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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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At least 973 records · Page 54
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

The alignment between brightest cluster galaxies and host clusters

The alignment between the brightest cluster galaxies (BCGs) and host clusters can reveal the mystery of the formation and evolution of galaxy clusters. We measure cluster orientations in optical based on the projected distribution of member galaxies and in X-ray by fitting the morphology of intracluster medium (ICM). Cluster orientations determined in the two wavelengths are generally consistent. The orientation alignment between BCGs and host clusters is confirmed and more significant than in previous works. We find that BCGs are more aligned with cluster orientations measured in X-ray than those from optical data. Clusters with a brighter BCG generally show a stronger alignment. We argue that the detected redshift evolution of the alignment is probably caused by observational bias rather than intrinsic evolution. The alignment is not related to the ellipticity of BCGs, and the richness, ellipticity, and dynamical state of host clusters. As a result, the strong alignment between BCGs and the morphology of ICMs may be the consequence of the co-evolution between the central massive galaxy and host clusters.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

HAWC+ Far-infrared Observations of the Magnetic Field Geometry in M51 and NGC 891

Stratospheric Observatory for Infrared Astronomy High-resolution Airborne Wideband Camera Plus polarimetry at 154 μm is reported for the face-on galaxy M51 and the edge-on galaxy NGC 891. For M51, the polarization vectors generally follow the spiral pattern defined by the molecular gas distribution, the far-infrared (FIR) intensity contours, and other tracers of star formation. The fractional polarization is much lower in the FIR-bright central regions than in the outer regions, and we rule out loss of grain alignment and variations in magnetic field strength as causes. When compared with existing synchrotron observations, which sample different regions with different weighting, we find the net position angles are strongly correlated, the fractional polarizations are moderately correlated, but the polarized intensities are uncorrelated. We argue that the low fractional polarization in the central regions must be due to significant numbers of highly turbulent segments across the beam and along lines of sight in the beam in the central 3 kpc of M51. For NGC 891, the FIR polarization vectors within an intensity contour of 1500 MJy sr{sup −1} are oriented very close to the plane of the galaxy. The FIR polarimetry is probably sampling the magnetic field geometry in NGC 891 much deeper into the disk than is possible with NIR polarimetry and radio synchrotron measurements. In some locations in NGC 891, the FIR polarization is very low, suggesting we are preferentially viewing the magnetic field mostly along the line of sight, down the length of embedded spiral arms. There is tentative evidence for a vertical field in the polarized emission off the plane of the disk.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Development of Parameters for the Particle Size Distribution of TATB

Laser light scattering (LLS), manual counting of scanning electron microscopy (SEM) images, and reduction of SEM images using ImageJ (open source program) to determine Feret (caliper) diameters were applied to determine particle size distribution (PSD) of four preparations of 1,3,5‐triamino‐2,4,6‐trinitrobenzene (TATB) and yttria‐stabilized zirconia (YSZ), an SEM certified standard. Mie theory was used to reduce the LLS data. The spherical nature of the YSZ made it a good candidate for LLS. Variations in n , the refractive index, and iκ, the imaginary component, produced very little change in the PSD. However, changing the carrier liquid from H 2 O to a 40% aqueous sucrose solution, thereby changing the carrier refractive index, n 0 , substantially affected the PSD. The Mie complex refractive indices for the YSZ were n = 2.200, iκ = 0.100, with a 40% aqueous sucrose solution, n 0 = 1.400. The triclinic crystal structure of TATB made refractive index determinations more difficult, so a study was conducted varying Mie parameters and comparing them to the same data reduced using the Fraunhofer theory. Changing the n and κ parameters produced PSD with a small concentration of particles less than 1 µm in size or none in this range. SEM images, Feret data, manual counting, and Fraunhofer data reduction indicate particles less than 1 µm are probably < 5% in concentration. The final selection of Mie parameters for TATB was n = 2.283, iκ = 0.1, and suspension medium, n 0 = 1.330. Finally, computations, using density functional theory produced similar parameters.

Feret diameter↗
DOE OSTI2025

In-Situ Synchrotron X-Ray Diffraction of Ultrasonic Microstructural Refinement During Solidification in a Commercial Al–Si–Mg Alloy

This study reports the first use of in-situ synchrotron X-ray diffraction (SXRD) to study the effects of ultrasonic melt processing (USMP) on phase and grain size evolution during solidification in a commercial Al–Si–Mg casting alloy. USMP is a technique that, when applied to aluminum as it solidifies, can be used to refine the local microstructure of large-scale castings. Analysis of the in-situ SXRD data to estimate the average grain size of primary α-Al grains during USMP demonstrates that USMP slows the growth rate of α-Al grains and reduces grain size by 36 pct. Furthermore, there is also evidence that USMP causes the primary α-Al grains to move relative to the X-ray beam; such motion increases the probability of primary α-Al grains colliding and fragmenting. This movement becomes constrained at the onset of the Al–Si binary eutectic, suggesting that USMP ceases to effectively refine the microstructure once the Al–Si binary eutectic begins to form. Complementary laboratory-scale X-ray diffraction (XRD) data were used to correlate the lattice parameters of the α-Al and Si (D-A4) phases with temperature to estimate cooling rate during solidification. Thus, this study can guide the design of novel castings with spatially distributed fine-grained regions produced using local ultrasonic processing.

Aluminum Alloys↗
DOE OSTI2020

Machine-Learning Enabled Evaluation of Probability of Piping Degradation In Secondary Systems of Nuclear Power Plants

The transition to condition-based, risk-informed automated maintenance will contribute to a significant reduction of operations and maintenance costs that account for the majority of nuclear power generation costs. Furthermore, of the operations and maintenance costs in U.S. plants, approximately 80% are labor costs. To address the issue of rising operating costs and economic viability, technologies used to perform online monitoring of piping and other secondary system structural components in commercial nuclear power plants (NPPs) are under evaluation. These online monitoring systems have the potential to identify when a more detailed inspection is needed using real time measurements, rather than at a pre-determined inspection interval thus reducing the maintenance cost. This paper describes distributed high-temperature stable fiber sensors fabricated in optical fibers through a roll-to-roll laser direct writing process using femtosecond lasers. Using phase-sensitive optical time domain reflectometry, distributed acoustic and vibration sensors can be developed and deployed to critical components and systems in NPPs to perform active measurements with spatial resolution down to 0.5-meter throughout the piping systems. Complex acoustic and vibration signatures harnessed by distributed sensors are registered and analyzed by artificial intelligence algorithms for degradation detection and flaw identification. Piping elbows with machined-in flaws were instrumented with fiber sensors. High-spatial-resolution data were used to develop and validate machine learning algorithms, including both linear and nonlinear regression, and classification. Additionally, classification and sensor analysis were also performed for data analysis. The paper concludes with recommendations and future work on applications of machine learning enabled high-resolution fiber sensors for piping degradation monitoring in current or future NPPs.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Analysis and Mitigation of Cascading Failures Using a Stochastic Interaction Graph with Eigen-analysis

In studies on complex network systems using graph theory, eigen-analysis is typically performed on an undirected graph model of the network. However, when analyzing cascading failures in a power system, the interactions among failures suggest the need for a directed graph beyond the topology of the power system to model directions of failure propagation. To accurately quantify failure interactions for effective mitigation strategies, this paper proposes a stochastic interaction graph model and associated eigen-analysis. Different types of modes on failure propagations are defined and characterized by the eigenvalues of a stochastic interaction matrix, whose absolute values are unity, zero, or in between. Finding and interpreting these modes helps identify the probable patterns of failure propagation, either local or widespread, and the participating components based on eigenvectors. Then, by lowering the failure probabilities of critical components highly participating in a mode of widespread failures, cascading can be mitigated. Here, the validity of the proposed stochastic interaction graph model, eigen-analysis and the resulting mitigation strategies is demonstrated using simulated cascading failure data on an NPCC 140-bus system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Extraction of the higher-twist parton distribution $e(x)$ from CLAS data

We present the first point-by-point extraction of a twist-3 PDF. The scalar PDF, e(x), is accessed through the analysis of the data for the sin φR-moment of the beam-spin asymmetry for dihadron production in semi-inclusive DIS off proton target at CLAS and CLAS12. The dihadron formalism allows for use of collinear framework, hence calling for a minimal set of approximations and hypotheses. The extracted PDF e(x) carries insights into the physics of the largely-unexplored quark-gluon correlations, and its first Mellin moment is related to the marginally known scalar charge of the nucleon. We show that the proton flavor combination of the scalar PDF is nonzero at more than 74% probability.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

A Preliminary Radiological Risk Assessment Model for Disposition of Remote-Handled Transuranic Wastes at Los Alamos National Laboratory Area G - 20116

The U.S. Department of Energy (DOE) operates a low-level radioactive waste (LLW) disposal site at Material Disposal Area G, in Los Alamos, New Mexico, USA. Area G has been the primary LLW disposal site for Los Alamos National Laboratory (LANL) since the 1960's. In addition to LLW, Area G is host to a variety of other wastes, the disposition of which must be determined before closure of the site. A probabilistic Radiological Risk Assessment (RRA) for Area G is used in order to support decision making regarding some wastes that are not addressed in the extant Area G Performance Assessment (PA) and Composite Analysis (CA). Between 1979 and 1987, 33 special shafts were augered into the Bandelier Tuff at Area G. This volcanic tuff is present across Pajarito Plateau on the eastern slopes of the Jemez Mountains, and varies widely in its consistency, from weakly indurated non-welded layers to welded layers that uphold the mesa cliffs of the Plateau. These mesas are home to LANL, Area G, and the townsites of Los Alamos and White Rock, with residences about 1400 m from Area G. The 33 Shafts were lined with steel casing, and contain remote-handled (RH) transuranic wastes (TRU) resulting from experiments and analysis performed in special glove boxes at the Chemistry and Metallurgy Research (CMR) facility at LANL. Some of these wastes originated as used nuclear fuel. The purpose of the Area G RRA is to evaluate the potential future risk to humans and the environment from the RH TRU in the 33 Shafts in the context of the risk associated with the surrounding wastes at Area G. The analysis is responsive to expectations outlined in DOE Order 458.1, Radiation Protection of the Public and the Environment, and is informed by the Manual and Guidance accompanying DOE O 435.1, Radioactive Waste Management. Because the waste meets the definition of TRU, the regulatory context necessarily takes into consideration the regulation governing the disposal of TRU from the U.S. Environmental Protection Agency (EPA): 40 CFR 191, Environmental Radiation Protection Standards for Management and Disposal of Spent Nuclear Fuel, High-Level and Transuranic Radioactive Wastes. Given the broader regulatory context for the RRA, the analysis is subject to different assumptions from those made in the existing DOE O 435.1 PA and CA, such as allowing for future occupation of the site. The analysis begins with a comprehensive evaluation of features, events, processes, and exposure scenarios (FEPS) for Area G and the wastes it contains. These FEPSs are screened to eliminate from further consideration those of extremely low probability and/or consequence, and a conceptual site model (CSM) is subsequently developed. The scope and structure of the Area G RRA Model is informed by this CSM, and the Area G RRA Model is developed using the GoldSim systems analysis modeling platform. This paper presents the initial version of a defensible, transparent, and reasonably realistic model, which is based on the state of knowledge of the wastes, the site, and the FEPSs that govern contaminant transport from wastes into the environment and subsequent exposures to humans and other biota. Probabilistic model input distributions represent uncertainties inherent in the real and modeled systems. The results of the Area G RRA Model inform decisions regarding the disposition of the RH TRU in the 33 Shafts. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Voltage Probability Density Function Shaping Control Strategy Considering Grid Operational Uncertainties

It is well-known that power systems operation always affected by various uncertainties which make the bus voltage a random process that can be characterized by its probability density function (PDF) at any time instant. In this context, this paper presents a novel PDF-based voltage control framework for power systems. By modeling voltage as a stochastic process, we formulate a stochastic differential equationthat captures grid uncertainties. The associated Fokker-Planck-Kolmogorov equation is derived to describe the evolution of the voltage PDF, which enables the formulation of a PDF-shaping control strategy. To simplify the PDF control formulation, a B-spline neural network is introduced for real-time estimation and regulation of the voltage distribution. The proposed PDF control law updates voltage references for energy storage systems and synchronous generators using real-time PDF measurements and feedback signals. The proposed method is validated on a modified Kundur’s two-area system. Simulation results demonstrate that the controller can significantly improve the voltage stability under stochastic conditions, highlighting its effectiveness in modern inverter-rich grids.

Gui, Yonghao [ORNL] (ORCID:0000000250435534)↗
DOE OSTI2022

Data-driven Minimum Entropy Control for Stochastic Nonlinear Systems using the Cumulant-Generating Function

Here, we present a novel minimum entropy control algorithm for a class of stochastic nonlinear systems subjected to non-Gaussian noises. The entropy control can be considered as an optimization problem for the system randomness attenuation, but the mean value has to be considered separately. To overcome this disadvantage, a new representation of the system stochastic properties was given using the cumulant-generating function based on the moment-generating function, in which the mean value and the entropy was reflected by the shape of the cumulant-generating function. Based on the samples of the system output and control input, a time-variant linear model was identified, and the minimum entropy optimization was transformed to system stabilization. Then, an optimal control strategy was developed to achieve the randomness attenuation, and the boundedness of the controlled system output was analyzed. The effectiveness of the presented control algorithm was demonstrated by a numerical example. In this paper, a data-driven minimum entropy design is presented without pre-knowledge of the system model; entropy optimization is achieved by the system stabilization approach in which the stochastic distribution control and minimum entropy are unified using the same identified structure; and a potential framework is obtained since all the existing system stabilization methods can be adopted to achieve the minimum entropy objective.

42 ENGINEERING↗
DOE OSTI2025

Karhunen–Loève deep learning method for surrogate modeling and approximate Bayesian parameter estimation

We evaluate the performance of the Karhunen-Loève Deep Neural Network (KL-DNN) framework for surrogate modeling and approximate Bayesian parameter estimation in partial differential equation models. In the surrogate model, the Karhunen-Loève (KL) expansions are used for the dimensionality reduction of the number of unknown parameters and variables, and a deep neural network is employed to relate the reduced space of parameters to that of the state variables. The KL-DNN surrogate model is used to formulate a maximum-a-posteriori-like least-squares problem, which is randomized to draw samples of the posterior distribution of the parameters. We test the proposed framework for a hypothetical unconfined aquifer via comparison with the forward MODFLOW and inverse PEST++ iterative ensemble smoother (IES) solutions as well as the state-of-the-art Fourier neural operator (FNO) and deep operator networks (DeepONets) operator learning surrogate models. Our results show that the KL-DNN surrogate model outperforms FNO and DeepONet for forward predictions. For solving inverse problems, the randomized algorithm provides the same or more accurate Bayesian predictions of the parameters than IES as evidenced by the higher log-predictive probability of both the estimated parameter field and the forecast hydraulic head. The posterior mean obtained from the randomized algorithm is closer to the reference parameter field than that obtained with FNO as the maximum a posteriori estimate.

Approximate Bayesian inference↗
DOE OSTI2022

β − decay of exotic P and S isotopes with neutron number near 28

β - decay of very neutron-rich isotopes of P and S, studied at the National Superconducting Cyclotron Laboratory (NSCL) using the Beta Counting Station (BCS) consisting of a Double Sided Strip Detector surrounded by clovers detectors for observing delayed γ transitions is reported here. β-decay half-lifes and delayed neutron emission probabilities were extracted for 42,43,44 P and 44,46 S by analyzing spatial and temporal correlations between implants and decay events in the Si detector with further coincidence with γ transitions. Detection of delayed γ rays allowed for the identification of negative parity 1p1h states in 42 S for the first time, also constraining the parent ( 42 P) spin/parity to 2 - or 3 - . For the most exotic isotope studied, 46 S, no strong γ transition was observed unlike lighter even-even S isotopes, thus implying the shift of Gamow Teller (GT) strength distribution to higher energies. In conclusion, comparison of experimental observations to detailed shell-model calculations using the SDPFSDG-MU interaction allowed us to infer the importance and role of First Forbidden (FF) β transitions as the neutron number approaches and then exceeds N = 28.

39 ≤ A ≤ 58↗
DOE OSTI2020

Preliminary results in using Deep Learning to emulate BLOB, a nuclear interaction model

Purpose: A reliable model to simulate nuclear interactions is fundamental for Ion-therapy. We already showed how BLOB (“Boltzmann-Langevin One Body”), a model developed to simulate heavy ion interactions up to few hundreds of MeV/u, could simulate also 12 C reactions in the same energy domain. However, its computation time is too long for any medical application. For this reason we present the possibility of emulating it with a Deep Learning algorithm. Methods: The BLOB final state is a Probability Density Function (PDF) of finding a nucleon in a position of the phase space. We discretised this PDF and trained a Variational Auto-Encoder (VAE) to reproduce such a discrete PDF. As a proof of concept, we developed and trained a VAE to emulate BLOB in simulating the interactions of 12 C with 12 C at 62 MeV/u. To have more control on the generation, we forced the VAE latent space to be organised with respect to the impact parameter (b) training a classifier of b jointly with the VAE. Results: In this work, the distributions obtained from the VAE are similar to the input ones and the computation time needed to use the VAE as a generator is negligible. Conclusions: We show that it is possible to use a Deep Learning approach to emulate a model developed to simulate nuclear reactions in the energy range of interest for Ion-therapy. We foresee the implementation of the generation part in C++ and to interface it with the most used Monte Carlo toolkit: Geant4.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Molecular Tuning of Reactivity of Zeolite Protons in HZSM-5

In acidic HZSM-5 zeolite, the reactivity of a methanol molecule interacting with the zeolite proton is amenable to modification via coadsorbing a stochiometric amount of an electron density donor E to form the [(E)(CH 3 OH)(HZ)] complex. The rate of the methanol in this complex undergoing dehydration to dimethyl ether was determined for a series of E with proton affinity (PA) ranging from 659 kJ mol –1 for C 6 F 6 to 825 kJ mol –1 for C 4 H 8 O and was found to follow the expression: Ln(Rate) – Ln(Rate N 2 ) = β(PA – PA N 2 ) γ , where E = N 2 is the reference and β and γ are constants. This trend is probably due to the increased stability of the solvated proton in the [(E)(CH 3 OH)(HZ)] complex with increasing PA. Importantly, this is also observed in steady-state flow reactions when stoichiometric quantities of E are preadsorbed on the zeolite. As demonstrated with E being D 2 O, the effect on methanol reactivity diminishes when E is present in excess of the [(E)(CH 3 OH)(HZ)] complex. It is proposed that the methanol dehydration reaction involves [(E)(CH 3 OH)(CH 3 OH)(HZ)] as the transition state, which is supported by the isotopologue distribution of the initial dimethyl ether formed when a flow of CH 3 OH was passed over ZSM-5 containing one CD 3 OH per zeolite proton. Furthermore, the implication of this on the mechanism of catalytic methanol dehydration on HZSM-5 is discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

On the Role of Macrophysics and Microphysics in Km-Scale Simulations of Mixed-Phase Clouds During Cold Air Outbreaks

Regional atmospheric models struggle to maintain supercooled liquid in mixed-phase clouds during polar cold-air outbreaks (CAOs). Previous studies focused on the parameterization of aerosol, microphysics and turbulence to understand the origin of this widespread model bias. This study investigates the role of sub-grid cloud fraction parameterizations (CFP) in the simulation of mixed-phase cloud. Km-scale simulations are performed for a large number of CAO cases over Norway, for which continuous ground observations were collected at one site over 6 months. We use a novel analysis that attributes the cloud-radiative errors to deficiencies in specific cloud regimes. We show that the CFP matters for cloud-radiative effects in CAOs, but that it is probably not the primary cause of the lack of liquid water in simulated mixed-phase clouds. Of all the CFP sensitivities explored in this study, the prognostic representation of both liquid and ice shows most promise in increasing the liquid water path. A newly proposed hybrid CFP with prognostic frozen and diagnostic liquid cloud fraction reproduces some of the benefits of the prognostic scheme at reduced cost and complexity. All simulations appear to produce too large precipitation particles. As a result, reducing the snow deposition rate increases the liquid water path and improves the precipitation size distributions, while simulations are less sensitive to reduced riming rates.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Variations of the physical parameters of the blazar Mrk 421 based on analysis of the spectral energy distributions

Abstract We report on the variations of the physical parameters of the jet observed in the blazar Mrk 421, and discuss the origin of X-ray flares in the jet, based on analysis of several spectral energy distributions (SEDs). The SEDs are modeled using the one-zone synchrotron self-Compton model, its parameters determined using a Markov chain Monte Carlo method. The lack of data at TeV energies means many of the parameters cannot be uniquely determined and are correlated. These are studied in detail. We find that the optimal solution can be uniquely determined only when we apply a constraint to one of four parameters: the magnetic field (B), the Doppler factor, the size of the emitting region, and the normalization factor of the electron energy distribution. We used 31 sets of SEDs from 2009 to 2014 with optical–UV data observed with UVOT/Swift and the Kanata telescope, X-ray data with XRT/Swift, and γ-ray data with the Fermi Large Area Telescope. The result of our SED analysis suggests that, in the X-ray faint state, the emission occurs in a relatively small area (∼1016 cm) with a relatively strong magnetic field (B ∼ 10−1 G). The X-ray bright state shows a tendency opposite to that of the faint state, that is, a large emitting area (∼1018 cm), probably downstream of the jet, and a weak magnetic field (B ∼ 10−3 G). The high X-ray flux was due to an increase in the maximum energy of electrons. On the other hand, the presence of two kinds of emitting areas implies that the one-zone model is unsuitable for reproducing at least part of the observed SEDs.

Yamada, Yurika↗
DOE OSTI2022

Using ultrasonic attenuation in cortical bone to infer distributions on pore size

Here, in this work we infer the underlying distribution on pore radius in human cortical bone samples using ultrasonic attenuation data. We first discuss how to formulate polydisperse attenuation models using a probabilistic approach and the Waterman Truell model for scattering attenuation. We then compare the Independent Scattering Approximation and the higher-order Waterman Truell models’ forward predictions for total attenuation in polydisperse samples. Following this, we formulate an inverse problem under the Prohorov Metric Framework coupled with variational regularization to stabilize this inverse problem. We then use experimental attenuation data taken from human cadaver samples and solve inverse problems resulting in nonparametric estimates of the probability density function on pore radius. We compare these estimates to the “true” microstructure of the bone samples determined via microCT imaging. We find that our methodology allows us to reliably estimate the underlying microstructure of the bone from attenuation data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Characterization of the CMS Endcap Timing Layer readout chip prototype with charge injection

We present the characterization of a readout Application-Specific Integrated Circuit (ASIC) for the CMS Endcap Timing Layer (ETL) of the High-Luminosity LHC upgrade with charge injection. The ASIC, named ETROC and developed in a 65 nm CMOS technology, reads out a 16× 16 pixel matrix of the Low-Gain Avalanche Detector (LGAD). The jitter contribution from ETROC is required to be below 40 ps to achieve the 50 ps overall time resolution per hit. The analog readout circuits in ETROC consist of the preamplifier and the discriminator. The preamplifier handles the LGAD charge signal with the most probable value of around 15 fC. The discriminator generates the digital pulse, which provides the Time-Of-Arrival (TOA, leading edge) and Time-Over-Threshold (TOT, pulse width) information. The prototype of ETROC (ETROC0) that implements a single channel of analog readout circuits has been evaluated with charge injection. The jitter of the analog readout circuits, measured from the discriminator's leading edge, is better than 16 ps for a charge larger than 15 fC with the sensor capacitance. The time walk resulting from different pulse heights can be corrected using the TOT measurement. The time resolution distribution has a standard deviation of 29 ps after the time-walk correction from the charge injection. At room temperature, the preamplifier's power consumption is measured to be 0.74 mW and 1.53 mW per pixel in the low- and high-power mode, respectively. Finally, the measured power consumption of the discriminator is 0.84 mW per pixel. With the ASIC alone or the LGAD sensor, The characterization performances fulfill the ETL's challenging requirements.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗