SEARCH · Search NASA

Results for “uncertainty characterization”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 667 records · Page 37

All sources NASA NTRS DOE OSTI

NASA NTRS1989

Unmanned and manned Mars missions - Aeroassist technology needs and issues

The critical aeroassist technologies are considered, with consideration given to the manned and unmanned Mars missions. Attention is given to the aerothermal characterization, the thermal-protection system (TPS), the guidance navigation and control systems, and atmospheric uncertainties. Particular consideration is given to the aerobrake assembly concepts for an unmanned Mars mission, and to programmatics.

Willcockson, W. H.↗

NASA NTRS1994

Sensor biases effect on the estimation algorithm for performance-seeking controllers

The performance-seeking-control algorithm (PSC) is designed to continuously optimize the performance of propulsion systems. The PSC uses a nominal model of the propulsion system and estimates, in flight, the engine deviation parameters (EDPs) characterizing engine deviations with respect to nominal conditions. In practice, the measurement biases (or model uncertainties) may prevent the estimated EDPs from reflecting the engine's actual off-nominal condition. This factor has a direct impact on the PSC scheme exacerbated by the open-loop character of the algorithm. An observability analysis shows that the biases cannot be estimated together with the EDPs. Moreover, biases and EDPs turn out to have equivalent effects on the measurements, leaving it undecided whether the estimated EDPs represent the actual engine deviation or whether they simply reflect the measurement biases. In this article, the effects produced by unknown measurement biases over the estimation algorithm are evaluated. This evaluation allows for identification of the most critical measurements for application of the PSC algorithm to an F100 engine.

F100 ENGINE↗

NASA NTRS2000

Remote Sensing of Global Wetland Dynamics

Although natural wetlands only cover about 4% of the earth's ice-free land surface, they are the world's largest methane (CH4) source and the only one dominated by climate. In addition, wetlands affect climate by modulating temperatures and heat fluxes, storing water, increasing evaporation, and altering the seasonality of runoff and river discharge to the oceans. Current CH4 emissions from wetlands are relatively well understood but the sensitivity of wetlands and their emissions to climate variations remains the largest uncertainty in the global CH4 cycle and could strongly influence predictions of future climate. Therefore, characterizing climate-sensitive processes prevailing in the world's wetlands is crucial to understanding and predicting physical and biogeochemical responses of wetlands to interannual and longer-term climate variations. Recent research has resulted in the first generation of models to predict methane emissions from wetlands but the models must still be applied to static data on wetland distributions. Moreover, no models currently exist to realistically predict the distribution and dynamics of wetlands themselves for the current, or any other, climate. The dominant obstacle to modeling wetland dynamics has been lack of remote sensing techniques and data useful for characterizing quantitatively the seasonal and interannual variations of wetlands. We report on initial remote sensing studies undertaken to validate a global hydrological model linking rivers, takes and wetlands. Using a combination of SSM/I microwave and TOPEX Poseidon altimetry data sets, we developed and applied techniques to quantify inundation extent and duration for several large wetlands in tropical Africa and South America. Our initial results indicate that seasonally-inundated wetlands can be well characterized over large spatial scales and at monthly time scales using these remote sensing data. The results also confirm that currently available remote sensing products can be applied to provide crucial data to the research community modeling global hydrological and biogeochemical cycles.

Matthews, Elaine↗

NASA NTRS2014

An Adaptive Nonlinear Aircraft Maneuvering Envelope Estimation Approach for Online Applications

A nonlinear aircraft model is presented and used to develop an overall unified robust and adaptive approach to passive trim and maneuverability envelope estimation with uncertainty quantification. The concept of time scale separation makes this method suitable for the online characterization of altered safe maneuvering limitations after impairment. The results can be used to provide pilot feedback and/or be combined with flight planning, trajectory generation, and guidance algorithms to help maintain safe aircraft operations in both nominal and off-nominal scenarios.

nonlinearity↗

NASA NTRS2021

Computational Inference of Vibratory System with Incomplete Modal Information Using Parallel, Interactive and Adaptive Markov Chains

Inverse analysis of vibratory system is an important subject in fault identification, model updating, and robust design and control. It is challenging subject because 1) the problem is oftentimes underdetermined while the measurements are limited and/or incomplete; 2) many combinations of parameters may yield results that are similar with respect to actual response measurements; and 3) uncertainties inevitably exist. The aim of this research is to leverage upon computational intelligence through statistical inference to facilitate an enhanced, probabilistic framework using incomplete modal response measurement. This new framework is built upon efficient inverse identification through optimization, whereas Bayesian inference is employed to account for the effect of uncertainties. To overcome the computational cost barrier, we adopt Markov chain Monte Carlo (MCMC) to characterize the target function/distribution. Instead of using single Markov chain in conventional Bayesian approach, we develop a new sampling theory with multiple parallel, interactive and adaptive Markov chains and incorporate it into Bayesian inference. This can harness the collective power of these Markov chains to realize the concurrent search of multiple local optima. The number of required Markov chains and their respective initial model parameters are automatically determined via Monte Carlo simulation-based sample pre-screening followed by K-means clustering analysis. These enhancements can effectively address the aforementioned challenges in finite element inverse analysis. The validity of this framework is systematically demonstrated through case studies.

K Zhou↗

NASA NTRSDate not supplied

Aerothermodynamic Measurements in Hypersonic Non-Equilibrium Flows

High enthalpy arc-jets are unique facilities particularly suited for simulating complex flows in the aerospace field, such as the aerothermodynamics of a re-entry vehicle. Arc-jets are often used to evaluate important design factors such as heat shield materials and vehicle design. Characterization of these facilities is important, as studies often match specific in-flight environments during experiments. Due to the complex environment produced by an arc-jet, with effects such as thermodynamic and chemical non-equilibrium occurring in the flow, characterization experiments are significantly more difficult than in traditional blow-down wind tunnels. The current work aims to characterize an arc-jet facility in terms of flow temperature, velocity and unsteadiness, in a spatially-resolved analysis. To achieve this goal, a non-intrusive imaging technique called “planar laser-induced fluorescence” was performed in the NASA Langley Hypersonic Materials Environ-mental Test System arc-jet facility. The experimental data was analyzed to produce the quantitative measurements in multiple regions of the flow around a blunt body sample. A quasi-1D low fidelity solver was created to numerically simulate the flow to investigate non-equilibrium effects occur-ring outside the imaging region in the arc-jet nozzle. Unsteadiness in the test section was minimized by analyzing a subset of data assessing the gas injection location. Radial velocity, rotational temperature and translational temperature measurements are provided that can be used to validate future high-fidelity computational studies. The temperature measurements revealed rotational non-equilibrium potentially occurring in the flow. This work provides a methodology for characterizing arc-jet facilities, identifying and correcting sources of systematic error, and estimating the uncertainty associated with the quantitative measurements.

laser induced fluorescence↗

NASA NTRS2024

Deciphering the Spectra of Flowers to Map Landscape-scale Blooming Dynamics

Like leaves, floral coloration is driven by inherent optical properties, which are determined by pigments, scattering structure, and thickness. However, establishing the relative contribution of these factors to canopy spectral signals is usually limited to in-situ observations. Modeling flowering dynamics (e.g., blooming duration, spatial distribution) at the landscape scale may reveal insights into ecological processes and phenological adaptations to environmental changes. Multitemporal visible to shortwave infrared (VSWIR) imaging spectroscopy observations are especially suited for such efforts. Reflectance in this spectral range is sensitive to major flower pigments, flowering phenology traces, and biophysical differences between flowers and other plant parts. We explored how flowers contribute to spectral signals using a time series of imagery from the Airborne Visible InfraRed Imaging Spectrometer - Next Generation (AVIRIS-NG) collected as part of the SBG High-Frequency Time Series (SHIFT) campaign as a case study. Airborne data were collected weekly during the spring of 2022 across two natural reserves in California. Field spectra were gathered from blooming plots at leaf, flower, and canopy levels at two time points during the campaign. The processed data was used to investigate flowering species' spectro-temporal variation and spatial distribution using Spectral Mixture Residual, Gaussian clustering techniques, and a proposed narrow-band flowering index. Linear spectral unmixing allowed the computation of the weighted contribution of four major high-variance endmembers (leaves, flowers, soil, dark) and low-variance residual signal that comprises subtle spectral features used to track biophysical processes. The reflectance residual was projected on a low principal component basis to characterize flowering clusters' variation and spatial distribution based on the Gaussian mixture model, providing an uncertainty metric to assess the results. Mapping flowering events from modeling spectro-temporal dynamics throughout the season, from pre-blooming to post-flowering stages, allowed us to identify gradient variations in spectral features within the VSWIR spectral range linked to flowering pigments. Time series of the Mixture Residual Blooming Index and the Red-Edge Normalized Difference Vegetation Index revealed specific flowering and greenness phenophases across the two main species (Coreopsis gigantea, Artemisia californica) in the flowering areas. Overall, our approach opens opportunities for future satellite monitoring of floral cycles at broader scales.

Yoseline Angel↗

DOE OSTI2025

Measurement of $\nu_\mu$ CC Interactions With Two-Proton Final State in MINERvA

This dissertation presents a measurement of charged–current (CC) muon–neutrino interactions with exactly two protons and no pions in the final state (CC~$2p\,0\pi$), using data collected by the MINERvA detector in the NuMI medium–energy beam at Fermilab. Such two–proton topologies are a sensitive probe of nuclear dynamics in the few–GeV regime, including multi–nucleon correlations (npnh, notably $2p2h$) and intranuclear final–state interactions (FSI) such as pion absorption and nucleon rescattering. A precise experimental characterization of these processes is essential both for neutrino–interaction theory and for reducing systematic uncertainties in oscillation experiments that rely on accurate modeling of neutrino–nucleus interactions. Events are selected by requiring a $\nu_\mu$ CC interaction with a reconstructed $\mu^-$ and two proton tracks originating from a common vertex in MINERvA’s finely segmented scintillator tracker, with no reconstructed mesons. Muon charge and momentum are constrained by matching to the MINOS Near Detector, while proton identification exploits energy–loss profiles and stopping–proton features. Backgrounds from pion–producing channels that enter the signal region through FSI or reconstruction effects are constrained with data–driven sidebands (Michel–electron and isolated–cluster “blob” samples) and tuned via a simultaneous fit across signal and sideband regions. To correct detector resolution and acceptance effects, the analysis employs iterative Bayesian unfolding with extensive validation: statistical pseudo–experiments, and robustness checks against generator systematic “universes” and additional strong shape warps. Single–differential cross sections are reported for three observables tailored to the two–proton final state: the opening–angle cosine $\cos\!\left(\theta_{pp}\right)$, the leading–proton momentum, and the subleading–proton momentum. Systematic uncertainties include contributions from neutrino flux, interaction modeling (e.g., npnh and resonance parameters, pion FSI), and detector response (calibration, reconstruction efficiencies). The resulting distributions provide targeted constraints on the interplay of multi–nucleon dynamics and FSI that shape CC~$2p\,0\pi$ final states on hydrocarbon. Comparisons to modern GENIE–based simulations highlight kinematic regions where model components require refinement. These measurements thus inform generator tuning and improve the reliability of neutrino–energy reconstruction strategies for current and future long–baseline oscillation programs.

Syrotenko, Vladyslav S. [Tufts U.]↗

NASA NTRS2011

Assessment of MODIS Reflected Solar Calibration Uncertainty

Determination of the calibration accuracy and traceability of a remote sensing instrument is a driving issue in the use of satellite data for calibration inter-comparisons and studying climate change. The Terra and Aqua MODerate Resolution Imaging Spectroradiometer (MODIS) instruments have successfully operated for more than 11 and 9 years, respectively. Twenty of the thirty six MODIS spectral bands are in the reflected solar region with center wavelengths ranging from 0.41 to 2.2 microns. MODIS reflective solar band (RSB) on-orbit calibration is reflectance based through the use of an on-board solar diffuser (SO). The calibration uncertainty requirements are +/-2.0% for the RSB reflectance factors at sensor specified typical scene reflectances or radiances. The SO bi-directional reflectance factor (BRF) was characterized pre-launch and its on-orbit changes are tracked by an on-board solar diffuser stability monitor (SDSM). This paper provides an assessment of MODIS RSB on-orbit calibration traceability and uncertainty for its Level 1B (L1B) reflectance factors. It examines in details each of the uncertainty contributors, including those from pre-launch measurements as well as on-orbit observations. Common challenging issues and differences due to individual sensors' specific characteristics and on-orbit performance are also discussed in this paper. Guidance and recommendations are presented, based on lessons from MODIS RSB calibration uncertainty assessment, for the development of future instrument calibration and validation plans.

Xiong, Xiaoxiong↗

DOE OSTI2025

Two-Stage Distributionally Robust Conic Linear Programming over 1-Wasserstein Balls

Here, this paper studies two-stage distributionally robust conic linear programming under constraint uncertainty over type-1 Wasserstein balls. We present optimality conditions for the dual of the worst-case expectation problem, which characterizes worst-case uncertain parameters for its inner maximization problem. This condition offers an alternative proof, a counterexample, and an extension to previous works. Additionally, the condition highlights the potential advantage of a specific distance metric for out-of-sample performance, as exemplified in a numerical study on a facility location problem with demand uncertainty. Furthermore, cutting-plane-based algorithms, equipped with a unified scenario generation framework, are proposed for addressing both unbounded support and second-stage dual feasible regions, with a finite convergence proof under less stringent assumptions.

Wasserstein↗

DOE OSTI2023

Experimental measurement of the effective contact angle for solvent/packing interactions in a structured packed column for CO 2 capture

The contact angle is a critical factor for determining the effective mass transfer area for carbon dioxide (CO 2 ) capture via the chemical absorption process in a packed column, and thus the overall capture efficiency of the packed column. Many widely used commercial packings involve microscale features (perforation, corrugation, etc.) that may also affect the wetting behavior. This study proposes a systematic method of using a modified Wilhelmy plate to measure the effective contact angle to characterize the solvent and featured packing interaction. In lieu of computational efforts relying on assumptions guided by semi-informed correlation, the proposed method directly measures the effective contact angle as a function of the solvent and packing thermophysical and hydrodynamic properties. The characterization of the effective contact angle is then integrated in the computational fluid dynamics modeling to reduce uncertainty in the prediction of effective mas transfer area. Experiments were conducted for stainless steel coupons using water and aqueous sodium hydroxide (NaOH) solvent for verification of the proposed experimental protocol. The surface tension of the aqueous NaOH solvent was altered using surfactant and antifoam. The effective contact angle increases with the increased value of the surface tension for flat stainless steel sheets. On the other hand, effective contact angles do not vary in Mellapak coupons for aqueous monoethanolamine (MEA) and NaOH solvent. In this case, surface textures and sheet design play a dominant role in the surface tension of solvents. Furthermore, CO 2 loading has a significant effect on the contact angle for Mellapak coupons. As expected, the contact angle decreases with the increasing temperature of CO 2 capture solvents (MEA, EEMPA). The effective contact angle measurement using the Wilhelmy plate method can provide more accurate and efficient solvents for characterizing the solvent-packing surface interactions. Subsequently, the present method enhances the accuracy of the prediction of the effective mass transfer area in carbon capture by solvent absorption.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Uncertainty-Aware Machine Learning for Small-Angle X-ray Scattering Analysis in Autonomous Experimentation

Small-angle X-ray scattering (SAXS) is a powerful high-throughput characterization tool for probing nanoscale structure in native sample environments, providing real-time morphological information such as nanoparticle size and shape during synthesis. However, automated SAXS data analysis for extracting meaningful structural parameters is non-trivial and remains a bottleneck in closed-loop experimentation towards autonomous materials discovery, which demands fast, reliable, and uncertainty-aware data analysis. Here, we develop a machine-learning approach for automated SAXS analysis tailored to closed-loop nanoparticle synthesis. A Random Forest (RF) regression model is trained on 100,000 synthetic SAXS curves generated from polydisperse spherical nanoparticles with realistic background contributions. Using normalized one-dimensional SAXS intensity profiles as input, the RF model directly predicts nanoparticle radius, size polydispersity, and background parameters, while the ensemble standard deviation across trees provides built-in uncertainty quantification (UQ). On synthetic data, we show that combining fit-quality metrics (R 2 , MAE) with thresholds on prediction uncertainty reliably identifies accurate parameter estimates without access to ground truth. We then apply the trained model to 365 experimental SAXS profiles of citrate-reduced gold nanoparticles synthesized using an automated droplet-flow microreactor with in situ SAXS at a synchrotron beamline, classifying the results into high- and low-confidence subsets based on UQ metrics. Finally, we integrate RF-based SAXS analysis into a simulated closed-loop optimization campaign using Gaussian process Bayesian optimization to minimize nanoparticle polydispersity, benchmarking against conventional automated Levenberg–Marquardt fitting. The RF-guided campaign exhibits substantially faster convergence and lower relative opportunity cost (∼0.07 vs ∼0.3), demonstrating that uncertainty-aware machine-learning SAXS analysis significantly enhances the efficiency and robustness of autonomous nanomaterials synthesis workflows.

Bayesian optimization↗

NASA NTRS2016

Probabilistic Damage Characterization Using the Computationally-Efficient Bayesian Approach

This work presents a computationally-ecient approach for damage determination that quanti es uncertainty in the provided diagnosis. Given strain sensor data that are polluted with measurement errors, Bayesian inference is used to estimate the location, size, and orientation of damage. This approach uses Bayes' Theorem to combine any prior knowledge an analyst may have about the nature of the damage with information provided implicitly by the strain sensor data to form a posterior probability distribution over possible damage states. The unknown damage parameters are then estimated based on samples drawn numerically from this distribution using a Markov Chain Monte Carlo (MCMC) sampling algorithm. Several modi cations are made to the traditional Bayesian inference approach to provide signi cant computational speedup. First, an ecient surrogate model is constructed using sparse grid interpolation to replace a costly nite element model that must otherwise be evaluated for each sample drawn with MCMC. Next, the standard Bayesian posterior distribution is modi ed using a weighted likelihood formulation, which is shown to improve the convergence of the sampling process. Finally, a robust MCMC algorithm, Delayed Rejection Adaptive Metropolis (DRAM), is adopted to sample the probability distribution more eciently. Numerical examples demonstrate that the proposed framework e ectively provides damage estimates with uncertainty quanti cation and can yield orders of magnitude speedup over standard Bayesian approaches.

Warner, James E.↗

NASA NTRS2016

Characterize Aerosols from MODIS MISR OMI MERRA-2: Dynamic Image Browse Perspective

Among the known atmospheric constituents, aerosols still represent the greatest uncertainty in climate research. To understand the uncertainty is to bring altogether of observational (in-situ and remote sensing) and modeling datasets and inter-compare them synergistically for a wide variety of applications that can bring far-reaching benefits to the science community and the broader society. These benefits can best be achieved if these earth science data (satellite and modeling) are well utilized and interpreted. Unfortunately, this is not always the case, despite the abundance and relative maturity of numerous satellite-borne sensors routinely measure aerosols. There is often disagreement between similar aerosol parameters retrieved from different sensors, leaving users confused as to which sensors to trust for answering important science questions about the distribution, properties, and impacts of aerosols. NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) have developed a new visualization service (NASA Level 2 Data Quality Visualization, DQViz)supporting various visualization and data accessing capabilities from satellite Level 2(MODISMISROMI) and long term assimilated aerosols from NASA Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2 displaying at their own native physical-retrieved spatial resolution. Functionality will include selecting data sources (e.g., multiple parameters under the same measurement), defining area-of-interest and temporal extents, zooming, panning, overlaying, sliding, and data subsetting and reformatting.

data quality↗

NASA NTRSDate not supplied

Uncertainty Quantification of Classical Theories of Dendritic Growth Kinetics Applied to Nickel-Based Alloys

The solidification velocity in a model nickel-alloy single crystal during laser spot melting was recently characterized using synchrotron X-ray imaging. The measured solidification velocity was found to exceed the absolute stability threshold predicted by the Kurz-Giovanola-Trivedi (KGT) model. The discrepancies between the model and experiments motivate the further assessment of accurate material properties. This work quantifies the impact material property uncertainty has on model predictions of the absolute stability threshold velocities. Properties from the literature are reviewed and compared to those calculated using computational thermodynamics to provide uncertainty estimates on input properties to the KGT model. Global sensitivity analysis is used to quantify the influence of each uncertain input property on the predicted threshold velocity. This work supports the understanding of the nickel-alloy solidification during powder bed fusion additive manufacturing and identifies the solidification material properties that are the most important to assess from first-principles computations and experiments.

Computational thermodynamics↗

DOE OSTI2020

A non-intrusive optical (NIO) approach to characterize heliostats in utility-scale power tower plants: Sensitivity study

Power towers, a type of concentrating power tower technology, use a large number of heliostats to concentrate sunlight and produce renewable energy. Optical errors of heliostats can cause drastic losses in the efficiency of power-tower plants. Accurately measuring optical errors is crucial to assessing and improving plant performance. This analysis discusses an innovate non-intrusive optical (NIO) approach to measure slope, canting, and tracking errors by detecting distortions in the reflected tower structure in heliostat images. Further in this work a sensitivity study is carefully conducted to determine the uncertainty requirements to allow the method to calculate slope errors with an accuracy of 0.25 mrad. Measurement uncertainty sources include camera resolution and position uncertainty, tower position uncertainty, and number of collected images. Each uncertainty source is investigated to determine its impact on the accuracy of the slope-error calculation. A combination of theoretical results and experimental results obtained from data collected on a heliostat at Sandia National Laboratories is used to determine and validate uncertainty requirements. The analysis shows that a measurement uncertainty of 0.25 mrad can be realized by realistically controlling uncertainty sources when implementing the NIO method. It demonstrates the superior performance of NIO in performing in-situ optical characterization.

14 SOLAR ENERGY↗

DOE OSTI2023

The Dark Energy Camera Plane Survey 2 (DECaPS2): More Sky, Less Bias, and Better Uncertainties

Abstract Deep optical and near-infrared imaging of the entire Galactic plane is essential for understanding our Galaxy’s stars, gas, and dust. The second data release of the Dark Energy Camera (DECam) Plane Survey extends the five-band optical and near-infrared survey of the southern Galactic plane to cover 6.5% of the sky, ∣ b ∣ ≤ 10°, and 6° > ℓ > −124°, complementary to coverage by Pan-STARRS1. Typical single-exposure effective depths, including crowding effects and other complications, are 23.5, 22.6, 22.1, 21.6, and 20.8 mag in g , r , i , z , and Y bands, respectively, with around 1″ seeing. The survey comprises 3.32 billion objects built from 34 billion detections in 21,400 exposures, totaling 260 hr open shutter time on the DECam at Cerro Tololo. The data reduction pipeline features several improvements, including the addition of synthetic source injection tests to validate photometric solutions across the entire survey footprint. A convenient functional form for the detection bias in the faint limit was derived and leveraged to characterize the photometric pipeline performance. A new postprocessing technique was applied to every detection to debias and improve uncertainty estimates of the flux in the presence of structured backgrounds, specifically targeting nebulosity. The images and source catalogs are publicly available at http://decaps.skymaps.info/ .

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Adversarial autoencoder ensemble for fast and probabilistic reconstructions of few-shot photon correlation functions for solid-state quantum emitters

Second-order photon correlation measurements [g (2) (τ) functions] are widely used to classify single-photon emission purity in quantum emitters or to measure the multiexciton quantum yield of emitters that can simultaneously host multiple excitations – such as quantum dots – by evaluating the value of g (2) (τ = 0). Accumulating enough photons to accurately calculate this value is time consuming and could be accelerated by fitting of few-shot photon correlations. Here, we develop an uncertainty-aware, deep adversarial autoencoder ensemble (AAE) that reconstructs noise-free g (2) (τ) functions from noise-dominated, few-shot inputs. The model is trained with simulated g (2) (τ) functions that are facilely generated by Poisson sampling time bins. The AAE reconstructions are performed orders-of-magnitude faster, with reconstruction errors and estimates of g (2) (τ = 0) that are lower in variance and similar in accuracy compared to Maximum likelihood estimation and Levenberg-Marquardt least-squares fitting approaches, for simulated and experimentally measured few-shot g (2) (τ) functions (~100 two-photon events) of InP/ZnS/ZnSe and CdS/CdSe/CdS quantum dots. The deep-ensemble model comprises eight individual autoencoders, allowing for probabilistic reconstructions of noise-free g (2) (τ) functions, and we show that the predicted variance scales inversely with number of shots, with comparable uncertainties to computationally intensive Markov chain Monte Carlo sampling. Furthermore, this work demonstrates the advantage of machine learning models to perform uncertainty-aware, fast, and accurate reconstructions of simple Poisson-distributed photon correlation functions, allowing for on-the-fly reconstructions and accelerated materials characterization of solid-state quantum emitters.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗