Measurements of t t ¯ differential cross-sections of highly boosted top quarks decaying to all-hadronic final states in p p collisions at s = 13 TeV using the ATLAS detector
Not Available
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
SEARCH · Search NASA
Results for “methods: data analysis”
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.
Measurement of Differential Branching Fractions of Inclusive B → X u ℓ + ν ℓ Decays
Not Available
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Search for the Lepton Flavor Violating Decays B + → K + τ ± ℓ ∓ ( ℓ = e , μ ) at Belle
We present a search for the lepton flavor violating decays B + → K + $\tau$ ± ℓ ∓ , with ℓ = (e,μ), using the full data sample of 772×10 6 B$\overline{B}$ pairs recorded by the Belle detector at the KEKB asymmetric-energy e + e - collider. We use events in which one B meson is fully reconstructed in a hadronic decay mode. We find no evidence for B ± → K ± $\tau$ℓ decays and set upper limits on their branching fractions at the 90% confidence level in the (1 - 3) × 10 -5 range. The obtained limits are the world’s best results.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Comparison of unfolding methods using RooFitUnfold
In this study we describe RooFitUnfold, an extension of the RooFit statistical software package to treat unfolding problems, and which includes most of the unfolding methods that commonly used in particle physics. The package provides a common interface to these algorithms as well as common uniform methods to evaluate their performance in terms of bias, variance and coverage. In this paper we exploit this common interface of RooFitUnfold to compare the performance of unfolding with the Richardson–Lucy, Iterative Dynamically Stabilized, Tikhonov, Gaussian Process, bin-by-bin and inversion methods on several example problems.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Characterizing the Sample Selection for Supernova Cosmology
Type Ia supernovae (SNe Ia) are used as distance indicators to infer the cosmological parameters that specify the expansion history of the universe. Parameter inference depends on the criteria by which the analysis SN sample is selected. Only for the simplest selection criteria and population models can the likelihood be calculated analytically, otherwise it needs to be determined numerically, a process that inherently has error. Numerical errors in the likelihood lead to errors in parameter inference. This article presents toy examples where the distance modulus is inferred given a set of SNe at a single redshift. Parameter estimators and their uncertainties are calculated using Monte Carlo techniques. The relationship between the number of Monte Carlo realizations and numerical errors is presented. The procedure can be applied to more realistic models and used to determine the computational and data management requirements of the transient analysis pipeline.
79 ASTRONOMY AND ASTROPHYSICS↗
A Hard X-ray Study of a Manganese-Terpyridine Dimer Catalyst in a Chromium-based Metal Organic Framework - Oral Presentation [Slides]
Cleaner forms of energy are needed, and H 2 produced from water splitting is a possible source. However, a robust catalyst is necessary to carry out the water oxidation reaction. Plants utilize Photosystem II to catalyze water oxidation as a part of photosynthesis, and many synthetic water oxidation catalysts use Photosystem II as a model. In this study, the catalyst of interest was [(terpy)Mn(μ-O)2Mn(terpy)]3+ (MnTD), which was synthesized in a chromium-based Metal Organic Framework (MOF) to avoid degradation of MnTD molecules. Hard X-ray powder diffraction was the primary method of analysis. The diffraction data was used to detect the presence of MOF in samples at different catalytic stages, and lattice parameters were assigned to the samples containing MOF. Fourier maps were constructed to determine the contents of the MOF as preliminary studies suggested that MnTD may not be present. Results showed that MOF is present before catalysis occurs, but disappears in the initial stages of catalysis. Changes in the MOF’s lattice parameters suggest attractive interactions between the MOF and catalyst; these interactions may lead to the observed MOF degradation. Fourier maps also reveal limited, if any, amounts of MnTD in the system. Molecular manganese oxide may be the source of the high rate of water oxidation catalysis in the studied system.
08 HYDROGEN↗
(U) Boundary Conditions for Ejecta Source Models with Stationary Velocity Distributions
We show that certain apparently universal features of the piezoelectric voltage traces measured in HE-driven tin coupon experiments [1, 2] constrain the functional form of any ejecta source model with a stationary velocity distribution. Building on an analytic formalism that has been extensively documented elsewhere [3, 4, 5, 6, 7], we derive simple expressions for V(t 0 a ), V'(t 0 a ), and the discontinuity in V(t break ) where t 0 a is the time of first ejecta arrival at the sensor and t break is the “depletion” time for the fastest particles arriving at the sensor. These three quantities appear to be zero, or nearly so, in the Vo- gan voltage data [1] (modulo noise and any smoothing effects created by the circuit); however, the predicted voltages for this general class of source models do not automatically achieve these conditions unless special boundary conditions are met. Source models which violate these boundary conditions will produce voltage predictions at odds with the measurements. The RMI+SSVD ejecta source model in its current formulation satisfies none of these boundary conditions, and therefore predictions from a stationary-velocity-distribution approximation to the RMI+SSVD source model deviate significantly from the voltage data. This analysis suggests straightforward methods for potentially improving the predictions of the RMI+SSVD ejecta source model. We test this with a simple adjustment to the SSVD and find, as anticipated, the adjustment corrects three properties of the voltage prediction. Furthermore, the adjusted model’s prediction for ejecta mass accumulation at the sensor exhibits a smoother and more gradual rise from the baseline than that produced by the original source model, thereby improving overall model/data agreement.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Domain Adaptation for Measurements of Strong Gravitational Lenses
Upcoming surveys are predicted to discover galaxy-scale strong lenses on the order of 10\textsuperscript{5}, making deep learning methods necessary in lensing data analysis. Currently, there is insufficient real lensing data to train deep learning algorithms, but the alternative of training only on simulated data results in poor performance on real data. Domain Adaptation may be able to bridge the gap between simulated and real datasets. We utilize domain adaptation for the estimation of Einstein radius ($\Theta_E$) in simulated galaxy-scale gravitational lensing images with different levels of observational realism. We evaluate two domain adaptation techniques - Domain Adversarial Neural Networks (DANN) and Maximum Mean Discrepancy (MMD). We train on a source domain of simulated lenses and apply it to a target domain of lenses simulated to emulate noise conditions in the Dark Energy Survey (DES). We show that both domain adaptation techniques can significantly improve the model performance on the more complex target domain dataset. This work is the first application of domain adaptation for a regression task in strong lensing imaging analysis. Our results show the potential of using domain adaptation to perform analysis of future survey data with a deep neural network trained on simulated data.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
On Computation of Koopman Operator from Sparse Data
In this paper, we propose a novel approach to compute the Koopman operator from sparse time series data. In recent years there has a considerable interest in operator theoretic methods for data-driven analysis of dynamical systems and existing techniques for the approximation of the Koopman operator require rich enough data-sets. However, in many applications, the data set may not be rich enough to approximate the operators to acceptable limits. In this paper, using ideas from robust optimization, we propose an algorithm to compute the Koopman operator from sparse data. In particular, we enrich the sparse data set with artificial data points and use robust optimization techniques to obtain the transfer operator. We illustrate the efficiency of our proposed approach in three different dynamical systems, namely, a linear system, a nonlinear system and a dynamical system governed by a partial differential equation.
Sinha, Subhrajit↗
Visualizing Solution Structure at Solid-Liquid Interfaces using Three-Dimensional Fast Force Mapping
Amongst the challenges for a variety of research fields are the visualization of solid-liquid interfaces and understanding how they are affected by the solution conditions such as ion concentrations, pH, ligands, and trace additives, as well as the underlying crystallography and chemistry. Here, in this context, three-dimensional fast force mapping (3D FFM) has emerged as a promising tool for investigating solution structure at interfaces. This capability is based on atomic force microscopy (AFM) and allows the direct visualization of interfacial regions in three spatial dimensions with sub-nanometer resolution. Here we provide a detailed description of the experimental protocol for acquiring 3D FFM data. The main considerations for optimizing the operating parameters depending on the sample and application are discussed. Moreover, the basic methods for data processing and analysis are discussed, including the transformation of the measured instrument observables into tip-sample force maps that can be linked to the local solution structure. Finally, we shed light on some of the outstanding questions related to 3D FFM data interpretation and how this technique can become a central tool in the repertoire of surface science.
Nakouzi, Elias↗
Characterizing and Improving the Data Reduction Pipeline for the Keck OSIRIS Integral Field Spectrograph
OSIRIS is a near-infrared (1.0–2.4 μm) integral field spectrograph operating behind the adaptive optics system at Keck Observatory and one of the first lenslet-based integral field spectrographs. Since its commissioning in 2005, it has been a productive instrument, producing nearly half the laser guide star adaptive optics papers on Keck. The complexity of its raw data format necessitated a custom data reduction pipeline (DRP) delivered with the instrument in order to iteratively assign flux in overlapping spectra to the proper spatial and spectral locations in a data cube. Other than bug fixes and updates required for hardware upgrades, the bulk of the DRP has not been updated since initial instrument commissioning. We report on the first major comprehensive characterization of the DRP using on-sky and calibration data. We also detail improvements to the DRP, including characterization of the flux assignment algorithm, exploration of spatial rippling in the reduced data cubes, and improvements to several calibration files, including the rectification matrix, bad-pixel mask, and wavelength solution. We present lessons learned from over a decade of OSIRIS data reduction that are relevant to the next generation of integral field spectrograph hardware and data reduction software design.
79 ASTRONOMY AND ASTROPHYSICS↗
Deciphering Degradation: Machine Learning on Real-World Performance Data (Final Report)
This project addresses a fundamental flaw in solar PV research and solar project financing; the assumed rate of degradation for solar plants. The solar industry currently relies on an out-dated report that observed a 0.5% degradation rate based on a small sample size of systems (~100). While the research conducted at the time was new and innovative, the solar community has not updated this research and universally applies this 0.5% degradation assumption in financial models. Our project updates this assumption by analyzing observed degradation from the industry’s largest dataset of operating solar assets (>10,000 systems) and creating the first machine-learning model based on these observed results to quantify and identify features that drive degradation. There are two strategic goals for this award: reduce the cost of capital (enable solar to attract more capital) and improve the reliability of solar itself. These dual goals are achieved by leveraging an industry dataset to observe system degradation on a large scale, deploying advanced data analysis and machine learning methods to quantify and predict system reliability, and engaging with industry stakeholders to help them accurately price degradation in financial models.
14 SOLAR ENERGY↗
Domain Adaptation for Measurements of Strong Gravitational Lenses
Upcoming surveys are predicted to discover galaxy-scale strong lenses on the order of $10^5$, making deep learning methods necessary in lensing data analysis. Currently, there is insufficient real lensing data to train deep learning algorithms, but the alternative of training only on simulated data results in poor performance on real data. Domain Adaptation may be able to bridge the gap between simulated and real datasets. We utilize domain adaptation for the estimation of Einstein radius ($\Theta_E$) in simulated galaxy-scale gravitational lensing images with different levels of observational realism. We evaluate two domain adaptation techniques - Domain Adversarial Neural Networks (DANN) and Maximum Mean Discrepancy (MMD). We train on a source domain of simulated lenses and apply it to a target domain of lenses simulated to emulate noise conditions in the Dark Energy Survey (DES). We show that both domain adaptation techniques can significantly improve the model performance on the more complex target domain dataset. This work is the first application of domain adaptation for a regression task in strong lensing imaging analysis. Our results show the potential of using domain adaptation to perform analysis of future survey data with a deep neural network trained on simulated data.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Domain Adaptation for Measurements of Strong Gravitational Lenses
Upcoming surveys are predicted to discover galaxy-scale strong lenses on the magnitude of 105, making deep learning methods necessary in lensing data analysis. Currently, there is insufficient real lensing data to train deep learning algorithms, but training only on simulated data results in poor performance on real data. Domain adaptation can bridge the gap between simulated and real datasets. We adopt domain adaptation on the estimation of Einstein radius in simulated galaxy-scale gravitational lensing images. We evaluate two domain adaptation techniques - domain adversarial neural networks (DANN) and maximum mean discrepancy (MMD). We train on a source domain of simulated lenses and apply it to a target domain with emulation of DES survey conditions. We show that both domain adaptation techniques can significantly improve the model performance on the more complex target domain datasets. Our results show the potential of using domain adaptation to perform analysis on future survey data with a deep neural network trained on simulated data.
79 ASTRONOMY AND ASTROPHYSICS↗
A High-Quality Genome-Scale Model for Rhodococcus opacus Metabolism
Rhodococcus opacus is a bacterium that has a high tolerance to aromatic compounds and can produce significant amounts of triacylglycerol (TAG). Here, we present iGR1773, the first genome-scale model (GSM) of R. opacus PD630 metabolism based on its genomic sequence and associated data. The model includes 1773 genes, 3025 reactions, and 1956 metabolites, was developed in a reproducible manner using CarveMe, and was evaluated through Metabolic Model tests (MEMOTE). We combine the model with two Constraint-Based Reconstruction and Analysis (COBRA) methods that use transcriptomics data to predict growth rates and fluxes: E-Flux2 and SPOT (Simplified Pearson Correlation with Transcriptomic data). Growth rates are best predicted by E-Flux2. Flux profiles are more accurately predicted by E-Flux2 than flux balance analysis (FBA) and parsimonious FBA (pFBA), when compared to 44 central carbon fluxes measured by 13C-Metabolic Flux Analysis (13C-MFA). Under glucose-fed conditions, E-Flux2 presents an R2 value of 0.54, while predictions based on pFBA had an inferior R2 of 0.28. We attribute this improved performance to the extra activity information provided by the transcriptomics data. For phenol-fed metabolism, in which the substrate first enters the TCA cycle, E-Flux2’s flux predictions display a high R2 of 0.96 while pFBA showed an R2 of 0.93. We also show that glucose metabolism and phenol metabolism function with similar relative ATP maintenance costs. These findings demonstrate that iGR1773 can help the metabolic engineering community predict aromatic substrate utilization patterns and perform computational strain design.
Roell, Garrett W.↗
Dynamics retrieval from stochastically weighted incomplete data by low-pass spectral analysis
Time-resolved serial femtosecond crystallography (TR-SFX) provides access to protein dynamics on sub-picosecond timescales, and with atomic resolution. Due to the nature of the experiment, these datasets are often highly incomplete and the measured diffracted intensities are affected by partiality. To tackle these issues, one established procedure is that of splitting the data into time bins, and averaging the multiple measurements of equivalent reflections within each bin. This binning and averaging often involve a loss of information. Here, we propose an alternative approach, which we call low-pass spectral analysis (LPSA). In this method, the data are projected onto the subspace defined by a set of trigonometric functions, with frequencies up to a certain cutoff. This approach attenuates undesirable high-frequency features and facilitates retrieving the underlying dynamics. A time-lagged embedding step can be included prior to subspace projection to improve the stability of the results with respect to the parameters involved. Subsequent modal decomposition allows to produce a low-rank description of the system's evolution. Using a synthetic time-evolving model with incomplete and partial observations, we analyze the LPSA results in terms of quality of the retrieved signal, as a function of the parameters involved. We compare the performance of LPSA to that of a range of other sophisticated data analysis techniques. We show that LPSA allows to achieve excellent dynamics reconstruction at modest computational cost. Finally, we demonstrate the superiority of dynamics retrieval by LPSA compared to time binning and merging, which is, to date, the most commonly used method to extract dynamical information from TR-SFX data.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Determining biomolecular structures near room temperature using X-ray crystallography: concepts, methods and future optimization
For roughly two decades, cryocrystallography has been the overwhelmingly dominant method for determining high-resolution biomolecular structures. Competition from single-particle cryo-electron microscopy and micro-electron diffraction, increased interest in functionally relevant information that may be missing or corrupted in structures determined at cryogenic temperature, and interest in time-resolved studies of the biomolecular response to chemical and optical stimuli have driven renewed interest in data collection at room temperature and, more generally, at temperatures from the protein–solvent glass transition near 200 K to ∼350 K. Fischer has recently reviewed practical methods for room-temperature data collection and analysis [Fischer (2021), Q. Rev. Biophys. 54 , e1]. Here, the key advantages and physical principles of, and methods for, crystallographic data collection at noncryogenic temperatures and some factors relevant to interpreting the resulting data are discussed. For room-temperature data collection to realize its potential within the structural biology toolkit, streamlined and standardized methods for delivering crystals prepared in the home laboratory to the synchrotron and for automated handling and data collection, similar to those for cryocrystallography, should be implemented.
59 BASIC BIOLOGICAL SCIENCES↗
Chapter 20: Data Center IT Efficiency Measures Evaluation Protocol. The Uniform Methods Project: Methods for Determining Energy Efficiency Savings for Specific Measures
This chapter focuses on IT measures in the data center and examines the techniques and analysis methods used to verify savings that result from improving the efficiency of two specific pieces of IT equipment: servers and data storage.
24 POWER TRANSMISSION AND DISTRIBUTION↗