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S. Kunori

Publications and source records attributed to S. Kunori.

10 recordsLinked to original sources
arXiv2025

Vertex Imaging Hadron Calorimetry Using AI/ML Tools

The fluctuations in energy loss to processes that do not generate measurable signals, such as binding energy losses, set the limit on achievable hadronic energy resolution in traditional energy reconstruction techniques. The correlation between the number of hadronic interaction vertices in a shower and invisible energy is found to be strong and is used to estimate invisible energy fraction in highly granular calorimeters in short time intervals (<10 ns). We simulated images of hadronic showers using GEANT4 and deployed a neural network to analyze the images for energy regression. The neural network-based approach results in significant improvement in energy resolution, from 13 % to 4 % in the case of a Cherenkov calorimeter for 100 GeV pion showers. We discuss the significance of the phenomena responsible for this improvement and the plans for experimental verification of these results and further development.

physics.ins-det↗
arXiv2024

High-granularity Dual-readout Calorimeter: Evolution of a Classic Prototype

The original dual-readout calorimeter prototype (DREAM), constructed two decades ago, has proven instrumental in advancing our understanding of calorimetry. It has facilitated a multitude of breakthroughs by leveraging signals from complementary media (Cherenkov and scintillation) to capture fluctuations in electromagnetic energy fraction within hadronic showers. Over the years, extensive studies have shed light on the performance characteristics of this module, rendering it exceptionally well-understood. Drawing on this wealth of experience, we have embarked on enhancing the detectors' capabilities further by integrating fast silicon photomultipliers (SiPMs) with finer transverse segmentation, $\sim$1 cm$^2$, as well as longitudinal segmentation by timing measuring better than 10 cm. This configuration will allow us to image hadronic showers with high granularity (HG-DREAM). We argue that the spatial information provided by such a granular detector in a short time window ($\approx$5 ns) leads to substantial enhancement in energy resolution when advanced neural networks are employed in energy reconstruction. We briefly present the current status of work, new concepts that have been introduced to the detector, and expectations from simulations.

physics.ins-det↗
arXiv2022

Perspectives on the Calibration of CNN Energy Reconstruction in Highly Granular Calorimeters

We present a study which shows encouraging stability of the response linearity for a simulated high granularity calorimeter module reconstructed by a CNN model to miscalibration, bias, and noise effects. Our results also show an intuitive, quantifiable relationship between these factors and the calibration parameters. We trained a CNN model to reconstruct energy in the calorimeter module using simulated single-pion events; we then observed the response of the model under various miscalibration, bias, and noise conditions that affected the model input. From these data, we estimated linear response models to calibrate the CNN. We also quantified the relationship between these factors and the calibration parameters by regression analysis.

physics.ins-det↗
arXiv2022

On the Use of Neural Networks for Energy Reconstruction in High-granularity Calorimeters

We contrasted the performance of deep neural networks - Convolutional Neural Network (CNN) and Graph Neural Network (GNN) - to current state of the art energy regression methods in a finely 3D-segmented calorimeter simulated by GEANT4. This comparative benchmark gives us some insight to assess the particular latent signals neural network methods exploit to achieve superior resolution. A CNN trained solely on a pure sample of pions achieved substantial improvement in the energy resolution for both single pions and jets over the conventional approaches. It maintained good performance for electron and photon reconstruction. We also used the Graph Neural Network (GNN) with edge convolution to assess the importance of timing information in the shower development for improved energy reconstruction. We implement a simple simulation based correction to the energy sum derived from the fraction of energy deposited in the electromagnetic shower component. This serves as an approximate dual-readout analogue for our benchmark comparison. Although this study does not include the simulation of detector effects, such as electronic noise, the margin of improvement seems robust enough to suggest these benefits will endure in real-world application. We also find reason to infer that the CNN/GNN methods leverage latent features that concur with our current understanding of the physics of calorimeter measurement.

physics.ins-det↗
arXiv2019

Cerium-Doped Fused-Silica Fibers as Wavelength Shifters

We have evaluated the performance of a Ce-doped fused-silica fiber as wavelength shifter coupled to a CeF$_{3}$ crystal using electron beams at CERN. The pulse shape and collection efficiency were measured using irradiated (100 kGy) and un-irradiated fibers. In addition, we evaluated the light yield of various Ce-doped fibers and explored the possibility of using them in the future, including for precision timing applications in a high-luminosity collider environment.

physics.ins-det↗
arXiv2014

LArIAT: Liquid Argon In A Testbeam

Liquid Argon Time Projection Chambers (LArTPCs) are ideal detectors for precision neutrino physics. These detectors, when located deep underground, can also be used for measurements of proton decay, and astrophysical neutrinos. The technology must be completely developed, up to very large mass scales, and fully mastered to construct and operate these detectors for this physics program. As part of an integrated plan of developing these detectors, accurate measurements in LArTPC of known particle species in the relevant energy ranges are now deemed as necessary. The LArIAT program aims to directly achieve these goals by deploying LArTPC detectors in a dedicated calibration test beam line at Fermilab. The set of measurements envisaged here are significant for both the short-baseline (SBN) and long-baseline (LBN) neutrino oscillation programs in the US, starting with MicroBooNE in the near term and with the adjoint near and far liquid argon detectors in the Booster beam line at Fermilab envisioned in the mid-term, and moving towards deep underground physics such as with the long-baseline neutrino facility (LBNF) in the longer term.

physics.ins-det↗
arXiv2007

Search for a Standard Model Higgs Boson in CMS via Vector Boson Fusion in the H->WW->lνlνChannel

We present the potential for discovering the Standard Model Higgs boson produced by the vector-boson fusion mechanism. We considered the decay of Higgs bosons into the W+W- final state, with both W-bosons subsequently decaying leptonically. The main background is ttbar with one or more jets produced. This study is based on a full simulation of the CMS detector, and up-to-date reconstruction codes. The result is that a signal of 5 sigma significance can be obtained with an integrated luminosity of 12-72 1/fb for Higgs boson masses between 130-200 GeV. In addition, the major background can be measured directly to 7% from the data with an integrated luminosity of 30 1/fb. In this study, we also suggested a method to obtain information in Higgs mass using the transverse mass distributions.

hep-ex↗
arXiv2000

Studies of the Response of the Prototype CMS Hadron Calorimeter, Including Magnetic Field Effects, to Pion, Electron, and Muon Beams

We report on the response of a prototype CMS hadron calorimeter module to charged particle beams of pions, muons, and electrons with momenta up to 375 GeV/c. The data were taken at the H2 and H4 beamlines at CERN in 1995 and 1996. The prototype sampling calorimeter used copper absorber plates and scintillator tiles with wavelength shifting fibers for readout. The effects of a magnetic field of up to 3 Tesla on the response of the calorimeter to muons, electrons, and pions are presented, and the effects of an upstream lead tungstate crystal electromagnetic calorimeter on the linearity and energy resolution of the combined calorimetric system to hadrons are evaluated. The results are compared with Monte Carlo simulations and are used to optimize the choice of total absorber depth, sampling frequency, and longitudinal readout segmentation.

hep-ex↗
arXiv1998

Discovery potential for supersymmetry in CMS

This work summarizes and puts in an overall perspective studies done within CMS concerning the discovery potential for squarks and gluinos, sleptons, charginos and neutralinos, SUSY dark matter, lightest Higgs, sparticle mass determination methods and the detector design optimisation in view of SUSY searches. It represents the status of our understanding of these subjects as of Summer 1997. As a benchmark model we used the minimal supergravity-inspired super- symmetric standard model (mSUGRA) with a stable LSP. Discovery of SUSY at the LHC should be relatively straightforward. It may occur through the observation of a large excesses of events in missing E_T + jets, or with one or more isolated leptons. An excess of trilepton events or of isolated dileptons with E_T^miss, exhibiting a characteristic signature in the l^+l^- invariant mass distribution could also be the first manifestation of SUSY production. Squark and gluino production may represent a copious source of Higgs bosons through cascade decays. The lightest SUSY Higgs h->bbbar may be reconstructed with a signal/background ratio of order 1. The lightest supersymmetric particle of SUSY models with conserved R-parity represents a very good candidate for the cosmological dark matter. The region of parameter space where this is true is well-covered by our searches, at least for tan(beta)=2. If supersymmetry exists at electroweak scale it could hardly escape detection in CMS, and the study of supersymmetry will form a central part of our physics program.

hep-ph↗