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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

The LHC Olympics 2020 a community challenge for anomaly detection in high energy physics

A new paradigm for data-driven, model-agnostic new physics searches at colliders is emerging, and aims to leverage recent breakthroughs in anomaly detection and machine learning. In order to develop and benchmark new anomaly detection methods within this framework, it is essential to have standard datasets. To this end, we have created the LHC Olympics 2020, a community challenge accompanied by a set of simulated collider events. Participants in these Olympics have developed their methods using an R&D dataset and then tested them on black boxes: datasets with an unknown anomaly (or not). Furthermore, methods made use of modern machine learning tools and were based on unsupervised learning (autoencoders, generative adversarial networks, normalizing flows), weakly supervised learning, and semi-supervised learning. This paper will review the LHC Olympics 2020 challenge, including an overview of the competition, a description of methods deployed in the competition, lessons learned from the experience, and implications for data analyses with future datasets as well as future colliders.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Quench detection using Hall sensors in high-temperature superconducting CORC ® -based cable-in-conduit-conductors for fusion applications

Advanced magnet systems for fusion applications would greatly benefit from the use of high-temperature superconductors (HTS). These materials allow fusion magnets to operate at higher magnetic fields, allowing for more compact fusion machines, and allow for operation at elevated temperatures, enabling demountable coils that provide access for maintenance of the fusion reactor. Quench detection remains a major challenge in the protection of HTS magnets that are vulnerable to localized conductor burnout due to their low quench propagation velocities. One of the methods explored is the use of Hall sensors that are incorporated in or near the magnet terminations that can detect local field variations that occur as a result of current redistribution within the conductor to bypass a hotspot within the magnet winding. This method is potentially well suited for Cable in Conduit Conductors, such as those made from Conductor on Round Core (CORC) cables, in which sub-cables containing HTS tapes are connected to the terminations at a low resistance. Here, to demonstrate the technique, a CORC ® triplet consisting of three sub-cables, rated for 4 kA operation at 77 K, was manufactured and Hall sensors were used to measure local field variations next to the terminations due to current redistribution between the cables. The Hall response was compared to voltages that developed over the cables and terminations as a local hotspot was applied to different cables in the triplet. It was found that the Hall sensors were faster and more sensitive than voltage contact measurements and were able to reliably detect current redistribution of only a few amperes caused by a hotspot, well before the triplet exceeded its critical current. The method also allowed the detection of heater-induced hotspots during high ramp rates of 2 kA s<:sup>–1 relevant for fusion applications. Hall sensors have a distinct benefit of being less sensitive to inductive pickup of AC interference compared to voltage contact measurements that make quench detection through voltage measurements in magnets especially challenging. The method can also be used for diagnostic measurements of current redistribution caused by other sources such as inhomogeneous current injection from faulty joints, or localized conductor damage. The Hall sensors are likely capable of detecting the onset of a quench that may occur a far distance away from the sensor location, presenting a breakthrough in HTS quench detection that potentially removes one of the remaining barriers to reliable operation of large HTS magnet systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Magnetic ac susceptibility of superconducting Ta films for quantum computing

Recently, breakthroughs were reported in the long coherence time (>0.5 ms) transmon qubit using tantalum films (Ta). Identifying the loss mechanism in Ta films will help further improve the performance of Ta-based superconducting qubits. We report a study of superconducting properties in two sets of Ta films grown on c-cut and a-cut sapphires respectively using contact transport and noncontact magnetic ac susceptibility measurements. Although the resistive transition appears to be similar in both films, we found strikingly different responses in their complex magnetic susceptibilities χ' + iχ'$^{'}$ .χ'$^{'}$ in the c-cut films exhibits a sharp peak at superconducting transition Tc and becomes featureless below Tc indicating a strongly coupled superconducting state. In contrast, χ'$^{'}$ \ in the a-cut films exhibits a broad peak near Tc and a second peak appears below Tc, indicating granular superconductivity behavior. This second peak χ'$^{'}$ in is associated with the hysteresis loss at weak-link-type grain boundaries, which is believed to be a leading source of decoherence in the a-cut Ta films. The derived magnetic loss tangent in the a-cut films is about one order of magnitude higher than in the c-cut films. This study demonstrates that ac susceptibility is an excellent probe for characterizing bulk superconducting properties of the constituent superconducting materials used in qubits.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Interfacial dynamics and catalytic behavior of single Ni atom site

Single-atom catalysts (SACs) have garnered significant interest due to their ability to reduce metal particles to the atomic scale, enabling finely tunable local environments and enhanced catalytic properties in terms of reactivity and selectivity. Despite this potential, their application has largely been confined to small-molecule transformations as metal-catalyzed reaction. Here, in this study, we present a diverse single-atom nickel (Ni) catalyst established via a nanoporous carbon (NPC) supported practice. This catalyst represents a breakthrough by achieving the bond formation between carbon and nitrogen and interfacial dynamics in the SAC. The present first principle-based density functional simulations establish the reaction dynamics and catalytic behaviour of such SAC. This dynamic nature comprises an exclusive nitrogen intercalated site showing excellent base effects. This base quickly tunes the interfacial atmosphere, enabling dynamic movement of adatoms into the NPC species, significantly changing the reaction path in Ni SACs due to superior steric effects. The research demonstrates that SACs can extend the capabilities of catalytic systems to include a wider range of complex reactions, offering substantial promise for the development of new, efficient synthetic methods for creating value-added molecular products.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Automatic Detection and Classification of Radio Galaxy Images by Deep Learning

Abstract Surveys conducted by radio astronomy observatories, such as SKA, MeerKAT, Very Large Array, and ASKAP, have generated massive astronomical images containing radio galaxies (RGs). This generation of massive RG images has imposed strict requirements on the detection and classification of RGs and makes manual classification and detection increasingly difficult, even impossible. Rapid classification and detection of images of different types of RGs help astronomers make full use of the observed astronomical image data for further processing and analysis. The classification of FRI and FRII is relatively easy, and there are more studies and literature on them at present, but FR0 and FRI are similar, so it is difficult to distinguish them. It poses a greater challenge to image processing. At present, deep learning has made breakthrough progress in the field of image analysis and processing and has preliminary applications in astronomical data processing. Compared with classification algorithms that can only classify galaxies, object detection algorithms that can locate and classify RGs simultaneously are preferred. In target detection algorithms, YOLOv5 has outstanding advantages in the classification and positioning of small targets. Therefore, we propose a deep-learning method based on an improved YOLOv5 object detection model that makes full use of multisource data, combining FIRST radio with SDSS optical image data, and realizes the automatic detection of FR0, FRI, and FRII RGs. The innovation of our work is that on the basis of the original YOLOv5 object detection model, we introduce the SE Net attention mechanism, increase the number of preset anchors, adjust the network structure of the feature pyramid, and modify the network structure, thereby allowing our model to demonstrate galaxy classification and position detection effects. Our improved model produces satisfactory results, as evidenced by experiments. Overall, the mean average precision (mAP@0.5) of our improved model on the test set reaches 89.4%, which can determine the position (R.A. and decl.) and automatically detect and classify FR0s, FRIs, and FRIIs. Our work contributes to astronomy because it allows astronomers to locate FR0, FRI, and FRII galaxies in a relatively short time and can be further combined with other astronomically generated data to study the properties of these galaxies. The target detection model can also help astronomers find FR0s, FRIs, and FRIIs in future surveys and build a large-scale star RG catalog. Moreover, our work is also useful for the detection of other types of galaxies.

Astronomy & Astrophysics↗
DOE OSTI2021

LAMOST Time-Domain survey: first results of four K2 plates

From Oct. 2019 to Apr. 2020, LAMOST performed a time-domain (TD) spectroscopic survey of four K2 plates with both low- and medium-resolution observations. The low-resolution spectroscopic survey acquired 282 exposures ( ≈ 46.6 h) over 25 nights, yielding a total of about 767 000 spectra, and the medium-resolution survey took 177 exposures ( ≈ 49.1 h) over 27 nights, collecting about 478 000 spectra. More than 70%/50% of low-resolution/medium-resolution spectra have signal-to-noise ratio higher than 10. We determine stellar parameters (e.g., T eff , log g, [Fe/H]) and radial velocity (RV) with different methods, including LASP, DD-Payne and SLAM. In general, these parameter estimations from different methods show good agreement, and the stellar parameter values are consistent with those of APOGEE. We use the Gaia DR2 RV values to calculate a median RV zero point (RVZP) for each spectrograph exposure by exposure, and the RVZP-corrected RVs agree well with the APOGEE data. The stellar evolutionary and spectroscopic masses are estimated based on the stellar parameters, multi-band magnitudes, distances and extinction values. Finally, we construct a binary catalog including about 2700 candidates by analyzing their light curves, fitting the RV data, calculating the binarity parameters from medium-resolution spectra and cross-matching the spatially resolved binary catalog from Gaia EDR3. In conclusion, the LAMOST TD survey is expected to represent a breakthrough in various scientific topics, such as binary systems, stellar activity, stellar pulsation, etc.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Thermal and Electrical Properties of Isocyanate Derived Organic Aerogels for Cryogenic Insulation Applications

The use and storage of cryogens such as liquefied nitrogen, helium, hydrogen among others requires reliable and efficient thermal insulation systems. Passive insulation from high performance materials that are well-known for their inherent low thermal conductivity would reduce the overall costs involved in design, manufacture and maintenance of such systems. One such class of materials are referred to as aerogels. These materials are known for their low density, high mesoporosity, high surface areas, low thermal conductivity and high acoustic impedance. Aerogels were invented by S.S. Kistler in 1931 and the most common type are those made of silica. However, the inherent fragility of silica aerogels makes them hard to mass produce, and therefore applications have been limited. A major breakthrough was introduced by our team almost 20 years ago with the invention of polymer crosslinked silica aerogels. Those materials shifted attention to all-polymer aerogels that have overcome all fragility issues associated with their inorganic counterparts. This study focuses on such polymeric aerogels that can be mass produced as large monoliths while maintaining the low thermal conductivity of traditional silica aerogels over a wide temperature range. Manufacturing flexibility of polymeric aerogels allows fabrication of blocks and sheets that can be applied in various configurations to insulate cryogenic and superconducting devices. The thermal conductivity with 80 K and room temperature boundary are reported as well as other properties (electrical), that need to be considered when designing devices for cryogenic applications.

Churu, Gitogo↗
DOE OSTI2021

Beyond the Anderson rule: importance of interfacial dipole and hybridization in van der Waals heterostructures

Vertical stacking of two-dimensional materials with weak van der Waals (vdW) interactions has laid the ground for breakthroughs in physics as well as in technological applications. Although vdW interactions dominate interlayer binding, interlayer electronic coupling may not be negligible and can lead to properties beyond the superposition of constituent monolayers. Here, studying heterobilayers of transition-metal dichalcogenides (MQ 2 ; M = Mo, Ni, Pt; Q = S, Se) by means of density functional theory calculations, we show two mechanisms that influence the band gaps of vdW heterostructures beyond the Anderson rule: (1) interfacial hybridization (mainly involving out-of-plane states, such as chalcogen p z -states), which leads to an upshift in the valence band maxima and accordingly a decrease in the band gap. (2) Formation of an interfacial electric dipole, resulting in an effective gap increase in type-II junctions. While the former is material specific, depending on the proximity of p z -states to each other and the valence band maxima, the latter can be generally described using a model based on the charge density decay outside the monolayers and the pristine band edge positions with respect to the vacuum level, irrespective of junction type.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Metallic and complex hydride-based electrochemical storage of energy

Abstract The development of efficient storage systems is one of the keys to the success of the energy transition. There are many ways to store energy, but among them, electrochemical storage is particularly valuable because it can store electrons produced by renewable energies with a very good efficiency. However, the solutions currently available on the market remain unsuitable in terms of storage capacity, recharging kinetics, durability, and cost. Technological breakthroughs are therefore expected to meet the growing need for energy storage. Within the framework of the Hydrogen Technology Collaboration Program—H 2 TCP Task-40, IEA’s expert researchers have developed innovative materials based on hydrides (metallic or complex) offering new solutions in the field of solid electrolytes and anodes for alkaline and ionic batteries. This review presents the state of the art of research in this field, from the most fundamental aspects to the applications in battery prototypes.

Cuevas, Fermin (ORCID:0000000290555880)↗
DOE OSTI2021

Watch and learn—a generalized approach for transferrable learning in deep neural networks via physical principles

Transfer learning refers to the use of knowledge gained while solving a machine learning task and applying it to the solution of a closely related problem. Such an approach has enabled scientific breakthroughs in computer vision and natural language processing where the weights learned in state-of-the-art models can be used to initialize models for other tasks which dramatically improve their performance and save computational time. Here we demonstrate an unsupervised learning approach augmented with basic physical principles that achieves fully transferrable learning for problems in statistical physics across different physical regimes. By coupling a sequence model based on a recurrent neural network to an extensive deep neural network, we are able to learn the equilibrium probability distributions and inter-particle interaction models of classical statistical mechanical systems. Our approach, distribution-consistent learning, DCL, is a general strategy that works for a variety of canonical statistical mechanical models (Ising and Potts) as well as disordered interaction potentials. Using data collected from a single set of observation conditions, DCL successfully extrapolates across all temperatures, thermodynamic phases, and can be applied to different length-scales. This constitutes a fully transferrable physics-based learning in a generalizable approach.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

FINETUNA: fine-tuning accelerated molecular simulations

Abstract Progress towards the energy breakthroughs needed to combat climate change can be significantly accelerated through the efficient simulation of atomistic systems. However, simulation techniques based on first principles, such as density functional theory (DFT), are limited in their practical use due to their high computational expense. Machine learning approaches have the potential to approximate DFT in a computationally efficient manner, which could dramatically increase the impact of computational simulations on real-world problems. However, they are limited by their accuracy and the cost of generating labeled data. Here, we present an online active learning framework for accelerating the simulation of atomic systems efficiently and accurately by incorporating prior physical information learned by large-scale pre-trained graph neural network models from the Open Catalyst Project. Accelerating these simulations enables useful data to be generated more cheaply, allowing better models to be trained and more atomistic systems to be screened. We also present a method of comparing local optimization techniques on the basis of both their speed and accuracy. Experiments on 30 benchmark adsorbate-catalyst systems show that our method of transfer learning to incorporate prior information from pre-trained models accelerates simulations by reducing the number of DFT calculations by 91%, while meeting an accuracy threshold of 0.02 eV 93% of the time. Finally, we demonstrate a technique for leveraging the interactive functionality built in to Vienna ab initio Simulation Package (VASP) to efficiently compute single point calculations within our online active learning framework without the significant startup costs. This allows VASP to work in tandem with our framework while requiring 75% fewer self-consistent cycles than conventional single point calculations. The online active learning implementation, and examples using the VASP interactive code, are available in the open source FINETUNA package on Github.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Selective Biosorption of Valuable Rare Earth Elements Among Co-Occurring Lanthanides

To meet the increasing demand for rare earth elements (REEs) essential for low carbon-intensity energy technologies, new methods are needed for selective REE extraction from unconventional resources. Only a few REEs have significant economic value, but isolation of target rare earths from co-occurring lanthanides is challenging due to their similar chemical behavior. We present a novel approach to enhance separation of specific REEs from lanthanide mixtures. Escherichia coli cells engineered with lanthanide binding tags (LBTs) were immobilized in nonadsorbing, permeable polyethylene glycol diacrylate beads and packed into continuous flow, fixed-bed columns. Breakthrough of 15 rare earths in the +3 oxidation state resulted in notable differences in adsorption selectivity, with greatest separation between europium (Eu) and lanthanum (La) due to competitive displacement. REE adsorption onto fixed-bed columns was predicted by coupling a surface complexation model to a calibrated one-dimensional dual porosity transport model that accounts for interbead advective and intrabead diffusive transport. A tradeoff between high-abundance, low-affinity native carboxyl sites and low-abundance, high-affinity engineered LBT sites dictates process recovery efficiency and selectivity. Key chemical and operational parameters are identified to maximize selective extraction of high-value lanthanides, achieving a threefold enhancement of Eu recovery relative to La in a mixed REE solution.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Current and future directions in network biology

Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseases across biological systems and scales. Although the field has been around for two decades, it remains nascent. It has witnessed rapid evolution, accompanied by emerging challenges. These stem from various factors, notably the growing complexity and volume of data together with the increased diversity of data types describing different tiers of biological organization. We discuss prevailing research directions in network biology, focusing on molecular/cellular networks but also on other biological network types such as biomedical knowledge graphs, patient similarity networks, brain networks, and social/contact networks relevant to disease spread. In more detail, we highlight areas of inference and comparison of biological networks, multimodal data integration and heterogeneous networks, higher-order network analysis, machine learning on networks, and network-based personalized medicine. Following the overview of recent breakthroughs across these five areas, we offer a perspective on future directions of network biology. Additionally, we discuss scientific communities, educational initiatives, and the importance of fostering diversity within the field. This article establishes a roadmap for an immediate and long-term vision for network biology.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Water alternative gas (WAG) optimization for a heterogeneous Brazilian pre-salt carbonate reservoir

Water alternating gas (WAG) is a cyclical process that involves alternating water and gas injections with the primary goal to improve sweep efficiency by maintaining initial high pressure, slowing water and gas breakthrough, and lowering oil viscosity. The objective of this work is to apply and optimize a WAG strategy on a carbonate field with light oil, compare it to the initially planned water-flooding strategy, and investigate the capability of WAG to improve field production. In this research, a compositional reservoir simulator was used to model a WAG process by injecting produced gas into the reservoir, using the same well structure as an optimized water-flooding strategy. Subsequently, a WAG strategy was created, optimizing the number and locations of wells, to facilitate a comparative analysis of the two recovery methods. The WAG optimization involved a detailed assessment of variables such as bottom hole pressure (BHP), WAG cycle duration, maximum gas oil ratio (GOR), and well positioning, to achieve a high net present value (NPV). The study focuses on the application of WAG optimization modeling in unconventional reservoirs, specifically pre-salt carbonate reservoirs, and investigates its implications on production strategy and forecast, emphasizing its potential for maximizing NPV and oil recovery in a recently producing field. The results showed that WAG improved reservoir performance when compared to water injection and produced a greater amount of oil. This solution showed potential to be tested under uncertainties (reservoir heterogeneity, faults, fractures, karsts, vugs, etc.) as future steps.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Molecular-Resolution Electron Imaging of Defects and Dynamics at the Ice-Water Interface

Water crystallization into hexagonal ice (type I h ) is one of the most critical processes relevant to the Earth’s environment and human activities. However, despite recent breakthroughs in imaging non-equilibrium condensed ice structures, the ice-water interface has never been imaged at a molecular resolution. This is primarily due to the low stability of the hydrogen bonds in ice under high-resolution microscopy conditions and a lack of methods to prepare compatible samples. Here, this presentation describes the first molecular-resolution imaging of ice crystallized from liquid water and the ice-water interface using high-resolution transmission electron microscopy (HRTEM). By encapsulating deionized (DI) water between two amorphous carbon (a-C) TEM grids and subsequently freezing it with liquid N 2 on a cryo sample stage, we generated two types of ice: non-equilibrium, condensed ice from the atmosphere and encapsulated ice from the DI water (Fig. 1A). Condensed ice usually shows irregular, spherulitic shapes (Fig. 1B). Selected area electron diffraction (SAED) shows that they are a mixture of cubic and hexagonal crystals (Fig. 1C). On the contrary, encapsulated ice forms thin films that contain large-area single-crystalline regions of hexagonal ice oriented along the [0001] zone axis (Fig. 1D). Differential electron energy-loss spectroscopy (EELS) confirmed the high purity of the encapsulated ice free from organic contaminations that are common in other encapsulation methods for HRTEM such as graphene liquid cells. These single-crystalline areas are robust under the electron beam up to ~100 e/Å 2 s. Aberration-corrected HRTEM imaging in these areas achieved a line resolution of ~1.3 Å (Fig. 1E and F). This platform allows us to study near-equilibrium ice structures and dynamics at an unprecedented spatial resolution (Fig. 2). For example, we discovered subdomain-rich regions near the defective crystal edges despite the structure appearing single-crystalline according to diffraction criteria. These subdomains connect via low-angle grain boundaries with flat energy landscapes as a function of tilt angles (according to simulations), showing the high tolerance of ice to defect structures. When we tuned the sample temperature and electron flux rate, we observed radiolysis-controlled bubble generation and dissolution in ice single crystals near a steady state of bubble dynamics. Furthermore, rich beam-induced melting and recrystallization dynamics were observed at the ice-water interface with lattice resolution. These data represent the first observation of the ice-water phase transformation at the sub-nanometer level. In summary, the methods developed in this work enabled molecular-resolution observations of ice and the ice-water interface and shed light on the microstructures and phase transformation pathways. Finer control on the temperature, electron irradiation profile, and imaging detector could eventually lead to real-time observation of ice nucleation in water and address long-standing questions in the nucleation pathways.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2023

Structural Characteristics and Phase Evolution of Calcium-Reduced (Sm,Zr)(Fe,Co,Ti) 12 Particles

Magnetic materials are essential for applications in electronics, energy conversion and many other industrial and technological sectors. Among the permanent magnets, rare-earth magnets exhibit the strongest properties and dominate the market shares in related applications. However, due to the increasing cost and unstable supply, the research on reducing the rare-earth elements in magnets attracts significant increasing interest. Rare-earth-lean samarium transition metal (TM) compounds (SmTM 12 or 1:12 for short) have made breakthroughs in 2020 and the (Sm,Zr)(Fe,Co,Ti) 12 monocrystalline particles synthesized by a Ca reduction of elemental oxides at high temperature demonstrated an order of magnitude improvement in coercivity at room temperature. In this study, we made structural analysis of (Sm,Zr)(Fe,Co,Ti) 12 particles to elucidate the impact of synthesis conditions, microstructure and phase transformation on the evolution of magnetic properties.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Focused Interest Groups Propel Innovation in the Emerging Data-Driven Hardware Ecosystem

Over the past several decades, the science of electron microscopy (EM) has risen to become one of the cornerstone approaches to understanding material structure, chemistry, and defects at exceptional spatial resolution. This rise can be attributed to the hard work of researchers and companies who have pushed the bounds of instrumentation and analysis, but it is also the result of advocacy by members of professional societies. Furthermore, such advocacy is exemplified by the Aberration Corrected EM (ACEM) focused interest group (FIG) of the Microscopy Society of America (MSA), which has played an important role in facilitating new hardware developments, promoting the exchange of ideas to catalyze discovery, and building the next generation of leaders in electron microscopy. From the early 2000s to the present, aberration correction has moved from proof-of-concept instrumentation to an established technique, providing unprecedented improvements in spatial and chemical resolution for breakthroughs in chemistry, physics, and materials science.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Time delay lens modelling challenge

ABSTRACT In recent years, breakthroughs in methods and data have enabled gravitational time delays to emerge as a very powerful tool to measure the Hubble constant H0. However, published state-of-the-art analyses require of order 1 yr of expert investigator time and up to a million hours of computing time per system. Furthermore, as precision improves, it is crucial to identify and mitigate systematic uncertainties. With this time delay lens modelling challenge, we aim to assess the level of precision and accuracy of the modelling techniques that are currently fast enough to handle of order 50 lenses, via the blind analysis of simulated data sets. The results in Rungs 1 and 2 show that methods that use only the point source positions tend to have lower precision ($10\!-\!20{{\ \rm per\ cent}}$) while remaining accurate. In Rung 2, the methods that exploit the full information of the imaging and kinematic data sets can recover H0 within the target accuracy (|A| < 2 per cent) and precision (<6 per cent per system), even in the presence of a poorly known point spread function and complex source morphology. A post-unblinding analysis of Rung 3 showed the numerical precision of the ray-traced cosmological simulations to be insufficient to test lens modelling methodology at the percent level, making the results difficult to interpret. A new challenge with improved simulations is needed to make further progress in the investigation of systematic uncertainties. For completeness, we present the Rung 3 results in an appendix and use them to discuss various approaches to mitigating against similar subtle data generation effects in future blind challenges.

Ding, X.↗