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DOE OSTI2018

Universality Classes of Interaction Structures for NK Fitness Landscapes

Kauffman’s NK-model is a paradigmatic example of a class of stochastic models of genotypic fitness landscapes that aim to capture generic features of epistatic interactions in multilocus systems. Genotypes are represented as sequences of L binary loci. The fitness assigned to a genotype is a sum of contributions, each of which is a random function defined on a subset of k≤L loci. These subsets or neighborhoods determine the genetic interactions of the model. Whereas earlier work on the NK model suggested that most of its properties are robust with regard to the choice of neighborhoods, recent work has revealed an important and sometimes counter-intuitive influence of the interaction structure on the properties of NK fitness landscapes. Here we review these developments and present new results concerning the number of local fitness maxima and the statistics of selectively accessible (that is, fitness-monotonic) mutational pathways. In particular, we develop a unified framework for computing the exponential growth rate of the expected number of local fitness maxima as a function of L, and identify two different universality classes of interaction structures that display different asymptotics of this quantity for large k. Moreover, we show that the probability that the fitness landscape can be traversed along an accessible path decreases exponentially in L for a large class of interaction structures that we characterize as locally bounded. Finally, we discuss the impact of the NK interaction structures on the dynamics of evolution using adaptive walk models.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

Propagation of priors for more accurate and efficient spectroscopic functional fits and their application to ferroelectric hysteresis

Multi-dimensional spectral-imaging is a mainstay of the scanning probe and electron microscopies, micro-Raman, and various forms of chemical imaging. In many cases, individual spectra can be fit to a specific functional form, with the model parameter maps, providing direct insight into material properties. Since spectra are often acquired across a spatial grid of points, spatially adjacent spectra are likely to be similar to one another; yet, this fact is almost never used when considering parameter estimation for functional fits. On datasets tried here, we show that by utilizing proximal information, whether it be in the spatial or spectral domains, it is possible to improve the reliability and increase the speed of such functional fits by ~2-3x, as compared to random priors. We explore and compare three distinct new methods: (1) spatially averaging neighborhood spectra, and propagating priors based on functional fits to the averaged case, (2) hierarchical clustering-based methods where spectra are grouped hierarchically based on response, with the priors propagated progressively down the hierarchy, and (3) regular clustering without hierarchical methods with priors propagated from fits to cluster means. Our results highlight that utilizing spatial and spectral neighborhood information is often critical for accurate parameter estimation in noisy environments, which we show for ferroelectric hysteresis loops acquired on a prototypical PbTiO3 thin film with piezoresponse spectroscopy. This method is general and applicable to any spatially measured spectra where functional forms are available. Examples include exploring the superconducting gap with tunneling spectroscopy, using the Dynes formula, or current-voltage curve fits in conductive atomic force microscopy mapping. Here we explore the problem for ferroelectric hysteresis, which, given its large parameter space, constitutes a more difficult task than, for example, fitting current-voltage curves with a Schottky emission formula.

42 ENGINEERING↗

DOE OSTI2023

Insufficient reporting of x-ray photoelectron spectroscopy instrumental and peak fitting parameters (metadata) in the scientific literature

This study was motivated by earlier observations. It is a systematic examination of the adequacy of reporting of information (metadata) necessary to understand x-ray photoelectron spectroscopy (XPS) data collection and data analysis in the scientific literature. The information for this study was obtained from papers published in three high-quality journals over a six-month period in 2019 and throughout 2021. Each paper was evaluated to determine whether the authors had reported (percentages of the papers properly providing the information are given in parentheses) the spectrometer (66%), fitting software (15%), x-ray source (40%), pass energy (10%), spot size (5%), synthetic peak shapes in fits (10%), backgrounds in fits (10%), whether the XPS data are shown in the main body of the paper or in the supporting information (or both), and whether fitted or unfitted spectra were shown (80% of published spectra are fit). The Shirley background is the most widely used background in XPS peak fitting. The Al Kα source is the most widely used x-ray source for XPS data collection. CASAXPS is the most widely used fitting program for XPS data analysis. Further, there is good agreement between the results gathered during the two years of our survey. There are some hints the situation may be improving. This study also provides a list of the information/parameters that should be reported when XPS is performed.

47 OTHER INSTRUMENTATION↗

DOE OSTI2015

Rapid quantification of mutant fitness in diverse bacteria by sequencing randomly bar-coded transposons

Transposon mutagenesis with next-generation sequencing (TnSeq) is a powerful approach to annotate gene function in bacteria, but existing protocols for TnSeq require laborious preparation of every sample before sequencing. Thus, the existing protocols are not amenable to the throughput necessary to identify phenotypes and functions for the majority of genes in diverse bacteria. Here, we present a method, random bar code transposon-site sequencing (RB-TnSeq), which increases the throughput of mutant fitness profiling by incorporating random DNA bar codes into Tn5 and mariner transposons and by using bar code sequencing (BarSeq) to assay mutant fitness. RB-TnSeq can be used with any transposon, and TnSeq is performed once per organism instead of once per sample. Each BarSeq assay requires only a simple PCR, and 48 to 96 samples can be sequenced on one lane of an Illumina HiSeq system. We demonstrate the reproducibility and biological significance of RB-TnSeq with Escherichia coli , Phaeobacter inhibens , Pseudomonas stutzeri , Shewanella amazonensis , and Shewanella oneidensis . To demonstrate the increased throughput of RB-TnSeq, we performed 387 successful genome-wide mutant fitness assays representing 130 different bacterium-carbon source combinations and identified 5,196 genes with significant phenotypes across the five bacteria. In P. inhibens , we used our mutant fitness data to identify genes important for the utilization of diverse carbon substrates, including a putative D-mannose isomerase that is required for mannitol catabolism. RB-TnSeq will enable the cost-effective functional annotation of diverse bacteria using mutant fitness profiling. A large challenge in microbiology is the functional assessment of the millions of uncharacterized genes identified by genome sequencing. Transposon mutagenesis coupled to next-generation sequencing (TnSeq) is a powerful approach to assign phenotypes and functions to genes. However, the current strategies for TnSeq are too laborious to be applied to hundreds of experimental conditions across multiple bacteria. Here, we describe an approach, random bar code transposon-site sequencing (RB-TnSeq), which greatly simplifies the measurement of gene fitness by using bar code sequencing (BarSeq) to monitor the abundance of mutants. We performed 387 genome-wide fitness assays across five bacteria and identified phenotypes for over 5,000 genes. RB-TnSeq can be applied to diverse bacteria and is a powerful tool to annotate uncharacterized genes using phenotype data.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Predicting the viability of beta-lactamase: How folding and binding free energies correlate with beta-lactamase fitness

One of the long-standing holy grails of molecular evolution has been the ability to predict an organism’s fitness directly from its genotype. With such predictive abilities in hand, researchers would be able to more accurately forecast how organisms will evolve and how proteins with novel functions could be engineered, leading to revolutionary advances in medicine and biotechnology. In this work, we assemble the largest reported set of experimental TEM-1 β-lactamase folding free energies and use this data in conjunction with previously acquired fitness data and computational free energy predictions to determine how much of the fitness of β-lactamase can be directly predicted by thermodynamic folding and binding free energies. We focus upon β-lactamase because of its long history as a model enzyme and its central role in antibiotic resistance. Based upon a set of 21 β-lactamase single and double mutants expressly designed to influence protein folding, we first demonstrate that modeling software designed to compute folding free energies such as FoldX and PyRosetta can meaningfully, although not perfectly, predict the experimental folding free energies of single mutants. Interestingly, while these techniques also yield sensible double mutant free energies, we show that they do so for the wrong physical reasons. We then go on to assess how well both experimental and computational folding free energies explain single mutant fitness. We find that folding free energies account for, at most, 24% of the variance in β-lactamase fitness values according to linear models and, somewhat surprisingly, complementing folding free energies with computationally-predicted binding free energies of residues near the active site only increases the folding-only figure by a few percent. This strongly suggests that the majority of β-lactamase’s fitness is controlled by factors other than free energies. Overall, our results shed a bright light on to what extent the community is justified in using thermodynamic measures to infer protein fitness as well as how applicable modern computational techniques for predicting free energies will be to the large data sets of multiply-mutated proteins forthcoming.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2017

Data from: Crop-associated virus infection in a native perennial grass: reduction in plant fitness and dynamic patterns of virus detection

To understand the eco-evolutionary significance of plant viruses in nature, we must (i) quantify the effects of infection on plant fitness and (ii) recognize that native plants are increasingly exposed to crop-associated viruses. Studies of perennials are particularly needed: most of our knowledge of plant-virus interactions is from annuals, yet long-lived species dominate landscapes. Here we used aster models for life-history analysis and longitudinal measures of plant virus status to evaluate multi-year consequences of crop virus infection in a native perennial. We used Barley yellow dwarf virus acquired from wheat to inoculate seedlings of Panicum virgatum L. (switchgrass), a North American prairie grass. We grew inoculated and mock-inoculated individuals of two ecotypes for 3 years in the field. We measured plant size, infection status and fitness components. Aster modelling provided integrated multi-year measures of fitness. Crop virus inoculation reduced multi-year native plant fitness by 30% over 2 years despite generally asymptomatic infection and evidence of resistance. This reduction was greater than predicted from individual fitness components or most size measures. Ecotypes differed in response, with the lowland ecotype experiencing higher apparent recovery from infection. Virus treatment in the upland ecotype delayed flowering phenology and reduced seed filling. Synthesis. Our use of field experimentation, surveys of plant infection status and aster modelling demonstrates a rigorous and broadly applicable approach for quantifying the effects of viruses and other microbes on multi-year plant fitness. We found that a crop virus had negative multi-year effects on native plant fitness even after infection was no longer detected. Viruses may have substantial effects on native vegetation with domestication of landscapes and agricultural expansion.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS1969

Methods of Achieving and Maintaining Physical Fitness for Prolonged Space Flight

This final summary report covers the five experiments that were conducted over a 24-month period beginning May 1, 1967 and ending April 30, 1969. Experiment I revealed that running and riding a bicycle ergometer produced similar gains in physical fitness variables. In Experiment I the subjects exercising at a 180 heart rate made a greater improvement in physical fitness than did those exercising a t a 140 or 160 heart rate. In Experiment II the subjects who exercised sixty minutes per day made greater gains on specified components of physical fitness than did those who exercised twenty or forty minutes per day, twelve times per week made greater gains on specified components of physical fitness than did those who exercised three or six times per week. In Experiment V, it was found that subjects could maintain a moderate level of fitness by exercising at a pulse rate of 160 beats per minute for twenty-minute periods three times per week, that subjects who "overtrained" by exercising twice daily to near exhaustion increased in fitness and that those subjects who discontinued training decreased in fitness.

Olree, Harry D.↗

NASA NTRS1987

Simplified curve fits for the thermodynamic properties of equilibrium air

New, improved curve fits for the thermodynamic properties of equilibrium air have been developed. The curve fits are for pressure, speed of sound, temperature, entropy, enthalpy, density, and internal energy. These curve fits can be readily incorporated into new or existing computational fluid dynamics codes if real gas effects are desired. The curve fits are constructed from Grabau-type transition functions to model the thermodynamic surfaces in a piecewise manner. The accuracies and continuity of these curve fits are substantially improved over those of previous curve fits. These improvements are due to the incorporation of a small number of additional terms in the approximating polynomials and careful choices of the transition functions. The ranges of validity of the new curve fits are temperatures up to 25 000 K and densities from 10 to the -7 to 10 to the 3d power amagats.

Srinivasan, S.↗

NASA NTRS1986

Simplified curve fits for the thermodynamic properties of equilibrium air

New improved curve fits for the thermodynamic properties of equilibrium air were developed. The curve fits are for p = p(e,rho), a = a(e,rho), T = T(e,rho), s = s(e,rho), T = T(p,rho), h = h(p,rho), rho = rho(p,s), e = e(p,s) and a = a(p,s). These curve fits can be readily incorporated into new or existing Computational Fluid Dynamics (CFD) codes if real-gas effects are desired. The curve fits were constructed using Grabau-type transition functions to model the thermodynamic surfaces in a piecewise manner. The accuracies and continuity of these curve fits are substantially improved over those of previous curve fits appearing in NASA CR-2470. These improvements were due to the incorporation of a small number of additional terms in the approximating polynomials and careful choices of the transition functions. The ranges of validity of the new curve fits are temperatures up to 25,000 K and densities from 10 to the minus 7th to 100 amagats (rho/rho sub 0).

Srinivasan, S.↗

NASA NTRS1994

Microconical interface fitting and interface grasping tool

A small and light weight microconical interface fitting may be attached to the surface of a space vehicle or equipment to provide an attachment device for an astronaut or robot to capture the space vehicle or equipment. The microconical interface fitting of the present invention has an axisymmetrical conical body having a base portion with a torque reaction surface for preventing rotation of the interface grasping tool; a cavitated, sunken or hollowed out intermediate locking portion which has a cavity shaped for receiving the latches of the grasping tool and an upper guiding portion for guiding the grasping tool into axial alignment with the microconical interface fitting. The capture is accomplished with an interface grasping tool. The grasping tool comprises an outer sleeve with a handle attached, an inner sleeve which may be raised and lowered within the outer sleeve with a plurality of latches supported at the lower end and a cam to raise and lower the inner sleeve. When the inner sleeve is at its lowest position, the latches form the largest diameter opening for surrounding the microconical fitting and the latches form the smallest diameter or a locking, grasping position when raised to the highest position within the outer sleeve. The inner sleeve may be at an intermediate, capture position which permits the latches to be biased outwardly when contacting the microconical fitting under very low forces to grasp the fitting and permits capture (soft docking) without exact alignment of the fitting and the tool.

Gernhardt, Michael L.↗

NASA NTRS2008

Fpack and Funpack Utilities for FITS Image Compression and Uncompression

Fpack is a utility program for optimally compressing images in the FITS (Flexible Image Transport System) data format (see http://fits.gsfc.nasa.gov). The associated funpack program restores the compressed image file back to its original state (as long as a lossless compression algorithm is used). These programs may be run from the host operating system command line and are analogous to the gzip and gunzip utility programs except that they are optimized for FITS format images and offer a wider choice of compression algorithms. Fpack stores the compressed image using the FITS tiled image compression convention (see http://fits.gsfc.nasa.gov/fits_registry.html). Under this convention, the image is first divided into a user-configurable grid of rectangular tiles, and then each tile is individually compressed and stored in a variable-length array column in a FITS binary table. By default, fpack usually adopts a row-by-row tiling pattern. The FITS image header keywords remain uncompressed for fast access by FITS reading and writing software. The tiled image compression convention can in principle support any number of different compression algorithms. The fpack and funpack utilities call on routines in the CFITSIO library (http://hesarc.gsfc.nasa.gov/fitsio) to perform the actual compression and uncompression of the FITS images, which currently supports the GZIP, Rice, H-compress, and PLIO IRAF pixel list compression algorithms.

Pence, W.↗

DOE OSTI2026

Measurements of three-flavor neutrino oscillations from a PISCES two-detector fit to the NOvA Experiment data

NOvA is a long-baseline neutrino oscillation experiment with two functionally identical detectors: a Near Detector (ND) at Fermilab, placed 1 km from the neutrino source, and a Far Detector (FD) located 810 km away from the ND in Minnesota. NOvA s primary physics goals are to measure the neutrino oscillation parameters $\theta_{23}$ and $\Delta m^2_{32}$ with high precision, determine the neutrino mass hierarchy, and constrain the value of $\delta_{CP}$, primarily via the study of muon neutrino to electron neutrino oscillation. Extracting values for oscillation parameters from fits to data usually relies on treating systematic uncertainties as nuisance parameters, a strategy that suffers from poor scalability as the number of uncertainties becomes larger. This work introduces PISCES (Parameter Inference with Systematic Covariance and Exact Statistics), a novel method that circumvents this scalability problem by encoding systematic uncertainties into a covariance matrix. PISCES utilizes a nested minimization in which optimal systematic pulls are first computed using the covariance matrix in an inner minimization step, then the oscillation parameters are profiled over in the outer minimization. PISCES also uses a Poisson Likelihood term, making it ideal for the inclusion of low-statistic samples in the fits. PISCES is a flexible framework that also supports complex fits, such as a joint Near and Far detector fit. In the standard NOvA analysis, oscillation parameters are extracted using an extrapolation technique in which the ND data indirectly constrain the FD prediction via a ratio method. PISCES, on the other hand, enables a simultaneous ND+FD fit, allowing the high-statistics ND data to directly constrain systematic uncertainties across all samples. This thesis presents the full PISCES joint ND+FD fit for the NOvA three-flavor analysis, details its implementation, and evaluates its performance through extensive robustness tests and fake data studies. It also provides a comparison between the PISCES joint ND+FD results and the standard NOvA extrapolation method using the full NOvA 10-year data set. The results demonstrate that PISCES can successfully fit NOvA data while incorporating the constraints from the ND detectors consistently, using physically motivated systematic uncertainties to account for data/MC discrepancies.

Rajaoalisoa, Miriama [Cincinnati U.]↗

DOE OSTI2017

Automated single-ion peak fitting as an efficient approach for analyzing complex chromatographic data

Chromatography provides important detail on the composition of environmental samples and their chemical processing. However, the complexity of these samples and their tendency to contain many structurally and chemically similar compounds frequently results in convoluted or poorly resolved data. Data reduction from raw chromatograms of complex environmental data into integrated peak areas consequently often requires substantial operator interaction. This difficulty has led to a bottleneck in analysis that increases analysis time, decreases data quality, and will worsen as advances in field-based instrumentation multiply the quantity and informational density of data produced. In this work, we develop and validate an automated approach to fitting chromatographic data within a target retention time window with a combination of multiple idealized peaks (Gaussian peaks either with or without an exponential decay component). Herein we compare this single-ion peak fitting approach to drawn baseline integration methods of more than 70,000 peaks collected by field-based chromatographs spanning across a wide range of volatilities and functionalities. Accuracy of peak fitting under real-world conditions is found to be within 10%. The quantitative parameters describing the fit (e.g. coefficients, fit residuals, etc.) are found to provide valuable information to increase the efficiency of quality control and provide constraints to accurately integrate peaks that are significantly convoluted with neighboring peaks. Implementation of the peak fitting method is shown to yield accurate integration of peaks otherwise too poorly resolved to separate into individual compounds and improved quantitative metrics to determine the fidelity of the data reduction process, while substantially decreasing the time spent by operators on data reduction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

High–throughput measurement of plant fitness traits with an object detection method using Faster R–CNN

Revealing the contributions of genes to plant phenotype is frequently challenging because loss-of-function effects may be subtle or masked by varying degrees of genetic redundancy. Such effects can potentially be detected by measuring plant fitness, which reflects the cumulative effects of genetic changes over the lifetime of a plant. However, fitness is challenging to measure accurately, particularly in species with high fecundity and relatively small propagule sizes such as Arabidopsis thaliana. An image segmentation-based method using the software ImageJ and an object detection-based method using the Faster Region-based Convolutional Neural Network (R-CNN) algorithm were used for measuring two Arabidopsis fitness traits: seed and fruit counts. The segmentation-based method was error-prone (correlation between true and predicted seed counts, r 2 = 0.849) because seeds touching each other were undercounted. By contrast, the object detection-based algorithm yielded near perfect seed counts (r 2 = 0.9996) and highly accurate fruit counts (r 2 = 0.980). Comparing seed counts for wild-type and 12 mutant lines revealed fitness effects for three genes; fruit counts revealed the same effects for two genes. Our study provides analysis pipelines and models to facilitate the investigation of Arabidopsis fitness traits and demonstrates the importance of examining fitness traits when studying gene functions.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2015

Application of Fireworks Algorithm in Gamma-Ray Spectrum Fitting for Radioisotope Identification

We present that identification of radioisotopic signature patterns in gamma-ray spectra is of paramount importance in various applications of gamma spectroscopy. Therefore, there are several active research efforts to develop accurate and precise methods to perform automated spectroscopic analysis and subsequently recognize gamma-ray signatures. In this work, the authors present a new method for radioisotope identification in gamma-ray spectra obtained with a low resolution radiation detector. The method fits the obtained spectrum with a linear combina-tion of known template signature patterns. Coefficients of the linear combination are evaluated by computing the solution of a single objective optimization problem, whose objective is the Theil-1 inequality coefficient. Optimization of the problem is performed by the Fireworks Algorithm, which identifies a set of coefficients that minimize the Theil-1 value. The computed coefficients are statistically tested for being significantly different than zero or not, and if at least one is found to be zero then the Fireworks Algorithm is used to reiterate fitting using the non-zero templates. Fitting iterations are continued up to the point that no linear coefficients are found to be zero. The output of the method is a list that contains the radioisotopes that have been identified in the measured spectrum. The method is tested on a set of both simulated and real experimental gamma-ray spectra comprised of a variety of isotopes, and compared to a multiple linear regression fitting, and genetic algorithm Theil-1 based fitting. Finally, results demonstrate the potentiality of the Fireworks Algorithm based method, expressed as higher accuracy and similar precision over the other two tested methodologies for radioisotope signature pattern identification in the framework of gamma-ray spectrum fitting.

Theil Coefficient↗

NASA NTRS2010

An Algorithm for the Fitting of Planet Models to Kepler Light Curves

We describe an algorithm which fits model planetary system parameters to light curves from Kepler Mission target stars. The algorithm begins by producing an initial model of the system which is used to seed the fit,with particular emphasis on obtaining good transit timing parameters. An attempt is then made to determine whether the observed transits are more likely due to a planet or an eclipsing binary. In the event that the transits are consistent with a transiting planet, an iterative fitting process is initiated: a wavelet-based whitening filter is used to eliminate stellar variations on timescales long compared to a transit; a robust nonlinear fitter operating on the whitened light curve produces a new model of the system; and the procedure iterates until convergence upon a self-consistent whitening filter and planet model. The fitted transits are removed from the light curve anda search for additional planet candidates is performed upon the residual light curve. The fitted models are used in additional tests which identify false positive planet detections: multiple planet candidates with near-identical fitted periods are far more likely to be an eclipsing binary, for example, while target stars in which the model lightcurve is correlated with the star centroid position may indicate a background eclipsing binary, and subtraction of all model planet candidates yields a light curve of pure noise and stellar variability, which can be used to study the probability that the planet candidates result from statistical fluctuations in the data.

Exoplanet↗

DOE OSTI2018

Collective Dispersal Leads to Variance in Fitness and Maintains Offspring Size Variation within Marine Populations

Variance in fitness is well known to influence the outcome of evolution but is rarely considered in the theory of marine reproductive strategies. In coastal environments, turbulent mesoscale eddies can collect larvae into packets, resulting in collective dispersal. Larvae in packets return to the coast or are lost offshore in groups, producing variance in fitness. Using a Markov process to calculate fixation probabilities for competing phenotypes, we examine the evolution of offspring size and spawning duration in species with benthic adults and pelagic offspring. The offspring size that provides mothers with the highest mean fitness also generates the greatest variance in fitness, but pairwise invasion plots show that bet-hedging strategies are not evolutionarily stable; maximizing expected fitness correctly predicts the unique evolutionarily stable strategy. Nonetheless, fixation can take a long time. We find that selection to increase spawning duration as a risk avoidance strategy to reduce the negative impacts of stochastic recruitment success can allow multiple offspring sizes to coexist in a population for extended periods. This has two important consequences for offspring size: (1) coexistence occurs over a broader range of sizes and is longer when spawning duration is longer because longer spawning durations reduce variation in fitness and increase the time to fixation, and (2) longer spawning durations can compensate for having a nonoptimal size and even allow less optimal sizes to reach fixation. Collective dispersal and longer spawning durations could effectively maintain offspring size variation even in the absence of good and bad years or locations. Finally, empirical comparisons of offspring size would therefore not always reflect environment-specific differences in the optimal size.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2015

Improved profile fitting and quantification of uncertainty in experimental measurements of impurity transport coefficients using Gaussian process regression

The need to fit smooth temperature and density profiles to discrete observations is ubiquitous in plasma physics, yet the prevailing techniques for this have many shortcomings that cast doubt on the statistical validity of the results. This issue is amplified in the context of validation of gyrokinetic transport models (Holland et al 2009 Phys. Plasmas 16 052301), where the strong sensitivity of the code outputs to input gradients means that inadequacies in the profile fitting technique can easily lead to an incorrect assessment of the degree of agreement with experimental measurements. In order to rectify the shortcomings of standard approaches to profile fitting, we have applied Gaussian process regression (GPR), a powerful non-parametric regression technique, to analyse an Alcator C-Mod L-mode discharge used for past gyrokinetic validation work (Howard et al 2012 Nucl. Fusion 52 063002). We demonstrate that the GPR techniques can reproduce the previous results while delivering more statistically rigorous fits and uncertainty estimates for both the value and the gradient of plasma profiles with an improved level of automation. Moreover, we discuss how the use of GPR can allow for dramatic increases in the rate of convergence of uncertainty propagation for any code that takes experimental profiles as inputs. The new GPR techniques for profile fitting and uncertainty propagation are quite useful and general, and we describe the steps to implementation in detail in this paper. These techniques have the potential to substantially improve the quality of uncertainty estimates on profile fits and the rate of convergence of uncertainty propagation, making them of great interest for wider use in fusion experiments and modelling efforts.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗