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DOE OSTI2016

A mass graph-based approach for the identification of modified proteoforms using top-down tandem mass spectra

Motivation: Although proteomics has rapidly developed in the past decade, researchers are still in the early stage of exploring the world of complex proteoforms, which are protein products with various primary structure alterations resulting from gene mutations, alternative splicing, post-translational modifications, and other biological processes. Proteoform identification is essential to mapping proteoforms to their biological functions as well as discovering novel proteoforms and new protein functions. Top-down mass spectrometry is the method of choice for identifying complex proteoforms because it provides a “bird’s eye view” of intact proteoforms. The combinatorial explosion of various alterations on a protein may result in billions of possible proteoforms, making proteoform identification a challenging computational problem. Results: We propose a new data structure, called the mass graph, for efficient representation of proteoforms and design mass graph alignment algorithms. We developed TopMG, a mass graph-based software tool for proteoform identification by top-down mass spectrometry. Experiments on top-down mass spectrometry data sets showed that TopMG outperformed existing methods in identifying complex proteoforms.

Dynamic Programming↗

DOE OSTI2022

Mapping variations of redshift distributions with probability integral transforms

We present a method for mapping variations between probability distribution functions and apply this method within the context of measuring galaxy redshift distributions from imaging survey data. This method, which we name PITPZ for the probability integral transformations it relies on, uses a difference in curves between distribution functions in an ensemble as a transformation to apply to another distribution function, thus transferring the variation in the ensemble to the latter distribution function. This procedure is broadly applicable to the problem of uncertainty propagation. In the context of redshift distributions, for example, the uncertainty contribution due to certain effects can be studied effectively only in simulations, thus necessitating a transfer of variation measured in simulations to the redshift distributions measured from data. We illustrate the use of PITPZ by using the method to propagate photometric calibration uncertainty to redshift distributions of the Dark Energy Survey Year 3 weak lensing source galaxies. For this test case, we find that PITPZ yields a lensing amplitude uncertainty estimate due to photometric calibration error within 1 per cent of the truth, compared to as much as a 30 per cent underestimate when using traditional methods.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

Photometry and astrometry with JWST – III. A NIRCam-Gaia DR3 analysis of the open cluster NGC 2506

ABSTRACT In the third paper of this series aimed at developing the tools for analysing resolved stellar populations using the cameras on board of the James Webb Space Telescope (JWST), we present a detailed multiband study of the 2 Gyr Galactic open cluster NGC 2506. We employ public calibration data sets collected in multiple filters to: (i) derive improved effective Point Spread Functions (ePSFs) for 10 NIRCam filters; (ii) extract high-precision photometry and astrometry for stars in the cluster, approaching the main sequence (MS) lower mass of ∼0.1 M⊙; and (iii) take advantage of the synergy between JWST and Gaia DR3 to perform a comprehensive analysis of the cluster’s global and local properties. We derived a MS binary fraction of ∼57.5 per cent, extending the Gaia limit (∼0.8 M⊙) to lower masses (∼0.4 M⊙) with JWST. We conducted a study on the mass functions (MFs) of NGC 2506, mapping the mass segregation with Gaia data, and extending MFs to lower masses with the JWST field. We also combined information on the derived MFs to infer an estimate of the cluster present-day total mass. Lastly, we investigated the presence of white dwarfs (WDs) and identified a strong candidate. However, to firmly establish its cluster membership, as well as that of four other WD candidates and of the majority of faint low-mass MS stars, further JWST equally deep observations will be required. We make publicly available catalogues, atlases, and the improved ePSFs.

Nardiello, D. (ORCID:0000000311493659)↗

DOE OSTI2025

Seedless nonresonant gas-flow velocimetry with single-shot coherent Rayleigh-Brillouin scattering

We demonstrate neutral gas-flow velocimetry by exploiting the optical dipole forces exerted by precisely tailored high-intensity laser fields on polarizable particles, in the form of single-shot coherent Rayleigh-Brillouin scattering (CRBS). This enables mapping of the velocity distribution function of the particles in a single laser shot of approximately 200 ns duration, allowing us to perform spectroscopic measurements in the time, rather than the frequency, domain. Importantly, CRBS results in a coherent signal beam, which renders the technique ideal for measurements in optically noisy environments. By correlating the time shift of the resulting spectrum to the Doppler shift caused by the bulk motion of particles, we perform neutral gas-flow velocimetry without the need to seed the flow, nondestructively, for a variety of gases, atomic or molecular. We demonstrate gas-flow velocimetry at flow velocities as low as 1 m ⁢s −1 , with similar velocity resolution, while temperature and gas density can also be estimated from the same single-shot spectra. To prove the effectiveness of the measurement technique, we benchmark it against Pitot tube measurements, which show excellent agreement.

Gerakis, Alexandros [Luxembourg Institute of Scien↗

DOE OSTI2023

Cosmological constraints from the Subaru Hyper Suprime-Cam year 1 shear catalogue lensing convergence probability distribution function

Here we utilize the probability distribution function (PDF) of normalized convergence maps reconstructed from the Subaru Hyper Suprime-Cam (HSC) year 1 shear catalogue, in combination with the power spectrum, to measure the matter clustering amplitude S 8 = σ 8 $\sqrt {Ω_{m}/0.3}$. The large-scale structure’s statistical properties are incompletely described by the traditional two-point statistics, motivating our investigation of the PDF—a complementary higher-order statistic. By defining the PDF over the standard-deviation-normalized convergence map, we are able to isolate the non-Gaussian information. We use tailored simulations to compress the data vector and construct a likelihood approximation. We mitigate the impact of survey and astrophysical systematics with cuts on smoothing scales, redshift bins, and data vectors. We find S 8 = $0.860^{+0.066}_{–0.109}$ from the PDF alone and S 8 = 0.798$^{+0.029}_{–0.042}$ from the combination of the PDF and power spectrum (68% confidential level (CL)). The PDF improves the power-spectrum-only constraint by about 10%.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

Development of a machine-learning-based ionic-force correction model for quantum molecular dynamic simulations of warm dense matter

In this work Δ learning is used to map orbital-free density functional theory (OF-DFT) ionic forces to the corresponding Kohn-Sham (KS) DFT ionic forces. The development of the approximate force difference in terms of the ion positions is constructed and serves as a stand in for the ground truth force difference. Descriptor vectors for ion configurations are constructed using all distance between ions in conjunction with an indexing based on a nearest neighbor ranking. It is demonstrated that such a scheme of descriptors can uniquely describe an ionic configuration up to a rotation and reflection when no ambiguity in the nearest neighbor ranking exists. How to handle the case when an ambiguity exists in the nearest neighbor ranking is discussed. As a proof of principle, the model is trained and tested on warm dense hydrogen at temperatures between 1 and 15 eV. Once tested, the model was used to perform molecular dynamic simulations of warm dense hydrogen. Furthermore, the resulting energies and pressures are within 1% and 2% of their respective target KS values.

36 MATERIALS SCIENCE↗

DOE OSTI2016

Cross-sectional transport imaging in a multijunction solar cell

Here, we combine a highly localized electron-beam point source excitation to generate excess free carriers with the spatial resolution of optical near-field imaging to map recombination in a cross-sectioned multijunction (Ga 0.5 In 0.5 P/GaIn 0.01 As/Ge) solar cell. By mapping the spatial variations in emission of light for fixed generation (as opposed to traditional cathodoluminescence (CL), which maps integrated emission as a function of position of generation), it is possible to directly monitor the motion of carriers and photons. We observe carrier diffusion throughout the full width of the middle (GaInAs) cell, as well as luminescent coupling from point source excitation in the top cell GaInP to the middle cell. Supporting CL and near-field photoluminescence (PL) measurements demonstrate the excitation-dependent Fermi level splitting effects that influence cross-sectioned spectroscopy results, as well as transport limitations on the spatial resolution of conventional cross-sectional far-field measurements.

14 SOLAR ENERGY↗

DOE OSTI2016

WE-AB-209-07: Explicit and Convex Optimization of Plan Quality Metrics in Intensity-Modulated Radiation Therapy Treatment Planning

Purpose: To formulate objective functions of a multicriteria fluence map optimization model that correlate well with plan quality metrics, and to solve this multicriteria model by convex approximation. Methods: In this study, objectives of a multicriteria model are formulated to explicitly either minimize or maximize a dose-at-volume measure. Given the widespread agreement that dose-at-volume levels play important roles in plan quality assessment, these objectives correlate well with plan quality metrics. This is in contrast to the conventional objectives, which are to maximize clinical goal achievement by relating to deviations from given dose-at-volume thresholds: while balancing the new objectives means explicitly balancing dose-at-volume levels, balancing the conventional objectives effectively means balancing deviations. Constituted by the inherently non-convex dose-at-volume measure, the new objectives are approximated by the convex mean-tail-dose measure (CVaR measure), yielding a convex approximation of the multicriteria model. Results: Advantages of using the convex approximation are investigated through juxtaposition with the conventional objectives in a computational study of two patient cases. Clinical goals of each case respectively point out three ROI dose-at-volume measures to be considered for plan quality assessment. This is translated in the convex approximation into minimizing three mean-tail-dose measures. Evaluations of the three ROI dose-at-volume measures on Pareto optimal plans are used to represent plan quality of the Pareto sets. Besides providing increased accuracy in terms of feasibility of solutions, the convex approximation generates Pareto sets with overall improved plan quality. In one case, the Pareto set generated by the convex approximation entirely dominates that generated with the conventional objectives. Conclusion: The initial computational study indicates that the convex approximation outperforms the conventional objectives in aspects of accuracy and plan quality.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

WE-FG-202-12: Investigation of Longitudinal Salivary Gland DCE-MRI Changes

Purpose: To determine the correlation between dose and changes through treatment in dynamic contrast enhanced (DCE) MRI voxel parameters (Ktrans, kep, Ve, and Vp) within salivary glands of head and neck oropharyngeal squamous cell carcinoma (HNSCC) patients. Methods: 17 HNSCC patients treated with definitive radiation therapy completed DCE-MRI scans on a 3T scanner at pre-treatment, mid-treatment, and post-treatment time points. Mid-treatment and post-treatment DCE images were deformably registered to pre-treatment DCE images (Velocity software package). Pharmacokinetic analysis of the DCE images used a modified Tofts model to produce parameter maps with an arterial input function selected from each patient’s perivertebral space on the image (NordicICE software package). In-house software was developed for voxel-by-voxel longitudinal analysis of the salivary glands within the registered images. The planning CT was rigidly registered to the pre-treatment DCE image to obtain dose values in each voxel. Voxels within the lower and upper dose quartiles for each gland were averaged for each patient, then an average of the patients’ means for the two quartiles were compared. Dose-relationships were also assessed by Spearman correlations between dose and voxel parameter changes for each patient’s gland. Results: Changes in parameters’ means between time points were observed, but inter-patient variability was high. Ve of the parotid was the only parameter that had a consistently significant longitudinal difference between dose quartiles. The highest Spearman correlation was Vp of the sublingual gland for the change in the pre-treatment to mid-treatment values with only a ρ=0.29. Conclusion: In this preliminary study, there was large inter-patient variability in the changes of DCE voxel parameters with no clear relationship with dose. Additional patients may reduce the uncertainties and allow for the determination of the existence of parameter and dose relationships.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

MIONet: Learning Multiple-Input Operators via Tensor Product

As an emerging paradigm in scientific machine learning, neural operators aim to learn operators, via neural networks, that map between infinite-dimensional function spaces. Several neural operators have been recently developed. However, all the existing neural operators are only designed to learn operators defined on a single Banach space; i.e., the input of the operator is a single function. Here, for the first time, we study the operator regression via neural networks for multiple-input operators defined on the product of Banach spaces. We first prove a universal approximation theorem of continuous multiple-input operators. We also provide a detailed theoretical analysis including the approximation error, which provides guidance for the design of the network architecture. Based on our theory and a low-rank approximation, we propose a novel neural operator, MIONet, to learn multiple-input operators. MIONet consists of several branch nets for encoding the input functions and a trunk net for encoding the domain of the output function. Here, we demonstrate that MIONet can learn solution operators involving systems governed by ordinary and partial differential equations. In our computational examples, we also show that we can endow MIONet with prior knowledge of the underlying system, such as linearity and periodicity, to further improve accuracy.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Compressor is A Sensor: A Universal Refrigerant Charge Fault Detection and Diagnostics Method Based on Pump Down Operation

The primary goal of this report is to develop a universal charge fault detection method that requires only a few experimental data with high prediction accuracy. Currently, pump-down operations is typical practices by HVAC technicians when they need to open the refrigerant circuit to make a repair. In addition, compressors have a built-in low-pressure cut-off protection function, and the compressor performance maps are commonly available from manufacturers. The new charge fault detection and diagnostics method innovatively utilizes the typical pump down operation, the compressor’s low-pressure cut-off protection, and the compressor performance map. It does not require any geometry information of heat exchangers, refrigerant lines, or charge buffers.

42 ENGINEERING↗

DOE OSTI2020

US East Coast synthetic aperture radar wind atlas for offshore wind energy

We present the first synthetic aperture radar (SAR) offshore wind atlas of the US East Coast from Georgia to the Canadian border. Images from RADARSAT-1, Envisat, and Sentinel-1A/B are processed to wind maps using the geophysical model function (GMF) CMOD5.N. Extensive comparisons with 6008 collocated buoy observations of the wind speed reveal that biases of the individual systems range from -0.8 to 0.6 m s -1 . Unbiased wind retrievals are crucial for producing an accurate wind atlas, and intercalibration of the SAR observations is therefore applied. Wind retrievals from the intercalibrated SAR observations show biases in the range of to -0.2 to 0.0 m s -1 , while at the same time improving the root-mean-squared error from 1.67 to 1.46 m s -1 . The intercalibrated SAR observations are, for the first time, aggregated to create a wind atlas at the height 10 m a.s.l. (above sea level). The SAR wind atlas is used as a reference to study wind resources derived from the Wind Integration National Dataset Toolkit (WTK), which is based on 7 years of modelling output from the Weather Research and Forecasting (WRF) model. Comparisons focus on the spatial variation in wind resources and show that model outputs lead to lower coastal wind speed gradients than those derived from SAR. Areas designated for offshore wind development by the Bureau of Ocean Energy Management are investigated in more detail; the wind resources in terms of the mean wind speed show spatial variations within each designated area between 0.3 and 0.5 m s -1 for SAR and less than 0.2m s -1 for the WTK. Our findings indicate that wind speed gradients and variations might be underestimated in mesoscale model outputs along the US East Coast.

17 WIND ENERGY↗

DOE OSTI2024

Data Files for P. Mai et al., "Fluctuating charge-density-wave correlations in the three-band Hubbard model" (2024)

The high-temperature superconducting cuprates host unidirectional spin- and charge-density-wave orders that can intertwine with superconductivity in nontrivial ways. While the charge components of these stripes have now been observed in nearly all cuprate families, their detailed evolution with doping varies across different materials and at high and low temperatures. We address this problem using nonperturbative determinant quantum Monte Carlo calculations for the three-band Hubbard model. Using an efficient implementation, we can resolve the model’s fluctuating spin and charge modulations and map their evolution as a function of the charge transfer energy and doping. We find that the incommensurability of the charge modulations is decoupled from the spin modulations and decreases with hole doping, consistent with experimental measurements at high temperatures. These findings support the proposal that the high-temperature charge correlations are distinct from the intertwined stripe order observed at low-temperature and in the single-band Hubbard model.

cuprates↗

DOE OSTI2015

Few-body physics

Few-body hadronic observables play an essential role in a wide number of processes relevant for both particle and nuclear physics. In order for Lattice QCD to offer insight into the interpretation of few-body states, a theoretical infrastructure must be developed to map Euclidean-time correlation functions to the desired Minkowski-time few-body observables. In this talk, I will first review the formal challenges associated with the studies of such systems via Lattice QCD, as first introduced by Maiani and Testa, and then review methodology to circumvent said limitations. The first main example of the latter is the formalism of Luscher to analyze elastic scattering and a second is the method of Lellouch & Luscher to analyze weak decays. I will then proceed to discus recent theoretical generalizations of these frameworks that allow for the determination of scattering amplitudes, resonances, transition and elastic form factors. Finally, I will outline outstanding problems, including those that are now beginning to be addressed.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2022

A Universal Refrigerant Charge Fault Detection and Diagnostics Method Based on Pump Down Operation

The performance of the heat pump system varies greatly depending on the refrigerant charge amount. Improving the refrigerant charge fault detection and diagnostics (FDD) method of vapor compression systems have the potential for increasing energy efficiency and reducing service cost. Previous studies to predict refrigerant charge amount are mostly empirical methods which require significant amount of experimental data for high accuracy. The primary goal of this research is to develop a universal charge fault detection method which requires only a few experimental data with high prediction accuracy.Currently, pump down operations are typical practices by HVAC technicians when they need to open the refrigerant circuit to make a repairment. In addition, compressors have a built-in low-pressure cut-off protection function, and the compressor performance maps are commonly available from manufacturers. The proposed method innovatively utilizes the typical pump down operation, the compressor low-pressure cut-off protection, and the compressor performance map. It does not require any geometry information of heat exchangers, refrigerant lines, or charge buffers.The new charge prediction method is firstly formulated through theoretical analysis, then verified and calibrated by a quasi-steady-state simulation of the pump down process for a residential heat pump system. The quasi steady-state simulation uses an HVAC system simulation framework driven by DOE/ORNL Heat Pump Design Model (HPDM). Preliminary experiment validations with heat pump refrigerant leakage tests demonstrate the deviation of the proposed charge prediction method compared with measurement is within 8%. This technology makes refrigerant charge amount available at the technician’s fingertips and leads to shorter maintenance time and fewer site visits.

Li, Zhenning↗

DOE OSTI2010

QCD and Light-Front Holography

The soft-wall AdS/QCD model, modified by a positive-sign dilaton metric, leads to a remarkable one-parameter description of nonperturbative hadron dynamics. The model predicts a zero-mass pion for zero-mass quarks and a Regge spectrum of linear trajectories with the same slope in the leading orbital angular momentum L of hadrons and the radial quantum number N. Light-Front Holography maps the amplitudes which are functions of the fifth dimension variable z of anti-de Sitter space to a corresponding hadron theory quantized on the light front. The resulting Lorentz-invariant relativistic light-front wave equations are functions of an invariant impact variable {zeta} which measures the separation of the quark and gluonic constituents within the hadron at equal light-front time. The result is to a semi-classical frame-independent first approximation to the spectra and light-front wavefunctions of meson and baryon light-quark bound states, which in turn predict the behavior of the pion and nucleon form factors. The theory implements chiral symmetry in a novel way: the effects of chiral symmetry breaking increase as one goes toward large interquark separation, consistent with spectroscopic data, and the the hadron eigenstates generally have components with different orbital angular momentum; e.g., the proton eigenstate in AdS/QCD with massless quarks has L = 0 and L = 1 light-front Fock components with equal probability. The soft-wall model also predicts the form of the non-perturbative effective coupling {alpha}{sub s}{sup AdS} (Q) and its {beta}-function which agrees with the effective coupling {alpha}{sub g1} extracted from the Bjorken sum rule. The AdS/QCD model can be systematically improved by using its complete orthonormal solutions to diagonalize the full QCD light-front Hamiltonian or by applying the Lippmann-Schwinger method in order to systematically include the QCD interaction terms. A new perspective on quark and gluon condensates is also reviewed.

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 OSTI2015

Calculation of photodetachment cross sections and photoelectron angular distributions of negative ions using density functional theory

Recently, the development of photoelectron velocity map imaging makes it much easier to obtain the photoelectron angular distributions (PADs) experimentally. However, explanations of PADs are only qualitative in most cases, and very limited works have been reported on how to calculate PAD of anions. In the present work, we report a method using the density-functional-theory Kohn-Sham orbitals to calculate the photodetachment cross sections and the anisotropy parameter β. The spherical average over all random molecular orientation is calculated analytically. A program which can handle both the Gaussian type orbital and the Slater type orbital has been coded. The testing calculations on Li{sup −}, C{sup −}, O{sup −}, F{sup −}, CH{sup −}, OH{sup −}, NH{sub 2}{sup −}, O{sub 2}{sup −}, and S{sub 2}{sup −} show that our method is an efficient way to calculate the photodetachment cross section and anisotropy parameter β for anions, thus promising for large systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗