Deep learning techniques for energy clustering in the CMS ECAL

• URL: http://arxiv.org/abs/2204.10277v1
• Date: Thu, 21 Apr 2022 17:23:43 GMT
• Title: Deep learning techniques for energy clustering in the CMS ECAL
• Authors: Davide Valsecchi
• Abstract summary: Reconstruction of electrons and photons in CMS depends on topological clustering of the energy deposited by an incident particle. New methods are being investigated that exploit state-of-the-art deep learning architectures like Graph Neural Networks (GNN) and self-attention algorithms.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The reconstruction of electrons and photons in CMS depends on topological clustering of the energy deposited by an incident particle in different crystals of the electromagnetic calorimeter (ECAL). These clusters are formed by aggregating neighbouring crystals according to the expected topology of an electromagnetic shower in the ECAL. The presence of upstream material (beampipe, tracker and support structures) causes electrons and photons to start showering before reaching the calorimeter. This effect, combined with the 3.8T CMS magnetic field, leads to energy being spread in several clusters around the primary one. It is essential to recover the energy contained in these satellite clusters in order to achieve the best possible energy resolution for physics analyses. Historically satellite clusters have been associated to the primary cluster using a purely topological algorithm which does not attempt to remove spurious energy deposits from additional pileup interactions (PU). The performance of this algorithm is expected to degrade during LHC Run 3 (2022+) because of the larger average PU levels and the increasing levels of noise due to the ageing of the ECAL detector. New methods are being investigated that exploit state-of-the-art deep learning architectures like Graph Neural Networks (GNN) and self-attention algorithms. These more sophisticated models improve the energy collection and are more resilient to PU and noise, helping to preserve the electron and photon energy resolution achieved during LHC Runs 1 and 2. This work will cover the challenges of training the models as well the opportunity that this new approach offers to unify the ECAL energy measurement with the particle identification steps used in the global CMS photon and electron reconstruction.

Related papers

• Reconstruction of Particle Flow Energy Distribution Using Deep Learning Algorithms [8.5980103509356]
  Recent advancements involve using deep learning to process calorimeter images from various sub-detectors for energy map reconstruction. This paper compares classical algorithms-MLP, CNN, U-Net, and RNN-with variants that include self-attention and 3D convolution modules. Test dataset of jet events is utilized to analyze and compare models' performance in handling anomalous high-energy events.
  arXiv  Detail & Related papers  (2024-10-08T11:49:18Z)
• Lowering the Exponential Wall: Accelerating High-Entropy Alloy Catalysts Screening using Local Surface Energy Descriptors from Neural Network Potentials [0.0]
  We propose a rapid method for predicting HEA properties using data from monometallic systems. We developed high-precision models by employing both classical and quantum machine learning. The proposed approach accelerates the exploration of the vast HEA chemical space, facilitating the design of novel catalysts.
  arXiv  Detail & Related papers  (2024-04-12T11:54:06Z)
• Neutron-nucleus dynamics simulations for quantum computers [49.369935809497214]
  We develop a novel quantum algorithm for neutron-nucleus simulations with general potentials. It provides acceptable bound-state energies even in the presence of noise, through the noise-resilient training method. We introduce a new commutativity scheme called distance-grouped commutativity (DGC) and compare its performance with the well-known qubit-commutativity scheme.
  arXiv  Detail & Related papers  (2024-02-22T16:33:48Z)
• Platinum-based Catalysts for Oxygen Reduction Reaction simulated with a Quantum Computer [2.8866494528688555]
  Hydrogen has emerged as a promising energy source, holding the key to achieve low-carbon and sustainable mobility. Here, we present a pioneering study that combines classical and quantum computational approaches to investigate the ORR on pure platinum and platinum/cobalt surfaces. Results highlight the involvement of strongly correlated species in the cobalt-containing catalyst, suggesting their potential as ideal candidates for showcasing quantum advantage in future applications.
  arXiv  Detail & Related papers  (2023-07-28T21:49:00Z)
• Ab initio electron-lattice downfolding: potential energy landscapes, anharmonicity, and molecular dynamics in charge density wave materials [0.0]
  Computational challenges arise especially for large systems, long time scales, in nonequilibrium, or in systems with strong correlations. We show how downfolding approaches facilitate complexity reduction on the electronic side and thereby boost the simulation of electronic properties and nuclear motion.
  arXiv  Detail & Related papers  (2023-03-13T16:41:37Z)
• Photonic Quantum Computing For Polymer Classification [62.997667081978825]
  Two polymer classes visual (VIS) and near-infrared (NIR) are defined based on the size of the polymer gaps. We present a hybrid classical-quantum approach to the binary classification of polymer structures.
  arXiv  Detail & Related papers  (2022-11-22T11:59:52Z)
• Electromagnetically induced transparency in inhomogeneously broadened divacancy defect ensembles in SiC [52.74159341260462]
  Electromagnetically induced transparency (EIT) is a phenomenon that can provide strong and robust interfacing between optical signals and quantum coherence of electronic spins. We show that EIT can be established with high visibility also in this material platform upon careful design of the measurement geometry. Our work provides an understanding of EIT in multi-level systems with significant inhomogeneities, and our considerations are valid for a wide array of defects in semiconductors.
  arXiv  Detail & Related papers  (2022-03-18T11:22:09Z)
• Quantum-Classical Hybrid Algorithm for the Simulation of All-Electron Correlation [58.720142291102135]
  We present a novel hybrid-classical algorithm that computes a molecule's all-electron energy and properties on the classical computer. We demonstrate the ability of the quantum-classical hybrid algorithms to achieve chemically relevant results and accuracy on currently available quantum computers.
  arXiv  Detail & Related papers  (2021-06-22T18:00:00Z)
• Demonstration of electron-nuclear decoupling at a spin clock transition [54.088309058031705]
  Clock transitions protect molecular spin qubits from magnetic noise. linear coupling to nuclear degrees of freedom causes a modulation and decay of electronic coherence. An absence of quantum information leakage to the nuclear bath provides opportunities to characterize other decoherence sources.
  arXiv  Detail & Related papers  (2021-06-09T16:23:47Z)
• Resource Efficient Chemistry on Quantum Computers with the Variational Quantum Eigensolver and The Double Unitary Coupled-Cluster approach [0.0]
  We show that the number of qubits scales linearly with the size of molecular basis. We employ the double unitary coupled-cluster (DUCC) method to effectively downfold correlation effects into the reduced-size orbital space. Using downfolding techniques, we demonstrate that properly constructed effective Hamiltonians can capture the effect of the whole orbital space in small-size active spaces.
  arXiv  Detail & Related papers  (2020-04-16T15:59:15Z)
• Entanglement generation via power-of-SWAP operations between dynamic electron-spin qubits [62.997667081978825]
  Surface acoustic waves (SAWs) can create moving quantum dots in piezoelectric materials. We show how electron-spin qubits located on dynamic quantum dots can be entangled.
  arXiv  Detail & Related papers  (2020-01-15T19:00:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.