnable summary statistic with respect to the expected sensitivity of an analysis. Doing this results in an optimisation process that is aware of the modelling and treatment of systematic uncertainties.
A neural-network-defined Gaussian mixture model for particle identification applied to the LHCb fixed-target programme
Authors: Mariani S , Anderlini L , Di Nezza P , Franzoso E , Graziani G , Pappalardo L L 
Published: February 2023

Abstract
Particle identification at high-energy physics experiments typically relies on classifiers combining different experimental observables. In this document, an innovative approach employing machine learning techniques to describe their dependence from the relevant features is presented. The proposed method is applied to the fixed-target programme at the LHCb experiment, where the sample size of the particle identification calibration channels affects the experimental performance. It is demonstrated to perform better than a model based on the LHCb detailed simulation and to be fast and suitable to a large variety of use cases.
New software technologies in the LHCb Simulation
Authors: Corti Gloria , Davis Adam , Kreps Michał , Mazurek Michal , Popov Dmitry , Gianluca Siddi Benedetto 
Published: February 2023

Abstract
Monte Carlo simulations are key to the design and commissioning of new detectors as well as the interpretation of physics measurements. The amount of simulated samples required for the Run 3 physics program of the LHCb experiment will increase significantly from 2022 onward to match the increase in the amount of data collected with respect to Run 1 and 2 operation. A new version of the LHCb Gauss simulation framework has been developed to better accommodate new simulation techniques and software technologies to produce the necessary samples within the computing resources allocated for the next few years. It provides the LHCb specific functionality while the generic simulation infrastructure has been encapsulated in an experiment-independent framework, GAUSSINO. The latter combines the GAUDI core software framework and the GEANT4 simulation toolkit and fully exploits their multi-threading capabilities. Fast simulation interface is the latest feature being developed in GAUSSINO to interact with GEANT4 giving the possibility of replacing its detailed description of physics processes with an extensive palette of fast simulation models for a specific LHCb sub-detector. A facility to ease the production of training datasets for fast simulations models has also been introduced.
New web based event data and geometry visualization for LHCb
Authors: Pappas A , Couturier B , Ponce S 
Published: February 2023

Abstract
The LHCb detector is undergoing a comprehensive upgrade for data taking in the LHC’s Run 3, which is scheduled to begin in 2022. The new Run 3 detector has a different, upgraded geometry and uses new tools for its description, namely DD4hep [2] and ROOT [12]. Besides, the visualization technologies have evolved quite a lot since Run 1, with the introduction of ubiquitous web based solutions or Augmented Reality (AR) for example. The LHCb collaboration has thus started the development of a new visualization solution, based on the Phoenix [6] framework, developed jointly by several experiments in the context of the HEP Software Foundation (HSF) [10]. We present here the architecture and implementation of this new solution, as well as the different contributions made to the Phoenix ecosystem. In particular we discuss a generic tool for exporting ROOT geometries to the visualization application, which can be used to display in a browser either the whole detector or subparts of it. Extensions to the Phoenix visualization primitives regarding calorimeters and performance improvements are also presented.
The Open Data Detector Tracking System
Authors: Gessinger-Befurt Paul , Salzburger Andreas , Niermann Joana 
Published: February 2023

Abstract
Charged particle reconstruction in High Energy Physics experiments is a significant part of overall event reconstruction. Depending on the physics environment, for instance in collider experiments with high multiplicities or luminosities, the tracking problem increases in complexity and often poses not only an algorithmic, but also a computational challenge. With the high-luminosity phase of the LHC at CERN approaching, research for new approaches and algorithms for track reconstruction has seen an increased interest. Both new technological approaches like hardware accelerators, as well as machine learning are being developed. However, testing and developing these new approaches against the existing experiments’ software stacks can prove to be challenging, as they typically focus on stable data taking, discouraging disruptive changes. This document presents a virtual tracking detector that is designed to be a simplified, but realistic model of a real-world detector, that can serve as a robust testbed for new developments.
Particle identification with the Belle II calorimeter using machine learning
Authors: Narimani Charan Abtin 
Published: February 2023

Abstract
I present an application of a convolutional neural network (CNN) to separate muons and pions in the Belle II electromagnetic calorimeter (ECL). The ECL is designed to measure the energy deposited by charged and neutral particles. It also provides important contributions to the particle identification (PID) system. Identification of low-momenta muons and pions in the ECL is crucial if they do not reach the outer muon detector. Track-seeded cluster energy images provide the maximal possible information. The shape of the energy depositions for muons and pions in the crystals around an extrapolated track at the entering point of the ECL is used together with crystal positions in θ − ϕ plane and transverse momentum of the track to train a CNN. The CNN exploits the difference between the dispersed energy depositions from pion hadronic interactions and the more localized muon electromagnetic interactions. Using simulation, the performance of the CNN algorithm is compared with other PID methods at Belle II which are based on track-matched clustering information. The results show that the CNN PID method improves muon-pion separation in low momentum.
Precise simulation of drift chamber in the CEPC experiment
Authors: Fang Wenxing , Huang Xingtao , Li Weidong , Lin Tao , Liu Mengyao , Yuan Ye , Zhang Xueyao , Zhang Yao 
Published: February 2023

Abstract
The Circular Electron Positron Collider (CEPC) is one of the future experiments which aims to precisely study the properties of the Higgs boson and to search for new physics beyond the Standard Model. To achieve that, it puts strict requirements on the particle reconstruction and identification ability of the CEPC detector. In order to obtain better particle identification performance, the drift chamber is chosen for the outer tracking detector which can provide information used by the cluster counting method for getting excellent particle identification performance. To evaluate the performance of the cluster counting method, it is necessary to develop a simulation tool that can be used to precisely simulate the response of the gaseous particle detector. This work proposes a new way to combine Geant4 and Garfield++ at the Geant4 step level for the drift chamber simulation. Due to the extreme time consumption of simulating the avalanche process in Garfield++, a fast signal response simulation method based on a neural network has been developed to replace detailed simulation. It shows the fast simulation can give consistent results with Garfield++ simulation and achieve the speedup by a factor of 200.
Probing highly collimated photon-jets with deep learning
Authors: Ai Xiaocong , Hsu Shih-Chieh , Li Ke , Lu Chih-Ting 
Published: February 2023

Abstract
Many extensions of the standard model (SM) predict the existence of axion-like particles and/or dark Higgs in the sub-GeV scale. Two new sub-GeV particles, a scalar and a pseudoscalar, produced through the Higgs boson exotic decays, are investigated. The decay signatures of these two new particles with highly collimated photons in the final states are discriminated from the ones of SM backgrounds using the Convolutional Neural Networks and Boosted Decision Trees techniques. The sensitivities of searching for such new physics signatures at the Large Hadron Collider are obtained.
RNTuple performance: Status and Outlook
Authors: Lopez-Gomez Javier , Blomer Jakob 
Published: February 2023

Abstract
Upcoming HEP experiments, e.g. at the HL-LHC, are expected to increase the volume of generated data by at least one order of magnitude. In order to retain the ability to analyze the influx of data, full exploitation of modern storage hardware and systems, such as low-latency high-bandwidth NVMe devices and distributed object stores, becomes critical.
To this end, the ROOT RNTuple I/O subsystem has been designed to address performance bottlenecks and shortcomings of ROOT’s current state of the art TTree I/O subsystem. RNTuple provides a backwards-incompatible redesign of the TTree binary format and access API that evolves the ROOT event data I/O for the challenges of the upcoming decades. It focuses on a compact data format, on performance engineering for modern storage hardware, for instance through making parallel and asynchronous I/O calls by default, and on robust interfaces that are easy to use correctly.
In this contribution, we evaluate the RNTuple performance for typical HEP analysis tasks. We compare the throughput delivered by RNTuple to popular I/O libraries outside HEP, such as HDF5 and Apache Parquet. We demonstrate the advantages of RNTuple for HEP analysis workflows and provide an outlook on the road to its use in production.
Self-Organizing Maps in High Energy Physics
Authors: Habermann Kai , Toerne Eckhard von 
Published: February 2023

Abstract
The Self-Organizing-Map (SOM) is a widely used neural network for dimensional reduction and clustering. It has yet to find its use in high energy physics. This paper discusses two applications of SOM: first, we find map regions with a high relative content of a rare process (H → WW*). Second we obtain Monte Carlo normalization factors for different physics processes by fitting the dimensionally reduced representation. Analysis and training are performed on ATLAS open data.
Simulation and Software Integration of the CMS MIP Timing Detector for High-Luminosity LHC
Authors: Sorrentino Giulia 
Published: February 2023

Abstract
The Compact Muon Solenoid detector at the Large Hadron Collider is undertaking an upgrade program in order to face the harsh conditions foreseen by the High-Luminosity era. This program comprises the installation of a new timing detector whose aim is to measure the time of MIPs, the minimum ionizing particles, with a resolution of around 30-40 ps. The time information provided by this new MIP Timing Detector will improve the rejection of spurious tracks and vertices, will enable particle identification based on the time of flight, and will bring unique physics opportunities for interesting signatures such as those including long-lived particles. All these capabilities require a full software infrastructure to simulate the timing detector and its digitization process, for locally reconstructing the time information associated with tracks, propagating it to the beam line, and contributing to the vertex building. In this paper, the main characteristics of this infrastructure and its integration into the offline software chain are discussed.
Sparse Convolutional Neural Networks for particle classification in ProtoDUNE-SP events
Authors: Abed Abud Adam 
Published: February 2023

Abstract
Deep Learning (DL) methods and Computer Vision are becoming important tools for event reconstruction in particle physics detectors. In this work, we report on the use of submanifold sparse convolutional neural networks (SparseNets) for the classification of track and shower hits from a DUNE prototype liquid-argon detector at CERN (ProtoDUNE-SP). By taking advantage of the three-dimensional nature of the problem we use a set of nine input features to classify sparse and locally dense hits associated to track or shower particles. The SparseNet has been trained on a test sample and shows promising results: efficiencies and purities greater than 90%. This has also been achieved with a considerable speedup and substantially less resource utilization with respect to other DL networks such as graph neural networks. This method offers great scalability advantages for future large neutrino detectors such as the planned DUNE experiment.
Supernova Light Curves Approximation based on Neural Network Models
Authors: Demianenko Mariia , Samorodova Ekaterina , Sysak Mikhail , Shiriaev Aleksandr , Malanchev Konstantin , Derkach Denis , Hushchyn Mikhail 
Published: February 2023

Abstract
Photometric data-driven classification of supernovae becomes a challenge due to the appearance of real-time processing of big data in astronomy. Recent studies have demonstrated the superior quality of solutions based on various machine learning models. These models learn to classify supernova types using their light curves as inputs. Preprocessing these curves is a crucial step that significantly affects the final quality. In this talk, we study the application of multilayer perceptron (MLP), bayesian neural network (BNN), and normalizing flows (NF) to approximate observations for a single light curve. We use these approximations as inputs for supernovae classification models and demonstrate that the proposed methods outperform the state-of-the-art based on Gaussian processes applying to the Zwicky Transient Facility Bright Transient Survey light curves. MLP demonstrates similar quality as Gaussian processes and speed increase. Normalizing Flows exceeds Gaussian processes in terms of approximation quality as well.
Using AI to predict calibration constants for the central drift chamber in GlueX at Jefferson Lab
Authors: Jeske Torri , McSpadden Diana , Kalra Nikhil , Britton Thomas , Jarvis Naomi , Lawrence David 
Published: February 2023

Abstract
The AI for Experimental Controls project team at Jefferson Lab has developed an AI system to control and calibrate a large drift chamber system in near-real time. The AI system will monitor environmental and experimental variables to recommend voltage settings that maintain consistent dE/dx gain and optimal resolution throughout the experiment. At present, calibrations are performed after data have been recorded and require a considerable amount of time and attention from experts. The calibrations currently require multiple iterations and depend on accurate tracking information. Our approach uses environmental data, such as atmospheric pressure and gas temperature, and beam conditions, such as the flux of incident particles, as inputs to a Gaussian Process Regression (GPR) model. For the data taken during the GlueX 2020 run period, the GPR is able to predict the existing gain correction factors to within 3.5%. This talk will briefly describe the development, testing, and future plans for this system at Jefferson Lab.