Detector Simulation [Slides]
Explore the source record for details and available documents.
SEARCH · Engineering Papers
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Explore the source record for details and available documents.
Abstract not provided.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
Simulations with the Monte Carlo N-Particle® (MCNP®) code are frequently used to study neutron coincidence counters for international safeguards. Reducing sources of uncertainty enables more accurate simulations, which reduces the need for expensive or impossible measurements. Accurately specifying the density of polyethylene is crucial for accurate simulations. The density of polyethylene ranges from 0.88 to 0.97 g/cm 3 and high-density polyethylene (HDPE) ranges from about 0.944 to 0.965 g/cm 3 . The density of a specific detector should be measured exactly for the best simulations. Here, the density of the blue/white Active Well Coincidence Counter (AWCC) bulk polyethylene was measured to be 0.9594 +- 0.0013 g/cm 3 at 1-sigma confidence. The density was calculated from repeated measurements of the various polyethylene dimensions for volume, and weighing the polyethylene for the mass. The AWCC neutron detection efficiency was simulated for each polyethylene density. The relative range of efficiencies (3σ) was 3.4% for unspecified polyethylene, 0.9% for HDPE, and 0.3% for the measured density. The impact of modeling small features such as screws was also studied and was found to be a negligible 0.2%. Measuring polyethylene density can reduce its role in uncertainty from one of the largest to one of the smallest contributors.
The structure of the CMS inner tracking system has been studied using nuclear interactions of hadrons striking its material. Data from proton-proton collisions at a center-of-mass energy of 13 TeV recorded in 2015 at the LHC are used to reconstruct millions of secondary vertices from these nuclear interactions. Precise positions of the beam pipe and the inner tracking system elements, such as the pixel detector support tube, and barrel pixel detector inner shield and support rails, are determined using these vertices. These measurements are important for detector simulations, detector upgrades, and to identify any changes in the positions of inactive elements.
The Intensity Frontier (IF) experiments at Fermilab require computing, software, data handling, and infrastructure development for detector and beamline design and to extract maximum scientific output from the data. The emphasis of computing at Fermilab for many years has been on the Tevatron collider Run 2 experiments and CMS. Using the knowledge and experience gained from those experiments as well as new computing developments, preparations for computing for IF experiments are ramping up. There are many challenges in IF computing. These include event generators and detector simulation, beamline simulation, detector design and optimization, data acquisition, data handling, data analysis, and all of the associated services required. In this presentation the computing challenges and requirements will be described and the approaches being taken to address them will be shown.
Accurate detector modeling is a requirement to design systems in many non-proliferation scenarios; by determining a Detector’s Response Function (DRF) to incident radiation, it is possible characterize measurements of unknown sources. DRiFT is intended to post-process MCNP® output and create realistic detector spectra. Capabilities currently under development include the simulation of semiconductor, gas, and (as is discussed in this work) scintillator detector physics. Energy spectra and pulse shape discrimination (PSD) trends for incident photon and neutron radiation have been reproduced by DRiFT.
Organic liquid scintillation detectors are valuable for the detection of special nuclear material since they are capable of detecting both neutrons and gamma rays. Scintillators can also provide energy information which is helpful in identification and characterization of the source. In order to design scintillation based measurement systems appropriate simulation tools are needed. MCNPX-PoliMi is capable of simulating scintillation detector response; however, simulations have traditionally been run in analog mode which leads to long computation times. In this paper, non-analog MCNPX-PoliMi mode which uses variance reduction techniques is applied and tested. The non-analog MCNPX-PoliMi simulation test cases use source biasing, geometry splitting and a combination of both variance reduction techniques to efficiently simulate pulse height distribution and then time-of-flight for a heavily shielded case with a {sup 252}Cf source. An improvement factor (I), is calculated for distributions in each of the three cases above to analyze the effectiveness of the non-analog MCNPX-PoliMi simulations in reducing computation time. It is found that of the three cases, the last case which uses a combination of source biasing and geometry splitting shows the most improvement in simulation run time for the same desired variance. For pulse height distributions speedup ranging from a factor 5 to 25 is observed, while for time-of-flights the speedup factors range from 3 to 10. (authors)
X-ray speckle visibility spectroscopy using X-ray free-electron lasers has long been proposed as a probe of fast dynamics in noncrystalline materials. In this paper, numerical modeling is presented to show how the data interpretation of visibility spectroscopy can be impacted by the nonidealities of real-life X-ray detectors. Using simulated detector data, this work provides a detailed analysis of the systematic errors of several contrast extraction algorithms in the context of low-count-rate X-ray speckle visibility spectroscopy and their origins are discussed. Here, it was found that the finite detector charge cloud and pixel size lead to an unavoidable `degeneracy' in photon position determination, and that the contrasts extracted using different algorithms can all be corrected by a simple linear model. The results suggest that experimental calibration of the correction coefficient at the count rate of interest is possible and essential. This allows computationally lightweight algorithms to be implemented for on-the-fly analysis.
Full detector simulation was among the largest CPU consumers in all CERN experiment software stacks for the first two runs of the Large Hadron Collider. In the early 2010s, it was projected that simulation demands would scale linearly with increasing luminosity, with only partial compensation from increasing computing resources. The extension of fast simulation approaches to cover more use cases that represent a larger fraction of the simulation budget is only part of the solution, because of intrinsic precision limitations. The remainder corresponds to speeding up the simulation software by several factors, which is not achievable by just applying simple optimizations to the current code base. In this context, the GeantV R&D project was launched, aiming to redesign the legacy particle transport code in order to benefit from features of fine-grained parallelism, including vectorization and increased locality of both instruction and data. This paper provides an extensive presentation of the results and achievements of this R&D project, as well as the conclusions and lessons learned from the beta version prototype.
The computational intensity of detector simulation and event reconstruction poses a significant difficulty for data analysis in collider experiments. This challenge inspires the continued development of machine learning techniques to serve as efficient surrogate models. We propose a fast emulation approach that combines simulation and reconstruction. In other words, a neural network generates a set of reconstructed objects conditioned on input particle sets. To make this possible, we advance set-conditional set generation with diffusion models. Using a realistic, generic, and public detector simulation and reconstruction package (COCOA), we show how diffusion models can accurately model the complex spectrum of reconstructed particles inside jets.
Here, this work presents the organic scintillation simulation capabilities of DRiFT, a post-processing Detector Response Function Toolkit for MCNPR output. DRiFT is used to create realistic scintillation detector response functions to incident neutron and gamma mixed- field radiation. As a post-processing tool, DRiFT leverages the extensively validated radiation transport capabilities of MCNPR ® 6, which also provides the ability to simulate complex sources and geometries. DRiFT is designed to be flexible, it allows the user to specify scintillator material, PMT type, applied PMT voltage, and quenching data used in simulations. The toolkit's capabilities, which include the generation of pulse shape discrimination plots and full-energy detector spectra, are demonstrated in a comparison of measured and simulated neutron contributions from 252 Cf and PuBe, and photon spectra from 22 Na and 228 Th sources. DRiFT reproduced energy resolution effects observed in EJ-301 measurements through the inclusion of scintillation yield variances, photon transport noise, and PMT photocathode and multiplication noise.