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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.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

X-Ray Bolometric Corrections for Compton-thick Active Galactic Nuclei

We present X-ray bolometric correction factors, κ{sub Bol} (≡L{sub Bol}/L{sub X}), for Compton-thick (CT) active galactic nuclei (AGNs) with the aim of testing AGN torus models, probing orientation effects, and estimating the bolometric output of the most obscured AGNs. We adopt bolometric luminosities, L{sub Bol}, from literature infrared (IR) torus modeling and compile published intrinsic 2–10 keV X-ray luminosities, L{sub X}, from X-ray torus modeling of NuSTAR data. Our sample consists of 10 local CT AGNs, where both of these estimates are available. We test for systematic differences in κ{sub Bol} values produced when using two widely used IR torus models and two widely used X-ray torus models, finding consistency within the uncertainties. We find that the mean κ{sub Bol} of our sample in the range of L{sub Bol}≈10{sup 42}--10{sup 45} erg s{sup −1} is log{sub 10} κ{sub Bol} = 1.44 ± 0.12 with an intrinsic scatter of ∼0.2 dex, and that our derived κ{sub Bol} values are consistent with previously established relationships between κ{sub Bol} and L{sub Bol} and κ{sub Bol} and Eddington ratio (λ{sub Edd}). We investigate if κ{sub Bol} is dependent on N{sub H} by comparing our results on CT AGNs to published results on less-obscured AGNs, finding no significant dependence. Since many of our sample are megamaser AGNs, known to be viewed edge-on, and furthermore under the assumptions of AGN unification whereby unobscured AGNs are viewed face-on, our result implies that the X-ray emitting corona is not strongly anisotropic. Finally, we present κ{sub Bol} values for CT AGNs identified in X-ray surveys as a function of their observed L{sub X}, where an estimate of their intrinsic L{sub X} is not available, and redshift, useful for estimating the bolometric output of the most obscured AGNs across cosmic time.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Action-based dynamical modeling for the Milky Way disk

We present RoadMapping, a full-likelihood dynamical modeling machinery that aims to recover the Milky Way’s (MW) gravitational potential from large samples of stars in the Galactic disk. RoadMapping models the observed positions and velocities of stars with a parameterized, three-integral distribution function (DF) in a parameterized axisymmetric potential. We investigate through differential test cases with idealized mock data how the breakdown of model assumptions and data properties affect constraints on the potential and DF. Our key results are: (i) If the MW’s true potential is not included in the assumed model potential family, we can—in the axisymmetric case—still find a robust estimate for the potential, with only ≲10% difference in surface density within |z|⩽1.1 kpc inside the observed volume. (ii) Modest systematic differences between the true and model DF are inconsequential. E.g., when binning stars to define sub-populations with simple DFs, binning errors do not affect the modeling as long as the DF parameters of neighboring bins differ by <20%. In addition, RoadMapping ensures unbiased potential estimates for either (iii) small misjudgements of the spatial selection function (i.e., ≲15% at the survey volume’s edge), (iv) if distances are known to within 10%, or (v) if proper motion uncertainties are known within 10% or are smaller than δμ≲1 mas yr{sup −1}. Challenges are the rapidly increasing computational costs for large sample sizes. Overall, RoadMapping is well suited to making precise new measurements of the MW’s potential with data from the upcoming Gaia releases.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

ReLU, Sparseness, and the Encoding of Optic Flow in Neural Networks

Accurate self-motion estimation is critical for various navigational tasks in mobile robotics. Optic flow provides a means to estimate self-motion using a camera sensor and is particularly valuable in GPS- and radio-denied environments. The present study investigates the influence of different activation functions—ReLU, leaky ReLU, GELU, and Mish—on the accuracy, robustness, and encoding properties of convolutional neural networks (CNNs) and multi-layer perceptrons (MLPs) trained to estimate self-motion from optic flow. Our results demonstrate that networks with ReLU and leaky ReLU activation functions not only achieved superior accuracy in self-motion estimation from novel optic flow patterns but also exhibited greater robustness under challenging conditions. The advantages offered by ReLU and leaky ReLU may stem from their ability to induce sparser representations than GELU and Mish do. Our work characterizes the encoding of optic flow in neural networks and highlights how the sparseness induced by ReLU may enhance robust and accurate self-motion estimation from optic flow.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Improving Convection Trigger Functions in Deep Convective Parameterization Schemes Using Machine Learning

Abstract Deficiencies in convection trigger functions, used in deep convection parameterizations in General Circulation Models (GCMs), have critical impacts on climate simulations. A novel convection trigger function is developed using the machine learning (ML) classification model XGBoost. The large‐scale environmental information associated with convective events is obtained from the long‐term constrained variational analysis forcing data from the Atmospheric Radiation Measurement (ARM) program at its Southern Great Plains (SGP) and Manaus (MAO) sites representing, respectively, continental mid‐latitude and tropical convection. The ML trigger is separately trained and evaluated per site, and jointly trained and evaluated at both sites as a unified trigger. The performance of the ML trigger is compared with four convective trigger functions commonly used in GCMs: dilute convective available potential energy (CAPE), undilute CAPE, dilute dynamic CAPE (dCAPE), and undilute dCAPE. The ML trigger substantially outperforms the four CAPE‐based triggers in terms of the F 1 score metric, widely used to estimate the performance of ML methods. The site‐specific ML trigger functions can achieve, respectively, 91% and 93% F 1 scores at SGP and MAO. The unified trigger also has a 91% F 1 score, with virtually no degradation from the site‐specific training, suggesting the potential of a global ML trigger function. The ML trigger alleviates a GCM deficiency regarding the overprediction of convection occurrence, offering a promising improvement to the simulation of the diurnal cycle of precipitation. Furthermore, to overcome the black box issue of the ML methods, insights derived from the ML model are discussed, which may be leveraged to improve traditional CAPE‐based triggers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Constraining radio mode feedback in galaxy clusters with the cluster radio AGNs properties to z ∼ 1

ABSTRACT We study the properties of the Sydney University Molonglo Sky Survey (SUMSS) 843 MHz radio active galactic nuclei (AGNs) population in galaxy clusters from two large catalogues created using the Dark Energy Survey (DES): ∼11 800 optically selected RM-Y3 and ∼1000 X-ray selected MARD-Y3 clusters. We show that cluster radio loud AGNs are highly concentrated around cluster centres to $z$ ∼ 1. We measure the halo occupation number for cluster radio AGNs above a threshold luminosity, finding that the number of radio AGNs per cluster increases with cluster halo mass as N ∝ M1.2 ± 0.1 (N ∝ M0.68 ± 0.34) for the RM-Y3 (MARD-Y3) sample. Together, these results indicate that radio mode feedback is favoured in more massive galaxy clusters. Using optical counterparts for these sources, we demonstrate weak redshift evolution in the host broad-band colours and the radio luminosity at fixed host galaxy stellar mass. We use the redshift evolution in radio luminosity to break the degeneracy between density and luminosity evolution scenarios in the redshift trend of the radio AGNs luminosity function (LF). The LF exhibits a redshift trend of the form (1 + $z$)γ in density and luminosity, respectively, of γD = 3.0 ± 0.4 and γP = 0.21 ± 0.15 in the RM-Y3 sample, and γD = 2.6 ± 0.7 and γP = 0.31 ± 0.15 in MARD-Y3. We discuss the physical drivers of radio mode feedback in cluster AGNs, and we use the cluster radio galaxy LF to estimate the average radio-mode feedback energy as a function of cluster mass and redshift and compare it to the core (<0.1R500) X-ray radiative losses for clusters at $z$ < 1.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Sub-barrier fusion of weakly bound Li 6 with Ni 58

Evaporation-proton yields were measured for the fusion of the weakly bound nucleus 6 Li on a 58 Ni target at six near- or sub-barrier bombarding energies. Effects of the one-neutron transfer reaction were estimated and corrections made. Total-fusion cross sections were deduced using calculated proton multiplicities. The resulting fusion excitation function shows a considerable enhancement with respect to calculations for a bare potential. Inelastic couplings are estimated to have insignificant effects on such fusion. Here, the sum of total fusion plus one-neutron transfer cross sections nearly saturates the total reaction cross section in the energy region measured. Comparison with previous results appropriately scaled for the 6 Li+( 59 Co, 64 Ni, 64 Zn) systems shows good consistency except for some data spread at the lower energies.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2019

A Novel Statistical Method for Measuring the Temperature–Density Relation in the IGM Using the b – N Hi Distribution of Absorbers in the Ly α Forest

We propose a new method for determining the thermal state of the intergalactic medium based on Voigt profile decomposition of the Lyα forest. The distribution of Doppler parameter and column density (b–N H i distribution) is sensitive to the temperature–density relation T = T 0 (ρ/ρ 0 ) γ-1 , and previous work has inferred T 0 and γ by fitting its low-b cutoff. This approach discards the majority of available data and is susceptible to systematics related to cutoff determination. We introduce an approach that exploits all information encoded in the b – N H i distribution by modeling its entire shape. We apply kernel density estimation to discrete absorption lines to generate model probability density functions, and then we use principal component decomposition to create an emulator that can be evaluated anywhere in thermal parameter space. We introduce a Bayesian likelihood based on these models enabling parameter inference via Markov Chain Monte Carlo. The method's robustness is tested by applying it to a large grid of thermal history simulations. By conducting 160 mock measurements, we establish that our approach delivers unbiased estimates and valid uncertainties for a 2D ( T 0 , γ) measurement. Furthermore, we conduct a pilot study applying this methodology to real observational data at z = 2. Using 200 absorbers, equivalent in path length to a single Lya forest spectrum, we measure $\mathrm{log}{T}_{0}={4.092}_{-0.055}^{+0.050}$ and $\gamma ={1.49}_{-0.074}^{+0.073}$ in excellent agreement with cutoff fitting determinations using the same data. Our method is far more sensitive than cutoff fitting, enabling measurements of log T 0 and γ with precision on $\mathrm{log}{T}_{0}$ (γ) nearly two (three) times higher for current data set sizes.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Time to Amyloid Positivity and Preclinical Changes in Brain Metabolism, Atrophy, and Cognition: Evidence for Emerging Amyloid Pathology in Alzheimer's Disease

Background: Aβ pathology is associated with longitudinal changes of brain metabolism, atrophy, and cognition, in cognitively healthy elders. However, Aβ information is usually measured cross-sectionally and dichotomized to classify subjects as Aβ-positive or Aβ-negative, making it difficult to evaluate when brain and cognitive changes occur with respect to emerging Aβ pathology. In this study, we use longitudinal Aβ information to combine the level and rate of change of Aβ to estimate the time to Aβ-positivity for each subject and test this temporal proximity to significant Aβ pathology for associations with brain structure, metabolism, and cognition. Methods: In 89 cognitively healthy elders with up to 10 years of follow-up, we estimated the points at which rates of fluorodeoxyglucose (FDG) PET, MRI, and cognitive and functional decline begin to accelerate with respect to the time to Aβ-positivity. Points of initial acceleration in rates of decline were estimated using mixed-effects models with penalized regression splines. Results: Acceleration of rates of FDG PET were observed to occur 20+ years before the conventional threshold for Aβ-positivity. Subtle signs of cognitive dysfunction were observed 10+ years before Aβ-positivity. Conclusions: Aβ may have subtle associations with other hallmarks of Alzheimer’s disease before Aβ biomarkers reach conventional thresholds for Aβ-positivity. Therefore, we propose that emerging Aβ pathology occurs many years before cognitively healthy elders reach the current threshold for Aβ positivity (preclinical AD). To allow prevention in the earliest disease stages, AD clinical trials may be designed to also include subjects with Aβ biomarkers in the sub-threshold range.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Functional renormalization group approach to the Yang-Lee edge singularity

Here, we determine the scaling properties of the Yang-Lee edge singularity as described by a one-component scalar field theory with imaginary cubic coupling, using the nonperturbative functional renormalization group in 3 ≤ d ≤ 6 Euclidean dimensions. We find very good agreement with high-temperature series data in d = 3 dimensions and compare our results to recent estimates of critical exponents obtained with the four-loop ϵ = 6 - d expansion and the conformal bootstrap. The relevance of operator insertions at the corresponding fixed point of the RG β functions is discussed and we estimate the error associated with O(∂ 4 ) truncations of the scale-dependent effective action.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2017

SIDSIM (Sub-Interval Distribution-function in Simulation) [SWR-17-22]

Time-series simulation is a technique used to estimate the energy delivery of a power generation system with output that changes under changing conditions. Such variable resources include photovoltaic energy systems that experience different levels of sunlight or wind farms with changing wind patterns. As distributed generation from these variable resources continues to flourish, having improved software tools to accurately measure, model, and estimate phenomena of interest to grid integration, in addition to energy output, will be crucial for safeguarding smooth integration and operation of these energy systems. Currently, time-series simulations assume conditions and power output are constant over the duration of a time-step (steady-state assumption). NREL researchers improve the accuracy with a new simulation software called SIDSIM (Sub-Interval Distribution Simulation) that replaces this steady value (solar or wind resource) with a distribution function that has a maximum value, a minimum value, and a shape to the curve within each time-step. The distribution function used for modeling varying values in the SIDSIM software is a novel equation and presents advantageous new benefits over the conventional methods. The first advantage comes in the ability to determine maximum instantaneous power within a time-step. This enables better protection of a system from overcurrent issues and it also allows for better forecasting and reservation of transmission capacity; such capabilities are essential for energy system integration. Second, a distribution function helps to determine the amount of energy loss due to “clipping” in cases where the photovoltaic direct current (DC) system capacity exceeds the inverter capacity, the AC interconnect capacity, or any other limiting component or threshold value. It improves estimates of battery throughput by allowing both charge and discharge of a battery within the duration of a time-step. Third, it provides a more refined estimate of utility-bill impacts by considering the delivered power may be higher than the site load or lower than the regular average power estimates that are made using a quasi-steady-state assumption. SEE ALSO OSTI ID 1,361,018

Walker, Andy↗
DOE OSTI2018

Universality of Poisson-driven plasma fluctuations in the Alcator C-Mod scrape-off layer

Large-amplitude, intermittent fluctuations are ubiquitous in the boundary region of magnetically confined plasmas and lead to detrimental plasma-wall interactions in next-generation, high duty cycle fusion power experiments. Using gas puff imaging data time series from the scrape-off layer in the Alcator C-Mod device, it is demonstrated in this work that the large-amplitude fluctuations can be described as a super-position of pulses with a fixed shape and a constant duration. By applying a new deconvolution algorithm on the data time series with a two-sided exponential pulse function, the arrival times and amplitudes of the pulses can be estimated, and the measurement time series can be reconstructed with high accuracy. The pulse amplitudes are shown to follow an exponential distribution. The waiting times between pulses are uncorrelated, their distribution has an exponential tail, and the number of arrivals is a linear function of time. This demonstrates that pulse arrivals follow a homogeneous Poisson process. Identical statistical properties apply to both ohmic and high confinement mode plasmas, clearly demonstrating the universality of the fluctuation statistics in the boundary region of Alcator C-Mod.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Extraction of neutron-capture cross sections on 92 Zr using the charge-exchange Oslo method

The 93 Nb (𝑡, 3 He ) reaction at 115 MeV/nucleon was studied to demonstrate that nuclear level densities and 𝛾-ray strength functions can be extracted from charge-exchange reactions at intermediate energies using the Oslo technique. The matrix of excitation energy in 93 Zr, reconstructed from the (𝑡, 3 He ) reaction, versus the energy of 𝛾 rays emitted by the excited 93 Zr nuclei, was obtained in an experiment with the S800 Spectrograph operated in coincidence with the GRETINA 𝛾 -ray detector. The extracted level density and 𝛾 -ray strength function obtained by applying the Oslo method to this matrix were used to estimate the 92 Zr⁢(𝑛,𝛾) ⁢93 Zr cross section by combining the new results with other experimental data and theoretical calculations for 𝐸⁢1 and 𝑀⁢1 strength functions at higher energies. Good agreement with direct measurements of the 92 Zr⁢(𝑛,𝛾)⁢ 93 Zr cross section was found. The contribution from the upbend in the extracted 𝛾-ray strength function was important to achieve the consistency, as the neutron-capture cross section without this contribution is significantly below the direct measurements otherwise. Since charge-exchange reactions at intermediate energies have long been used for extracting Gamow-Teller strengths, the successful demonstration of the charge-exchange Oslo method enables experiments in which (𝑛,𝛾) cross sections and Gamow-Teller strengths can be measured simultaneously, which is of benefit for astrophysical studies.

90 ≤ A ≤ 149↗
DOE OSTI2022

Adjusting solar-induced fluorescence to nadir-viewing provides a better proxy for GPP

Solar-induced fluorescence (SIF) provides key information for inferring terrestrial gross primary productivity (GPP). However, there is little research on analyzing the performance of nadir-adjusted SIF (SIF nadir ) on GPP, compared to total SIF emitted by all leaves (SIF total ) for reducing the viewing angle effects and estimating GPP. Besides, there have been controversial opinions on whether SIF total is better correlated to GPP than raw SIF observations (SIF obs ). Here we systematically analyzed the relationship between raw/nadir/total SIF (i.e., SIF obs , SIF nadir and SIF total ) and GPP and investigated the underlying mechanism, using multi-angular field measurements and eddy covariance data in wheat and corn crops at sub-daily scale. We further compared the performance of SIF obs , SIF nadir and SIF total in estimating GPP using the TROPOspheric Monitoring Instrument (TROPOMI) SIF and concurrent AmeriFlux measurements at daily scale. Furthermore, the results indicate that diurnal SIF nadir has stronger correlations to GPP than SIF obs for field measurements, with the increase of mean coefficient of determination (R 2 ) by 0.05–0.07 for far-red band and 0.11–0.20 for red band. SIF nadir shows comparable performance with SIF total for both far-red and red bands. Although the viewing angle effects dominate the difference in estimating GPP between SIF nadir and SIF obs , the correlation between light use efficiency (LUE) and fesc further determines the different performance of SIF nadir and SIF total in estimating GPP. TROPOMI-based analysis further confirms that SIF nadir overall has higher correlations to AmeriFlux GPP than SIF obs for different plant functional types and shows similar performance with SIF total . Compared to SIF total , the estimation of SIF nadir independent of viewing angle effects does not require any canopy structure parameters, and thus offers promising potential for reliably estimating regional and global GPP.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Physics-Informed Deep Neural Networks for Learning Parameters and Constitutive Relationships in Subsurface Flow Problems

In this paper, we present a physics informed deep neural network (DNN) method for estimating parameters and unknown physics (constitutive relationships) in partial differential equation (PDE) models. We use PDEs in addition to measurements to train DNNs to approximate unknown parameters and constitutive relationships as well as states. The proposed approach increases the accuracy of DNN approximations of partially known functions when a limited number of measurements is available and allows for training DNNs when no direct measurements of the functions of interest are available. We employ physics informed DNNs to estimate the unknown space-dependent diffusion coefficient in a linear diffusion equation and an unknown constitutive relationship in a non-linear diffusion equation. For the parameter estimation problem, we assume that partial measurements of the coefficient and states are available and demonstrate that under these conditions, the proposed method is more accurate than state-of-the-art methods. For the non-linear diffusion PDE model with a fully unknown constitutive relationship (i.e., no measurements of constitutive relationship are available), the physics informed DNN method can accurately estimate the non-linear constitutive relationship based on state measurements only. Finally, we demonstrate that the proposed method remains accurate in the presence of measurement noise.

42 ENGINEERING↗
DOE OSTI2020

The Evolving Distribution of Relative Humidity Conditional Upon Daily Maximum Temperature in a Warming Climate

Abstract The impacts of heat waves in a warming climate depend not only on changing temperatures but also on changing humidity. Using 35 simulations from the Community Earth System Model Large Ensemble (CESM LENS), we investigate the long‐term evolution of the joint distribution of summer relative humidity (RH) and daily maximum temperature ( Tmax ) near four U.S. cities (New York City, Chicago, Phoenix, and New Orleans) under the high‐emissions Representative Concentration Pathway (RCP) 8.5. We estimate the conditional quantiles of RH given Tmax with quantile regression models, using functions of temperature for each city in July for three time periods (1990–2005, 2026–2035, and 2071–2080). Quality‐of‐fit diagnostics indicate that these models accurately estimate conditional quantiles for each city. As expected, each quantile of Tmax increases from 1990–2005 to 2071–2080, while mean RH decreases modestly. Conditional upon a fixed quantile of Tmax , the median and high quantiles of RH decrease, while those of the Heat Index (HI) and dew point both increase. This result suggests that, despite a modest decrease in median relative humidity, heat stress measured by metrics considering both humidity and temperature in a warming climate will increase faster than that measured by temperatures alone would indicate. For a fixed Tmax , the high quantiles of RH (and thus of HI and dew point) increase from 1990–2005 to 2071–2080 in all four cities. This result suggests that the heat stress of a day at a given Tmax will increase in a warming climate due to the increase of RH.

Yuan, Jiacan↗
DOE OSTI2020

Data-driven predictive probability density function control of fiber length stochastic distribution shaping in refining process

Pulp is the most important raw material in paper in paper industries, whose Fiber Length Stochastic Distribution (FLSD) shaping directly determines the energy consumption and paper quality of the subsequent papermaking processes. However, the mean and variance are insufficient to describe the output FLSD shaping, which display non-Gaussian distribution properties. Therefore, the traditional control, method based on the mean and variance of fiber length is difficult to control the FLSD shaping effectively. In this paper, a novel data-driven predictive probability density function (PDF) control method is proposed for output FLSD shaping in refining process. Primarily, in order to improve the approximation accuracy of the PDF employing RBF neural network, the parameters (center value and width) of each RBF basis functions are tuned via utilizing iterative learning control (ILC) law, and the corresponding estimations of weights law can be obtained. Secondly, considering that the conventional linear model of weights vector has drawbacks of low accuracy and weak generalization ability, random vector functional link networks (RVFLNs) based data driven nonlinear modelling method is employed to characterize the prediction model between the input variables and weights vector. Finally, in order to reduce the randomness of the output FLSD, a minimum entropy control method under mean constraint is employed to design the predictive PDF controller for the output FLSD shaping with the help of the established nonlinear stochastic distribution model. Both simulations and experiments are given to illustrate the effectiveness and practicability of the proposed method.

Probability debsity function, Fiber length distrib↗
DOE OSTI2016

Maps of the Magellanic clouds from combined South Pole Telescope and Planck data

Here, we present maps of the Large and Small Magellanic Clouds from combined South Pole Telescope (SPT) and Planck data. Both instruments are designed to make measurements of the cosmic microwave background but are sensitive to any source of millimeter-wave (mm-wave) emission. The Planck satellite observes in nine mm-wave bands, while the SPT data used in this work were taken with the three-band SPT-SZ camera. The SPT-SZ bands correspond closely to three of the nine Planck bands, namely those centered at 1.4, 2.1, and 3.0 mm. The angular resolution of the Planck data in these bands ranges from 5 to 10 arcmin, while the SPT resolution in these bands ranges from 1.0 to 1.7 arcmin. The combined maps take advantage of the high resolution of the SPT data and the long-timescale stability of the space-based Planck observations to deliver high signal-to-noise and robust brightness measurements on scales from the size of the maps down to ~1 arcmin. In each of the three bands, we first calibrate and color-correct the SPT data to match the Planck data, then we use noise estimates from each instrument and knowledge of each instrument's beam, or point-spread function, to make the inverse-variance-weighted combination of the two instruments' data as a function of angular scale. Furthermore, we create maps assuming a range of underlying emission spectra (for the color correction) and at a range of final resolutions. We perform several consistency tests on the combined maps and estimate the expected noise in measurements of features in the maps. Finally, we compare the maps of the Large Magellanic Cloud (LMC) from this work to maps from the Herschel HERITAGE survey, finding general consistency between the datasets. Furthermore, the broad wavelength coverage provides evidence of different emission mechanisms at work in different environments in the LMC.

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↗