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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Refining fast calorimeter simulations with a Schrödinger Bridge

Machine learning-based simulations, especially calorimeter simulations, are promising tools for approximating the precision of classical high energy physics simulations with a fraction of the generation time. Nearly all methods proposed so far learn neural networks that map a random variable with a known probability density, like a Gaussian, to realistic-looking events. In many cases, physics events are not close to Gaussian and so these neural networks have to learn a highly complex function. We study an alternative approach: Schrödinger bridge Quality Improvement via Refinement of Existing Lightweight Simulations (SQuIRELS). SQuIRELS leverages the power of diffusion-based neural networks and Schrödinger bridges to map between samples where the probability density is not known explicitly. We apply SQuIRELS to the task of refining a classical fast simulation to approximate a full classical simulation. On simulated calorimeter events, we find that SQuIRELS is able to reproduce highly non-trivial features of the full simulation with a fraction of the generation time.

Calorimeter methods↗
DOE OSTI2024

Anomaly detection with flow-based fast calorimeter simulators

Recently, several normalizing flow-based deep generative models have been proposed to accelerate the simulation of calorimeter showers. Using caloflow as an example, we show that these models can simultaneously perform unsupervised anomaly detection with no additional training cost. As a demonstration, we consider electromagnetic showers initiated by one (background) or multiple (signal) photons. The caloflow model is designed to generate single-photon showers, but it also provides access to the shower likelihood. We use this likelihood as an anomaly score and study the showers tagged as being unlikely. As expected, the tagger struggles when the signal photons are nearly collinear but is otherwise effective. This approach is complementary to a supervised classifier trained on only specific signal models using the same low-level calorimeter inputs. While the supervised classifier is also highly effective at unseen signal models, the unsupervised method is more sensitive in certain regions, and, thus, we expect that the ultimate performance will require a combination of these approaches.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

CaloChallenge 2022: a community challenge for fast calorimeter simulation

Here, we present the results of the ‘Fast Calorimeter Simulation Challenge 2022’—the CaloChallenge. We study state-of-the-art generative models on four calorimeter shower datasets of increasing dimensionality, ranging from a few hundred voxels to a few tens of thousand voxels. The 31 individual submissions span a wide range of current popular generative architectures, including variational autoencoders (VAEs), generative adversarial networks (GANs), normalizing flows, diffusion models, and models based on conditional flow matching. We compare all submissions in terms of quality of generated calorimeter showers, as well as shower generation time and model size. To assess the quality we use a broad range of different metrics including differences in one-dimensional histograms of observables, KPD/FPD scores, AUCs of binary classifiers, and the log-posterior of a multiclass classifier. The results of the CaloChallenge provide the most complete and comprehensive survey of cutting-edge approaches to calorimeter fast simulation to date. In addition, our work provides a uniquely detailed perspective on the important problem of how to evaluate generative models. As such, the results presented here should be applicable for other domains that use generative AI and require fast and faithful generation of samples in a large phase space.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS2000

Imaging Calorimeter for ACCESS Simulations with GEANT/FLUKA

Imaging Calorimeter for ACCESS (ICA) is a candidate of the calorimeter for the NASA's ACCESS program. The ICA studies the origin and acceleration mechanism of cosmic rays by measuring the elemental composition of the cosmic rays in the energy up to 10(exp 16) eV. For the past year, Monte Carlo simulation study for the ICA has been conducted to predict the detector performance and to design the system for match the scientific objectives. Simulation results show that the detector response resembles a Gaussian distribution and the energy resolution with ICA can be achieved about 40%. In addition, simulations of the detector's response to an assumed bent power law spectra in the region where the knee occurs have been conducted and clearly show that this detector can provide sufficiently accurate estimates of the spectral parameters that are a science goal of ACCESS.

Lee, Jeongin↗
NASA NTRS2000

Imaging Calorimeter for ACCESS Simulations with GEANT/FLUKA

Imaging Calorimeter for ACCESS (ICA) is a candidate of the calorimeter for the NASA's ACCESS program to be flown on the International Space Station. The ICA studies the origin and acceleration mechanism of cosmic rays by measuring the elemental composition of the cosmic rays in the energy up to 10(exp 16) eV. For the past year, Monte Carlo simulation study for the ICA has been conducted using GEANT/FLUKA to predict the detector performance and to design the system for match the scientific objectives. Simulation results will be shown for the detector response and the energy resolution for various configurations.

Watts, John↗
DOE OSTI2025

Unifying simulation and inference with normalizing flows

There have been many applications of deep neural networks to detector calibrations and a growing number of studies that propose deep generative models as automated fast detector simulators. We show that these two tasks can be unified by using maximum likelihood estimation (MLE) from conditional generative models for energy regression. Unlike direct regression techniques, the MLE approach is prior independent and non-Gaussian resolutions can be determined from the shape of the likelihood near the maximum. Using an ATLAS-like calorimeter simulation, we demonstrate this concept in the context of calorimeter energy calibration. Published by the American Physical Society 2025

Hadronic calorimiters↗
NASA NTRS2011

Flowfield Analysis of a Small Entry Probe (SPRITE) Tested in an Arc Jet

Results of simulations of flow of an arc-heated stream around a 14-inch diameter 45 sphere-cone configuration are presented. Computations are first benchmarked against pressure and heat flux measurements made using copper slug calorimeters of different shapes and sizes. The influence of catalycity of copper on computed results is investigated. Good agreements between predictions and measurements are obtained by assuming the copper slug to be partially catalytic to atomic recombination. With total enthalpy estimates obtained from these preliminary computations, calculations are then performed for the test article, with the nozzle and test article considered as an integrated whole the same procedure adopted for calorimeter simulations. The resulting heat fluxes at select points on the test article (points at which fully instrumented plugs were placed) are used in material thermal response code calculations. Predicted time histories of temperature are compared against thermocouple data from the instrumented plugs, and recession determined. Good agreement is obtained for in-depth thermocouples.

Prabhu, Dinesh K.↗
DOE OSTI2026

CaloTrilogy: Toward a Breakthrough in One-Step, End-to-End, Physics-Guided Shower Generation for Modern Calorimeters

High-precision calorimeter simulation at current and future colliders imposes rapidly growing computational demands, motivating the development of machine-learning surrogates for traditional Monte Carlo tools such as Geant4. Flow matching and diffusion-based generative models have become leading approaches for high-dimensional fast simulation because of their sample quality, but typically require ${\cal O}(100)$ function evaluations at inference and often rely on auxiliary networks to constrain global observables, compromising streamlined end-to-end generation. We introduce a unified framework that improves the balance between speed, shower quality, and physics fidelity. The method combines: (i) an average velocity field integrator that enables sampling in one or a few evaluations; (ii) a learned generative prior in shower space, constructed from data rather than random noise; and (iii) physics-guided loss terms that impose inductive biases on key observables during training. These elements are training time regularizers, preserving end-to-end inference with no additional cost. With only one or a few evaluation steps, the model achieves shower quality competitive with state-of-the-art flow and diffusion approaches, tested on several public high granularity calorimeter datasets. The results demonstrate inter-layer shower structure consistent with the underlying physics, providing a strong candidate for future fast simulation workflows.

Jiang, Cheng [Edinburgh U.]↗
NASA NTRS2007

Performance of the Anti-Coincidence Detector on the GLAST Large Area Telescope

The Anti-Coincidence Detector (ACD), the outermost detector layer in the Gamma-ray Large Area Space Telescope (GLAST) Large Area Telescope (LAT), is designed to detect and veto incident cosmic ray charged particles, which outnumber cosmic gamma rays by 3-4 orders of magnitude. The challenge in ACD design is that it must have high (0.9997) detection efficiency for singly-charged relativistic particles, but must also have a low probability for self-veto of high-energy gammas by backplash radiation from interactions in the LAT calorimeter. Simulations and tests demonstrate that the ACD meete its design requirements. The performance of the ACD has remained stable thrugh stand-alone environmental testing, shipment across the U.S. installation onto the LAT, shipment back across the U.S., LAT environmental testing, and shipment to Arizona. As part of the fully-assembled GLAST observatory, the ACD is being readied for final testing before launch.

Thompson, D. J.↗
DOE OSTI2024

Simulating the CMS High Granularity Calorimeter with ML

Detector simulation is a key component of physics analysis and related activities in CMS. In the upcoming High Luminosity LHC era, simulation will be required to use a smaller fraction of computing in order to satisfy resource constraints. At the same time, CMS will be upgraded with the new High Granularity Calorimeter (HGCal), which requires significantly more resources to simulate than the existing CMS calorimeters. This computing challenge motivates the use of generative machine learning models as surrogates to replace full physics-based simulation. We study the application of state-of-the-art diffusion models to simulate particle showers in the CMS HGCal. We will discuss methods to overcome the challenges posed by the high-dimensional, irregular geometry of the HGCal. The quality of the showers produced by the diffusion model will be assessed by comparison to the full GEANT4-based simulation. The increase in simulation throughput will be quantified and methods to accelerate the diffusion model inference will also be discussed.

Amram, Oz↗
NASA NTRS2000

Simulations of a Thin Sampling Calorimeter with GEANT/FLUKA

The Advanced Cosmic-ray Composition Experiment for the Space Station (ACCESS) will investigate the origin, composition and acceleration mechanism of cosmic rays by measuring the elemental composition of the cosmic rays up to 10(exp 15) eV. These measurements will be made with a thin ionization calorimeter and a transition radiation detector. This paper reports studies of a thin sampling calorimeter concept for the ACCESS thin ionization calorimeter. For the past year, a Monte Carlo simulation study of a Thin Sampling Calorimeter (TSC) design has been conducted to predict the detector performance and to design the system for achieving the ACCESS scientific objectives. Simulation results show that the detector energy resolution function resembles a Gaussian distribution and the energy resolution of TSC is about 40%. In addition, simulations of the detector's response to an assumed broken power law cosmic ray spectra in the region where the 'knee' of the cosmic ray spectrum occurs have been conducted and clearly show that a thin sampling calorimeter can provide sufficiently accurate estimates of the spectral parameters to meet the science requirements of ACCESS. n

Lee, Jeongin↗
DOE OSTI2026

DECAL MDN Resolution Calibration

The simulated pixelated calorimeter uses 0.1 mm × 0.1 mm silicon pixels (0.0001 cm²) silicon pixels as active layers — far finer than current concepts like HGCAL (~0.5 cm²) — improving energy resolution through much finer segmentation. While the standard resolution formalism fits three numbers to a handful of discrete test-beam energies, this work is a first try at a more data-efficient alternative: learning the full response shape continuously in energy with a mixture density network. This continuous, differentiable surrogate is a natural building block for fast simulation of extremely complex calorimeters.

Wang, Peter [U. Chicago (main)]↗
DOE OSTI2025

Normalizing flows for high-dimensional detector simulations

Whenever invertible generative networks are needed for LHC physics, normalizing flows show excellent performance. In this work, we investigate their performance for fast calorimeter shower simulations with increasing phase space dimension. We use fast and expressive coupling spline transformations applied to the CaloChallenge datasets. In addition to the base flow architecture we also employ a VAE to compress the dimensionality and train a generative network in the latent space. We evaluate our networks on several metrics, including high-level features, classifiers, and generation timing. Our findings demonstrate that invertible neural networks have competitive performance when compared to autoregressive flows, while being substantially faster during generation.

Ernst, Florian↗
NASA NTRS2001

An Imaging Calorimeter for Access-Concept Study

A mission concept study to define the "Advanced Cosmic-ray Composition Experiment for Space Station (ACCESS)" was sponsored by the National Aeronautics and Space Administration (NASA). The ACCESS instrument complement contains a transition radiation detector and an ionization calorimeter to measure tile spectrum of protons, helium, and heavier nuclei up to approximately 10(exp 15) eV to search for the limit of S/N shock wave acceleration, or evidence for other explanations of the spectra. Several calorimeter configurations have been studied, including the "baseline" totally active bismuth germanate instrument and sampling calorimeters utilizing various detectors. The Imaging Calorimeter for ACCESS (ICA) concept comprises a carbon target and a calorimeter using a high atomic number absorber sampled approximately each radiation length (rl) by thin scintillating fiber (SCIFI) detectors. The main features and options of the ICA instrument configuration are described in this paper. Since direct calibration is not possible over most of the energy range, the best approach must be decided from simulations of calorimeter performance extrapolated from CERN calibrations at 0.375 TeV. This paper presents results from the ICA simulations study.

Parnell, T. A.↗
NASA NTRS2002

Simulation of Energy Response of the ATIC Calorimeter

ATIC (Advanced Thin Ionization Calorimeter) is a balloon borne experiment designed to measure the cosmic ray composition for elements from hydrogen to iron and their energy spectra from approx.50 GeV to near 100 TeV. It consists of a Si-matrix detector to determine the charge of a CR particle, a scintillator hodoscope for tracking, carbon interaction targets and a fully active BGO calorimeter. ATIC had its first flight from McMurdo, Antarctica from 28/12/2000 to 13/01/2001. The ATIC flight collected approximately 25 million events. For reconstruction of primary spectra from spectra of energy deposits measured in the experiment, correlations between kinetic energy of a primary particle E(sub kin) and energy deposit in the calorimeter E(sub d) should be known. For this purpose, simulations of energy response of the calorimeter on energy spectra of different nuclei were done. The simulations were performed by GEANT-3.21 code with QGSM generator for nucleus - nucleus interactions. The incident flux was taken as isotropic in the ATIC aperture. Primary spectra power-law by momentum were used as inputs according to standard models of cosmic ray acceleration. These spectra become power-law by kinetic energy at E(sub kin) higher than approx.20Mc(sup 2), where M is primary nucleus mass. It should be noted that energy deposit spectra measured by ATIC illustrate similar behavior. Distributions of ratio E(sub kin)/E(sub d) are presented for different energy deposits and for a set of primaries. For power-law regions of energy spectra at E(sub d)> or equal to 20Mc(sup 2) the obtained mean value of E(sub kin)/E(sub d) increases from approx.2.4 for protons to approx.3.1 for iron, while rms/ decreases from 50% for protons to about 15% for iron. These values were obtained for the spectral index gamma=1.6

Batkov, K. E.↗
NASA NTRS1999

Observation and Simulations of the Backsplash Effects in High-Energy Gamma-Ray Telescopes Containing a Massive Calorimeter

Beam test and simulation results are presented for a study of the backsplash effects produced in a high-energy gamma-ray detector containing a massive calorimeter. An empirical formula is developed to estimate the probability (per unit area) of backsplash for different calorimeter materials and thicknesses, different incident particle energies, and at different distances from the calorimeter. The results obtained are applied to the design of Anti-Coincidence Detector (ACD) for the Large Area Telescope (LAT) on the Gamma-ray Large Area Space Telescope (GLAST).

Moiseev, Alexander A.↗
DOE OSTI2025

Development of High-Granularity Dual-Readout Calorimetry with psec Timing

Dual-readout and particle flow algorithm (PFA) are technologies proposed for precise jet energy measurement in future colliders. While PFA requires highly granular calorimeters, dual-readout has mainly been used with fiber-based calorimeters that do not have highly segmented capabilities. It is still non-trivial to combine these two technologies in one calorimeter system because of the use of fibers in most of the dualreadout calorimeters, which is not compatible with the high granularity requirement of PFA technologies. The aim of this study is to develop a novel calorimetry that combines dual-readout and PFA by adopting a highly segmented tile-based configuration. This paper compares the improvement of energy resolution using dual-readout approach across several configurations of highly granular hadron calorimeters through simulation. Results indicate that setups with fine sampling and close placement of scintillators and Cherenkov detectors improve dual-readout performance.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗