Calorimetric Reconstruction Research Articles

Application of a deep learning method for shower axis reconstruction in a 3D imaging calorimeter

We present a deep learning method based on Convolutional Neural Networks (CNN) to reconstruct the shower axis of isotropic electrons in a three-dimensional (3D) imaging calorimeter, which is one of the core instruments of the High Energy cosmic-Radiation Detection (HERD) payload. The CNN method is evaluated against the Principal Component Analysis (PCA) method, utilizing isotropic Monte Carlo (MC) simulation data covering an energy spectrum from 10 GeV to 1000 GeV, as well as beam test data ranging from 50 GeV to 250 GeV. The comparative analysis results indicate that the CNN outperforms the PCA by about 40% at 100 GeV in terms of angular resolution under isotropic conditions. Both methods are further validated using beam test data. The results demonstrate the effectiveness of the CNN method in reconstructing the shower axis, and they underscore the potential of incorporating advanced deep learning techniques into the comprehensive task of calorimeter reconstruction.

Key4hep: Progress Report on Integrations

Key4hep: Progress Report on Integrations

GNN-based end-to-end reconstruction in the CMS Phase 2 High-Granularity Calorimeter

We present the current stage of research progress towards a one-pass, completely Machine Learning (ML) based imaging calorimeter reconstruction. The model used is based on Graph Neural Networks (GNNs) and directly analyzes the hits in each HGCAL endcap. The ML algorithm is trained to predict clusters of hits originating from the same incident particle by labeling the hits with the same cluster index. We impose simple criteria to assess whether the hits associated as a cluster by the prediction are matched to those hits resulting from any particular individual incident particles. The algorithm is studied by simulating two tau leptons in each of the two HGCAL endcaps, where each tau may decay according to its measured standard model branching probabilities. The simulation includes the material interaction of the tau decay products which may create additional particles incident upon the calorimeter. Using this varied multiparticle environment we can investigate the application of this reconstruction technique and begin to characterize energy containment and performance.

The trigger system for the MEG II experiment

Intending to improve the current sensitivity on μ→eγ decay by one order of magnitude, the MEG II experiment at Paul Scherrer Institute completed the integration phase in 2021 with all detectors successfully operated throughout the subsequent beamtime. Earlier in 2021, the WaveDAQ integrated Trigger and Data Acquisition (TDAQ) system, developed for the readout of the experiment, was completely commissioned. Receiving almost 9000 channels from the detectors, the MEG II TDAQ system is the largest WaveDAQ deployment so far, proving the scalability of the overall design, from bench-top setup through various smaller-size experiments. We will describe how MEG II trigger system reduces the ∼107 muon decays at the experiment target down to a 10 Hz event rate by exploiting the signal event characteristics at the online level. The trigger system performs the calorimetric reconstruction of the photon shower and then compares the timing and direction with positron candidates within a 600 ns hard latency time. The first release of the online reconstruction, deployed in 2021, achieved a 2.4% photon energy resolution at the signal energy of 52.8MeV and a ∼2ns coincidence time resolution among the child particles.

End-to-end multi-particle reconstruction in high occupancy imaging calorimeters with graph neural networks

We present an end-to-end reconstruction algorithm to build particle candidates from detector hits in next-generation granular calorimeters similar to that foreseen for the high-luminosity upgrade of the CMS detector. The algorithm exploits a distance-weighted graph neural network, trained with object condensation, a graph segmentation technique. Through a single-shot approach, the reconstruction task is paired with energy regression. We describe the reconstruction performance in terms of efficiency as well as in terms of energy resolution. In addition, we show the jet reconstruction performance of our method and discuss its inference computational cost. To our knowledge, this work is the first-ever example of single-shot calorimetric reconstruction of {mathcal {O}}(1000) particles in high-luminosity conditions with 200 pileup.

Calorimetric classification of track-like signatures in liquid argon TPCs using MicroBooNE data

The MicroBooNE liquid argon time projection chamber located at Fermilab is a neutrino experiment dedicated to the study of short-baseline oscillations, the measurements of neutrino cross sections in liquid argon, and to the research and development of this novel detector technology. Accurate and precise measurements of calorimetry are essential to the event reconstruction and are achieved by leveraging the TPC to measure deposited energy per unit length along the particle trajectory, with mm resolution. We describe the non-uniform calorimetric reconstruction performance in the detector, showing dependence on the angle of the particle trajectory. Such non-uniform reconstruction directly affects the performance of the particle identification algorithms which infer particle type from calorimetric measurements. This work presents a new particle identification method which accounts for and effectively addresses such non-uniformity. The newly developed method shows improved performance compared to previous algorithms, illustrated by a 93.7% proton selection efficiency and a 10% muon mis-identification rate, with a fairly loose selection of tracks performed on beam data. The performance is further demonstrated by identifying exclusive final states in νμCC interactions. While developed using MicroBooNE data and simulation, this method is easily applicable to future LArTPC experiments, such as SBND, ICARUS, and DUNE.

Physics object performance of the FCC-hh calorimeter system

The feasibility of a future proton-proton collider (FCC-hh) has been outined in Conceptual Design Reports published early 2019. FCC-hh would deliver collisions at a center of mass energy up to 100 TeV and unprecedented instantaneous luminosity (L=3 × 1034), resulting in extremely challenging radiation conditions up to a maximum of 5× 1018 neq/cm2 and a dose up to 5 GGy in the forward calorimeters (up to |η|=6) and up to 1000 simultaneous proton-proton interactions per bunch-crossing. By delivering an integrated luminosity of few tens of ab−1, the FCC-hh would provide an unrivalled discovery potential for new physics. Requiring high sensitivity for resonant searches at masses up to tens of TeV imposes strong constraints on the design of the calorimeters. Resonant searches in final states containing jets, taus and electrons require both excellent energy resolution at multi-TeV energies as well as outstanding ability to resolve highly collimated decay products resulting from extreme boosts. In addition, the FCC-hh provides the unique opportunity to precisely measure the Higgs self-coupling in the di-photon and b-jets channel. Excellent photon and jet energy resolution at low energies as well as excellent angular resolution for pion background rejection are required in this challenging environment. In this talk we will briefly review the electromagnetic and hadronic calorimeter current design and requirements (granularity, energy resolution, acceptance, …) and discuss the expected performance of the physics objects based on calorimeter reconstruction. We will then examine the impact of the object performance on the final sensitivity of the relevant benchmark physics analyses.

Calorimetry for low-energy electrons using charge and light in liquid argon

Precise calorimetric reconstruction of 5-50 MeV electrons in liquid argon time projection chambers (LArTPCs) will enable the study of astrophysical neutrinos in DUNE and could enhance the physics reach of oscillation analyses. Liquid argon scintillation light has the potential to improve energy reconstruction for low-energy electrons over charge-based measurements alone. Here we demonstrate light-augmented calorimetry for low-energy electrons in a single-phase LArTPC using a sample of Michel electrons from decays of stopping cosmic muons in the LArIAT experiment at Fermilab. Michel electron energy spectra are reconstructed using both a traditional charge-based approach as well as a more holistic approach that incorporates both charge and light. A maximum-likelihood fitter, using LArIAT's well-tuned simulation, is developed for combining these quantities to achieve optimal energy resolution. A sample of isolated electrons is simulated to better determine the energy resolution expected for astrophysical electron-neutrino charged-current interaction final states. In LArIAT, which has very low wire noise and an average light yield of 18 pe/MeV, an energy resolution of $\sigma/E \simeq 9.3\%/\sqrt{E} \oplus 1.3\%$ is achieved. Samples are then generated with varying wire noise levels and light yields to gauge the impact of light-augmented calorimetry in larger LArTPCs. At a charge-readout signal-to-noise of S/N $\simeq$ 30, for example, the energy resolution for electrons below 40 MeV is improved by $\approx$ 10%, $\approx$ 20%, and $\approx$ 40% over charge-only calorimetry for average light yields of 10 pe/MeV, 20 pe/MeV, and 100 pe/MeV, respectively.

Operation and performance of the LHCb calorimeters

The LHCb calorimeters play a key role in the hardware trigger of the experiment. They also serve the measurement of radiative heavy flavor decays and the identification of electrons. Located at twelve meters from the interaction region, they are composed of a plane of scintillating tiles, a preshower detector, an electromagnetic and a hadronic sampling calorimeters using scintillators as active elements. In these proceedings, technical and operational aspects of these detectors are described. Emphasis is then put on calorimeter reconstruction and calibration. Finally, performance for benchmark physics modes are briefly reported.

Comparison of the calorimetric and kinematic methods of neutrino energy reconstruction in disappearance experiments

To be able to achieve their physics goals, future neutrino-oscillation experiments will need to reconstruct the neutrino energy with very high accuracy. In this work, we analyze how the energy reconstruction may be affected by realistic detection capabilities, such as energy resolutions, efficiencies, and thresholds. This allows us to estimate how well the detector performance needs to be determined a priori in order to avoid a sizable bias in the measurement of the relevant oscillation parameters. We compare the kinematic and calorimetric methods of energy reconstruction in the context of two muon-neutrino disappearance experiments operating in different energy regimes. For the calorimetric reconstruction method, we find that the detector performance has to be estimated with a ~10% accuracy to avoid a significant bias in the extracted oscillation parameters. On the other hand, in the case of kinematic energy reconstruction, we observe that the results exhibit less sensitivity to an overestimation of the detector capabilities.

Energy reconstruction in the long-baseline neutrino experiment.

The Long-Baseline Neutrino Experiment aims at measuring fundamental physical parameters to high precision and exploring physics beyond the standard model. Nuclear targets introduce complications towards that aim. We investigate the uncertainties in the energy reconstruction, based on quasielastic scattering relations, due to nuclear effects. The reconstructed event distributions as a function of energy tend to be smeared out and shifted by several 100MeV in their oscillatory structure if standard event selection is used. We show that a more restrictive experimental event selection offers the possibility to reach the accuracy needed for a determination of the mass ordering and the CP-violating phase. Quasielastic-based energy reconstruction could thus be a viable alternative to the calorimetric reconstruction also at higher energies.

The LArIAT light readout system

Most neutrino experiments using liquid argon as a detector medium focus on obtaining information about the interaction from ionization electrons and choose to use the scintillation light as a trigger or an indication of interaction time. On the other hand, experiments investigating lower energy ranges, i.e. Dark Matter searches have shown that there is a wealth of information in the scintillation light, which by itself allows calorimetric reconstruction and particle identification based on the shape of the light signal. LArIAT is a an experiment set to calibrate the LAr Time Projection Chamber technology by placing the detector on a beam of charged particles of known type and momentum. One of its goals is to test a Dark Matter search-like light collection system, which could supplement the calorimetric and particle identification capabilities of the LArTPC. The plans to implement this setup in the LArIAT detector will be presented together with the small set-up being constructed to test the components.

Analysis of a large sample of neutrino-induced muons with the ArgoNeuT detector

ArgoNeuT, or Argon Neutrino Test, is a 170 liter liquid argon time projection chamber designed to collect neutrino interactions from the NuMI beam at Fermi National Accelerator Laboratory. ArgoNeuT operated in the NuMI low-energy beam line directly upstream of the MINOS Near Detector from September 2009 to February 2010, during which thousands of neutrino and anti-neutrino events were collected.The MINOS Near Detector was used to measure muons downstream of ArgoNeuT.Though ArgoNeuT is primarily an R&D project, the data collected provide a unique opportunity to measure neutrino cross sections in the 0.1–10 GeV energy range.Fully reconstructing the muon from these interactions is imperative for these measurements. This paper focuses on the complete kinematic reconstruction of neutrino-induced through-going muons tracks.Analysis of this high statistics sample of minimum ionizing tracks demonstrates the reliability of the geometric and calorimetric reconstruction in the ArgoNeuT detector.

Measurement of the μ decay spectrum with the ICARUS liquid Argon TPC

Examples are given which prove the ICARUS detector quality through relevant physics measurements. We study the muon decay energy spectrum from a sample of stopping muon events acquired during the test run of the ICARUS T600 detector. This detector allows the spatial reconstruction of the events with fine granularity, hence, the precise measurement of the range and dE/dx of the muon with high sampling rate. This information is used to compute the calibration factors needed for the full calorimetric reconstruction of the events. The Michel rho parameter is then measured by comparison of the experimental and Monte Carlo simulated muon decay spectra, obtaining rho = 0.72 +/- 0.06(stat.) +/- 0.08(syst.). The energy resolution for electrons below ~50 MeV is finally extracted from the simulated sample, obtaining (Emeas-Emc)/Emc = 11%/sqrt(E[MeV]) + 2%.

Results from the ICARUS T600 module

We have studied the μ decay energy spectrum from a sample of stopping μ events acquired during the test run of the ICARUS T600 prototype. This detector allows the spatial reconstruction of the events with fine granularity, hence the precise measurement of the μ range and dE/dx with high sampling rate. This information is used to compute the correction factors needed for the calorimetric reconstruction. The Michel ρ parameter is then measured by comparison of the experimental and Monte Carlo simulated μ decay spectra, obtaining $\rho = 0.72\pm 0.06 \textrm{ (stat.)} \pm 0.08 \textrm{ (syst.)}$ . PACS: 13.15.+g Neutrino interactions — 13.35.Bv Decays of muons

Investigation of ADAMO performance in the ZEUS calorimeter reconstruction program

The ADAMO package has been investigated for the reconstruction of jets simulated in the ZEUS calorimeter. The feasibility of ADAMO routines was tested under various aspects. The study was based on different versions of a cluster algorithm using ADAMO tools or direct BOS memory management tools. In particular,the amount of CPU time needed by each of the versions was determined. Results are quite promising and support an extensive use of the ADAMO package in the software development of high energy physics experiments.