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At least 271 records · Page 15

A search for Heavy Neutral Leptons with masses up to 150 MeV and studies on scintillation light detection in SBND

Introduction Neutrino physics is one of today’s most promising fields in particle physics. The discovery of neutrino oscillations is an evidence of their mass not being null. However, the Standard Model of elementary particles (SM) does not contain a mechanism that generates them, so we have to search beyond the SM to find answers. Other unknowns remain open, such as the amount of CP symmetry violated in the lepton sector that could help explain the differences between matter and antimatter that formed the present universe. Nor is it understood why these particles are so light, more than six orders of magnitude below the next fermion, the electron. A possible explanation is the existence of heavier neutrino mass states usually referred as heavy neutral leptons (HNLs). One of the most advanced technologies for detecting neutrino interactions is the liquid argon time projection chamber (LArTPC). LArTPC detectors form a 3D image of the ionized electrons in the interaction, producing a detailed description of each neutrino event. Argon is also a prolific scintillator (40k photons/MeV) with light signals indicating the interaction time. The Short-Baseline Near Detector (SBND), a 112-active mass LArTPC at Fermilab, will measure millions of neutrino interactions from the Booster Neutrino Beam (BNB). As the near (L=110 m) detector of the Short-Baseline neutrino program, SBND will search for a fourth light neutrino (∼ eV) in the coming years. This hypothesis could explain the anomalous data observed in the LSND and MiniBooNE experiments.

Álvarez Garrote, Rodrigo [Madrid U.] (ORCID:000000↗

Demonstration and performance of an online data selection algorithm for liquid argon time projection chambers using MicroBooNE

The MicroBooNE detector is a liquid argon time projection chamber (LArTPC) that produces three-dimensional images of particle interactions using ionization charge collected by anode wire plane arrays and scintillation light collected by a light detection system. In addition to testing long-standing experimental neutrino anomalies and performing measurements of neutrino interactions with argon nuclei using the Fermilab Booster Neutrino Beam, MicroBooNE aims to develop methodologies for rare beyond the Standard Model and off-beam physics searches. Looking ahead to the upcoming Deep Underground Neutrino Experiment (DUNE), with MicroBooNE serving as a valuable testbed, achieving high sensitivity and livetime for off-beam physics while satisfying data processing and storage constraints will require data-driven, intelligent, and online or real-time data selection techniques. These techniques are essential for reducing data rates and preserving rare signals with high accuracy. In this paper, we describe a fast data selection algorithm suitable for online execution to identify electrons from stopping cosmic ray muons in the MicroBooNE detector utilizing ionization charge information, and present its performance. This represents the first demonstration of online data selection in a LArTPC using real data and charge information exclusively and provides an important proof-of-principle for applying such techniques to other LArTPC experiments such as the Short-Baseline Near Detector and DUNE.

Abratenko, P. [Tufts U. (main)]↗

Active Optical Control with Machine Learning: A Proof of Concept for the Vera C. Rubin Observatory

Abstract The Active Optics System of the Vera C. Rubin Observatory (Rubin) uses information provided by four wave front sensors to determine deviations between the reconstructed wave front and the ideal wave front. The observed deviations are used to adjust the control parameters of the optical system to maintain image quality across the 3.°5 field of view. The baseline approach from the project is to obtain amplitudes of the Zernike polynomials describing the distorted wave front from out-of-focus images collected by the wave front sensors. These Zernike amplitudes are related via an “influence matrix” to the control parameters necessary to correct the wave front. In this paper, we use deep-learning methods to extract the control parameters directly from the images captured by the wave front sensors. Our neural net model uses anti-aliasing pooling to boost performance, and a domain-specific loss function to aid learning and generalization. The accuracy of the control parameters derived from our model exceeds Rubin requirements even in the presence of full-moon background levels and mis-centering of reference stars. Although the training process is time consuming, model evaluation requires only a few milliseconds. This low latency should allow for the correction of the optical configuration during the readout and slew interval between successive exposures.

Astronomy & Astrophysics↗

Assessment of an E10 gasoline surrogate: Qualitative and quantitative comparisons of in-cylinder spray morphology

A minimum-component gasoline fuel surrogate that captures both chemical and physical behaviors of a full-distillate fuel is needed for high-fidelity CFD simulations. This study evaluates gasoline spray characteristics in a direct-injection spark-ignition engine under motored operation. Two fuels are compared; PACE-20, which is a 9-component surrogate formulation of RD5-87, is compared with its target fuel RD5-87, which is a full-boiling range research grade E10 gasoline. The spray morphologies of both fuels are recorded for a centrally-located direct-injection 8-hole spray subject to intake air cross-flow during the early part of the intake stroke. High-speed imaging recorded scattered light of the side and axial projections of the liquid spray. Quantitative metrics were developed and employed to facilitate comparison of spray morphologies as well as to identify the transition in spray morphology due to flash boiling. This paper builds on a previous study of RD5-87 where coolant temperature (20°C–100°C), in-cylinder pressure (40–110 kPa), engine speed (650–1950 rpm), and injection pressure (60–180 bar) were systematically changed to span operating conditions with and without flash boiling. Images of the PACE-20 morphology are selected for a sub-set of operating conditions from the previous study where distinctive morphology changes occurred. Visual inspection of the images and quantitative metrics demonstrate that the PACE-20 spray morphology is equivalent to that of the RD5-87 in most cases. The exception was for changes in the ambient-gas pressure where the flash-boiling transition occurred at ∼5 kPa higher in-cylinder pressure for PACE-20. Three empirical metrics, Merging Index, Asymmetry, and Flash Index are proposed here and they were found to be useful both as quantitative comparisons of the fuel morphologies, and for identifying the transition in spray morphology due to flash boiling.

Kim, Namho↗

Proton Tunable Analog Transistor for Low Power Computing

This project was broadly motivated by the need for new hardware that can process information such as images and sounds right at the point of where the information is sensed (e.g. edge computing). The project was further motivated by recent discoveries by group demonstrating that while certain organic polymer blends can be used to fabricate elements of such hardware, the need to mix ionic and electronic conducting phases imposed limits on performance, dimensional scalability and the degree of fundamental understanding of how such devices operated. As an alternative to blended polymers containing distinct ionic and electronic conducting phases, in this LDRD project we have discovered that a family of mixed valence coordination compounds called Prussian blue analogue (PBAs), with an open framework structure and ability to conduct both ionic and electronic charge, can be used for inkjet-printed flexible artificial synapses that reversibly switch conductance by more than four orders of magnitude based on electrochemically tunable oxidation state. Retention of programmed states is improved by nearly two orders of magnitude compared to the extensively studied organic polymers, thus enabling in-memory compute and avoiding energy costly off-chip access during training. We demonstrate dopamine detection using PBA synapses and biocompatibility with living neurons, evoking prospective application for brain - computer interfacing. By application of electron transfer theory to in-situ spectroscopic probing of intervalence charge transfer, we elucidate a switching mechanism whereby the degree of mixed valency between N-coordinated Ru sites controls the carrier concentration and mobility, as supported by density functional theory (DFT) .

97 MATHEMATICS AND COMPUTING↗

Spatial and momentum imaging of the pion and kaon

We determine the leading Fock state light front wave functions (LFWFs) of the pion and kaon via light front projections of the covariant Bethe-Salpeter wave function. Using these LFWFs we study the multidimensional images of the valence quarks in the pion and kaon that are provided by their generalized parton distribution functions (GPDs) and transverse momentum dependent parton distribution functions (TMDs). Moments of the GPDs are taken to obtain the electromagnetic and gravitational form factors of the pion and kaon, and comparisons to available experimental and lattice data are made. Highlights from this study include predictions that the mean-squared impact parameter for the quarks in the pion and kaon are: ( b T 2 ) u π = 0.11 fm 2 , ( b T 2 ) s K = 0.08 fm 2 , and ( b T 2 ) u K = 0.13 fm 2 , and therefore the s quark in the kaon is much closer to the center of transverse momentum than the u quark. From the electromagnetic and gravitational form factors we find that the light-cone energy radii are about 60% smaller than the light-cone charge radii for each quark sector in the pion and kaon. A quantitative measure of the importance of the leading Fock state is obtained via comparison with a full Dyson-Schwinger equations calculation (containing an infinite tower of Fock states) for the pion form factor.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Advanced PGM-free Cathode Engineering for High Power Density and Durability

Polymer electrolyte fuel cells (PEFCs) are among the most promising technologies for future electric vehicles by using clean H2 with much-improved energy conversion efficiency, longer range, and rapid refueling. However, due to a large amount of platinum group metal (PGM) catalyst used in their electrodes, their prohibitively high cost hinders broad commercialization of PEFCs for transportation. Therefore, there is a critical need to develop low-cost, high-performance PGM-free cathode catalysts that have the potential to dramatically transform the economics of PEFC commercialization by reducing catalyst costs by one to two orders of magnitude. However, before PGM-free cathodes become viable, several technical challenges associated with PGM-free cathodes must be addressed, including insufficient activity and stability of the catalysts, as well as severe water flooding and large transport losses in the electrodes. Overcoming those barriers and ultimately meeting the challenging automotive PEFC performance targets was the focus of this comprehensive research and development effort on new PGM-free cathodes. To this end, we assembled a team including leading researchers from universities and industry with different but complementary expertise and capabilities. The project combined three novel and promising approaches: Advanced metal-organic framework (MOF)-derived M-N-C catalysts with a high activity and impressive durability, Novel PGM-free specific cathode architectures and fabrication strategies capable of addressing the substantial flooding and transport resistances in thicker cathodes by introducing engineered hydrophobicity through additives and support layers, and Advanced electrode ionomers with high proton conductivity for low ohmic losses across the electrode and more uniform catalyst utilization. The implementation of these new materials and electrode designs was supported by a suite of advanced experimental and simulation tools that allows us to identify performance and durability bottlenecks, devise solutions, and establish rational material design and synthesis targets. These methods include advanced electrochemical characterization, high-resolution imaging, and multi-scale modeling. In addition, the project team leveraged a broad cross-section of the ElectroCat consortium’s national laboratory facilities and expertise in advancing these materials and design strategies. Finally, the industry partners on the project facilitated the evaluation of scaled-up synthesis and manufacturing in the United States. Over its four-year period, the project made significant year-over-year advances in PGM-free cathode performance and viability. A combination of high activity and highly durable catalysts were developed through novel catalyst synthesis strategies, which met several performance and durability targets. More specifically, a catalyst prepared from MOFs and Fe2O3 nanoparticles with ammonium chloride and chemical vapor deposition treatments yielded a significant advancement in PGM-free cathode durability. Several novel strategies for fabricating cathodes were demonstrated, including those designed to reduce flooding and thickness of the cells for significantly increased volumetric power density. An optimized cathode with high conductivity ionomer and tuned ink processing for hydrophobicity yielded high fuel cell performance with new levels power density and maximum current. The scientific studies and modeling assessment also provided an outlook for future efforts, including a focus on catalysts with an increased density of the highly stable active sites developed in this project.

08 HYDROGEN↗

Geothermal Fault Zone and Fluid Imaging through Joint Airborne ZTEM and Ground MT Data Inversion Analysis

This project has aimed to achieve detailed electrical resistivity resolution at geothermal reservoir scales by combining airborne natural electromagnetic (EM) field surveying (ZTEM) with ground magnetotelluric (MT) measurements to approximate an airborne MT geophysical method. MT alone is relatively expensive and may have permitting challenges in sensitive areas. Airborne ZTEM field data contains only the magnetic field, requires a background assumption, and has been limited to relatively high frequencies, thus suffering uniqueness problems. Based on proto-type 2D simulations, ZTEM ambiguities may be reduced through formal incorporation with possibly sparse ground MT soundings, which we pursued in full 3D for this project. The methodology was tested at the high-temperature Roosevelt Hot Springs geothermal system, Utah, which was considered advantageous given the near total exposure of crystalline reservoir rocks across the project area. ZTEM and ground MT survey data were acquired in 2017, subcontracted to outside parties with which we have worked in the past. These included 80 remote-referenced tensor MT soundings over the Mineral Mountains and adjacent Roosevelt Hot Spring producing geothermal system. These MT stations abut later coverage of a similar number of MT stations taken for the Utah FORGE project providing excellent total data aperture to re-solve structure beneath both project areas better than either set alone. The airborne ZTEM survey covered 704 line kilometers in E-W flight lines with a 250 m line spacing. Although this survey was timed during a maintenance-related shutdown of power production at the Roosevelt Hot Springs, other noise sources difficult to identify but including two high-voltage state-scale transmission lines compromised the ZTEM survey badly leading to unusable responses. Thus, with DOE management concurrence, the project proceeded to emphasize inversion and interpretation of the joint SubTER-FORGE MT data sets with regard to the Roosevelt Hot Springs reservoir recharge and to deep heat sources for both it and the Utah FORGE EGS project area. We also investigated the joint ZTEM-MT sampling concept with data sets from the Eleven Mile Canyon prospect area donated by the U.S. Navy (A. Sabin, PoC). Inversion of the SubTER-FORGE MT data using the HexMT 3D finite element algorithm reveals a large, low-resistivity anomaly extending sub-vertically through the depth range of the crust beneath the western Mineral Mountains. The steep conductive zone connects in the lower crust to a more tabular conductor characteristic of much of the Great Basin that generally is ascribed to current mafic magmatic underplating, hybridization and fluid release. The location of the resolved anomaly relative to the recent (0.5-0.8 Ma) eruptive centers of the Mineral Mountains implicates it as remnants of the magma body which fed these centers. This structure appears to be currently feeding heat and fluids upward into the Roosevelt Hot Springs hydrothermal system, as well as heat laterally to the FORGE project area. Separate and joint inversion models were carried out for the donated Eleven Mile Canyon MT-ZTEM data set to demonstrate concept. ZTEM only inversion showed two main alteration zones in the western portion of the project area known from geological mapping. Joint inversion including an E-W profile of MT soundings sharpened these features considerably. It also resolved in much greater detail the graben related normal faulting structure of the central project area which lies at depths exceeding the sensitivity of ZTEM alone. The sparse number of MT da-ta relative to the ZTEM required upweighting the former by a factor of several, but an exact procedure awaits future research. Our final impression is that sparse MT data can improve resolution of the subsurface over that of ZTEM alone. However, well sampled MT data are to be preferred and offer the simplicity of interpreting just one data type, and possess the superior resolution capability coming with the electric field everywhere, and from their high bandwidth.

15 GEOTHERMAL ENERGY↗

Mask-side Hyper-NA EUV imaging on the SHARP microscope

Hyper-NA, the prospective successor to high-numerical aperture (NA) extreme ultraviolet lithography (EUVL) could be inserted soon after 2030. Hyper-NA poses a number of challenges, including reduced depth of focus, amplified mask three-dimensional effects, and increased mask-side angular range. A Hyper-NA capable extreme ultraviolet (EUV) mask-imaging tool can address these challenges and accelerate research and development toward Hyper-NA. The Sharp High-Numerical Aperture Actinic Reticle Review Project (SHARP) EUV mask microscope is supporting mask-side high-NA imaging since 2015. Implementing mask-side Hyper-NA imaging in 2024 enables research and development toward the corresponding nodes of EUVL. Hyper-NA zoneplates at 0.75 4x/8x NA with a 6.7-deg chief ray angle and 0.85 4x/8x NA with a 7.4-deg chief ray angle are added to the SHARP microscope. Imaging at mask-side Hyper-NA is demonstrated. Imaging of 5-nm half-pitch (wafer scale) horizontal lines and spaces is demonstrated using dipole illumination. Imaging of 5-nm half-pitch (wafer scale) vertical lines and spaces is demonstrated using frequency-doubled imaging of 40-nm hp (mask scale) lines and spaces. Normalized image log slope (NILS) and modulation of Hyper-NA image data match closely to simulations for horizontal lines and spaces. A reduction in NILS of 0.3 or less is observed for vertical lines and spaces in the two-beam imaging regime. Through-focus image data are discussed, comparing different dipole sources and mask-side NAs. Mask-side Hyper-NA photomask imaging has been implemented and demonstrated on the SHARP microscope and is now available to users of the instrument.

Benk, Markus↗

3D Source Reconstruction Using Coded Aperture Gamma-Ray Imaging

Recent measurements with coded-aperture imagers demonstrate material mass determination in a holdup setting to an accuracy within a few percent. This capability is of particular interest to the Surplus Plutonium Disposition (SPD) project, which aims to dilute and dispose of surplus plutonium oxide. Gamma-ray imagers can be used to determine holdup without interrupting normal operations. In this work, we examine techniques for 3D source localization and mass determination using gamma-ray imagers. Coded-aperture imagers provide excellent source localization within the 2D image plane; however, multiple imagers operating in tandem are necessary to identify source location in 3D space. A Maximum Likelihood Expectation-Maximization (MLEM) method for fitting detector mappings is a powerful tool for accomplishing this task. MLEM allows 3D source localization to be simultaneously constrained using multiple gamma-ray imagers by constructing the basis for the MLEM fit using detector mappings from different detector locations stitched together. Each of these basis points represents a singular response from a source in 3D space and is generated using Monte Carlo simulations of sources placed individually at different locations throughout the imager’s field of view. Additionally, implementing knowledge of the physical equipment in the simulations of the glovebox used for the SPD project incorporates attenuation effects that are needed to calculate material holdup.

Laminack, Alex↗

A dual high-energy radiography platform with 15 μ m resolution at the National Ignition Facility

To study matter at extreme densities and pressures, we need mega laser facilities such as the National Ignition Facility as well as creative methods to make observations during timescales of a billionth of a second. To facilitate this, we developed a platform and diagnostic to characterize a new point-projection radiography configuration using two micro-wires irradiated by a short pulse laser system that provides a large field of view with up to 3.6 ns separation between images. In this work, we used tungsten-carbide solid spheres as reference objects and inferred characteristics of the back-lighter source using a forward-fitting algorithm. The resolution of the system is inferred to be 15 μm (using 12.5 μm diameter wires). The bremsstrahlung temperature of the source is 70–300 keV, depending on laser energy and coupling efficiency. By adding the images recorded on multiple stacked image plates, the signal-to-noise of the system is nearly doubled. The imaging characterization technique described here can be adapted to most point-projection platforms where the resolution, spectral contrast, and signal-to-noise are important.

47 OTHER INSTRUMENTATION↗

Hardening DOE R&D Software Tools for Web-based Visualization SBIR Phase I Final Report

Ubiquitous web-based visualization is essential to delivering large-scale data visualization to various stakeholders, from the scientist to the board member. These stakeholders will not tolerate a stalled application or a pop-up window asking them to wait for the processing to complete. They require a responsive and interactive visualization environment with high-quality imagery suitable for detailed analysis and boardroom presentations. At Kitware, Inc., we have accomplished web visualization to this point, leveraging state-of-the-art tools like HTML5, CSS3, SVG, Canvas, and WebGL. Solutions that leverage a combination of these technologies are necessary to handle workloads that vary significantly in data size efficiently. However, it is not always practical to move large data to the web client for visualization. Kitware's ParaView as a Service combines client-side visualization using both distributed processing and remote rendering on big data impractical to move. Existing distributed processing and remote rendering solution's interactivity is below the expectations of web-based applications. Our project examined proposed solutions to the areas outlined above in ParaView as a Service. We have investigated concurrent pipelines, streaming images, progressive rendering, and optimization of algorithms and data movement to address these concerns. For the Phase I project, we completed the proposed work plan. As a result, the project produced three prototypes of essential importance for web visualization and the ParaView as a Service community. We created a simple desktop application for an interactive streamline placement prototype, a web-based interactive streamline placement prototype, and a web-based progressive rendering utilizing raytracing prototype. These prototypes relied on the hardening of emerging software toolkits funded by the Department of Energy (DOE) Advanced Scientific Computing Research (ASCR) program (such as ParaView, VTK-m, and Mochi). We blended these components into web-based visualization prototypes that meet the industry's expectations for interactivity and responsiveness. The Phase I project had four essential focus areas: 1. Develop prototype ParaView as a Service backend server using asynchronous, non-blocking design principles. 2. Develop a prototype web application that uses the ParaView as a Service backend server for remote data visualization. 3. Implement image streaming with encoding/compression and progressive rendering capabilities in the proposed platform. 4. Evaluate the prototype developed and summarize observations, including the challenges and pitfalls of our approach. After our successful completion of Phase I, we are strongly positioned to propose a successful Phase II project.

Geveci, Berk↗

Zero-Power Analog Optical Processing

The motivation behind this research is the growing challenge of handling the massive amounts of data generated by modern imaging systems. Conventional digital image processing techniques are struggling to keep pace with the demands of high-resolution and high-speed imaging systems for remote sensing due to their high-power consumption and data storage requirements. We present a novel approach based on analog photonics to address this challenge. The proposed system utilizes a silicon-photonics-based image encoder positioned after image formation and initial optical-to-electrical conversion. The photonic encoder compresses image data using a passive disordered photonic structure to perform kernel-type random projections of the raw data. The compressed data is then processed by a back-end neural network, which reconstructs the original image with high fidelity (structural similarity exceeding 90%). Our proposed approach has the potential to compress images with ~ 1000X lower power consumption compared to digital approaches with data rates exceeding 1 terapixel/second.

97 MATHEMATICS AND COMPUTING↗

Utah FORGE: Well 16B(78)-32 Logs from Schlumberger Technologies

This dataset is a collection of well logs provided by Schlumberger Technologies from the Utah FORGE well 16B(78)-32 drilling project. Information here includes critical borehole information collected by an ultrasonic borehole imager (UBI) and a fullbore formation microimager (FMI). Well 16B(78)-32 serves as the production well for reservoir creation, fluid circulation, and demonstration of heat extraction for the FORGE project. It has been drilled as a doublet approximately 300 feet parallel to and above the injection well 16A(78)-32. The total depth measured 10,947 feet and the vertical depth measured 8,357 feet.

11.5 inch intermediate casing logs↗

Galaxy Spin Classification. I. Z-wise versus S-wise Spirals with the Chirality Equivariant Residual Network

The angular momentum of galaxies (galaxy spin) contains rich information about the initial condition of the universe, yet it is challenging to efficiently measure the spin direction for the tremendous amount of galaxies that are being mapped by ongoing and forthcoming cosmological surveys. We present a machine-learning-based classifier for the Z-wise versus S-wise spirals, which can help to break the degeneracy in the galaxy spin direction measurement. The proposed chirality equivariant residual network (CE-ResNet) is manifestly equivariant under a reflection of the input image, which guarantees that there is no inherent asymmetry between the Z-wise and S-wise probability estimators. We train the model with Sloan Digital Sky Survey images, with the training labels given by the Galaxy Zoo 1 project. A combination of data augmentation techniques is used during the training, making the model more robust to be applied to other surveys. We find an ~30% increase in both types of spirals when Dark Energy Spectroscopic Instrument (DESI) images are used for classification, due to the better imaging quality of DESI. We verify that the ~7σ difference between the numbers of Z-wise and S-wise spirals is due to human bias, since the discrepancy drops to <1.8σ with our CE-ResNet classification results. We discuss the potential systematics relevant to future cosmological applications.

79 ASTRONOMY AND ASTROPHYSICS↗

Development of an advanced ultrasonic phased array for the characterization of thick, reinforced concrete components (Final Scientific/Technical Report)

There are no nondestructive evaluation (NDE) tools capable of characterizing microscale damage throughout the thickness of concrete components, due to the multiphase, heterogeneous and multiscale nature of concrete. Ultrasound is only scattered by features at the same length scale, or smaller, than the wavelength of a wave’s dominant frequency. Successful imaging of microscale damage using ultrasound requires that the ultrasonic wavelength be on the order of a few millimeters (or smaller), yet the heterogeneous microstructure of concrete, with its fine and coarse aggregates, is on this same (and higher) micrometer/millimeter length scale, causing excessive ultrasonic wave scattering even in “good” concrete. The proposed solution applies non-collinear wave mixing to spatially image microscale damage, while still maintaining penetration through thick concrete components. This microscale imaging is possible by combining nonlinear wave mixing, with advanced phased array hardware and software to develop a breakthrough tool that will bring revolutionary changes in NDE of concrete infrastructure in terms of image resolution and depth of penetration. This work uses non-collinear wave mixing which exploits the physics that material nonlinearities such as microscale damage, cause interactions between two intersecting ultrasonic waves due to cross-mixing, which can lead to the generation of a third wave with a frequency and wave number of the sum or difference of the incident waves. The concrete material volume at this mixing point is characterized/imaged. This project delivered a single-sided nonlinear ultrasonic phased array imaging device, that can image microscale damage (microcracks of 100 micrometers) through a 0.5 m thick concrete component and assessed the commercial feasibility of such arrays for improved crack detection.

42 ENGINEERING↗

Galaxy Cruise: Deep Insights into Interacting Galaxies in the Local Universe

Abstract We present the first results from GALAXY CRUISE, a community (or citizen) science project based on data from the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP). The current paradigm of galaxy evolution suggests that galaxies grow hierarchically via mergers, but our observational understanding of the role of mergers is still limited. The data from HSC-SSP are ideally suited to improve our understanding with improved identifications of interacting galaxies thanks to the superb depth and image quality of HSC-SSP. We launched a community science project, GALAXY CRUISE, in 2019 and have collected over two million independent classifications of 20686 galaxies at z < 0.2. We first characterize the accuracy of the participants’ classifications and demonstrate that it surpasses previous studies based on shallower imaging data. We then investigate various aspects of interacting galaxies in detail. We show that there is a clear sign of enhanced activities of super-massive black holes and star formation in interacting galaxies compared to those in isolated galaxies. The enhancement seems particularly strong for galaxies undergoing violent mergers. We also show that the mass growth rate inferred from our results is roughly consistent with the observed evolution of the stellar mass function. The second season of GALAXY CRUISE is currently underway and we conclude with future prospects. We make the morphological classification catalog used in this paper publicly available at the GALAXY CRUISE website, which will be particularly useful for machine-learning applications.

Tanaka, Masayuki↗

Machine Learning Enabled Advanced Electron Tomography for Resolving Chemical Inhomogeneity and Materials Dynamics in Lithium-Ion Battery Electrodes

The objective of this project is to develop machine learning-assisted electron microscopy, together with three-dimensional, cryogenic, and in-situ imaging techniques, for resolving chemical inhomogeneity and materials dynamics in lithium battery electrodes and interfaces. The project aims to expand the spatial, temporal, and dimensional resolution of transmission electron microscopy and to enable quantitative analysis of beam-sensitive battery materials.

25 ENERGY STORAGE↗