Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Track reconstruction”

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

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

At least 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Cosmic Background Rejection of the ICARUS experiment at Fermilab

The Short Baseline Neutrino program at Fermilab aims to explore significant regions of parameter space, applicable to sterile neutrinos at the eV mass scale, as suggested by existing experimental anomalies. To this purpose it exploits Liquid Argon Time Projection Chamber detectors located along the Booster Neutrino Beamline to measure both νe appearance and νµ disappearance: the Short Baseline Neutrino Detector and the ICARUS-T600 detector at 110 and 600 m from the neutrino source, respectively. The ICARUS T-600 Far Detector, located at shallow depth, is surrounded by a Cosmic Ray Tagger system to mitigate the cosmic ray background. On average ~ 11 muon tracks are expected to cross the detector during the ~ 1 ms drift time. The cosmic ray tagger is composed of plastic scintillator bars, ensuring near 4π coverage of the detector aiming at tagging cosmic muons and thus reject 𝛾s produced by muon interactions in the surrounding materials that can generate an electromagnetic showers mimicking a νe signal. The system allows one to disentangle cosmic rays from particles originated in a neutrino interaction inside the detector by measuring their position and crossing time. A synchronization of the cosmic ray tagger with the ICARUS photon detection system with a nanosecond accuracy allows one to reject cosmic particles recorded during the beam spill and thus select an enriched sample of neutrino triggered events ahead of the event reconstruction. An overview of the cosmic ray tagger system as well as its role in the neutrino events identification and cosmic background rejection will be presented.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Kinetic-controlled transformations of group-III arsenide nanocubes

Tracking the structural evolution of colloidal nanocrystals (NCs) facilitates the mechanistic studies of their materials chemistry. NC engineering via phase transformation reveals the chemical and physical determinants that drive lattice-scale dynamic processes such as cation exchange. Here, in this study, we employed NCs to demonstrate the cation exchange process from Cu 3 As to InAs and GaAs within nanocubes. The symmetry conversion in unit cells from cubic Cu 3 As to hexagonal InAs and GaAs can be described using a schematic cellular automaton model, which suggests a simplified cube-to-sphere transition. The strong covalent characteristics of III–V materials highlight the kinetic control that navigates the tailorable transformation through either an isotropic trajectory, leading to hollow structures, or a topotaxial trajectory, with abundant stacking faults. The reconstruction of complex covalent bonds is envisioned as the foundation for the synthesis of NCs.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2023

Safer Foundation Solar Demands Skill Collaborative

The milestones and accomplishments achieved in Safer’s Solar Demands Skill Training program required a collective effort of stakeholders. The ability to achieve the desired results and impact lives of the population we serve works best when collaborating with community stakeholders such as community-based organizations, faith-based organizations, community activists, local law enforcement, community residents, employers, and elected and appointed officials. We are working harder than ever to place clients in-demand careers and high-growth industry sectors. Our employer engagement with high growth sectors continues to increase, we have been able to deepen our relationships in this space by providing in demand stackable credential training. Launched our initiative in the green job space by partnering with organizations like ComEd, the Department of Energy, community-based organizations, faith-based organizations, and manufacturers that helped to train clients in the green jobs and renewable energy space. By the end of the year, we will have completed our tenth cohort of photovoltaic solar installation training. The training has been used as a foundation for solar panel installers to acquire new skills in in the green jobs career pathways, such as sales and customer service. It has also served as a pipeline to union-level trade positions through the skills participants obtained through partnership engagement. Safer Foundation's policy and advocacy team plays a major role in expanding opportunities for people with records to over one hundred occupations, including the trades. This reform has allowed many to secure living wage employment, reducing the high recidivism rate within Illinois. Safer continues to build our social enterprise with Reconstructive Technology Partners (RTP). RTP is introducing people with records to the construction trades such as solar, carpentry and electrical through residential remodeling of homes on smaller construction projects. Understanding the need to have a greater community presence, outreach was extended to a boots on the ground model, including, but not limited to door-to-door engagement, DE-EE0008571 Safer Foundation Page 4 of 29 local radio broadcasting and print ads in local newspapers. We continue to seek innovative ways to increase Safer Foundations' presence in the renewable energy space. Outreach efforts gave us an opportunity to share critical reentry information and opportunities about our PV installer program. Giving us reach with local and national audiences alike. We launched a systematic approach to improving client data and client tracking through an evidence-based practices initiative. That implemented an agency wide cross-functional data management system. The system supported our goal of reviewing, evaluating, and making recommendations to improve operational processes, program delivery, information sharing, and more. Played a significant part in the success of the solar program. Finally, economists around the nation agree that there is a significant labor shortage. The shortage directly threatens our ability to sustain our economic growth; if employers do not have access to the workers they need, it can lead to a shutdown in the economic recovery. The demand for Safer Foundation services is more important and impactful than ever. We will continue to build upon our 50 years of experience to address the challenges ahead and serve more people in a better way. We are confident we will accomplish beautiful things that benefit everyone involved because together, we are powerful.

14 SOLAR ENERGY↗
DOE OSTI2020

High-performance Generic Neutrino Detection in a LArTPC near the Earth's Surface with the MicroBooNE Detector

Large Liquid Argon Time Projection Chambers (LArTPCs) are being increasingly adopted in neutrino oscillation experiments because of their superb imaging capabilities through the combination of both tracking and calorimetry in a fully active volume. Active LArTPC neutrino detectors at or near the Earth's surface, such as the MicroBooNE experiment, present a unique analysis challenge because of the large flux of cosmic-ray muons and the slow drift of ionization electrons. We present a novel Wire-Cell-based high-performance generic neutrino-detection technique implemented in MicroBooNE. The cosmic-ray background is reduced by a factor of 1.4$\times10^{5}$ resulting in a 9.7% cosmic contamination in the selected neutrino candidate events, for visible energies greater than 200~MeV, while the neutrino signal efficiency is retained at 88.4% for $\nu_{\mu}$ charged-current interactions in the fiducial volume in the same energy region. This significantly improved performance compared to existing reconstruction algorithms, marks a major milestone toward reaching the scientific goals of LArTPC neutrino oscillation experiments operating near the Earth's surface.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Toward Drilling the Perfect Geothermal Well: An International Research Coordination Network for Geothermal Drilling Optimization Supported by Deep Machine Learning and Cloud Based Data Aggregation

The EDGE project, supported by the U.S. Department of Energy Geothermal Technologies Office under award DE-EE0008793, established a data-driven framework for improving the efficiency, cost-effectiveness, and reliability of geothermal well drilling. The project focused on developing scalable data infrastructure, advanced machine learning and probabilistic models, and integrated analytics tools to support continuous drilling optimization. A central objective was to reduce geothermal drilling costs by up to seventy percent while minimizing the risk of well failure through predictive diagnostics and adaptive planning. Over the project period, a comprehensive data repository was designed and deployed, incorporating records from over one hundred geothermal wells across varied geological settings. This repository supported both structured and unstructured data and adhered to FAIR data principles, enabling provenance tracking, quality control, and standardized metadata. The project introduced automated ingestion pipelines and a cloud-hosted platform that facilitated access to raw, processed, and derived datasets. This infrastructure served as the foundation for model development and analysis. Machine learning workflows were developed to predict key drilling metrics including rate of penetration, non-productive time, and total drilling costs. Self-organizing maps and dimensionality reduction methods were used to uncover operational patterns and outliers, while supervised learning algorithms such as random forests and deep neural networks were applied to forecast performance outcomes. The models were validated on heterogeneous datasets from both U.S. and Icelandic fields, demonstrating variable but significant predictive accuracy. The results indicated that finer temporal resolution, inclusion of lithological data, and consistency in operational annotations could substantially improve model performance. The project also implemented process mining techniques to reconstruct state-transition models from drilling event logs. These models enabled the identification of deviations from optimal workflows and provided insights into recurring failure modes. Analysis of non-productive time highlighted the impact of equipment failures, geological challenges, and human factors, offering opportunities for targeted mitigation strategies. The EDGE Dashboard was developed as a web-based expert system integrating data visualization, model outputs, and user-driven queries. It provided an accessible interface for operators to explore historical data, evaluate predicted outcomes, and compare drilling scenarios. Initial feedback from project partners suggested that the dashboard could serve as a foundation for more advanced advisory and optimization tools. Overall, the EDGE project demonstrated the feasibility and value of applying modern data science techniques to geothermal drilling. It delivered a set of interoperable tools and models that can support more efficient, lower-risk well development. The findings point toward a viable path for transitioning from advisory analytics to semi-autonomous drilling systems, contingent on continued collaboration, expanded datasets, and field validation. The project results have immediate relevance for drilling operations, data management practices, and future geothermal R&D efforts aimed at achieving reliable, cost-competitive geothermal energy at scale.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Changes in electric field noise due to thermal transformation of a surface ion trap

Here, we aim to illuminate how the microscopic properties of a metal surface map to its electric field noise characteristics. In our system, prolonged heat treatments of a metal film can induce a rise in the magnitude of the electric field noise generated by the surface of that film. We refer to this heat-induced rise in noise magnitude as a thermal transformation. The underlying physics of this thermal transformation process is explored through a series of heating, milling, and electron treatments performed on a single surface ion trap. Between these treatments, 40 Ca + ions trapped 70 μm above the surface of the metal are used as detectors to monitor the electric field noise at frequencies close to 1 MHz. An Auger spectrometer is used to track changes in the composition of the contaminated metal surface. With these tools we investigate contaminant deposition, chemical reactions, and atomic restructuring as possible drivers of thermal transformations.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Characterization of BNL and HPK AC-LGAD sensors with a 120 GeV proton beam

We present measurements of AC-LGADs performed at the Fermilab's test beam facility using 120 GeV protons. Here we studied the performance of various strip and pad AC-LGAD sensors that were produced by BNL and HPK. The measurements are performed with our upgraded test beam setup that utilizes a high precision telescope tracker, and a simultaneous readout of up to 7 channels per sensor, which allows detailed studies of signal sharing characteristics. These measurements allow us to assess the differences in designs between different manufacturers, and optimize them based on experimental performance. We then study several reconstruction algorithms to optimize position and time resolutions that utilize the signal sharing properties of each sensor. We present a world's first demonstration of silicon sensors in a test beam that simultaneously achieve better than 6–10 μm position and 30 ps time resolution. This represents a substantial improvement to the spatial resolution than would be obtained with binary readout of sensors with similar pitch.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Methods for safely sharing dual-use genetic data

Background: Some genetic data has dual-use potential. Sharing pathogen data has shown tremendous value. For example therapeutic development and lineage tracking during the COVID pandemic. This data sharing is complicated by the fact that these data have the potential to be used for harm. The genome sequence of a pathogen can be used to enable malicious genetic engineering approaches or to recreate the pathogen from synthetic DNA. Standard data security methods can be applied to genetic data, but when data is shared between institutions, ensuring appropriate security can be difficult. Sensitive data that is shared internationally among a wide array of institutions can be especially difficult to control. Methods for securely storing and sharing genetic data with potential for dual-use are needed to mitigate this potential harm.Results: Here we propose new methods that allow genetic data to be shared in a data format that prevents a nefarious actor from accessing sensitive aspects of the data. Our methods obfuscate raw sequence data by pooling reads from different samples. This approach can ensure that data is secure while stored and during electronic transfer. We demonstrate that by pooling raw sequence data from multiple samples of the same organism, the ability to fully reconstruct any individual sample is prevented. In the pooled data, most genomic information remains, but reads or mutations cannot be directly attributed to any individual sample. To further restrict access to information, regions of a genome can be removed from the reads.Conclusion: Our methods obscure genomic information within raw sequence reads. This method can allow genetic data to be stored and shared while preventing a nefarious actor from being able to perfectly reconstruct an organism. Broad-scale sequence information remains, while fine scale details about specific samples are difficult or impossible to reconstruct. Our software is available at https://github.com/Geneinfosec-Inc/ReadMixer.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Experimental study of multiple-orientation muon tomography with image optimization in sparse data environments

Due to the high penetrating power of cosmic-ray muons, they can be used to probe very thick and dense objects. As muons are charged particles, they can be tracked by ionization detectors, determining the position and direction of the muons. With detectors on either side of an object to measure particle direction change, scattering information within the object can be found. This can be used to produce a scattering-intensity image within the object related to density and atomic number. Such imaging is typically performed with a single detector-object orientation, taking advantage of the more intense downward flux of muons, producing planar imaging with some depth-of-field information in the third dimension. Several simulation studies were published with multiorientation tomography, which can form a three-dimensional representation faster than a single-orientation view. In this study, experimental muon-scatter-based tomography was performed using a concrete filled steel drum with several different metal wedges inside, with the drum between detector planes. Data were collected from different detector-object orientations by rotating the steel drum. The data collected from each orientation were combined using two different tomographic methods. A traditional inverse Radon transform approach used for computed tomography and a combination of multiple depth-of-field reconstructions were applied to the data. As cosmic-ray muon flux imaging is rate limited, the imaging techniques were compared for sparse data. Using the combined depth-of-field reconstruction technique, fewer detector-object orientations were needed to reconstruct images that could be used to differentiate the metal wedges.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

First experimental study of multiple orientation muon tomography, with image optimization in sparse data environments

Due to the high penetrating power of cosmic ray muons, they can be used to probe very thick and dense objects. As charged particles, they can be tracked by ionization detectors, determining the position and direction of the muons. With detectors on either side of an object, particle direction changes can be used to extract scattering information within an object. This can be used to produce a scattering intensity image within the object related to density and atomic number. Such imaging is typically performed with a single detector-object orientation, taking advantage of the more intense downward flux of muons, producing planar imaging with some depth-of-field information in the third dimension. Several simulation studies have been published with multi-orientation tomography, which can form a three-dimensional representation faster than a single orientation view. In this work we present the first experimental multiple orientation muon tomography study. Experimental muon-scatter based tomography was performed using a concrete filled steel drum with several different metal wedges inside, between detector planes. Data was collected from different detector-object orientations by rotating the steel drum. The data collected from each orientation were then combined using two different tomographic methods. Results showed that using a combination of multiple depth-of-field reconstructions, rather than a traditional inverse Radon transform approach used for CT, resulted in more useful images for sparser data. As cosmic ray muon flux imaging is rate limited, the imaging techniques were compared for sparse data. Using the combined depth-of-field reconstruction technique, fewer detector-object orientations were needed to reconstruct images that could be used to differentiate the metal wedge compositions.

Applied Physics (physics.app-ph)↗
DOE OSTI2021

Tomographic X-ray particle tracking velocimetry

We investigate the feasibility of in-laboratory tomographic X-ray particle tracking velocimetry (TXPTV) and consider creeping flows with nearly density matched flow tracers. Specifically, in these proof-of-concept experiments we examined a Poiseuille flow, flow through porous media and a multiphase flow with a Taylor bubble. For a full 360° computed tomography (CT) scan we show that the specially selected 60 micron tracer particles could be imaged in less than 3 seconds with a signal-to-noise ratio between the tracers and the fluid of 2.5, sufficient to achieve proper volumetric segmentation at each time step. In the pipe flow, continuous Lagrangian particle trajectories were obtained, after which all the standard techniques used for PTV or PIV (taken at visible wave lengths) could also be employed for TXPTV data. And, with TXPTV we can examine flows inaccessible with visible wave lengths due to opaque media or numerous refractive interfaces. In the case of opaque porous media we were able to observe material accumulation and pore clogging, and for flow with Taylor bubble we can trace the particles and hence obtain velocities in the liquid film between the wall and bubble, with thickness of liquid film itself also simultaneously obtained from the volumetric reconstruction after segmentation. While improvements in scan speed are anticipated due to continuing improvements in CT system components, we show that for the flows examined even the presently available CT systems could yield quantitative flow data with the primary limitation being the quality of available flow tracers.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Nanoscale trace-element zoning in pyrite framboids and implications for paleoproxy applications

Pyrite framboids (spherical masses of nanoscale pyrite) are among the earliest textures of pyrite to form in sediments. It has been proposed that their trace-element (TE) contents can be used to track the TE composition of the water column in which they formed. However, it is not clear how these TEs are associated with the framboidal pyrite grains. For instance, it is important to know whether they are incorporated uniformly or are enriched in different regions of the framboid. Here, we used high-resolution scanning transmission electron microscopy to identify chemical zoning within pyrite framboids. We found that initial, nanoscale pyrite euhedral crystals, which make up the volumetric majority of the framboids, are covered/infilled by later pyrite that templates on the earlier pyrite. Further, this later pyrite is enriched in TEs, suggesting that many TEs are incorporated in pyrite relatively late (during early diagenesis; not in the water column). This observation suggests that although chemical analyses of pyrite framboids may provide ocean-water chemistry trends through time, the details are complex. Specifically, the TEs found in pyrite may be linked to adsorption onto organic matter, detrital material, and authigenic minerals such as Fe- and Mn-oxide phases followed by desorption in the sediments or release via dissolution and incorporation into pyrite as overgrowths on the initial nanoscale euhedral crystals that make up framboids. While the use of pyrite chemistry to understand past ocean conditions remains promising, and even diagenetic additions may not preclude the utility of pyrite for reconstructing ancient ocean conditions, care must be taken in interpretations because the end concentration may be influenced by diagenesis.

58 GEOSCIENCES↗
DOE OSTI2021

Novel usage of deep learning and high-performance computing in long-baseline neutrino oscillation experiments

Mención Internacional en el título de doctorDeep-learning methods are playing a crucial role in numerous scientific and industrialapplications. Over the past two decades, these techniques have helped in the collection,reconstruction, and analysis of large data samples in particle physics experiments. Themain topic of this PhD research is the study of deep-learning techniques in long-baselineneutrino oscillation experiments. Neutrinos are mysterious light elementary particles,and their investigation is essential to shed light on some of the remaining open questionsin physics. The work presented here describes an algorithm based on a convolutionalneural network developed to provide highly accurate and efficient selections of electronneutrino and muon neutrino interactions in the Deep Underground Neutrino Experiment(DUNE). With this algorithm, the electron neutrino (antineutrino) selection efficiencypeaks at 90% (94%) and exceeds 85% (90%) for reconstructed neutrino energies between2-5 GeV. The selection efficiency for muon neutrino (antineutrino) interactions is foundto have a maximum of 96% (97%) and exceeds 90% (95%) efficiency for reconstructedneutrino energies above 2 GeV. When considering all electron neutrino and antineutrinointeractions as signal (both those appearing from oscillations and those intrinsic tothe beam), a selection purity of 90% is achieved. These event selections are criticalto maximise the sensitivity of the experiment to CP-violating effects, key to furtherunderstand the matter-antimatter asymmetry of the Universe.In high-energy physics experiments, deep learning has also been explored for producingfast simulations and physically-motivated manipulations of simulated images. Some ofthose simulations, such as the light production and detection, are very computationallyexpensive and require novel methods to produce the necessary samples while controllingthe varied underlying physics model parameters. To do so, we invented the model-assistedgenerative adversarial network (MAGAN), first validated on simple generic case studiesand then successfully applied to the DUNE photon-detector simulation.Moreover, we also developed graph neural networks for 3D-voxel classification ofambiguities and optical crosstalk for a different particle physics experiment, most preciselyfor the proposed SuperFGD. This novel 3D-granular plastic-scintillator neutrino detectorwill be used to upgrade the near detector of the T2K neutrino oscillation experiment, and our method reports efficiencies and purities of 94-96% per event in the classificationof particle track voxels.Due to the growth and complexity of deep neural networks, researchers have beeninvestigating techniques to train those networks in a more computationally-efficient way.Many efforts have been made by the community to optimise deep-learning models byparallelising or distributing their training computation across multiple devices. In thisthesis, we study an approach based on data locality for those neural networks that cannotbenefit from scaling their computation due to a significant bottleneck in the data I/O.The research also includes a detailed study on the performance of deep neural networkson hardware accelerator boards.Los métodos de aprendizaje profundo son cada vez más utilizados en numerosas aplicacionescientíficas e industriales hoy en día. Durante las dos últimas décadas, estastécnicas se han empleado en la recolección, reconstrucción y análisis de la gran cantidadde datos generados por experimentos de física de partículas. El tema principal de estatesis doctoral es el uso de estos modelos de aprendizaje profundo en experimentos defísica de neutrinos, en concreto en los experimentos de larga distancia DUNE y T2K. Losneutrinos, partículas fundamentales neutras, de las más ligeras del Universo, pueden serclave para explicar algunas de las cuestiones todavía sin resolver en física fundamental.Entre las diferentes contribuciones que esta tesis ha hecho a su estudio, cabe destacar eldesarrollo de un algoritmo basado en una red de neuronas convolucional para seleccionarcon gran eficiencia y precisión las interacciones de neutrinos electrónicos y muónicos enel Deep Underground Neutrino Experiment (DUNE). La eficiencia de selección obtenidapara neutrinos (antineutrinos) electrónicos alcanza un máximo del 90% (94%) y supera el85% (90%) para neutrinos con energías reconstruidas en el rango 2-5 GeV. La selección deneutrinos (antineutrinos) muónicos tiene una eficiencia máxima del 96% (97%) y excedeel 90% (95%) para neutrinos con energías reconstruidas de más de 2 GeV. Considerandocomo señal todas las interacciones de neutrinos y antineutrinos electrónicos (procedentestanto de oscilaciones como intrínsecos en el haz inicial), se logra una pureza en la seleccióndel 90%. Dichas selecciones de eventos son fundamentales para maximizar la sensibilidaddel experimento a los efectos de violació...

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Tracking nickel oxide reduction in solid oxide cells via ex-situ ptychographic nano-tomography

In solid oxide cell (SOC) electrodes, nickel oxide reduction is a fundamental step occurring during the initial stages of cell operation. The reduction procedure is considered to be crucial in producing the final fuel electrode microstructure. This work studies the reduction reaction occurring in a SOC fuel electrode by means of X-ray ptychographic nanotomography. The same microstructure is analyzed in its pristine state and after 1 h of reduction at 850 °C, in dry hydrogen. The microstructure evolution is discussed in terms of changes in volume fractions, surface areas, and connectivity of the different constituent materials. The pore network in the unreduced cermet is revealed to be fully isolated, implying that reduction proceeds inwards from the edges of the sample towards the center. For the given experimental conditions we report that reduction is complete after 1 h of treatment but the resulting nickel network is not yet percolated and detached from the yttria-stabilized zirconia.

25 ENERGY STORAGE↗
DOE OSTI2022

Real-time X-ray phase-contrast imaging using SPINNet—a speckle-based phase-contrast imaging neural network

X-ray phase-contrast imaging has become indispensable for visualizing samples with low absorption contrast. In this regard, speckle-based techniques have shown significant advantages in spatial resolution, phase sensitivity, and implementation flexibility compared with traditional methods. However, the computational cost associated with data inversion has hindered their wider adoption. By exploiting the power of deep learning, we developed a speckle-based phase-contrast imaging neural network (SPINNet) that significantly improves the imaging quality and boosts the phase retrieval speed by at least 2 orders of magnitude compared to existing methods. To achieve this performance, we combined SPINNet with a coded-mask-based technique, an enhanced version of the speckle-based method. Using this scheme, we demonstrate the simultaneous reconstruction of absorption and phase images on the order of 100 ms, where a traditional correlation-based analysis would take several minutes even with a cluster. In addition to significant improvement in speed, our experimental results show that the imaging and phase retrieval quality of SPINNet outperform existing single-shot speckle-based methods. Furthermore, we successfully demonstrate SPINNet application in x-ray optics metrology and 3D x-ray phase-contrast tomography. Our result shows that SPINNet could enable many applications requiring high-resolution and fast data acquisition and processing, such as in situ and in operando 2D and 3D phase-contrast imaging and real-time at-wavelength metrology and wavefront sensing.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Genetic and behavioral adaptation of Candida parapsilosis to the microbiome of hospitalized infants revealed by in situ genomics, transcriptomics, and proteomics

Background Candida parapsilosis is a common cause of invasive candidiasis, especially in newborn infants, and infections have been increasing over the past two decades. C. parapsilosis has been primarily studied in pure culture, leaving gaps in understanding of its function in a microbiome context. Results. Here, we compare five unique C. parapsilosis genomes assembled from premature infant fecal samples, three of which are newly reconstructed, and analyze their genome structure, population diversity, and in situ activity relative to reference strains in pure culture. All five genomes contain hotspots of single nucleotide variants, some of which are shared by strains from multiple hospitals. A subset of environmental and hospital-derived genomes share variants within these hotspots suggesting derivation of that region from a common ancestor. Four of the newly reconstructed C. parapsilosis genomes have 4 to 16 copies of the gene RTA3, which encodes a lipid translocase and is implicated in antifungal resistance, potentially indicating adaptation to hospital antifungal use. Time course metatranscriptomics and metaproteomics on fecal samples from a premature infant with a C. parapsilosis blood infection revealed highly variable in situ expression patterns that are distinct from those of similar strains in pure cultures. For example, biofilm formation genes were relatively less expressed in situ, whereas genes linked to oxygen utilization were more highly expressed, indicative of growth in a relatively aerobic environment. In gut microbiome samples, C. parapsilosis co-existed with Enterococcus faecalis that shifted in relative abundance over time, accompanied by changes in bacterial and fungal gene expression and proteome composition. Conclusions The results reveal potentially medically relevant differences in Candida function in gut vs. laboratory environments, and constrain evolutionary processes that could contribute to hospital strain persistence and transfer into premature infant microbiomes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Design and simulated performance of calorimetry systems for the ECCE detector at the electron ion collider

Here, we describe the design and performance the calorimeter systems used in the ECCE detector to achieve the overall performance specifications cost-effectively with careful consideration of appropriate technical and schedule risks. The calorimeter systems consist of three electromagnetic calorimeters, covering the combined pseudorapidity range from -3.7 to 3.8 and two hadronic calorimeters covering a combined range of -1.1<η<3.8. Key calorimeter performances which include energy and position resolutions, reconstruction efficiency, and particle identification will be presented.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

GEM detectors for the CMS endcap muon system: status of three new detector stations

The High-Luminosity LHC (HL-LHC, or Phase-2 LHC) will deliver proton-proton collisions at 5–7.5 times the nominal LHC luminosity, with an expected number of 140–200 pp-interactions per bunch crossing (Pile-up or PU). To maintain the performance of muon triggering and reconstruction under high background, the forward part of the Muon Spectrometer of the CMS experiment will be upgraded with Gas Electron Multipliers (GEM) and improved Resistive Plate Chambers (iRPC) detectors. A first GEM station (GE1/1) was installed during Long Shutdown 2 (LS2, 2019–2021), a 2 nd station (GE2/1) of Triple-GEM detectors will be installed in winter 2023–24 and 2024–25, while a new 6-layer station (ME0) will be installed in the third Long Shutdown (LS3, 2026–2028). GE11 is considered an early Phase-2 upgrade as it will reduce the p T threshold by combining GEM and Cathode Strip Chamber (CSC) hits in the forward muon system at twice the LHC design luminosity ($\mathcal{L}$ = 2 · 10 34 cm -2 s -1 , 50 PU). After a successful start of Run-3 in 2022, with almost 40 fb -1 collected, the commissioning of the GE1/1 detector is nearly complete. Most chambers are operated stably with an efficiency in excess of 95%, next being the demonstration of the combined CSC-GEM trigger in 2023. The lessons learnt with the first large-area GEM station have lead to improvements in detector and electronics design for the Phase 2 detectors GE2/1 and ME0. This proceeding will discuss the progress made since last MPGD Conference (MPGD 2019), discussing the commissioning and early performance of GE1/1; the design improvements and start of construction of GE2/1; and the R&D currently ongoing for ME0.

47 OTHER INSTRUMENTATION↗