Cross section for inelastic neutron scattering from 193Ir at 6 MeV
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SNM verification can be accomplished via two methods i.e. destructive analysis (DA) or nondestructive analysis (NDA). The DA modes are much more accurate compared to NDA modes; nevertheless, the sample's physical integrity is destroyed in the process. Techniques such as ICP-MS, ID-ICP-MS, TI-MS are few examples of DA, where a bulk material is ground and mixed with a solution (some form of acid) to acquire a homogenous sample. In contrast, NDA modes utilize signals emitted by the sample such as gamma, neutrons, light, or heat to analyze samples under investigation. HPGe detectors are some of the most frequently used gamma detectors to assay SNM. However, in a high activity scenario it provides a large uncertainty and sometimes becomes unfeasible to measure low energy gamma rays. Meanwhile, neutron detectors are not affected by the gamma background, therefore they can be used in addition to the gamma detectors to verify total fissile mass ({sup 235}U + {sup 239}Pu) of SNM. The AWCC is one of the thermal neutron NDA systems developed at the Los Alamos National Laboratory (LANL). It consists of 42 {sup 3}He detectors to measure gross neutrons (singles), coincidence neutrons (doubles), higher order multiplicity counting (triples, quadruples, etc.). It can effectively operate in both active and passive neutron interrogation modes. It can utilize AmLi, AmBe, or {sup 252}Cf active interrogation neutron sources. It is used to verify declared fissile mass ({sup 235}U or {sup 239}Pu) present in SNM. Objectives: Calibrate HPGe and AWCC for lightly irradiated (with enough {sup 137}Cs gamma background) ∼93% enriched HEU fuels; Verify fissile mass ({sup 235}U +{sup 239}Pu) content using AWCC and HPGe; Model and benchmark AWCC in MCNP6. Neutron coincidence and multiplicity counting: Two or more neutrons are coincident if they are detected by the system within the specified gate window; In AWCC: Gate window is 128 μsec; Induced fission in fissile material such as {sup 235}U and {sup 239}Pu - emission of time correlated neutrons - Singles, Doubles, Triples, or Quadruples; No induced fission in non-fissile materials- no correlated neutrons - only singles. SNM can be verified with high accuracy and precision using gamma and neutron NDA instruments such as HPGe and AWCC. Interaction with thermal neutrons. Non-fissile atoms - do not emit correlated neutrons. Fissile atoms - emit correlated neutrons during induced fission events. The time correlated neutrons can be detected by using coincidence or multiplicity counters such as AWCC. Likewise, {sup 235}U produces 186 keV gamma peak that can be measured by a HPGe gamma detector. Counts under 186 keV gamma peak provide information about {sup 235}U content. Combining AWCC and HPGe results can provide better understanding of special nuclear material under investigation.
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Here, we present results from a 3.25 kg–day target exposure of two silicon charge-coupled devices (CCDs), each with 24 megapixels and skipper readout, deployed in the DAMIC setup at SNOLAB. With a reduction in pixel readout noise of a factor of 10 relative to the previous detector, we investigate the excess population of low-energy events in the CCD bulk previously observed above expected backgrounds. We address the dominant systematic uncertainty of the previous analysis through a depth fiducialization designed to reject surface backgrounds on the CCDs. The measured bulk ionization spectrum confirms the presence of an excess population of low-energy events in the CCD target with characteristic rate of ~7 events per kg-day and electron-equivalent energies of ~80 eV, whose origin remains unknown.
Many analyses in particle and nuclear physics use simulations to infer fundamental, effective, or phenomenological parameters of the underlying physics models. When the inference is performed with unfolded cross sections, the observables are designed using physics intuition and heuristics. We propose to design targeted observables with machine learning. Unfolded, differential cross sections in a neural network output contain the most information about parameters of interest and can be well-measured by construction. The networks are trained using a custom loss function that rewards outputs that are sensitive to the parameter(s) of interest while simultaneously penalizing outputs that are different between particle-level and detector-level (to minimize detector distortions). We demonstrate this idea in simulation using two physics models for inclusive measurements in deep inelastic scattering. We find that the new approach is more sensitive than classical observables at distinguishing the two models and also has a reduced unfolding uncertainty due to the reduced detector distortions.
Will neutrinos find uses outside basic science? It may be too early to say, but neutrino physicists have already imagined a variety of possibilities from the relatively modest to the more blue-sky. In this review, we survey the range of proposed applications, most involving nuclear reactors and other fission sources. We give special attention to the most recent proposals, including verifying submarine reactor integrity, safeguarding advanced nuclear power plants, and monitoring spent nuclear fuel. All of these concepts take advantage of the fact that neutrinos pass through barriers other signals cannot penetrate. That same fact creates the central challenge for neutrino applications: the size and complexity of detectors needed to collect a signal. Although the weakly interacting nature of neutrinos makes them fundamentally difficult to use, developments in detector technology are making some ideas more feasible.
The assessment of neutron data requires understanding the uncertainty. The uncertainty of the final quantities of interest, neutron multiplication and mass, is a combination of many other uncertainties because it is a combination of many other variables. The equations for the values of neutron multiplication and mass are quite complex. The relations between the many contributing variables and their uncertainties are complicated leading to complications in combining them. Additionally, some are statistical in nature, while others are systematic. In an effort to better understand and quantify the total uncertainty in neutron multiplication and SNM mass, they have been subdivided into four categories: setup, detector response, nuclear data, and object characterization. This paper discusses the four categories and what types of uncertainties are in each of them. This effort attempts to generalize this process for any neutron detector, but many of our examples show the Next Generation Multiplicity Detector (MC-15).
Large area, position sensitive planar germanium detectors offer new opportunities in -ray spectroscopy. For in-beam studies remediating neutron damage is important. This work studied a mechanically cooled circular planar 9 × 1 cm wafer with orthogonal 16 × 16 amorphous germanium strip electrodes. Before neutron irradiation the wafer was heated in-cryostat to above 373 K for more than 24 h in order to test the robustness of the contacts and the mechanical cooler. No deterioration in performance was observed. The detector was then exposed to controlled doses of 2.2 MeV neutrons, produced from the 7 Li(p,n) 7 Be reaction until substantial damage was observed. Averaged over the surface of the wafer, a flux of 1.9(1) x 10 9 n/cm 2 was delivered. The detector then survived thermal cycling and 350 K (~77°C) annealing for 70 h which substantially reduced hole trapping. pulse-height correction is investigated to further mitigate the neutron damage. Higher temperature annealing at 365 K was not successful.
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DRiFT post-processes MCNP output and simulates realistic nuclear instrumentation response.
The dual radiator Ring Imaging Cherenkov (dRICH) detector is required to provide continuous hadron identification from ≈3 GeV/c up to ≈50 GeV/c, and to supplement electron and positron identification from a few hundred MeV/c up to about 15 GeV/c, in the forward (ion-side) end-cap of the ePIC experiment. Such an extended momentum range imposes the use of two radiators, gas and aerogel. The common imaging system, that ensures compactness and cost-effectiveness, is based on SiPM sensors to work in a high non-uniform magnetic field. During the R&D phase, the dual radiator principle and the single component performance have been validated. A status overview of the project is presented. The design and technological choices are discussed together with the results obtained from laboratory characterization of the component demonstrators and beam tests of the evolving prototypes.
Environmental screening of gamma radiation consists of detecting weak nuisance and anomaly signal in the presence of strong and highly varying background. In a typical scenario, a mobile detector-spectrometer continuously measures gamma radiation spectra in short, e.g., one-second, signal acquisition intervals. The measurement data is a 2D matrix, where one dimension is gamma ray energy, and the other dimension is the number of measurements or total time. In principle, gamma radiation sources can be detected and identified from the measured data by their unique spectral lines. Detecting sources from data measured in a search scenario is difficult due to the highly varying background because of naturally occurring radioactive material (NORM), and low signal-to-noise ratio (S/N) of spectral signal measured during one-second acquisition intervals. The objective of this work is to explore unsupervised machine learning (ML) algorithms for detection and identification of weak nuisances and anomalies events in the presence of highly fluctuating background. The challenge is that spectral lines of isotopes are difficult to observe in one-second measurements. Averaging over the entire measurement campaign data set reveals spectral lines of most common background isotopes. Spectral lines of orphan sources, which might appear only in a few measurements during the campaign, will be washed out if averaging is performed over the entire measurement data set. The approach we have explored consists of extracting one-second measurements containing weak spectral features through data clustering. Averaging one-second spectra in a cluster should reveal the presence of anomaly sources. We created two ML models using K-means clustering and Neural Network Self-organizing Map (SOM). Performance of these ML models was benchmarked using search data. One data set contained 137 Cs source, and another dataset contained 131 I source.
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The objective of this work was to fabricate and characterize a new X-ray imaging detector with micrometre-scale pixel dimensions (7.8 µm) and high detection efficiency for hard X-ray energies above 20 keV. A key technology component consists of a monolithic hybrid detector built by direct deposition of an amorphous selenium film on a custom designed CMOS readout integrated circuit. Characterization was carried out at the synchrotron beamline 1-BM-B at the Advanced Photon Source of Argonne National Laboratory. The direct conversion detector demonstrated micrometre-scale spatial resolution with a 63 keV modulation transfer function of 10% at Nyquist frequency. In addition, spatial resolving power down to 8 µm was determined by imaging a transmission bar target at 21 keV. X-ray signal linearity, responsivity and lag were also characterized in the same energy range. Finally, phase contrast edge enhancement was observed in a phase object placed in the beam path. This amorphous selenium/CMOS detector technology can address gaps in commercially available X-ray detectors which limit their usefulness for existing synchrotron applications at energies greater than 50 keV; for example, phase contrast tomography and high-resolution imaging of nanoscale lattice distortions in bulk crystalline materials using Bragg coherent diffraction imaging. Here, the technology will also facilitate the creation of novel synchrotron imaging applications for X-ray energies at or above 20 keV.
The Super High Momentum Spectrometer (SHMS) has been built for Hall C at the Thomas Jefferson National Accelerator Facility (Jefferson Lab). With a momentum capability reaching 11 GeV/ c , the SHMS provides measurements of charged particles produced in electron-scattering experiments using the maximum available beam energy from the upgraded Jefferson Lab accelerator. The SHMS is an ion-optics magnetic spectrometer comprised of a series of new superconducting magnets which transport charged particles through an array of triggering, tracking, and particle-identification detectors that measure momentum, energy, angle and position in order to allow kinematic reconstruction of the events back to their origin at the scattering target. The detector system is protected from background radiation by a sophisticated shielding enclosure. The entire spectrometer is mounted on a rotating support structure which permits measurements to be taken with a large acceptance over laboratory scattering angles from 5.5° to 40°, thus allowing a wide range of low cross-section experiments to be conducted. Finally, these experiments complement and extend the previous Hall C research program to higher energies.
JSNS 2 (J-PARC Sterile Neutrino Search at J-PARC Spallation Neutron Source) is an experiment that is searching for sterile neutrinos via the observation of $\overline{ν}$ μ → $\overline{ν}$ e appearance oscillations using neutrinos from muon decay-at-rest. For this search, rejecting cosmic-ray-induced neutron events by Pulse Shape Discrimination (PSD) is essential because the JSNS2 detector is located above ground, on the third floor of the building. We have achieved 94.95% ± 0.15% rejection of neutron events while keeping 92.82% ± 1.77% of signal, electron-like events using a data-driven likelihood method. This article will report the PSD technique using the full fiducial volume of the JSNS2 detector.