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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC

A novel solution is presented for the problem of estimating the backgrounds of a signal search using observed data while simultaneously maximizing the sensitivity of the search to the signal. The 'ABCD method' provides a reliable framework for background estimation by partitioning events into one signal-enhanced region (A) and three background-enhanced control regions (B, C, and D) via two smoothly varying, statistically independent variables. In practice, even slight correlations between the two variables can significantly undermine the method's performance. Thus, choosing appropriate variables by hand can present a formidable challenge, especially when background and signal differ only subtly. To address this issue, the ABCD with distance correlation (ABCDisCo) method was developed to construct two learned variables via a neural network trained to provide strong signal-background discrimination with small values of the distance correlation (DisCo) measure between the two learned variables. However, relying solely on minimizing the DisCo can result in learned variables that may not have distributions of background events that are smoothly varying and localized at extreme values, as necessary for the validity of the background estimation. The ABCDisCo training enhanced with closure (ABCDisCoTEC) method is introduced to solve this issue by directly minimizing the nonclosure, expressed as a dedicated differentiable loss term. This extended method is applied to a data set of proton-proton collisions at a center-of-mass energy of 13 TeV recorded by the CMS detector at the CERN Large Hadron Collider. Additionally, given the complexity of the minimization problem with constraints on multiple loss terms, the modified differential method of multipliers is applied and shown to greatly improve the stability and robustness of the ABCDisCoTEC method, compared to grid search hyperparameter optimization procedures.

Hayrapetyan, Aram [Yerevan Phys. Inst.]↗
DOE OSTI2025

Improved Gas Plume Identification Using Nearest Neighbor Methods for Background Estimation

Longwave infrared (LWIR) hyperspectral imaging (HSI) can be used for many tasks in remote sensing, including detecting and identifying effluent gases by LWIR sensors on airborne platforms. Identification is used after detection to increase confidence in weakly detected plumes, reduce false positives from detection, and distinguish between similar and confounding material signatures. Background estimation is an important step used to reveal the unique spectral characteristics of the detected gas, allowing the identification model to determine what the gas is specifically. The importance of proper background estimation increases when dealing with weak signals, large libraries of gases of interest, and uncommon or heterogeneous backgrounds. In this article, we propose two methods for background estimation: a novel k-nearest segments (KNS) algorithm and the standard k-nearest neighbors (KNN) algorithm. We test our methods and three existing background estimation methods for comparison against global background estimation to determine which performs best at estimating the true background radiance under a plume and for increasing identification confidence using a neural network classification model. We compare the different methods using 640 simulated weak plumes in an urban environment. For identification, our KNS algorithm improves median neural network identification confidence by 53.2%. For background radiance estimation, the KNN algorithm provides a median of 49 times less RMSE than global background estimation. Furthermore, KNN is the easiest method to tune for different plumes, making it an excellent “out of the box” background estimator.

47 OTHER INSTRUMENTATION↗
NASA NTRS1975

Background estimation in a (gnu e)-diffusion detector

Background conditions for detecting (gamma e)-diffusion are discussed with reference to the utilization of a hodoscope system consisting of wide area flat Geiger counters connected for coincidence and of a controllable spark chamber. When calculating the background, any organic compounds synthesized from C, O2 and H2 with radioactive admixtures is presumed to be the preferrable material for the construction of the detector. The number of background events simulating the effect sought is estimated by analyzing the spectrum of the organic glass gamma radiation caused by disintegration and fission of long lived isotopes and by nuclear reactions with the participation of alpha particles and neutrons.

Glotov, V. I.↗
DOE OSTI2025

Incorporating civilian radioxenon background estimates in anomaly detection

A nuclear explosion screening exercise in 2023 (Maurer et al., 2023) found challenges with discerning anomalous radioxenon activity concentrations relative to elevated background concentrations. Research has continued into methods to detect anomalous radioxenon concentrations by comparing samples to estimates of atmospheric radioxenon background concentrations caused by releases at nuclear reactors or medical isotope production facilities. A new approach estimates the sample concentrations using time-varying radioxenon release rates obtained using optimization techniques that constrain the facility release rates to plausible amounts based on historical data or facility knowledge. The purpose of the optimization is to determine whether any combination of plausible release rates from emitting facilities can explain a series of radioxenon measurements at one or more sampling stations. A case study uses radioxenon data collected at three locations in western Europe for a month in 2021 and considers releases from 77 locations. Fewer samples are identified as being anomalous using a simplistic flagging rule than from an application of the current International Monitoring System (IMS) activity concentration-level rule.

Environmental sciences↗
DOE OSTI2026

Signal-to-background estimates for the inverse-kinematics 78 Kr(n,γ) experiment at LANSCE

The idea of a neutron target enables the measurement of neutron-induced reactions in inverse kinematics. This idea is part of the LANSCE strategy to stay a worldwide leader for neutron-induced research. The proof-of-principle experiment outlined in is the core of the LDRD project 20240004DR. The current approach is to use an ion source capable of accelerating single-charged ions up to about 60 keV. A beam of 78 Kr + ions will be created to interact with a neutron target and implanted. During the passage of the target, neutrons can be captured and the freshly produced 79 Kr + ions will be implanted along with the main beam.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1989

Nucleon interaction data bases for background estimates

Nucleon interaction data bases available in the open literature are examined for potential use in a recently developed nucleon transport code. Particular attention is given to secondary particle penetration and the multiple charged ion products. A brief description of the transport algorithm is given.

Wilson, John W.↗
NASA NTRS1993

Diagnostic and quality-assurance tools for low-contrast images obtained from array detectors

We investigate methods of estimating a background image frame for subtraction from a data frame for use when a more suitable measured background frame is not available. We define background as any signal component that is not attributable to the phenomenon currently under investigation. We describe a technique that is based on pixel-by-pixel least-squares regression of images for computing a background frame from available data. We argue that the same technique can be a useful quality-assurance tool for evaluating instrument performance. For example, it can help to separate image structure resulting from the reading process from structure resulting from the characteristics of the detector itself. We demonstrate that background estimation can be nontrivial by comparing the results of different background estimation procedures by using data obtained from a CCD array detector. We investigate the temperature-dependent contributions of the detector and readout electronics to the total signal as a demonstration of the diagnostic capabilities of least-squares image regression.

Hatfield, D. B.↗
NASA NTRS1991

The estimation of background production by cosmic rays in high-energy gamma ray telescopes

A calculational method of estimating instrumental background in high-energy gamma-ray telescopes, using the hadronic Monte Carlo code FLUKA87, is presented. The method is applied to the SAS-2 and EGRET telescope designs and is also used to explore the level of background to be expected for alternative configurations of the proposed GRITS telescope, which adapts the external fuel tank of a Space Shuttle as a gamma-ray telescope with a very large collecting area. The background produced in proton-beam tests of EGRET is much less than the predicted level. This discrepancy appears to be due to the FLUKA87 inability to transport evaporation nucleons. It is predicted that the background in EGRET will be no more than 4-10 percent of the extragalactic diffuse gamma radiation.

Edwards, H. L.↗
NASA NTRS1986

Formulation and implementation of a practical algorithm for non-stationary adaptive state estimation

Background information on the Kalman filter is given first. A discussion of the filter parameters and their a priori determination follows. The discussion points out the need for adaptive determination of the process noise statistics. The filter innovations are presented as a means for developing the adaptive criteria. The criteria center around the estimation of the true mean and covariance of the filter innovations. A method for the numerical approximation of the mean and covariance of a locally stationary random process is presented. The definition of a local stationarity is presented. Local stationarity allows for the separation of the process statistics into a stationary component and a time-varying component. The separation method is discussed. A method for estimating the stationary and time-varying components is presented. As an example of its application to real problems, the algorithm is applied to the problem to the problem of reentry trajectory estimation for the Space Shuttle. Both the adaptive algorithm and the steady-state Kalman filter are applied to the problem. The results of the reconstructions are presented. The adaptive algorithm exhibits superior performance.

Whitemore, S. A.↗
NASA NTRS2002

XTE Observations of Intermediate Polars: RXTE TOO Observations of Supernovae

Two intermediate polar cataclysmic variables, PQ Gem and AO Psc, were observed jointly with the Rossi X-ray Timing Explorer (RXTE) and ground-based photometry. The analysis of intermediate polars (IPs) is complex because these objects exhibit light curves that behave differently as a function of energy and that behave differently when phased on the orbital or on the spin periods. The presence of two periods in one system is essentially equivalent to analyzing two different X-ray sources. A preliminary analysis of the PQ Gem data was carried out and presented at the Annapolis Workshop on Magnetic Cataclysmic Variables. The final analysis of the data were held up by problems with the background estimation. The RXTE PCA team has released a new version of the background estimator. The PQ Gem must be reanalyzed using the new background. We have also installed a spectral model that calculates the expected emission from an accretion column. That model is undergoing final testing before we apply it to the data.

Schlegel, E.↗
DOE OSTI2025

Searching for Neutrino Tridents in the NOvA Near Detector

This dissertation presents a search for neutrino trident production in the NOvA near detector through the coherent ``dimuon" channel: $\nu_\mu +\hspace{1pt}\text{X} \rightarrow \nu_\mu + \mu^- + \mu^+ +\hspace{1pt}\text{X}$. Trident production is a rare, purely electroweak process with sensitivity to physics beyond the Standard Model. The theoretical background, motivation for studying the process, and previous experimental measurements are reviewed. The analysis uses data collected by the NOvA near detector (ND) from Fermilab's Neutrinos at the Main Injector (NuMI) beam between November 2014 and February 2024, corresponding to an exposure of $25.5\times 10^{20}$ protons on target. The ND is a segmented tracking calorimeter located 800~m from the beam target, receiving neutrinos with a mean energy of 2~GeV. A multi-pass background reduction strategy is implemented, including the development of a novel dimuon-specific tracking technique. Trident candidates are identified using a boost ed decision tree classifier trained on simulated signal and background events. Limited background Monte Carlo statistics necessitate the use of functional fits to sideband data, which are extrapolated to estimate backgrounds in the signal region. The unblinded data contain 9 trident-like events, with an estimated background of 5.66 $\pm$ 5.15 events. This yields a best fit estimate of 3.34 tridents compared to the Standard Model prediction of 4.66. A profiled Feldman-Cousins method is used to determine a 90\% confidence interval of [0,9.1] on the number of signal events, corresponding to an upper limit of 1.95$\times$ the Standard Model prediction. This result represents the lowest energy search for trident events to date, and the first experimental contribution to the process in 27 years.

Bowles, Reed Scott [Indiana U.]↗
NASA NTRS2010

The Revised Space Environment Models in CREME-MC: A Replacement for CREME96

The CREME96 model has been available on the WWW for more than 10 years now. While principally for the estimation of radiation effects on spacecraft electronics, it contains space radiation environment models that have been used for instrument design calculations, estimation of instrumental background, estimation of radiation hazards and many other purposes. Because of the evolution of electronic part design we have found it necessary to revise CREME96, creating CREME-MC. As part of this revision, we are revising and extending the environmental models in CREME96. This talk will describe the revised radiation environment models that are being made available in CREME-MC

Adams, James H., Jr.↗
NASA NTRS2002

XTE Observations of Intermediate Polars: RXTE TOO Observations of Supernovae

Two Intermediate Mars were observed with XTE (X-Ray Timing Explorer): PQ Gem and AO Psc. A very preliminary analysis for PQ Gem was presented at the Annapolis Workshop on Magnetic Cataclysmic Variables. At about the same time, the XTE team created a new background estimator for faint sources. The PQ Gem data must be reanalyzed using the new background estimator. The data for AO Psc have been analyzed and await the analysis of PQ Gem.

Schlegel, E.↗
DOE OSTI2025

Estimation of backgrounds from jets misidentified as τ-leptons using the Universal Fake Factor method with the ATLAS detector

Processes with τ$$\tau $$-leptons in the final state are important for Standard Model measurements and searches for physics beyond the Standard Model. The ATLAS experiment at the Large Hadron Collider observes τ$$\tau $$-leptons produced in proton–proton collisions only through their decay products. Data analyses involving hadronically decaying τ$$\tau $$-leptons face challenges due to backgrounds from jets misidentified as τ$$\tau $$-leptons that are not modelled reliably by Monte Carlo simulations. Data-driven methods such as the fake-factor method allow such misidentified backgrounds to be predicted by measuring transfer factors, known as fake factors, in data from dedicated regions. This paper describes a refined technique for determining the fake factors, the Universal Fake Factor method. It evaluates the fake factors for a signal region by using fake factors from samples enriched in different sources of jets misidentified as τ$$\tau $$-leptons (light-quark, gluon, b-quark, and pile-up jets). Each fake factor is calculated as a linear combination of fake factors measured in these different enriched samples. For the full Run 2 data set, the systematic uncertainty of the calculated fake factors, evaluated using W(μν)$$W(\mu u )$$ enriched event sample, ranges from 15 to 35% depending on the τ$$\tau $$-lepton’s transverse momentum and charged-particle decay multiplicity.

Aad, G↗
NASA NTRS1989

High-energy radiation background in space

The radiation environment of near-earth space and its effects on biological and hardware systems are examined in reviews and reports. Sections are devoted to particle interactions and propagation, data bases, instrument background and dosimetry, detectors and experimental progress, biological effects, and future needs and strategies. Particular attention is given to angular distributions and spectra of geomagnetically trapped protons in LEO, bremsstrahlung production by electrons, nucleon-interaction data bases for background estimates, instrumental and atmospheric background lines observed by the SMM gamma-ray spectrometer, the GRAD high-altitude balloon flight over Antarctica, space protons and brain tumors, a new radioprotective antioxidative agent, LEO radiation measurements on the Space Station, and particle-background effects on the Hubble Space Telescope and the Lyman FUV Spectroscopic Explorer.

Rester, A. C., Jr.↗
NASA NTRS2014

Background Error Covariance Estimation Using Information from a Single Model Trajectory with Application to Ocean Data Assimilation

An attractive property of ensemble data assimilation methods is that they provide flow dependent background error covariance estimates which can be used to update fields of observed variables as well as fields of unobserved model variables. Two methods to estimate background error covariances are introduced which share the above property with ensemble data assimilation methods but do not involve the integration of multiple model trajectories. Instead, all the necessary covariance information is obtained from a single model integration. The Space Adaptive Forecast error Estimation (SAFE) algorithm estimates error covariances from the spatial distribution of model variables within a single state vector. The Flow Adaptive error Statistics from a Time series (FAST) method constructs an ensemble sampled from a moving window along a model trajectory.SAFE and FAST are applied to the assimilation of Argo temperature profiles into version 4.1 of the Modular Ocean Model (MOM4.1) coupled to the GEOS-5 atmospheric model and to the CICE sea ice model. The results are validated against unassimilated Argo salinity data. They show that SAFE and FAST are competitive with the ensemble optimal interpolation (EnOI) used by the Global Modeling and Assimilation Office (GMAO) to produce its ocean analysis. Because of their reduced cost, SAFE and FAST hold promise for high-resolution data assimilation applications.

Error Covariance↗
NASA NTRS2014

Background Error Covariance Estimation using Information from a Single Model Trajectory with Application to Ocean Data Assimilation into the GEOS-5 Coupled Model

An attractive property of ensemble data assimilation methods is that they provide flow dependent background error covariance estimates which can be used to update fields of observed variables as well as fields of unobserved model variables. Two methods to estimate background error covariances are introduced which share the above property with ensemble data assimilation methods but do not involve the integration of multiple model trajectories. Instead, all the necessary covariance information is obtained from a single model integration. The Space Adaptive Forecast error Estimation (SAFE) algorithm estimates error covariances from the spatial distribution of model variables within a single state vector. The Flow Adaptive error Statistics from a Time series (FAST) method constructs an ensemble sampled from a moving window along a model trajectory. SAFE and FAST are applied to the assimilation of Argo temperature profiles into version 4.1 of the Modular Ocean Model (MOM4.1) coupled to the GEOS-5 atmospheric model and to the CICE sea ice model. The results are validated against unassimilated Argo salinity data. They show that SAFE and FAST are competitive with the ensemble optimal interpolation (EnOI) used by the Global Modeling and Assimilation Office (GMAO) to produce its ocean analysis. Because of their reduced cost, SAFE and FAST hold promise for high-resolution data assimilation applications.

Data Assimilation↗
DOE OSTI2025

Search for new physics in final states with semivisible jets or anomalous signatures using the ATLAS detector

A search is presented for hadronic signatures of beyond the Standard Model (BSM) physics, with an emphasis on signatures of a strongly coupled hidden dark sector accessed via resonant production of a 𝑍′ mediator. The ATLAS experiment dataset collected at the Large Hadron Collider from 2015 to 2018 is used, consisting of proton-proton collisions at $\sqrt{𝑠}$ = 13 TeV and corresponding to an integrated luminosity of 140 fb −1 . The 𝑍′ mediator is considered to decay to two dark quarks, which each hadronize and decay to showers containing both dark and Standard Model particles, producing a topology of interacting and noninteracting particles within a jet known as “semivisible.” Machine learning methods are used to select these dark showers and reject the dominant background of mismeasured multijet events, including an anomaly detection approach to preserve broad sensitivity to a variety of BSM topologies. A resonance search is performed by fitting the transverse mass spectrum based on a functional form background estimation. No significant excess over the expected background is observed. Results are presented as limits on the production cross section of semivisible jet signals, parametrized by the fraction of invisible particles in the decay and the 𝑍′ mass, and by quantifying the significance of any generic Gaussian-shaped mass peak in the anomaly region.

particle dark matter↗