Scale factors are commonly used in HEP to improve shape agreement between distributions of data and simulation. We present a generalized deep-learning based architecture for producing shape changing scale factors, investigated in the context of bottom-quark jet- tagging algorithms within the CMS experiment.
The method utilizes an adversarial approach with three networks forming the central...
When measuring cosmic ray induced air showers through radio waves, recovering the full three-dimensional electromagnetic field from the recorded two-dimensional voltage of an antenna is a major challenge. Antennas project the electromagnetic field into a lower dimensional space while applying a frequency dependent response and are subjected to noise contamination during measurement. We use...
High energy physics (HEP) is moving towards extremely high statistical experiments and super-large-scale simulation of theory such as Standard Model. In order to handle the challenge of rapidly increase of data volumes, distributed computing and storage frameworks in Big Data area like Hadoop and Spark make computations easily to scale out. While in- memory RDD based programming model assumes...
CLUE (CLUstering of Energy) is a fast parallel clustering algorithm for High Granularity Calorimeters in High Energy Physics. In these types of detectors, such as that to be built to cover the endcap region in the CMS Phase-2 Upgrade for HL-LHC, the standard clusterisation algorithms using combinatorics are expected to fail due to large number of digitised energy deposits (hits) in the...
This talk summarizes the various storage options that we implemented for the CMSWEB cluster in Kubernetes infrastructure. All CMSWEB services require storage for logs, while some services also require storage for data. We also provide a feasibility analysis of various storage options and describe the pros/cons of each technique from the perspective of the CMSWEB cluster and its users. In the...
The Liquid Argon Time Projection Chamber (LArTPC) technology is widely used in high energy physics experiments, including the upcoming Deep Underground Neutrino Experiment (DUNE). Accurately simulating LArTPC detector responses is essential for analysis algorithm development and physics model interpretations. But because of the highly diverse event topologies that can occur in LArTPCs,...
Understanding the predictions of a machine learning model can be as important as achieving high performance, especially in critical application domains such as health care, cybersecurity, or financial services, among others. In scientific domains, the model interpretation can enhance the model's performance, helping to trust them accurately for its use on real data and for knowledge discovery....
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 parameter mean 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...
The Belle II experiment is located at the asymmetric SuperKEKB $e^+ e^-$ collider in Tsukuba, Japan. The Belle II electromagnetic calorimeter (ECL) is designed to measure the energy deposited by charged and neutral particles. It also provides important contributions to the particle identification system. Identification of low-momenta muons and pions in the ECL is crucial if they do not reach...
Identification of hadronic decays of highly Lorentz-boosted W/Z/Higgs bosons and top quarks provides powerful handles to a wide range of new physics searches and Standard Model measurements at the LHC. In this talk, we present ParticleNeXt, a new graph neural network (GNN) architecture tailored for jet tagging. With the introduction of novel components such as pairwise features, attentive...
Heterogeneous Computing will play a fundamental role in the CMS reconstruction to face the challenges that will be posed by the HL-LHC phase. Several computing architectures and vendors are currently available to build an Heterogeneous Computing Farm for the CMS experiment. However, specialized implementations for each of these architectures is not sustainable in terms of development,...
The pyrate framework provides a dynamic, versatile, and memory-efficient approach to data format transformations, object reconstruction and data analysis in particle physics.The framework is implemented with the python programming language, allowing easy access to the scientific python package ecosystem and commodity big data technologies. Developed within the context of the SABRE experiment...
The Reproducible Open Benchmarks for Data Analysis Platform (ROB)[1][2] is a platform developed to help evaluate data analysis workflows in a controlled competition-style environment. ROB was inspired by the Top Tagger Comparison analysis (2019)[3] that compared multiple different top tagger neural networks. ROB has two main goals: (1) reduce the amount of time required to organize and...
In an earlier work [1], we introduced dual-Parameterized Quantum Circuit (PQC) Generative Adversarial Networks (GAN), an advanced prototype of quantum GAN, which consists of a classical discriminator and two quantum generators that take the form of PQCs. We have shown the model can imitate calorimeter outputs in High-Energy Physics (HEP), interpreted as reduced size pixelated images. But the...
Lattice quantum chromodynamics (lattice QCD) is the non-perturbative definition of the QCD theory from first principle and can be systematically improved, meanwhile, it is one of the most important high performance computing application in high energy physics. The physics research of lattice QCD benefited enormously from the development of computer hardware and algorithm, and particle...
In classical deep learning, a number of studies have proven that noise plays a crucial role in the training of neural networks. Artificial noises are often injected in order to make the model more robust, faster converging, and stable. Meanwhile, quantum computing, a completely new paradigm of computation, is characterized by statistical uncertainty from its probabilistic nature. Furthermore,...
The ATLAS experiment at the Large Hadron Collider (LHC) relies heavily on simulated data, requiring the production of billions of Monte Carlo (MC)-based proton-proton collisions for every run period. As such, the simulation of collisions (events) is the single biggest CPU resource consumer for the experiment. ATLAS's finite computing resources are at odds with the expected conditions during...
