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The Portsmouth Background Values (https://rais.ornl.gov/tools/ports_background.html) are from a Soil Background Report for the Portsmouth Gaseous Diffusion Plant, which sought to develop a comprehensive soil dataset using surface and subsurface soil samples. The Portsmouth background dataset on the RAIS also includes background concentrations for groundwater.
The Mu2e experiment will search for the charged lepton flavour violating process of neutrinoless coherent muon to electron conversion in the field of an Al nucleus. The expected signal is a 104.97 MeV/c electron. One of the expected backgrounds is due to ¯ps produced by the proton beam at the Production Target and annihilating in the Stopping Target (ST). The background from ¯p annihilation is not a dominant one, but it has a large uncertainty and it cannot be suppressed by the timing cuts used to reduce the prompt background. However at Mu2e energies, p¯p annihilation is the only source of events with multiple simultaneous tracks coming from the ST. We exploit this unique feature and reconstruct the multi-track events to estimate the ¯p background.
The upcoming Hyper-Kamiokande (HyperK) experiment is expected to detect the Diffuse Supernova Neutrino Background (DSNB). This requires to ponder all possible sources of background. Sub-GeV dark matter (DM) which annihilates into neutrinos is a potential background that has not been considered so far. We simulate DSNB and DM signals, as well as backgrounds in the HyperK detector. We find that DM-induced neutrinos could indeed alter the extraction of the correct values of the parameters of interest for DSNB physics. Since the DSNB is an isotropic signal, and DM originates primarily from the Galactic centre, we show that this effect could be alleviated with an on-off analysis.
The Mu2e experiment will search for the CLFV process of neutrinoless, coherent conversion of muon to electron in the field of an Al nucleus. One of the expected backgrounds is antiprotons produced by the proton beam at the Production Target and annihilating in the Stopping Target to produce signal-like electrons. Although not a dominant background, it has a large uncertainty and cannot be suppressed by the timing cuts used to reduce the prompt background. However, at Mu2e energies, $p\bar{p}$ annihilation is the only source of events with multiple, simultaneous particles coming from the Stopping Target. We utilized this unique feature and developed a novel approach to reconstruct multi-track events and estimate the antiproton background.
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.
MicroBooNE provides a testbed for studying MeV-scale activity relevant to supernova neutrino detection in LArTPCs. This work focuses on classifying isolated mm-scale energy depositions ( blips ), in contrast to longer track depositions with lengths of ~O(10cm), as possible νₑ CC or elastic scatter candidates from a supernova burst. Cosmogenic blip backgrounds are characterized using CRT-tagged muons and spatial correlations with decay products. Selection cuts are defined for rejecting Tl-208 decay radiogenic blip backgrounds and cosmogenic blip backgrounds; they are based on blip energy and distance from nearby cosmic muon tracks. Results affirm strategies for background rejection and signal efficiency in future low-energy searches, including DUNE.
The Mu2e experiment at Fermilab will search for the Charged Lepton Flavour Violating (CLFV) process of neutrinoless conversion of muon to electron in the field of an Al nucleus. The experimental signature is a monochromatic 104.97 MeV conversion electron. One of the expected backgrounds to the conversion electron search is antiprotons produced by the proton beam at the Production Target and annihilating in the Stopping Target (ST). The background expected from antiprotons is low but highly uncertain due to the uncertainty in the antiproton production cross section for the Mu2e beam energy in the relevant angular region. Antiprotons are significantly slower than the other beam particles, so they cannot be efficiently suppressed by the time window cut used to reduce the prompt background. At Mu2e energies, antiproton annihilation at rest in the ST is the only source of events with multiple, simultaneous particle trajectories. We utilized this unique feature and developed a novel way to reconstruct the multi-track events and estimate the antiproton background in situ.
High diffraction signal-to-background ratios (SBRs), the ratios of diffraction peak integrated intensity over its background intensity, are desired for a neutron diffractometer to acquire good statistics for diffraction pattern measurements and subsequent data analysis. For a given detector, while the diffraction peak signals primarily depend on the characteristics of neutron beam and sample coherent scattering, the background largely originates from the sample incoherent scattering and the scattering from the instrument space. In this work, we investigated the effect of collimation on neutron diffraction SBRs of Si powder measurements using one high-angle area detector bank coupled with six different collimation configurations in a large and complex instrument space at the engineering materials diffractometer VULCAN, SNS, ORNL. The results revealed that the diffraction SBRs can be significantly improved by a proper coarse collimator that leaves no gap between the detector and the collimator, and the improvement of SBRs by a fine radial collimator was remarkable with a proper coarse collimator in place but not distinguishable without one. It was also found that the diffraction SBRs were not effectively improved by adding neutron absorbing element boron to the fine radial collimator body, which indicates that either the absorption of secondary scattered neutrons by the added boron is insignificant or the collimator base material (resin and ABS) alone attenuates background scattering sufficiently. These findings could serve as a useful reference for diffractometer developers and/or operators to optimize their collimation to achieve higher diffraction SBRs.
Deep underground environments are ideal for low background searches due to the attenuation of cosmic rays by passage through the earth. However, they are affected by backgrounds from γ -rays emitted by 40 K and the 238 U and 232 Th decay chains in the surrounding rock. The LUX-ZEPLIN (LZ) experiment will search for dark matter particle interactions with a liquid xenon TPC located within the Davis campus at the Sanford Underground Research Facility, Lead, South Dakota, at the 4850-foot level. In order to characterise the cavern background, in-situ γ -ray measurements were taken with a sodium iodide detector in various locations and with lead shielding. The integral count rates (0–3300 keV) varied from 596 Hz to 1355 Hz for unshielded measurements, corresponding to a total flux from the cavern walls of 1.9 ± 0.4 γ cm −2 s −1 . The resulting activity in the walls of the cavern can be characterised as 220 ± 60 Bq/kg of 40 K, 29 ± 15 Bq/kg of 238 U, and 13 ± 3 Bq/kg of 232 Th.
Here, we describe a neural network for predicting the background hit rate in the Belle II detector produced by the SuperKEKB electron-positron collider. The neural network, BGNet, learns to predict the individual contributions of different physical background sources, such as beam-gas scattering or continuous top-up injections into the collider, to Belle II sub-detector rates. The samples for learning are archived 1 Hz time series of diagnostic variables from the SuperKEKB collider subsystems and measured hit rates of Belle II used as regression targets. We test the learned model by predicting detector hit rates on archived data from different run periods not used during training. We show that a feature attribution method can help interpret the source of changes in the background level over time.
During Run2 the high instantaneous luminosity, up to 2.2 · 10 34 cm -2 s -1 , lead to a substantial hit rate in the Compact Muon Solenoid experiment’s muon chambers due to multiple background sources to physics processes sought for at LHC. In this article we will describe the analysis method devised to measure and identify the contributions to such background in the Resistive Plate Chambers. Thorough understanding of the background rates provides the base for the upgrade of the muon detectors for the High-Luminosity LHC.
ABSTRACT Gravitational lensing deflects the paths of photons, altering the statistics of cosmic backgrounds and distorting their information content. We take the cosmic infrared background (CIB), which provides plentiful information about galaxy formation and evolution, as an example to probe the effect of lensing on non-Gaussian statistics. Using the Websky simulations, we first quantify the non-Gaussianity of the CIB, revealing additional detail on top of its well-measured power spectrum. To achieve this, we use needlet-like multipole-band filters to calculate the variance and higher-point correlations. Using our simulations, we show the two-, three- and four-point spectra, and compare our calculated power spectra and bispectra to Planck values. We then lens the CIB, shell-by-shell with corresponding convergence maps, to capture the broad redshift extent of both the CIB and its lensing convergence. The lensing of the CIB changes the three- and four-point functions by a few tens of per cent at large scales, unlike with the power spectrum, which changes by less than two per cent. We expand our analyses to encompass the full intensity probability distribution functions (PDFs) involving all n-point correlations as a function of scale. In particular, we use the relative entropy between lensed and unlensed PDFs to create a spectrum of templates that can allow estimation of lensing. The underlying CIB model is missing the important role of star bursting, which we test by adding a stochastic lognormal term to the intensity distributions. The novel aspects of our filtering and lensing pipeline should prove useful for any radiant background, including line intensity maps.
The reach of sub-GeV dark-matter detectors is at present severely affected by low-energy events from various origins. We present the theoretical methods to compute the single- and few-electron events that arise from secondary radiation emitted by high-energy particles as they pass through detector materials and perform a detailed simulation to quantify them at (Skipper) CCD-based experiments, focusing on the SENSEI data collected at Fermilab near the MINOS cavern. The simulations account for the generation of secondaries from Cherenkov and luminescent recombination radiation; photo-absorption in the bulk, backside layer, pitch adapter, and epoxy; the photon reflection and refraction at interfaces; thin-film interference; the roughness of the interfaces; the dynamics of charges produced in the highly doped CCD-backside-layers; and the partial charge collection on the CCD backside. We consider several systematic uncertainties, notably those stemming from the backside modeling, which we estimate with a “fiducial” and an “extreme” charge-diffusion model, with the former model being preferred due to better agreement with partial-charge collection data. We find that Cherenkov photons constitute about 30% of the observed single-electron events for both diffusion models; radiative recombination contributes negligibly to the event rate for the fiducial model, although it can dominate over Cherenkov for the extreme model. We also estimate the fraction of 2-electron events that arise from 1-electron event coincidences in the same pixel, finding that the entire 2-electron rate can be explained by coincidences of radiative events and spurious charge. Accounting for both radiative and non-radiative backgrounds, we project the sensitivity of future Skipper-CCD-based experiments to different dark-matter models. For light-mediator models with dark-matter masses of 1, 5, and 10 MeV, we find that future experiments with 10-kg-year exposures and successful background mitigation could have a sensitivity that is larger by 9, 3, and 2 orders of magnitude, respectively, when compared to an experiment without background improvements.
The scattering of massless waves of helicity |$h$| = $0$, $\frac{1}{2}$, $1$ in Schwarzschild and Kerr backgrounds is revisited in the long-wavelength regime. Using a novel description of such backgrounds in terms of gravitating massive particles, we compute classical wave scattering in terms of 2 → 2 QFT amplitudes in flat space, to all orders in spin. The results are Newman-Penrose amplitudes which are in direct correspondence with solutions of the Regge-Wheeler/Teukolsky equation. By introducing a precise prescription for the point-particle limit, in Part I of this work we show how both agree for $h$ = 0 at finite values of the scattering angle and arbitrary spin orientation. Associated classical observables such as the scattering cross sections, wave polarizations and time delay are studied at all orders in spin. The effect of the spin of the black hole on the polarization and helicity of the waves is found in agreement with previous analysis at linear order in spin. In the particular limit of small scattering angle, we argue that wave scattering admits a universal, point-particle description determined by the eikonal approximation. We show how our results recover the scattering eikonal phase with spin up to second post-Minkowskian order, and match it to the effective action of null geodesics in a Kerr background. Using this correspondence we derive classical observables such as polar and equatorial scattering angles. This study serves as a preceding analysis to Part II, where the Gravitational Wave ($h$ = 2) case will be studied in detail.
Complete anomaly detection strategies that are both signal sensitive and compatible with background estimation have largely focused on resonant signals. Non-resonant new physics scenarios are relatively under-explored and may arise from off-shell effects or final states with significant missing energy. In this paper, we extend a class of weakly supervised anomaly detection strategies developed for resonant physics to the non-resonant case. Machine learning models are trained to reweight, generate, or morph the background, extrapolated from a control region. A classifier is then trained in a signal region to distinguish the estimated background from the data. The new methods are demonstrated using a semi-visible jet signature as a benchmark signal model, and are shown to automatically identify the anomalous events without specifying the signal ahead of time.
Among the most important problems for the world-wide nuclear explosion monitoring is the interference of naturally occurring and man-made radionuclides. The International Monitoring System (IMS) of the Comprehensive Nuclear-Test-Ban Treaty (CTBT) frequently detects these interferences using sensitive radionuclide measurement equipment. We commonly refer to the presence of radionuclides that are relevant to the CTBT but do not originate from a nuclear explosion as “background”. Backgrounds are highest near the sources but are known to have regional and global effects on the IMS. This review paper summarizes much of the relevant work in the area of background and discusses issues of interest for nuclear explosion detection.
In recent years, a Muon collider has attracted a lot of interest in the high-energy physics community, thanks to its ability of achieving clean interaction signatures at multi-TeV collision energies in the most cost-effective way. Estimation of the physics potential of such an experiment must take into account the impact of beam-induced background on the detector performance, which has to be carefully evaluated using full detector simulation. Tracing of all the background particles entering the detector region in a single bunch crossing is out of reach for any realistic computing facility due to the unprecedented number of such particles. To make it feasible a number of optimisations have been applied to the detector simulation workflow. This contribution presents an overview of the main characteristics of the beam-induced background at a Muon collider, the detector technologies considered for the experiment and how they are taken into account to strongly reduce the number of irrelevant computations performed during the detector simulation. Special attention is dedicated to the optimisation of track reconstruction with the conformal tracking algorithm in this high-occupancy environment, which is the most computationally demanding part of event reconstruction.
Highlights: • Two-dimensional electron gas above a neutralizing jellium background. • Analytic results for the value of the total energy. • Energy contribution originating from separation. • Added energy behaves as an effective kinetic energy term. We consider a two-dimensional electron gas system consisting of a layer of electrons suspended above its jellium neutralizing background counterpart. We investigate the energy of the system in the Hartree–Fock approximation as a function of separation distance between the layer of electrons and the background. We find that the energy of the system is influenced by the separation distance in such a way that the new energy contribution originating from such a separation behaves as an effective kinetic energy term. The increase of the overall energy with increase of the separation distance adversely affects the stability of electron liquid states at high density. The results are derived for a fully spin-polarized (spinless) system of electrons in the thermodynamic limit, but can easily be generalized to a spin unpolarized state.