Search NASA⌕ Search

SEARCH · Search NASA

Results for “inference methods”

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.

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

At least 1,621 records · Page 90
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Constraining Cosmology with Simulation-based inference and Optical Galaxy Cluster Abundance

We test the robustness of simulation-based inference (SBI) in the context of cosmological parameter estimation from galaxy cluster counts and masses in simulated optical datasets. We construct ``simulations'' using analytical models for the galaxy cluster halo mass function (HMF) and for the observed richness (number of observed member galaxies) to train and test the SBI method. We compare the SBI parameter posterior samples to those from an MCMC analysis that uses the same analytical models to construct predictions of the observed data vector. The two methods exhibit comparable performance, with reliable constraints derived for the primary cosmological parameters, ($\Omega_m$ and $\sigma_8$), and richness-mass relation parameters. We also perform out-of-domain tests with observables constructed from galaxy cluster-sized halos in the Quijote simulations. Again, the SBI and MCMC results have comparable posteriors, with similar uncertainties and biases. Unsurprisingly, upon evaluating the SBI method on thousands of simulated data vectors that span the parameter space, SBI exhibits worsened posterior calibration metrics in the out-of-domain application. We note that such calibration tests with MCMC is less computationally feasible and highlight the potential use of SBI to stress-test limitations of analytical models, such as in the use for constructing models for inference with MCMC.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Development of a Severe Accident Analysis Engine Using Approximate Reasoning

In hypothetical severe accident scenarios, nuclear power plant safety is challenged by core degradation and release of fission products. To aid the plant operator and technical support center staff, research has been performed to develop understanding of severe accident phenomenology, assessing their risk and devise measures to mitigate accident consequences (Sehgal, 2011). Nuclear power plant accidents involve highly complex physical and chemical interactions, a large number of safety related systems, and emergency operating procedures (EOP) and severe accident management guidelines (SAMG). Large number of factors with uncertainty and stress can overwhelm the operators. The overall objective of the present research is to develop a methodology and tools that an operator can use to predict severe accident progression and make effective decisions to arrest and manage the accident under large uncertainties. Towards this goal, this study examines a novel approach that brings to bear a combined use of techniques in artificial intelligence (Russel and Norvig, 2010), including Bayesian Network (BN), Answer Set Programming (ASP) and Fuzzy Inference (FI) to achieve faster-than-real-time, yet sufficiently accurate analysis of severe accident progression. Bayesian network (BN) is a type of probabilistic graphical models that can be used to represent knowledge and conditional dependencies about an uncertain realm. It is useful for capturing the causal relationships in a complex system. It also allows prediction via Bayesian inference (Kelly and Smith, 2012). It is composed of nodes and edges, which express random variables and probabilistic relationships between the nodes, respectively. This relationship, given by conditional probability is calculated using stochastic methods. The BN is used in several systems for analysis and management of accident risk; e.g. (Knochenhauer and Frid, 2010). In those systems, the nodes or so-called 'effective factors' are presumably known within the network by system descriptions in SAMGs. There are two steps to configure the BN, namely (i) identifying compositions within the network and (ii) defining conditional dependency between nodes. The first step is performed following the spirit of ASP (Baral, 2003) using Prolog (Wielemaker, 2005). Knowledge representation is used to build databases required for processing by ASP. Knowledge representation is defined as the expression of knowledge in a way that is understandable to both human and computers. Originally knowledge indicates data, information and facts. To focus, knowledge should be formed to achieve a goal. In a severe accident, key physics and safety related components status are defined according to a failure mode. The conditional dependencies between nodes are generated by approximate reasoning using Fuzzy inference (Zadeh, 1995). Fuzzy logic is a multiple-valued logic to treat ambiguous features such as linguistic description. It indicates possibility, which is distinguished from probability. Fuzzy set consists of linguistic variables according to the fixed rule. In other words, even uncertain states could be expressed as not binary logic, but as many-value logic by rule-based technology. Fuzzy control system is comprised of Fuzzy inference engine that is performed based on the 'If-Then Rule'. The 'If-Then Rule' is of the following form. If Y1 is B11 and Y2 is B12 then Z is C1. If Y1 is B21 and Y2 is B22 then Z is C2. Y1 and Y2 are inputs, which indicate conditional variables. Z indicates decision-making and both B and C are linguistic variables. With this rule-based technique, the approximate reasoning can be realized by Fuzzy inference. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2013

Effect Of Oxidation On Chromium Leaching And Redox Capacity Of Slag-Containing Waste Forms

The rate of oxidation is important to the long-term performance of reducing salt waste forms because the solubility of some contaminants, e.g., technetium, is a function of oxidation state. TcO 4 - in the salt solution is reduced to Tc(IV) and has been shown to react with ingredients in the waste form to precipitate low solubility sulfide and/or oxide phases [Shuh, et al., 1994, Shuh, et al., 2000, Shuh, et al., 2003]. Upon exposure to oxygen, the compounds containing Tc(IV) oxidize to the pertechnetate ion, Tc(VII)O 4 - , which is very soluble. Consequently the rate of technetium oxidation front advancement into a monolith and the technetium leaching profile as a function of depth from an exposed surface are important to waste form performance and ground water concentration predictions. An approach for measuring contaminant oxidation rate (effective contaminant specific oxidation rate) based on leaching of select contaminants of concern is described in this report. In addition, the relationship between reduction capacity and contaminant oxidation is addressed. Chromate was used as a non-radioactive surrogate for pertechnetate in simulated waste form samples. Depth discrete subsamples were cut from material exposed to Savannah River Site (SRS) field cured conditions. The subsamples were prepared and analyzed for both reduction capacity and chromium leachability. Results from field-cured samples indicate that the depth at which leachable chromium was detected advanced further into the sample exposed for 302 days compared to the sample exposed to air for 118 days (at least 50 mm compared to at least 20 mm). Data for only two exposure time intervals is currently available. Data for additional exposure times are required to develop an equation for the oxidation front progression. Reduction capacity measurements (per the Angus-Glasser method, which is a measurement of the ability of a material to chemically reduce Ce(IV) to Ce(III) in solution) performed on depth discrete samples could not be correlated with the amount of chromium leached from the depth discrete subsamples or with the oxidation front inferred from soluble chromium (i.e., effective Cr oxidation front). Exposure to oxygen (air or oxygen dissolved in water) results in the release of chromium through oxidation of Cr(III) to highly soluble chromate, Cr(VI). Residual reduction capacity in the oxidized region of the test samples indicates that the remaining reduction capacity is not effective in re-reducing Cr(VI) in the presence of oxygen. Consequently, this method for determining reduction capacity may not be a good indicator of the effective contaminant oxidation rate in a relatively porous solid (40 to 60 volume percent porosity). The chromium extracted in depth discrete samples ranged from a maximum of about 5.8 % at about 5 mm (118 day exposure) to about 4 % at about 10 mm (302 day exposure). The use of reduction capacity as an indicator of long-term performance requires further investigation. The carbonation front was also estimated to have advanced to at least 28 mm in 302 days based on visual observation of gas evolution during acid addition during the reduction capacity measurements. Depth discrete sampling of materials exposed to realistic conditions in combination with short term leaching of crushed samples has potential for advancing the understanding of factors influencing performance and will support conceptual model development.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2016

Release history and transport parameters of relativistic solar electrons inferred from near-the-sun in situ observations

We study four consecutive 300–800 keV electron events observed on 1980 May 28 by Helios-1, when the spacecraft was located at 0.31 au from the Sun. We use two different techniques to extract the release time history of electrons at the Sun: (1) a data-driven method based on the assumption that particles conserve their magnetic moment as they propagate between the Sun and the spacecraft and (2) an inversion method that utilizes particle transport simulation results. Both methods make use of the particle angular distributions measured relative to the local direction of the magnetic field. The general characteristics of the release time profiles obtained by these two techniques are similar, especially during their rising phases. We find indications that the strength of the interplanetary scattering varies with the size of the solar parent event, suggesting that scattering processes are not necessarily an inherent property of the medium but are related to the amount of released particles at the Sun. We use the inferred release profiles to compute the expected intensities at 1 au. In contrast to simultaneous near-Earth observations by the Interplanetary Monitoring Platform (IMP-8), our simulations predict the observation of four separate events at 1 au. Processes that could contribute to the observation of one single time-extended event at 1 au include (1) distinct magnetic connections of the spacecraft to the particle sources, (2) the spatio-temporal evolution of the particle sources, and (3) different particle transport conditions, including a variation of λ{sub r} with radial distance and/or heliolongitude, as well as the possibility that electrons reached IMP-8 by diffusion perpendicular to the interplanetary magnetic field.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRSDate not supplied

Using Federated Learning to Overcome Data Gravity in Space

Humans intend to take longer missions to outer space. Understanding the impact that space has on human health is paramount to the success of these missions. Controlled experiments with model organisms are run to infer the impact of space conditions on human health, but the data these experiments generate are too large to transfer to Earth for building models. The same is true for space-relevant data generated on Earth. Ideally, these datasets should be combined to improve statistical power and model accuracy without having to transfer data. Federated learning is such a method which trains an algorithm across decentralized computing systems, each of which has their own local copy of training and testing data. In this research, made possible by NASA@Work, the AI for Life in Space group at NASA demonstrates the use of federated learning to train an ensemble of causality inference models on a combination of data residing on the International Space Station (ISS) and in the cloud. Our work leverages CRISP, a causal inference platform developed during the 2020 Frontier Development Lab’s “Astronaut Health Challenge.” We also leverage the OpenFL federated learning library which was collaboratively developed at Intel and UPenn. We used publicly available data from the NASA Ames Life Sciences Data Archive to identify features in ionizing radiation experiments as causal of changes in cardiac blood velocity. This research demonstrates, for the first time, the possibility of running machine learning algorithms on datasets separated by astronomical distances. In this experiment, all the data were generated in terra, half of which were transferred to the ISS and analyzed on the Spaceborne Computer. In the future, our research will leverage federated learning on data generated in situ on the ISS with data generated terrestrially to predict the impact of spaceflight on mammalian female reproductive capacity.

James Casaletto↗
DOE OSTI2022

Simultaneous single-shot rotation–vibration non-equilibrium thermometry using pure rotational fs/ps CARS coherence beating

Here, we report the development of a simple and sensitive two-beam hybrid femtosecond/picosecond pure rotational coherent anti-Stokes Raman scattering (fs/ps CARS) method to simultaneously measure the rotational and vibrational temperatures of diatomic molecules. Rotation–vibration non-equilibrium plays a key role in the chemistry and thermalization in low-temperature plasmas as well as thermal loading of hypersonic vehicles. This approach uses time-domain interferences between ground state and vibrationally excited N 2 molecules to intentionally induce coherence beating that leads to apparent non-Boltzmann distributions in the pure rotational spectra. These distortions enable simultaneous inference of both the rotational and vibrational temperatures. Coherence beating effects were observed in single-shot fs/ps CARS measurements of a 75 Torr N 2 DC glow discharge and were successfully modeled for rotational and vibrational temperature extraction. We show that this method can be more sensitive than a pure rotational fs/ps CARS approach using a spectrally narrow probe pulse. Lastly, we experimentally measured the beat frequencies via Fourier transform of the time-domain response and obtained excellent agreement with the model.

42 ENGINEERING↗
DOE OSTI2016

Structural, electronic and elastic properties of RERu{sub 2} (RE=Pr and Nd) Laves phase intermetallic compounds

We have performed the first-principles calculations to study the structural, electronic and elastic properties of RERu{sub 2} (RE = Pr and Nd) Laves phase intermetallic compounds using full-potential linearized augmented plane wave (FP-LAPW) method based on density functional theory (DFT) within the generalized gradient approximation (GGA) for exchange and correlation potential. The optimized lattices constant are in reasonable agreement with available experimental data. The electronic properties are analyzed in terms of band structures, total and partial density of states, which confirm their metallic character. The calculated elastic constants infer that these compounds are mechanically stable in C15 (MgCu{sub 2} type) structure and found to be ductile in nature.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Label Assist: Personalized Travel Models for Longitudinal Data Collection

Understanding travel behavior is crucial to transportation decarbonization. OpenPATH is an open-source mobility platform which collects and analyzes human travel behavior at the individual level. The mobile application passively senses trips and prompts users to label them. However, users find the labeling process burdensome; less than half the trips are typically labeled, making much of the data unusable in aggregate analyses of mobility patterns. Prior work has addressed the response fatigue challenge through automated mode inference using sensor data, but sensors cannot capture all aspects of travel behavior. We explore an alternative approach in which we leverage prior user input to predict travel choices in novel trips. We first explore trip clustering methods and develop a novel two-step pipeline using DBSCAN and SVMs to extract realistic geospatial clusters. We then propose two strategies to predict trip labels: (i) clustering trips and extrapolating labels for similar trips, and (ii) random forest classification. The random forest approach is able to achieve - $70-80% accuracy (purpose: 72%, mode: 79%, replaced mode: 81%). These novel approaches to trip classification allow us to increase the rate of user labeling by suggesting predicted labels to be verified by the user. Unlabeled trips can also contribute to aggregate analyses, using label predictions and their associated confidences as a substitute. While there exist other travel survey apps with the ability to infer travel choices, to our knowledge, this is the first paper to describe such a supervised system and rigorously evaluate it.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗
DOE OSTI2014

SU-E-T-249: Neutron Model Upgrade for Radiotherapy Patients Monitoring Using a New Online Detector

Purpose: The purpose of this work is to improve the existing methodology to estimate neutron equivalent dose in organs during radiotherapy treatments, based on a Static Random Access Memory neutron detector (SRAMnd) [1]. This is possible thanks to the introduction of a new digital detector with improved characteristics, which is able to measure online the neutron fluence rate in the presence of an intense photon background [2]. Its reduced size, allows the direct estimation of doses in specific points inside an anthropomorphic phantom (NORMA) without using passive detectors as TLD or CR-39. This versatility will allow not only to improve the existing models (generic abdomen and H and N [1]) but to generate more specific ones for any technique. Methods: The new Thermal Neutron Rate Detector (TNRD), based on a diode device sensitized to thermal neutrons, have been inserted in 16 points of the phantom. These points are distributed to infer doses to specific organs. Simultaneous measurements of these devices and a reference one, located in front of the gantry, have been performed for the mentioned generic treatments, in order to improve the existing model. Results: These new devices have shown more precise since they agree better with Monte Carlo simulations. The comparison of the thermal neutron fluence, measured with TNRD, and the existing models, converted from events to fluence, shows an average improvement of (3.90±3.37) % for H and N and (12.61±9.43) % for abdomen, normalized to the maximum value. Conclusion: This work indicates the potential of these new devices for more precise neutron equivalent dose estimation in organs, as a consequence of radiotherapy treatments. The simplicity of the process makes possible to establish more specific models that will provide a better dose estimation. References[1] Phys Med Biol 2012; 57:6167–6191.[2] A new active thermal neutron detector. Radiat. Prot. Dosim. (in press)

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Impact of three-dimensional hot-spot flow asymmetry on ion-temperature measurements in inertial confinement fusion experiments

Three-dimensional (3-D) implosion asymmetries lead to signficant variations in ion-temperature measurements in inertial confinement fusion experiments. We present an analytical method to generalize the physical properties of velocity variance in the Brysk ion-temperature model. This analysis provides a consistent explanation for the 3-D effects of inferred ion-temperature variations for various single modes and multimodes modeled by the deceleration-phase hydrocode DEC3D and the neutron transport code IRIS3D . The effect of the hot-spot ow asymmetry on variations in ion-temperature measurement is shown uniquely determined by a complete set of six hot-spot flow parameters. An approximated solution to the minimum inferred ion temperature is derived and shown to be close to the thermal ion temperature for low mode l = 1, which exhibits the largest anisotropic velocity variance in the single-mode spectrum. In conclusion, the isotropic velocity variance for low mode l = 2 is shown to result in the minimum inferred ion temperatures being well above the thermal ion temperature.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

A Bayesian Approach to Regional Decadal Predictability: Sparse Parameter Estimation in High-Dimensional Linear Inverse Models of High-Latitude Sea Surface Temperature Variability

Stochastic reduced models are an important tool in climate systems whose many spatial and temporal scales cannot be fully discretized or underlying physics may not be fully accounted for. One form of reduced model, the linear inverse model (LIM), has been widely used for regional climate predictability studies—typically focusing more on tropical or midlatitude studies. However, most LIM fitting techniques rely on point estimation techniques deriving from fluctuation–dissipation theory. In this methodological study we explore the use of Bayesian inference techniques for LIM parameter estimation of sea surface temperature (SST), to quantify the skillful decadal predictability of Bayesian LIM models at high latitudes. We show that Bayesian methods, when compared to traditional point estimation methods for LIM-type models, provide better calibrated probabilistic skill, while simultaneously providing better point estimates due to the regularization effect of the prior distribution in high-dimensional problems. We compare the effect of several priors, as well as maximum likelihood estimates, on 1) estimating parameter values on a perfect model experiment and 2) producing calibrated 1-yr SST anomaly forecast distributions using a preindustrial control run of the Community Earth System Model (CESM). Finally, we employ a host of probabilistic skill metrics to determine the extent to which an LIM can forecast SST anomalies at high latitudes. We find that the choice of prior distribution has an appreciable impact on estimation outcomes, and priors that emphasize physically relevant properties enhance the model’s ability to capture variability of SST anomalies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

2020 ETI Annual Summer School: Data Science and Engineering

The Consortium for Enabling Technologies & Innovation (ETI) was established in 2019 to address emerging technologies within the context of nuclear nonproliferation. ETI creates a research and education environment to support cross-cutting technologies across three core disciplines: 1) computer and engineering science research specifically in a form of machine learning and high performance computing (HPC), 2) advanced manufacturing, and 3) nuclear detection technologies. For outreach and development, ETI hosted the first of three summer schools from August 24-28, 2020 with the theme of “Data Science and Engineering”. The school was hosted in an on-line format and had over 200 participants. The recorded content is available on-line as a resource for students. The summer school had four modules: 1) Fundamentals of data Applications, 2) Computational Machine Learning, 3) Bayesian Modeling and Inference, and 4) Data Science for Safeguards. Modules contained both lectures as well as student exercises. Poll Everywhere was utilized in some modules as an on-line method to engage large groups of students. Upcoming ETI Summer Schools include Novel Instrumentation in 2021 and Advanced Manufacturing in 2022.

Biegalski, Steven R.↗
DOE OSTI2022

Towards Quantitative Inference of Nanoscale Defects in Irradiated Metals and Alloys

Quantifying the population of nanoscale defects that are formed in metals and alloys exposed to extreme radiation environments remains a pressing challenge in materials science. These defects both fundamentally alter material properties and seed long-timescale performance degradation, which often limits the lifespan of engineering systems. Unlike ceramic and semiconducting materials, these defects in metals and alloys are not spectroscopically active, forcing characterization to rely on indirect measurements from which the distribution of nanoscale defects may be inferred. In this mini-review, different experimental methodologies which have been employed for defect inference are highlighted to capture the current state of the art. Future directions in this area are proposed, which, by combining data streams from multiple and complementary characterization methods in concert with multi-scale modeling and simulation, will enable the ultimate goal of quantifying the full spectrum of defects in irradiated metals and alloys.

Hirst, Charles A.↗
DOE OSTI2019

Single-throughput Complementary High-resolution Analytical Techniques for Characterizing Complex Natural Organic Matter Mixtures

Natural organic matter (NOM) is composed of a highly complex mixture of thousands of organic compounds which, historically, proved difficult to characterize. However, to understand the thermodynamic and kinetic controls on greenhouse gas (carbon dioxide [CO 2 ] and methane [CH 4 ]) production resulting from the decomposition of NOM, a molecular-level characterization coupled with microbial proteome analyses is necessary. Further, climate and environmental changes are expected to perturb natural ecosystems, potentially upsetting complex interactions that influence both the supply of organic matter substrates and the microorganisms performing the transformations. A detailed molecular characterization of the organic matter, microbial proteomics, and the pathways and transformations by which organic matter is decomposed will be necessary to predict the direction and magnitude of the effects of environmental changes. This article describes a methodological throughput for comprehensive metabolite characterization in a single sample by direct injection Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS), gas chromatography mass spectrometry (GC-MS), nuclear magnetic resonance (NMR) spectroscopy, liquid chromatography mass spectrometry (LC-MS), and proteomics analysis. Furthermore, this approach results in a fully-paired dataset which improves statistical confidence for inferring pathways of organic matter decomposition, the resulting CO 2 and CH 4 production rates, and their responses to environmental perturbation. Herein we present results of applying this method to NOM samples collected from peatlands; however, the protocol is applicable to any NOM sample (e.g., peat, forested soils, marine sediments, etc.).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1992

C Language Integrated Production System, Ada Version

CLIPS/Ada provides capabilities of CLIPS v4.3 but uses Ada as source language for CLIPS executable code. Implements forward-chaining rule-based language. Program contains inference engine and language syntax providing framework for construction of expert-system program. Also includes features for debugging application program. Based on Rete algorithm which provides efficient method for performing repeated matching of patterns. Written in Ada.

Culbert, Chris↗
NASA NTRS1999

Human sympathetic and vagal baroreflex responses to sequential nitroprusside and phenylephrine

We evaluated a method of baroreflex testing involving sequential intravenous bolus injections of nitroprusside followed by phenylephrine and phenylephrine followed by nitroprusside in 18 healthy men and women, and we drew inferences regarding human sympathetic and vagal baroreflex mechanisms. We recorded the electrocardiogram, photoplethysmographic finger arterial pressure, and peroneal nerve muscle sympathetic activity. We then contrasted least squares linear regression slopes derived from the depressor (nitroprusside) and pressor (phenylephrine) phases with 1) slopes derived from spontaneous fluctuations of systolic arterial pressures and R-R intervals, and 2) baroreflex gain derived from cross-spectral analyses of systolic pressures and R-R intervals. We calculated sympathetic baroreflex gain from integrated muscle sympathetic nerve activity and diastolic pressures. We found that vagal baroreflex slopes are less when arterial pressures are falling than when they are rising and that this hysteresis exists over pressure ranges both below and above baseline levels. Although pharmacological and spontaneous vagal baroreflex responses correlate closely, pharmacological baroreflex slopes tend to be lower than those derived from spontaneous fluctuations. Sympathetic baroreflex slopes are similar when arterial pressure is falling and rising; however, small pressure elevations above baseline silence sympathetic motoneurons. Vagal, but not sympathetic baroreflex gains vary inversely with subjects' ages and their baseline arterial pressures. There is no correlation between sympathetic and vagal baroreflex gains. We recommend repeated sequential nitroprusside followed by phenylephrine doses as a simple, efficientmeans to provoke and characterize human vagal and sympathetic baroreflex responses.

NASA Discipline Cardiopulmonary↗
DOE OSTI2020

Flow-based likelihoods for non-Gaussian inference

We investigate the use of data-driven likelihoods to bypass a key assumption made in many scientific analyses, which is that the true likelihood of the data is Gaussian. In particular, we suggest using the optimization targets of flow-based generative models, a class of models that can capture complex distributions by transforming a simple base distribution through layers of nonlinearities. We call these flow-based likelihoods (FBL). We analyze the accuracy and precision of the reconstructed likelihoods on mock Gaussian data, and show that simply gauging the quality of samples drawn from the trained model is not a sufficient indicator that the true likelihood has been learned. We nevertheless demonstrate that the likelihood can be reconstructed to a precision equal to that of sampling error due to a finite sample size. We then apply FBLs to mock weak lensing convergence power spectra, a cosmological observable that is significantly non-Gaussian (NG). We find that the FBL captures the NG signatures in the data extremely well, while other commonly used data-driven likelihoods, such as Gaussian mixture models and independent component analysis, fail to do so. This suggests that works that have found small posterior shifts in NG data with data-driven likelihoods such as these could be underestimating the impact of non-Gaussianity in parameter constraints. By introducing a suite of tests that can capture different levels of NG in the data, we show that the success or failure of traditional data-driven likelihoods can be tied back to the structure of the NG in the data. Here, unlike other methods, the flexibility of the FBL makes it successful at tackling different types of NG simultaneously. Because of this, and consequently their likely applicability across datasets and domains, we encourage their use for inference when sufficient mock data are available for training.

79 ASTRONOMY AND ASTROPHYSICS↗