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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

PNNL Superfund Research Program Analytics (srpAnalytics Data Release v.1.0)

The OSU/PNNL superfund Research Program represents a longstanding collaboration to quantify Polycyclic Aromatic Hydrocarbons at various superfund sites in the Pacific Northwest and assess their potential impact on human health. To link the chemical measurements to biological activity, we describe the use of the zebrafish as a high-throughput developmental model of human exposure that provides quantitative measurements of the biological ramifications of exposure to toxicants. The PNNL Superfund have implemented a dose-response modelling pipeline to calculate benchmark dose parameters that enable the comparison of potency across chemicals and phenotypes. Our portal provides public access to this dataset and an interactive web site designed to enable exploration and re-use of this data by the scientific community at http://srp.pnnl.gov.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

PNNL Superfund Research Program Analytics (srpAnalytics Data Release v.1.0)

The OSU/PNNL superfund Research Program represents a longstanding collaboration to quantify Polycyclic Aromatic Hydrocarbons at various superfund sites in the Pacific Northwest and assess their potential impact on human health. To link the chemical measurements to biological activity, we describe the use of the zebrafish as a high-throughput developmental model of human exposure that provides quantitative measurements of the biological ramifications of exposure to toxicants. The PNNL Superfund have implemented a dose-response modelling pipeline to calculate benchmark dose parameters that enable the comparison of potency across chemicals and phenotypes. Our portal provides public access to this dataset and an interactive web site designed to enable exploration and re-use of this data by the scientific community at http://srp.pnnl.gov.

OSU-PNNL Superfund Research Program Center SRP↗
DOE OSTI2022

Toward the Detection of Polyglot Files

Standardized file types play a key role in the development and use of computer software. However, it is possible to confound standardized file type processing by creating a file that is valid in multiple file types. The resulting polyglot (many languages) file can confuse file type identification, allowing elements of the file to evade analysis. This is especially problematic for malware detection systems that rely on file type identification for feature extraction. Although work has been done to identify file types using more comprehensive methods than file signatures, accurate identification of polyglot files remains an open problem. Since malware detection systems routinely perform file type-specific feature extraction, polyglot files need to be filtered out prior to ingestion by these systems. Otherwise, malicious content could pass through undetected. To address the problem of polyglot detection we assembled a data set using the mitra tool. We then evaluated the performance of the most commonly used file identification tools, including file, polydet, binwalk, and TrID. Our analysis demonstrates that existing file type detection tools fail to provide reliable polyglot detection. We then evaluated the ability of a range of machine and deep learning models to detect polyglot files. The most performant models were MalConv2 and Catboost, which demonstrated the highest recall on our data set with 95.16% and 95.45%, respectively. These models outperformed existing methods and could be incorporated into a malware detector’s file processing pipeline to filter out potentially malicious polyglots before file type-dependent feature extraction takes place.

Koch, Luke↗
DOE OSTI2022

Toward the Detection of Polyglot Files

Standardized file types play a key role in the development and use of computer software. However, it is possible to confound standardized file type processing by creating a file that is valid in multiple file types. The resulting polyglot (many languages) file can confuse file type identification, allowing elements of the file to evade analysis. This is especially problematic for malware detection systems that rely on file type identification for feature extraction. Although work has been done to identify file types using more comprehensive methods than file signatures, accurate identification of polyglot files remains an open problem. Since malware detection systems routinely perform file type-specific feature extraction, polyglot files need to be filtered out prior to ingestion by these systems. Otherwise, malicious content could pass through undetected. To address the problem of polyglot detection we assembled a data set using the mitra tool. We then evaluated the performance of the most commonly used file identification tools, including file, polydet, binwalk, and TrID. Our analysis demonstrates that existing file type detection tools fail to provide reliable polyglot detection. We then evaluated the ability of a range of machine and deep learning models to detect polyglot files. The most performant models were MalConv2 and Catboost, which demonstrated the highest recall on our data set with 95.16% and 95.45%, respectively. These models outperformed existing methods and could be incorporated into a malware detector’s file processing pipeline to filter out potentially malicious polyglots before file type-dependent feature extraction takes place.

Koch, Lucas↗
DOE OSTI2022

Uncertainties associated with GAN-generated datasets in high energy physics

Recently, Generative Adversarial Networks (GANs) trained on samples of traditionally simulated collider events have been proposed as a way of generating larger simulated datasets at a reduced computational cost. In this paper we point out that data generated by a GAN cannot statistically be better than the data it was trained on, and critically examine the applicability of GANs in various situations, including a) for replacing the entire Monte Carlo pipeline or parts of it, and b) to produce datasets for usage in highly sensitive analyses or sub-optimal ones. We present our arguments using information theoretic demonstrations, a toy example, as well as in the form of a formal statement, and identify some potential valid uses of GANs in collider simulations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Applying the ATOM drug discovery platform to small-molecule antivirals (Annual Report)

The objective of this project is to advance our understanding of the ML approaches needed to find the most potent inhibitor through an AI guided search that simultaneously optimizes for limited off-target safety activity and desirable pharmacokinetics (PK) properties. The methods will be tested for inhibiting SARS-CoV-2 activity through inhibition of the main protease as a demonstration, but the methodology will be developed to apply to any biothreat target using a small-molecule protein binding-based intervention, with limited experimental data. Computational experiments are conducted using our ATOM generative molecular design (GMD) loop software, which is the implementation of our AI/ML drug design pipeline.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Opportunities in Hydropower and Pumped Storage Hydropower

Hydropower and pumped storage hydropower (PSH) are established technologies with a long-standing presence in the United States, but the industry remains dynamic with an active relicensing pipeline and growing interest in PSH to provide large-scale energy storage and grid services. NLR has developed a range of tools, data, and analysis that supports the evaluation of hydropower and PSH opportunities, complementing other DOE-supported efforts in this space. This presentation initiates a conversation around the future of hydropower and PSH and how it can inform the National Academy of Sciences investigation of possible DOE-supported regional energy-water technology pilots.

13 HYDRO ENERGY↗
DOE OSTI2020

Big Sky Regional Carbon Sequestration Partnership (Phase III Final Scientific/Technical Report)

The Big Sky Carbon Sequestration Partnership (BSCSP) pursued a Phase III demonstration project at Kevin Dome in north central Montana. Kevin Dome covers approximately 700 square miles and is a naturally occurring CO 2 reservoir that is flanked by oil and gas fields. The carbon dioxide (CO 2 ) is in the upper Devonian Duperow (carbonate) formation and does not reach the spill point of the dome; therefore, the dome has potential as a CO 2 sequestration reservoir, a CO 2 supply, or as both if anthropogenic sources and enhanced oil recovery (EOR) operations are associated with the dome. Kevin Dome could potentially act as a buffer to continue accepting anthropogenic CO 2 when EOR flooding operations are interrupted or completed. The project objective was to produce one million tonnes of CO 2 from the gas cap of Kevin Dome, pipe it laterally, inject, and re-store it in the brine leg of the same formation to test the hub / buffer storage concept. This was to be accomplished by drilling up to five production wells, building a short pipeline and compression facilities, and drilling an injection well and several monitoring wells. BSCSP commenced outreach and site characterization activities including acquiring baseline data for near-surface insurance monitoring, acquiring 3-dimensional, 9-component surface seismic over the project area, drilling two test wells (one in the production area and one in the injection area), coring key intervals, and performing comprehensive logging. Well tests of those wells revealed two barriers to the project. The production (Danielson 33-17) well was perforated in multiple zones but failed to produce any significant CO 2 . This was despite being drilled in the near vicinity of a historic well that had produced 3700 MCF per day in a drill stem test. Modeling indicated that this was likely due to a phase change during production causing a temperature drop resulting in hydrate and/or water ice formation that clogged the formation. Tests of the injection zone (Wallewein 22-1) well indicated total dissolved solids (TDS) slightly below the EPA required 10,000 parts per million (ppm) for a Class VI underground injection control permit. While the project was initiated before Class VI rules were promulgated, and this was an experimental project (seemingly qualified for a Class V permit), the Environmental Protection Agency (EPA) indicated that injection would require a Class VI permit. The low salinity result was unexpected as contours plotted based on regional formation water quality data indicated an expected TDS above 20,000 ppm, and wells between the recharge zone and the Wallewein well tested above 10,000 ppm. Faced with the inability to obtain an injection permit, the demonstration project could not proceed. However, the project had generated valuable samples and data on a large natural analog including 32 sq. mi. of 3-D, 9-C seismic, 430 ft. of carbonate core covering seven different depositional environments taken from areas with, and without the presence of CO 2 , 30 ft. of core of two caprocks, a tight carbonate and an anhydrite, a full set of modern logs on both wells, and well tests. DOE decided to re-scope the project around completing studies utilizing this data. This report covers both the initial scope and the re-scope (Task / Section numbers preceded with an R). While the report covers a wide range of project activities, highlights of this work include: Development of a geostatic model using neural nets to match well logs to facies and using multi-waveform seismic to inform reservoir heterogeneity; Unique mechanical testing of permeability – stress relationship in two caprock materials; Development of full waveform inversion to generate a high resolution velocity model; Model development for dual permeability (fracture and matrix) systems to better account for matrix-matrix interactions; Joint seismic wave inversion (including the first quadr-joint inversion) exhibiting better imaging of a challenging reservoir zone in stiff rock; Core flow and core flood results on a reactive carbonate; and Innovative laboratory measurements of seismic response of fractured core as a function of fluid fill.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

The Atacama Cosmology Telescope: High-resolution component-separated maps across one-third of the sky

Observations of the millimeter sky contain valuable information on a number of signals, including the blackbody cosmic microwave background (CMB), Galactic emissions, and the Compton-$y$ distortion due to the thermal Sunyaev-Zel'dovich (tSZ) effect. Extracting new insight into cosmological and astrophysical questions often requires combining multi-wavelength observations to spectrally isolate one component. In this work, we present a new arcminute-resolution Compton-$y$ map, which traces out the line-of-sight-integrated electron pressure, as well as maps of the CMB in intensity and E-mode polarization, across a third of the sky (around 13,000 sq.~deg.). We produce these through a joint analysis of data from the Atacama Cosmology Telescope (ACT) Data Release 4 and 6 at frequencies of roughly 93, 148, and 225 GHz, together with data from the \textit{Planck} satellite at frequencies between 30 GHz and 545 GHz. We present detailed verification of an internal linear combination pipeline implemented in a needlet frame that allows us to efficiently suppress Galactic contamination and account for spatial variations in the ACT instrument noise. These maps provide a significant advance, in noise levels and resolution, over the existing \textit{Planck} component-separated maps and will enable a host of science goals including studies of cluster and galaxy astrophysics, inferences of the cosmic velocity field, primordial non-Gaussianity searches, and gravitational lensing reconstruction of the CMB.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Unveiling Hidden Lyman Alpha Emitters in the DESI DR1 Data

We present an automatic method based on machine-learning convolutional neural network (CNN) architecture to detect Lyman alpha emitters (LAE) hidden in the Data Release 1 spectroscopic dataset of the Dark Energy Spectroscopic Instrument (DESI). Those LAEs mostly have incorrect redshift estimations because the current DESI pipeline is not designed to detect and measure the redshifts of galaxies at $z>2$. To uncover those sources, we first visually inspect thousands of DESI spectra and construct a sample, consisting of both LAEs and non-LAEs, for training and testing the CNN-based model to (1) detect LAEs in DESI spectra and (2) determine their Ly$α$ redshifts. The final model yields $95.2\%$ purity and $95.9\%$ completeness for detecting LAEs. We apply this model to approximately $2\times10^{6}$ spectra of sources targeted as emission-line galaxies and detect 19,685 LAEs from $z\sim2$ to $3.5$ within 12 minutes with a single GPU, illustrating the high efficiency of this model for identifying LAEs. The detected LAEs are mostly at the bright end of the luminosity function with Ly$α$ luminosity $L_{\rm Lyα} \gtrsim 10^{43}$ erg/s. The high signal-to-noise composite spectrum of the detected LAEs further shows various spectral features, including P-Cygni profiles of metal lines and MgII emission lines, possible indicators of Lyman continuum escape fraction, revealing the rich astrophysical information in this LAE sample. Finally, this sample can be used to train and validate the pipelines for redshift determination of LAEs for the preparation of the DESI-II survey.

Chan, Jui-Kuan [Taiwan, Natl. Taiwan U.] (ORCID:00↗
DOE OSTI2024

Measurement and calibration of non-linear shear terms in galaxy cluster fields

Introduction Galaxy cluster lensing is a powerful tool for measuring the mass of galaxy clusters, but accurate shear measurement and calibration are critical to obtaining reliable results. This study focuses on the measurement and calibration of weak lensing shears to improve mass estimates in cluster lensing. To deal with the problem, we first developed an image simulation pipeline, jedisim, which utilizes galaxy images extracted from the Hubble Space Telescope (HST) Ultra Deep Field (UDF) and the Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey (CANDELS). Methods The simulations represent realistic galaxy distributions and morphologies as input sources. The foreground halo with a Navarro–Frenk–White (NFW) profile is constructed such that the lensing signals of background galaxies can be measured by the Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST) Science Pipelines. By comparing the measured reduced shear g meas and the true reduced shear g true , we observe non-linearity up to g ≲ 0.6 . We fit polynomials to the data with quadratic correction adequate to g ≲ 0.4 . Meanwhile, we conduct mass estimates using the pzmassfitter code on four different clusters. Results The mass estimate results are significantly improved after applying the shear calibration derived from the present work—from 4.954 ± 0.504 × 1 0 14 M ⊙ to 10.507 ± 0.498 × 1 0 14 M ⊙ after calibration for a simulated cluster with the mass of 10 × 1 0 14 M ⊙ . In multiple cases of validation, the estimated results are all consistent with true cluster mass. Discussion This study yields the first relationship between reality and shape measurement of the LSST Science Pipelines and serves as the first step toward the overall goal of mass calibration in cluster lensing. By addressing the challenges in shear measurement and calibration, we aim to enhance the accuracy and reliability of mass estimates in galaxy cluster lensing studies.

Astronomy & Astrophysics↗
DOE OSTI2025

DESI Spectroscopy of HETDEX Emission-line Candidates. I. Line Discrimination Validation

The Hobby–Eberly Dark Energy Experiment (HETDEX) is an untargeted spectroscopic galaxy survey that uses Lyα-emitting galaxies (LAEs) as tracers of 1.9 < z < 3.5 large-scale structure. Most detections consist of a single emission line, whose identity is inferred via a Bayesian analysis of ancillary data. To determine the accuracy of these line identifications, HETDEX detections were observed with the Dark Energy Spectroscopic Instrument (DESI). In two DESI pointings, high-confidence spectroscopic redshifts are obtained for 1157 sources, including 982 LAEs. The DESI spectra are used to evaluate the accuracy of the HETDEX object classifications and tune the methodology to achieve the HETDEX science requirement of ≲2% contamination of the LAE sample by low-redshift emission-line galaxies, while still assigning 96% of the true Lyα emission sample with the correct spectroscopic redshift. We compare emission-line measurements between the two experiments assuming a simple Gaussian line fitting model. Fitted values for the central wavelength of the emission line, the measured line flux, and line widths are consistent between the surveys within uncertainties. Derived spectroscopic redshifts, from the two classification pipelines, when both agree as an LAE classification, are consistent to within $\langle$Δz/(1 + z)$\rangle$ = 6.9 × 10 −5 with an rms scatter of 3.3 × 10 −4 . Data are available at https://data.desi.lbl.gov/desi/public/dr1/vac/dr1/hetdex.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

HOLISMOKES IX. Neural network inference of strong-lens parameters and uncertainties from ground-based images

Modeling of strong gravitational lenses is a necessity for further applications in astrophysics and cosmology. With the large number of detections in current and upcoming surveys, such as the Rubin Legacy Survey of Space and Time (LSST), it is pertinent to investigate automated and fast analysis techniques beyond the traditional and time-consuming Markov chain Monte Carlo sampling methods. Building upon our (simple) convolutional neural network (CNN), we present here another CNN, specifically a residual neural network (ResNet), that predicts the five mass parameters of a singular isothermal ellipsoid (SIE) profile (lens center x and y, ellipticity e x and e y , Einstein radius θ E ) and the external shear (γ ext, 1 , γ ext, 2 ) from ground-based imaging data. In contrast to our previous CNN, this ResNet further predicts the 1σ uncertainty for each parameter. To train our network, we use our improved pipeline to simulate lens images using real images of galaxies from the Hyper Suprime-Cam Survey (HSC) and from the Hubble Ultra Deep Field as lens galaxies and background sources, respectively. We find very good recoveries overall for the SIE parameters, especially for the lens center in comparison to our previous CNN, while significant differences remain in predicting the external shear. From our multiple tests, it appears that most likely the low ground-based image resolution is the limiting factor in predicting the external shear. Given the run time of milli-seconds per system, our network is perfectly suited to quickly predict the next appearing image and time delays of lensed transients. Therefore, we use the network-predicted mass model to estimate these quantities and compare to those values obtained from our simulations. Unfortunately, the achieved precision allows only a first-order estimate of time delays on real lens systems and requires further refinement through follow-up modeling. Nonetheless, our ResNet is able to predict the SIE and shear parameter values in fractions of a second on a single CPU, meaning that we are able to efficiently process the huge amount of galaxy-scale lenses expected in the near future.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Building a framework to genetically characterize “feather spots” and understand demographic impacts of solar energy sites on migratory bird populations

The lack of data on the impact of utility-scale solar facilities on avian species and populations adds to the cost of siting and operation. As much as 32 percent of the avian biological material (feathers and carcasses) recovered from solar facilities remain unidentified, because they often take the form of “feather spots”. Feather spots are remains of impacted animals that can be separated into two broad categories: 1) those remains that may be visually identified to a species, or 2) those that cannot be visually identified to a species due to degradation from the environment and/or scavenger activity (listed as “unknown”). Even when feather spots can be identified to species, they cannot be visually assigned to particular breeding populations. In some cases, it is unknown whether multiple feather spots represent single or multiple individuals. This project’s objectives were to: 1. Use a developed, genetic-based technique to identify and determine the species, population of origin, and number of individuals found in feather spots recovered from solar facilities. 2. Implement collected data and resulting analyses to develop a publicly accessible web-based decision-making tool that can be used by the solar industry, regulators and other stakeholders to inform siting, mitigation, and conservation management efforts. 3. Establish a not-for-profit fee-for-service center at UCLA to ensure collection and identification of feather spots continue after the project period of performance. During the Project Period, we proposed to establish a pipeline for collecting, transporting, and storing of avian biological material collected at solar facilities and the collection and identification of feather spots to species and individual. We proposed the development of a genetic-based framework that would recover viable DNA from feather spots, amplify this DNA (i.e., make millions of copies of the original DNA), and use it to match the resulting sequences to a national database of known species of birds. The result would be the identification of feathers spots that were previously unidentified, and the incorporation of these samples into a larger database that included all samples recovered from solar facilities. The resulting report (below) details the result of this work and its alignment with proposed activities. We proposed the use of the data collected to assess the comparative risk to specific species or populations of species from solar facilities. For some species, we have already identified genomic markers of specific breeding populations and developed “genoscapes,” maps of unique genetic variation across the full breeding range of a species. We used these (previously and newly developed) genoscapes to probabilistically link a feather spot to the specific breeding populations from which it originated (assignment probabilities range from 75%-100% depending on species and population groups). For those species without genoscapes, we developed a vulnerability and susceptibility estimate that determines the relative local and regional risk to populations that are in geographic proximity to solar facilities, using citizen science data (Breeding Bird Survey (BBS) and eBird). These two feather spot processing pipelines (see Figure 1 below) provide quantitative estimates as to the numbers of individuals from a given population of origin that are affected by solar facilities, and ultimately can reduce costs to the consumer by reducing the industry costs associated with mitigation and siting strategies for future solar energy development.

14 SOLAR ENERGY↗
DOE OSTI2025

DESI-DR1 $3 \times 2$-pt analysis: consistent cosmology across weak lensing surveys

We present a joint cosmological analysis of projected galaxy clustering observations from the Dark Energy Spectroscopic Instrument Data Release 1 (DESI-DR1), and overlapping weak gravitational lensing observations from three datasets: the Kilo-Degree Survey (KiDS-1000), the Dark Energy Survey (DES-Y3), and the Hyper-Suprime-Cam Survey (HSC-Y3). This combination of large-scale structure probes allows us to measure a set of $3 \times 2$-pt correlation functions, breaking the degeneracies between parameters in cosmological fits to individual observables. We obtain mutually-consistent constraints on the parameter $S_8 = σ_8 \sqrt{Ω_{\rm m}/0.3} = 0.786^{+0.022}_{-0.019}$ from the combination of DESI-DR1 and DES-Y3, $S_8 = 0.760^{+0.020}_{-0.018}$ from KiDS-1000, and $S_8 = 0.771^{+0.026}_{-0.027}$ from HSC-Y3. These parameter determinations are consistent with fits to the Planck Cosmic Microwave Background dataset, albeit with $1.5-2σ$ lower values in the $S_8-Ω_{\rm m}$ plane. We perform our analysis with a unified pipeline tailored to the requirements of each cosmic shear survey, which self-consistently determines cosmological and astrophysical parameters. We generate an analytical covariance matrix for the correlation data including all cross-covariances between probes, and we design a new blinding procedure to safeguard our analysis against confirmation bias, whilst leaving goodness-of-fit statistics unchanged. Our study is part of a suite of papers that present joint cosmological analyses of DESI-DR1 and weak gravitational lensing datasets.

Porredon, A. [Madrid, CIEMAT; Edinburgh U., Inst. ↗
DOE OSTI2025

Mapping Hsp104 interactions using cross‐linking mass spectrometry

Molecular machines from the AAA+ (ATPases Associated with diverse cellular Activity) superfamily of protein disaggregases play important roles in protein folding, disaggregation and DNA processing. Recent cryo-EM structures of AAA+ molecular machines have uncovered nuanced changes in their conformation that underlie their specialized functions. Structural knowledge of these molecular machines in complex with substrates begins to explain their mechanism of activity. Here, we explore how cross-linking mass spectrometry (XL-MS) can be used to interpret changes in conformation induced by ATP in Hsp104 and how a substrate may interact with Hsp104. We applied a panel of cross-linking reagents to produce cross-linking maps of Hsp104 and interpret our data on previously determined X-ray and cryo-EM structures of Hsp104 from a thermophilic yeast, Calcarisporiella thermophila. We developed an analysis pipeline to differentiate between intra-subunit and inter-subunit contacts within the hexameric homo-oligomer. We identify cross-links that break the asymmetry that is present in Hsp104 in an ATP-hydrolysis competent conformation but is absent in an ATP-hydrolysis-defective mutant. Finally, we identify contacts between Hsp104 and a selected protein (proprotein convertase subtilisin/kexin type 9 PCSK9) to reveal contacts on the central channel of Hsp104 across the length of this protein indicating that we might have trapped interactions consistent with its translocation. Our simple and robust XL-MS-based experiments and methods help interpret how these molecular machines change conformation and bind to other proteins even in the context of homo-oligomeric assemblies enabling coupling state-of-the-art modeling approaches with XL-MS.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

The GlueX beamline and detector

The GlueX experiment at Jefferson Lab has been designed to study photoproduction reactions with a 9-GeV linearly polarized photon beam. The energy and arrival time of beam photons are tagged using a scintillator hodoscope and a scintillating fiber array. The photon flux is determined using a pair spectrometer, while the linear polarization of the photon beam is determined using a polarimeter based on triplet photoproduction. Charged-particle tracks from interactions in the central target are analyzed in a solenoidal field using a central straw-tube drift chamber and six packages of planar chambers with cathode strips and drift wires. Electromagnetic showers are reconstructed in a cylindrical scintillating fiber calorimeter inside the magnet and a lead-glass array downstream. Charged particle identification is achieved by measuring energy loss in the wire chambers and using the flight time of particles between the target and detectors outside the magnet. The signals from all detectors are recorded with flash ADCs and/or pipeline TDCs into memories allowing trigger decisions with a latency of 3.3 $μs$. The detector operates routinely at trigger rates of 40 kHz and data rates of 600 megabytes per second. Here, we describe the photon beam, the GlueX detector components, electronics, data-acquisition and monitoring systems, and the performance of the experiment during the first three years of operation.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Consistency of cosmic shear analyses in harmonic and real space

ABSTRACT Recent cosmic shear studies have reported discrepancies of up to 1σ on the parameter ${S_{8}=\sigma _{8}\sqrt{{\Omega _{\rm m}}/0.3}}$ between the analysis of shear power spectra and two-point correlation functions, derived from the same shear catalogues. It is not a priori clear whether the measured discrepancies are consistent with statistical fluctuations. In this paper, we investigate this issue in the context of the forthcoming analyses from the third year data of the Dark Energy Survey (DES Y3). We analyse DES Y3 mock catalogues from Gaussian simulations with a fast and accurate importance sampling pipeline. We show that the methodology for determining matching scale cuts in harmonic and real space is the key factor that contributes to the scatter between constraints derived from the two statistics. We compare the published scales cuts of the KiDS, Subaru-HSC, and DES surveys, and find that the correlation coefficients of posterior means range from over 80 per cent for our proposed cuts, down to 10 per cent for cuts used in the literature. We then study the interaction between scale cuts and systematic uncertainties arising from multiple sources: non-linear power spectrum, baryonic feedback, intrinsic alignments, uncertainties in the point spread function, and redshift distributions. We find that, given DES Y3 characteristics and proposed cuts, these uncertainties affect the two statistics similarly; the differential biases are below a third of the statistical uncertainty, with the largest biases arising from intrinsic alignment and baryonic feedback. While this work is aimed at DES Y3, the tools developed can be applied to Stage-IV surveys where statistical errors will be much smaller.

79 ASTRONOMY AND ASTROPHYSICS↗