Statistical evaluation of experimental determinations of neutrino mass hierarchy
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Qian, X.↗
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Results for “Bayesian sensitivity analysis”
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Automated ICRF heating surrogate modeling via machine learning
This work introduces automated machine learning workflows that address critical bottlenecks in surrogate model development for Ion Cyclotron Range of Frequencies (ICRF) heating applications. The automated framework includes data analysis tools that transform raw datasets into actionable insights in seconds, replacing weeks of manual exploratory effort and ensuring consistent, reproducible dataset characterization. By integrating advanced hyperparameter optimization (HPO) methods including Bayesian optimization via BoTorch and Tree-structured Parzen Estimators (TPE), the framework significantly reduces model development time from weeks to hours, decreasing computational cost and required expertise, while enabling high-accuracy surrogate models. Compared to traditional hyperparameter scanning (HPS) techniques such as methodical, randomized, and grid searches, HPO methods achieve superior convergence and predictive performance, even when compared to already well-tuned reference models. On NSTX High Harmonic Fast Wave (HHFW) heating datasets, both Random Forest Regressor (RFR) and neural network surrogates demonstrate improved accuracy, achieving R 2 values beyond 0.97 and 0.98, respectively. The results show that while HPO gains are modest for robust architectures like RFR, they become essential for more sensitive models such as neural networks, highlighting the trade-offs across optimization strategies. Through automated workflows that eliminate manual hyperparameter tuning and require minimal ML expertise, this work enables widespread adoption of high-fidelity surrogate models across the fusion community for real-time plasma control, uncertainty quantification, rapid experimental scenario development, and integrated system optimization.
Sanchez-Villar, Alvaro [Princeton Plasma Physics L↗
An adaptive least-squares global sensitivity method and application to a plasma-coupled combustion prediction with parametric correlation
We introduce an efficient non-intrusive surrogate-based methodology for global sensitivity analysis and uncertainty quantification. Modified covariance-based sensitivity indices (mCov-SI) are defined for outputs that reflect correlated effects. The overall approach is applied to simulations of a complex plasma-coupled combustion system with disparate uncertain parameters in sub-models for chemical kinetics and a laser-induced breakdown ignition seed. The surrogate is based on an Analysis of Variance (ANOVA) expansion, such as widely used in statistics, with orthogonal polynomials representing the ANOVA subspaces and a polynomial dimensional decomposition (PDD) representing its multi-dimensional components. The coefficients of the PDD expansion are obtained using a least-squares regression, which both avoids the direct computation of high-dimensional integrals and affords an attractive flexibility in choosing sampling points. This facilitates importance sampling using a Bayesian calibrated posterior distribution, which is fast and thus particularly advantageous in common practical cases, such as our large-scale demonstration, for which the asymptotic convergence properties of polynomial expansions cannot be realized due to computation expense. Effort, instead, is focused on efficient finite-resolution sampling. Standard covariance-based sensitivity indices (Cov-SI) are employed to account for correlation of the uncertain parameters. Magnitude of Cov-SI is unfortunately unbounded, which can produce extremely large indices that limit their utility. Alternatively, mCov-SI are then proposed in order to bound this magnitude $\in$ [0, 1]. The polynomial expansion is coupled with an adaptive ANOVA strategy to provide an accurate surrogate as the union of several low-dimensional spaces, avoiding the typical computational cost of a high-dimensional expansion. It is also adaptively simplified according to the relative contribution of the different polynomials to the total variance. Finally, the approach is demonstrated for a laser-induced turbulent combustion simulation model, which includes parameters with correlated effects.
42 ENGINEERING↗
Developing a Continuous Nuclear Data Evaluation Method for the Resolved and Unresolved Resonance Regions With FITAPI and SAMMY [Poster]
SAMMY is an R-matrix Bayesian fitting program used in the analysis of nuclear cross section data. SAMMY produces separate cross section models in the resolved and unresolved resonance regions. This project develops a new method of continuous evaluation joining both energy regions with FITAPI. Full energy spectrum model introduces cross covariance between physics models allowing more accurate description of uncertainty. Consistency developed between physics models by the use of a unique set of prior parameters. An unresolved region cross section developed with sensitivity to resolved cross section. Incorporation into ORNL FITAPI provides access to greater number of parameter estimation algorithms.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
THE LYOT PROJECT DIRECT IMAGING SURVEY OF SUBSTELLAR COMPANIONS: STATISTICAL ANALYSIS AND INFORMATION FROM NONDETECTIONS
The Lyot project used an optimized Lyot coronagraph with extreme adaptive optics at the 3.63 m Advanced Electro-Optical System telescope to observe 86 stars from 2004 to 2007. In this paper, we give an overview of the survey results and a statistical analysis of the observed nondetections around 58 of our targets to place constraints on the population of substellar companions to nearby stars. The observations did not detect any companion in the substellar regime. Since null results can be as important as detections, we analyzed each observation to determine the characteristics of the companions that can be ruled out. For this purpose, we use a Monte Carlo approach to produce artificial companions and determine their detectability by comparison with the sensitivity curve for each star. All the non-detection results are combined using a Bayesian approach and we provide upper limits on the population of giant exoplanets and brown dwarfs for this sample of stars. Our nondetections confirm the rarity of brown dwarfs around solar-like stars and we constrain the frequency of massive substellar companions (M>40 M{sub J}) at orbital separation between and 10 and 50 AU to be {approx_lt}20%.
79 ASTRONOMY AND ASTROPHYSICS↗
Search for neutrinoless β-plus-EC decay of 120 Te with CUORE
The Cryogenic Underground Observatory for Rare Events (CUORE) is a large-scale cryogenic experiment searching for neutrinoless double-beta decay (0νββ) in 130 Te. The CUORE detector is made of natural tellurium, providing the possibility of rare event searches on isotopes other than 130 Te. In this work we describe a search for neutrinoless positron-emitting electron capture (β + EC ) decay in 120 Te with a total TeO 2 exposure of 355.7 kg yr, corresponding to 0.2405 kg yr of 120 Te. Albeit 0νββ with two final-state electrons represents the most promising channel, the emission of a positron and two 511-keV γ 's make 0νβ + EC decay signature extremely clear. To fully exploit the potential offered by the detector modularity we include events with different topology and perform a simultaneous fit of five selected signal signatures. Using blinded data we extract a median exclusion sensitivity of 3.4 × 10 22 yr at 90% credibility interval (C.I.). After unblinding we find no evidence of 0νβ + EC signal and set a 90% C.I. Bayesian lower limit of 2.9 × 10 22 yr on 120 Te half-life. This result improves by an order of magnitude the existing limit from the combined analysis of CUORE-0 and Cuoricino.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Uncertainty quantification of property models: Methodology and its application to CO 2 -loaded aqueous MEA solutions
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CO2 capture↗
Bayesian analysis of (3 +1)D relativistic nuclear dynamics with the RHIC beam energy scan data
This work presents a Bayesian inference study for relativistic heavy-ion collisions in the beam energy scan program at the BNL Relativistic Heavy-Ion Collider. The theoretical model simulates event-by-event (3+1)-dimensional [(3+1)D] collision dynamics using hydrodynamics and hadronic transport theory. We analyze the model's 20-dimensional posterior distributions obtained using three model emulators with different accuracy and demonstrate the essential role of training an accurate model emulator in the Bayesian analysis. Our analysis provides robust constraints on the quark-gluon plasma's transport properties and various aspects of (3+1)D relativistic nuclear dynamics. By running full model simulations with 100 parameter sets sampled from the posterior distribution, we make predictions for p T -differential observables and estimate their systematic theory uncertainty. Here, a sensitivity analysis is performed to elucidate how individual experimental observables respond to different model parameters, providing useful physics insights into the phenomenological model for heavy-ion collisions.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Study of Single Top Quark Production Using Bayesian Neural Networks With D0 Detector at the Tevatron
Top quark, the heaviest and most intriguing among the six known quarks, can be created via two independent production mechanisms in {\pp} collisions. The primary mode, strong {\ttbar} pair production from a $gtt$ vertex, was used by the {\d0} and CDF collaborations to establish the existence of the top quark in March 1995. The second mode is the electroweak production of a single top quark or antiquark, which has been observed recently in March 2009. Since single top quarks are produced at hadron colliders through a $Wtb$ vertex, thereby provide a direct probe of the nature of $Wtb$ coupling and of the Cabibbo-Kobayashi-Maskawa matrix element, $V_{tb}$. So this mechanism provides a sensitive probe for several, standard model and beyond standard model, parameters such as anomalous $Wtb$ couplings. In this thesis, we measure the cross section of the electroweak produced top quark in three different production modes, $s+t$, $s$ and $t$-channels using a technique based on the Bayesian neural networks. This technique is applied for analysis of the 5.4 $fb^{-1}$ of data collected by the {\d0} detector. From a comparison of the Bayesian neural networks discriminants between data and the signal-background model using Bayesian statistics, the cross sections of the top quark produced through the electroweak mechanism have been measured as: \[\sigma(p\bar{p}→tb+X,tqb+X) = 3.11^{+0.77}_{-0.71}\;\rm pb\] \[\sigma(p\bar{p}→tb+X) = 0.72^{+0.44}_{-0.43}\;\rm pb\] \[\sigma(p\bar{p}→tqb+X) = 2.92^{+0.87}_{-0.73}\;\rm pb\] % The $s+t$-channel has a gaussian significance of $4.7\sigma$, the $s$-channel $0.9\sigma$ and the $t$-channel~$4.7\sigma$. The results are consistent with the standard model predictions within one standard deviation. By combining these results with the results for two other analyses (using different MVA techniques) improved results \[\sigma(p\bar{p}→tb+X,tqb+X) = 3.43^{+0.73}_{-0.74}\;\rm pb\] \[\sigma(p\bar{p}→tb+X) = 0.68^{+0.38}_{-0.35}\;\rm pb\] \[\sigma(p\bar{p}→tqb+X) = 2.86^{+0.69}_{-0.63}\;\rm pb\] % were obtained with a significance of $5.4\sigma$, $1.8\sigma$ and $5.0\sigma$ respectively for $s+t$, $s$ and $t$-channels. Using this measured cross section and constraining $0 \leq |V_{tb}|^2 \leq 1$, the lower limit has been calculated to be $|V_{tb}| > 0.79$ with 95\% confidence level (C.L.). Another measurement of $tqb$ production cross section is done using the same dataset and discriminant but without any assumption on the $tb$ production rate. From this measurement, we obtain a cross section of $2.90^{+0.59}_{-0.59}$~pb for $t$-channel and corresponding significance of $5.5\sigma$. In addition to the above mentioned work, a search is made for the anomalous $Wtb$ couplings in single top quark production. Within the Standard Model, the $Wtb$ vertex is purely left-handed, and its amplitude is given by the $V_{tb}$, related to weak interaction between a top and a $b$-quark. In a more general way, additional anomalous couplings such as right-handed vectorial couplings and left and right-handed tensorial couplings can also be considered. An analysis based on the Bayesian neural networks method is used to separate the signal from expected backgrounds. We find no evidence for anomalous couplings and set 95\% C.L. limits on these couplings as $|V_{tb} \cdot f_{L_T}|^2<0.06$, $|V_{tb} \cdot f_{R_V}|^2<0.93$ and $|V_{tb} \cdot f_{R_T}|^2<0.13$. This result represents the most stringent direct constraints on anomalous $Wtb$ interactions. This work has been done in collaboration with \d0 experiment but the analyses and results presented in this thesis are my contribution.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Top Quark Produced Through the Electroweak Force: Discovery Using the Matrix Element Analysis and Search for Heavy Gauge Bosons Using Boosted Decision Trees
The top quark produced through the electroweak channel provides a direct measurement of the V tb element in the CKM matrix which can be viewed as a transition rate of a top quark to a bottom quark. This production channel of top quark is also sensitive to different theories beyond the Standard Model such as heavy charged gauged bosons termed W'. This thesis measures the cross section of the electroweak produced top quark using a technique based on using the matrix elements of the processes under consideration. The technique is applied to 2.3 fb -1 of data from the D0 detector. From a comparison of the matrix element discriminants between data and the signal and background model using Bayesian statistics, we measure the cross section of the top quark produced through the electroweak mechanism σ(p$\bar{p}$ → tb + X, tqb + X) = 4.30 -1.20 +0.98 pb. The measured result corresponds to a 4.9σ Gaussian-equivalent significance. By combining this analysis with other analyses based on the Bayesian Neural Network (BNN) and Boosted Decision Tree (BDT) method, the measured cross section is 3.94 ± 0.88 pb with a significance of 5.0σ, resulting in the discovery of electroweak produced top quarks. Using this measured cross section and constraining |V tb | < 1, the 95% confidence level (C.L.) lower limit is |V tb | > 0.78. Additionally, a search is made for the production of W' using the same samples from the electroweak produced top quark. An analysis based on the BDT method is used to separate the signal from expected backgrounds. No significant excess is found and 95% C.L. upper limits on the production cross section are set for W' with masses within 600-950 GeV. For four general models of W{prime} boson production using decay channel W' → t$\bar{p}$, the lower mass limits are the following: M(W' L with SM couplings) > 840 GeV; M(W' R ) > 880 GeV or 890 GeV if the right-handed neutrino is lighter or heavier than W{prime}{sub R}; and M(W' L+R ) > 915 GeV.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
The Casualidad method for uncertainty evaluation of best-estimate system thermal-hydraulics calculations
The present paper deals with the description of the salient features of three independent approaches for estimating uncertainties associated with predictions of complex system codes. The first approach is the 'standard' one and the most used at the industrial level: it is based upon the selection of input uncertain parameters, on assigning related ranges of variations and, possibly, PDF (Probability Density Functions) and on performing a suitable number of code runs to get the combined effect of variation on the results. In the second approach the uncertainty derives from the comparison between relevant measured data and results of corresponding code calculations. The third approach is based on the Bayesian inference technique and on the availability of experimental data by which computer model predictions can be improved and the ranges of variation of (in theory) 'all' input parameters can be characterized. More details are provided in respect with the third approach that has been named CASUALIDAD (Code with the capability of Adjoint Sensitivity and Uncertainty Analysis by Internal Data Adjustment and assimilation). (authors)
42 ENGINEERING↗
Global Framework for Emulation of Nuclear Calculations
We introduce a hierarchical framework that combines ab initio many-body calculations with a Bayesian neural network, developing emulators capable of accurately predicting nuclear properties across isotopic chains simultaneously and being applicable to different regions of the nuclear chart. We benchmark our developments using the oxygen isotopic chain, achieving accurate results for ground-state energies and nuclear charge radii, while providing robust uncertainty quantification. Our framework enables global sensitivity analysis of nuclear binding energies and charge radii with respect to the low-energy constants that describe the nuclear force.
FOS: Computer and information sciences↗
Inverse Uncertainty Quantification of Reactor Simulation with Polynomial Chaos Surrogate Model
Reliable uncertainty quantification (UQ) and sensitivity analysis (SA) is significant part of modelling and simulation. This is particularly important in the nuclear community due to the fact that the licensing of nuclear installations has shifted from conservative to Best Estimate plus Uncertainty (BEPU) methodologies. UQ can be broadly classified as forward UQ and inverse UQ. Forward UQ is the process of quantifying the uncertainties in simulation outputs by propagating the uncertainties in input parameters. It requires knowledge in the input uncertainty, which is often specified by ad-hoc expert judgment which lacks mathematical rigor. The inverse UQ is the process to determine the uncertainty in input parameters given observations of the output. The purpose of this paper is to replace such ad-hoc expert judgment of the statistical properties of input model parameters in nuclear reactor simulation. This summary presents work on inverse UQ of a nonlinear Point Reactor Kinetics Equations - lumped thermal-hydraulics parameter coupled problem. Bayesian inference and Markov Chain Monte Carlo (MCMC) are used for the inverse UQ. Because MCMC requires a large number of simulations which is impractical for models that are computationally expensive, surrogate models is constructed by generalized Polynomial Chaos (PC) expansion technique. (authors)
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Initial conditions and sampling for multifield inflation
We investigate the initial conditions problem for multifield inflation. In these scenarios the pre-inflationary dynamics can be chaotic, increasing the sensitivity of the onset of inflation to the initial data even in the homogeneous limit. To analyze physically equivalent scenarios we compare initial conditions at fixed energy. This ensures that each trajectory is counted once and only once, since the energy density decreases monotonically. We present a full analysis of hybrid inflation that reveals a greater degree of long range order in the set of ''successful'' initial conditions than was previously apparent. In addition, we explore the effective smoothing scale for the fractal set of successful initial conditions induced by the finite duration of the pre-inflationary phase. The role of the prior information used to specify the initial data is discussed in terms of Bayesian sampling.
79 ASTRONOMY AND ASTROPHYSICS↗
A unifying Bayesian framework for merging X-ray diffraction data
Novel X-ray methods are transforming the study of the functional dynamics of biomolecules. Key to this revolution is detection of often subtle conformational changes from diffraction data. Diffraction data contain patterns of bright spots known as reflections. To compute the electron density of a molecule, the intensity of each reflection must be estimated, and redundant observations reduced to consensus intensities. Systematic effects, however, lead to the measurement of equivalent reflections on different scales, corrupting observation of changes in electron density. Here, we present a modern Bayesian solution to this problem, which uses deep learning and variational inference to simultaneously rescale and merge reflection observations. We successfully apply this method to monochromatic and polychromatic single-crystal diffraction data, as well as serial femtosecond crystallography data. We find that this approach is applicable to the analysis of many types of diffraction experiments, while accurately and sensitively detecting subtle dynamics and anomalous scattering.
59 BASIC BIOLOGICAL SCIENCES↗
Impact and mitigation of polarized extragalactic foregrounds on Bayesian cosmic microwave background lensing
Future low-noise cosmic microwave background (CMB) lensing measurements from e.g., CMB-S4 will be polarization dominated, rather than temperature dominated. In this new regime, statistically optimal lensing reconstructions outperform the standard quadratic estimator, but their sensitivity to extragalactic polarized foregrounds has not been quantified. Using realistic simulations of polarized radio and infrared point sources, we show for the first time that optimal Bayesian lensing from a CMB-S4-like experiment is insensitive to the expected level of polarized extragalactic foregrounds after masking, as long as an accurate foreground power spectrum is included in the analysis. For more futuristic experiments where these foregrounds could cause a detectable bias, we propose a new method to jointly fit for lensing and the Poisson foregrounds, generalizing the bias hardening from the standard quadratic estimator to Bayesian lensing.
cosmic microwave background↗
Data-driven background model for the CUORE experiment
Here, we present the model we developed to reconstruct the CUORE radioactive background based on the analysis of an experimental exposure of 1038.4 kg yr. The data reconstruction relies on a simultaneous Bayesian fit applied to energy spectra over a broad energy range. The high granularity of the CUORE detector, together with the large exposure and extended stable operations, allow for an in-depth exploration of both spatial and time dependence of backgrounds. We achieve high sensitivity to both bulk and surface activities of the materials of the setup, detecting levels as low as 10 nBq kg −1 and 0.1 nBq cm −2 , respectively. We compare the contamination levels we extract from the background model with prior radio-assay data, which informs future background risk mitigation strategies. The results of this background model play a crucial role in constructing the background budget for the CUPID experiment as it will exploit the same CUORE infrastructure.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Reference priors for high energy physics
Bayesian inferences in high energy physics often use uniform prior distributions for parameters about which little or no information is available before data are collected. The resulting posterior distributions are therefore sensitive to the choice of parametrization for the problem and may even be improper if this choice is not carefully considered. Here we describe an extensively tested methodology, known as reference analysis, which allows one to construct parametrization-invariant priors that embody the notion of minimal informativeness in a mathematically well-defined sense. We apply this methodology to general cross section measurements and show that it yields sensible results. A recent measurement of the single-top quark cross section illustrates the relevant techniques in a realistic situation.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗