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We report observations of eight Jovian irregular satellites with JWST's NIRSpec instrument: Himalia, Elara, Pasiphae, Sinope, Lysithea, Carme, Ananke, and Themisto. Irregular satellite families, which are presumed to have formed via collisions, contain various Trojan-like and C-type-asteroid-like surfaces. We sample the three largest members of the Himalia satellite family, detecting the presence of complexed CO$_2$ and a unique absorption band from $\sim2.7-3.6\ \mu m$ whose character correlates with satellite size. The two largest irregular satellites, Himalia family members Himalia and Elara, contain ammoniated phyllosilicates that are not seen in the meteorite inventory. We propose that the Himalia parent body was heterogeneous and formed with materials similar to Ceres-like ammonium-bearing asteroids. Several small ($D\sim 10km$) irregular satellites closely track the colors and absorption bands of ``red'' Jovian Trojans, demonstrating that these compositions are retained amongst the products of collisions that occurred after Jovian capture. We report the first detection of aqueous alteration products in the retrograde satellite swarm, finding Ananke's 3 micron band to closely match phyllosilicates seen in C2 chondrites. Notably, objects with OH absorption features similar to the Trojan asteroid Eurybates are found in both the retrograde Pasiphae family and the prograde Himalia family, confounding a simple link between such materials and a single surface type. The irregular satellites appear consistent with some materials that experienced alteration from liquid water and others that did not. Consequently, Jupiter may have captured bodies that formed from different initial compositions, or bodies that experienced different levels of heating, driving differential alteration processes., Comment: 20 Pages, 11 Figures, 2 Tables. Submitted to PSJ

Despite the increasing importance of stochastic processes on linear networks and graphs, current literature on multivariate (vector-valued) Gaussian random fields on metric graphs is elusive. This paper challenges several aspects related to the construction of proper matrix-valued kernels structures. We start by considering matrix-valued metrics that can be composed with scalar- or matrix-valued functions to implement valid kernels associated with vector-valued Gaussian fields. We then provide conditions for certain classes of matrix-valued functions to be composed with the univariate resistance metric and ensure positive semidefiniteness. Special attention is then devoted to Euclidean trees, where a substantial effort is required given the absence of literature related to multivariate kernels depending on the $\ell_1$ metric. Hence, we provide a foundational contribution to certain classes of matrix-valued positive semidefinite functions depending on the $\ell_1$ metric. This fact is then used to characterise kernels on Euclidean trees with a finite number of leaves. Amongst those, we provide classes of matrix-valued covariance functions that are compactly supported.

Context. The supernova remnant (SNR) W44 and its surroundings are a prime target for studying the acceleration of cosmic rays (CRs). Several previous studies established an extended gamma-ray emission that is set apart from the radio shell of W44. This emission is thought to originate from escaped high-energy CRs that interact with a surrounding dense molecular cloud complex. Aims. We present a detailed analysis of Fermi-LAT data with an emphasis on the spatial and spectral properties of W44 and its surroundings. We also report the results of the observations performed with the MAGIC telescopes of the northwestern region of W44. Finally, we present an interpretation model to explain the gamma-ray emission of the SNR and its surroundings. Methods. We first performed a detailed spatial analysis of 12 years of Fermi-LAT data at energies above 1 GeV, in order to exploit the better angular resolution, while we set a threshold of 100MeV for the spectral analysis. We performed a likelihood analysis of 174 hours of MAGIC data above 130 GeV using the spatial information obtained with Fermi-LAT. Results. The combined spectra of Fermi-LAT and MAGIC, extending from 100MeV to several TeV, were used to derive constraints on the escape of CRs. Using a time-dependent model to describe the particle acceleration and escape from the SNR, we show that the maximum energy of the accelerated particles has to be ' 40 GeV. However, our gamma-ray data suggest that a small number of lower-energy particles also needs to escape. We propose a novel model, the broken-shock scenario, to account for this effect and explain the gamma-ray emission.

For over three decades, scanning probe microscopy (SPM) has been a key method for exploring material structures and functionalities at nanometer and often atomic scales in ambient, liquid, and vacuum environments. Historically, SPM applications have predominantly been downstream, with images and spectra serving as a qualitative source of data on the microstructure and properties of materials, and in rare cases of fundamental physical knowledge. However, the fast growing developments in accelerated material synthesis via self-driving labs and established applications such as combinatorial spread libraries are poised to change this paradigm. Rapid synthesis demands matching capabilities to probe structure and functionalities of materials on small scales and with high throughput, which are characteristically inherent to SPM. Here, we overview SPM methods applicable to these emerging applications and emphasize their quantitativeness, focusing on piezoresponse force microscopy, electrochemical strain microscopy, conductive, and surface photovoltage measurements. We discuss the challenges and opportunities ahead, asserting that SPM will play a crucial role in closing the loop from material prediction and synthesis to characterization., Comment: 64 pages, 15 figures

The advent of large language models (LLMs) has paved the way for a new era of programming tools with both significant capabilities and risks, as the generated code lacks guarantees of correctness and reliability. Developers using LLMs currently face the difficult task of optimizing, integrating, and maintaining code generated by AI. We propose an embedded domain-specific language (DSL), Pythoness, to address those challenges. In Pythoness, developers program with LLMs at a higher level of abstraction. Rather than interacting directly with generated code, developers using Pythoness operate at the level of behavioral specifications when writing functions, classes, or an entire program. These specifications can take the form of unit tests and property-based tests, which may be expressed formally or in natural language. Guided by these specifications, Pythoness generates code that both passes the tests and can be continuously checked during execution. We posit that the Pythoness approach lets developers harness the full potential of LLMs for code generation while substantially mitigating their inherent risks. We describe our current prototype implementation of Pythoness and demonstrate that it can successfully leverage a combination of tests and code generation to yield higher quality code than specifications alone., Comment: 5 pages

There exists a plethora of parametric models for positive definite kernels, and their use is ubiquitous in disciplines as diverse as statistics, machine learning, numerical analysis, and approximation theory. Usually, the kernel parameters index certain features of an associated process. Amongst those features, smoothness (in the sense of Sobolev spaces, mean square differentiability, and fractal dimensions), compact or global supports, and negative dependencies (hole effects) are of interest to several theoretical and applied disciplines. This paper unifies a wealth of well-known kernels into a single parametric class that encompasses them as special cases, attained either by exact parameterization or through parametric asymptotics. We furthermore characterize the Sobolev space that is norm equivalent to the RKHS associated with the new kernel. As a by-product, we infer the Sobolev spaces that are associated with existing classes of kernels. We illustrate the main properties of the new class, show how this class can switch from compact to global supports, and provide special cases for which the kernel attains negative values over nontrivial intervals. Hence, the proposed class of kernel is the reproducing kernel of a very rich Hilbert space that contains many special cases, including the celebrated Mat\'ern and Wendland kernels, as well as their aliases with hole effects.

A huge literature in statistics and machine learning is devoted to parametric families of correlation functions, where the correlation parameters are used to understand the properties of an associated spatial random process in terms of smoothness and global or compact support. However, most of current parametric correlation functions attain only non-negative values. This work provides two new families that parameterize negative dependencies (aka hole effects), along with smoothness, and global or compact support. They generalize the celebrated Mat\'ern and Generalized Wendland models, respectively, which are attained as special cases. A link between the two new families is also established, showing that a specific reparameterization of the latter includes the former as a special limit case. Their performance in terms of estimation accuracy and goodness of best linear unbiased prediction is illustrated through synthetic and real data.

BACKGROUND: Radiomics provides quantitative features of pulmonary nodules (PNs) which could aid lung cancer diagnosis, but medical image acquisition variability is an obstacle to clinical application. Acquisition effects may differ between radiomic features from benign vs. malignant PNs. PURPOSE: We evaluated how to account for differences between benign and malignant PNs when correcting radiomic features' acquisition dependency. METHODS: We used 567 chest CT scans grouped as benign, malignant, or lung cancer screening (mixed benign, malignant). ComBat harmonization was applied to extracted features for variation in 4 acquisition parameters. We compared: harmonizing without distinction, harmonizing with a covariate to preserve distinctions between subgroups, and harmonizing subgroups separately. Significant ($p\le0.05$) Kruskal-Wallis tests showed whether harmonization removed acquisition dependency. A LASSO-SVM pipeline was trained on successfully harmonized features to predict malignancy. To evaluate predictive information in these features, the trained harmonization estimators and predictive model were applied to unseen test sets. Harmonization and predictive performance were assessed for 10 trials of 5-fold cross-validation. RESULTS: An average 2.1% of features (95% CI:1.9-2.4%) were acquisition-independent when harmonized without distinction, 27.3% (95% CI:25.7-28.9%) when harmonized with a covariate, and 90.9% (95% CI:90.4-91.5%) when harmonized separately. Data harmonized separately or with a covariate trained models with higher ROC-AUC for screening scans than data harmonized without distinction between benign and malignant PNs (Delong test, adjusted $p\le0.05$). CONCLUSIONS: Radiomic features of benign and malignant PNs need different corrective transformations to recover acquisition-independent distributions. This can be done by harmonizing separately or with a covariate., Comment: 15 pages, 3 figures, plus supplemental material; updated author list, corrected result in paragraph 3 of Discussion, updated Figure S1

In Tennenholtz's program equilibrium, players of a game submit programs to play on their behalf. Each program receives the other programs' source code and outputs an action. This can model interactions involving AI agents, mutually transparent institutions, or commitments. Tennenholtz (2004) proves a folk theorem for program games, but the equilibria constructed are very brittle. We therefore consider simulation-based programs -- i.e., programs that work by running opponents' programs. These are relatively robust (in particular, two programs that act the same are treated the same) and are more practical than proof-based approaches. Oesterheld's (2019) $\epsilon$Grounded$\pi$Bot is such an approach. Unfortunately, it is not generally applicable to games of three or more players, and only allows for a limited range of equilibria in two player games. In this paper, we propose a generalisation to Oesterheld's (2019) $\epsilon$Grounded$\pi$Bot. We prove a folk theorem for our programs in a setting with access to a shared source of randomness. We then characterise their equilibria in a setting without shared randomness. Both with and without shared randomness, we achieve a much wider range of equilibria than Oesterheld's (2019) $\epsilon$Grounded$\pi$Bot. Finally, we explore the limits of simulation-based program equilibrium, showing that the Tennenholtz folk theorem cannot be attained by simulation-based programs without access to shared randomness.

Effectively using immersive multi-user environments for digital applications (via virtual, augmented and mixed reality technologies) beckons the future of healthcare delivery in the metaverse. We aimed to evaluate the feasibility and effectiveness of these environments used in health applications, while identifying their design features. We systematically searched MEDLINE, PsycINFO, and Emcare databases for peer-reviewed original reports, published in English, without date restrictions until Aug 30, 2023, and conducted manual citation searching in Feb 2024. All studies using fully immersive extended reality technologies (e.g., head-mounted displays, smart glasses) while engaging more than one participant in an intervention with direct health benefits were included. A qualitative synthesis of findings is reported. The quality of research was assessed using JBI Critical Checklists. The review was pre-registered on PROSPERO (CRD42023479155). Of 2862 identified records, 10 studies were eligible. Included studies were mostly conducted with healthy young adults (five studies) and older adults (four studies). While they all used different models of Oculus/Meta headsets, their environments' designs were distinctive and aligned with their objectives. Findings indicated varying degrees of positive health outcomes, for engagement in rehabilitation, meaningful interactions across distances, positive affect, transformative experiences, mental health therapies, and motor skill learning. Participants reported high usability, motivation, enjoyment, presence and copresence. They also expressed the need for more training time with technology. Adopting an intentional intervention design, considering factors affecting presence and copresence, as well as integrating co-creation of the program with participants, seems integral to achieving positive health outcomes., Comment: 45 pages, 2 figures, submitted to Human Behavior and Emerging Technologies

Non-rigid shape deformations pose significant challenges, and most existing methods struggle to handle partial deformations effectively. We present Partial Non-rigid Deformations and interpolations of the human body Surfaces (PaNDAS), a new method to learn local and global deformations of 3D surface meshes by building on recent deep models. Unlike previous approaches, our method enables restricting deformations to specific parts of the shape in a versatile way and allows for mixing and combining various poses from the database, all while not requiring any optimization at inference time. We demonstrate that the proposed framework can be used to generate new shapes, interpolate between parts of shapes, and perform other shape manipulation tasks with state-of-the-art accuracy and greater locality across various types of human surface data. Code and data will be made available soon.

The study of measurements in quantum mechanics exposes many of the ways in which the quantum world is different. For example, one of the hallmarks of quantum mechanics is that observables may be incompatible, implying among other things that it is not always possible to find joint probability distributions which fully capture the joint statistics of multiple measurements. Instead, one must employ more general tools such as the Kirkwood-Dirac quasiprobability (KDQ) distribution, which may exhibit negative or non-real values heralding non-classicality. In this Letter, we consider the KDQ distributions describing arbitrary collections of measurements on disjoint components of some generic multipartite system. We show that the system dynamics ensures that these distributions are classical if and only if the Hamiltonian supports Quantum Darwinism. Thus, we demonstrate a fundamental relationship between these two notions of classicality and their emergence in the quantum world., Comment: 6 pages, 1 figure

We introduce a dataset of natural-language questions in the decision theory of so-called Newcomb-like problems. Newcomb-like problems include, for instance, decision problems in which an agent interacts with a similar other agent, and thus has to reason about the fact that the other agent will likely reason in similar ways. Evaluating LLM reasoning about Newcomb-like problems is important because interactions between foundation-model-based agents will often be Newcomb-like. Some ways of reasoning about Newcomb-like problems may allow for greater cooperation between models. Our dataset contains both capabilities questions (i.e., questions with a unique, uncontroversially correct answer) and attitude questions (i.e., questions about which decision theorists would disagree). We use our dataset for an investigation of decision-theoretical capabilities and expressed attitudes and their interplay in existing models (different models by OpenAI, Anthropic, Meta, GDM, Reka, etc.), as well as models under simple prompt-based interventions. We find, among other things, that attitudes vary significantly between existing models; that high capabilities are associated with attitudes more favorable toward so-called evidential decision theory; and that attitudes are consistent across different types of questions., Comment: 48 pages, 15 figures; code and data at https://github.com/casparoe/newcomblike_questions_dataset; corrected error in funding acknowledgments

In Newcomb's problem, causal decision theory (CDT) recommends two-boxing and thus comes apart from evidential decision theory (EDT) and ex ante policy optimisation (which prescribe one-boxing). However, in Newcomb's problem, you should perhaps believe that with some probability you are in a simulation run by the predictor to determine whether to put a million dollars into the opaque box. If so, then causal decision theory might recommend one-boxing in order to cause the predictor to fill the opaque box. In this paper, we study generalisations of this approach. That is, we consider general Newcomblike problems and try to form reasonable self-locating beliefs under which CDT's recommendations align with an EDT-like notion of ex ante policy optimisation. We consider approaches in which we model the world as running simulations of the agent, and an approach not based on such models (which we call 'Generalised Generalised Thirding', or GGT). For each approach, we characterise the resulting CDT policies, and prove that under certain conditions, these include the ex ante optimal policies.

OT 081 is a well-known, luminous blazar that is remarkably variable in many energy bands. We present the first broadband study of the source which includes very-high-energy (VHE, $E>$100\,GeV) $\gamma$-ray data taken by the MAGIC and H.E.S.S. imaging Cherenkov telescopes. The discovery of VHE $\gamma$-ray emission happened during a high state of $\gamma$-ray activity in July 2016, observed by many instruments from radio to VHE $\gamma$-rays. We identify four states of activity of the source, one of which includes VHE $\gamma$-ray emission. Variability in the VHE domain is found on daily timescales. The intrinsic VHE spectrum can be described by a power-law with index $3.27\pm0.44_{\rm stat}\pm0.15_{\rm sys}$ (MAGIC) and $3.39\pm0.58_{\rm stat}\pm0.64_{\rm sys}$ (H.E.S.S.) in the energy range of 55--300\,GeV and 120--500\,GeV, respectively. The broadband emission cannot be sucessfully reproduced by a simple one-zone synchrotron self-Compton model. Instead, an additional external Compton component is required. We test a lepto-hadronic model that reproduces the dataset well and a proton-synchrotron dominated model that requires an extreme proton luminosity. Emission models that are able to successfully represent the data place the emitting region well outside of the Broad Line Region (BLR) to a location at which the radiative environment is dominated by the infrared thermal radiation field of the dusty torus. In the scenario described by this flaring activity, the source appears to be an FSRQ, in contrast with past categorizations. This suggests that the source can be considered to be a transitional blazar, intermediate between BL~Lac and FSRQ objects., Comment: Accepted on MNRAS Corresponding authors: M. Manganaro, J. Becerra Gonz\'alez, M. Seglar-Arroyo, D. A. Sanchez

Imaging atmospheric Cherenkov telescopes (IACTs) are used to observe very high-energy photons from the ground. Gamma rays are indirectly detected through the Cherenkov light emitted by the air showers they induce. The new generation of experiments, in particular the Cherenkov Telescope Array Observatory (CTAO), sets ambitious goals for discoveries of new gamma-ray sources and precise measurements of the already discovered ones. To achieve these goals, both hardware and data analysis must employ cutting-edge techniques. This also applies to the LST-1, the first IACT built for the CTAO, which is currently taking data on the Canary island of La Palma. This paper introduces a new event reconstruction technique for IACT data, aiming to improve the image reconstruction quality and the discrimination between the signal and the background from misidentified hadrons and electrons. The technique models the development of the extensive air shower signal, recorded as a waveform per pixel, seen by CTAO telescopes' cameras. Model parameters are subsequently passed to random forest regressors and classifiers to extract information on the primary particle. The new reconstruction was applied to simulated data and to data from observations of the Crab Nebula performed by the LST-1. The event reconstruction method presented here shows promising performance improvements. The angular and energy resolution, and the sensitivity, are improved by 10 to 20% over most of the energy range. At low energy, improvements reach up to 22%, 47%, and 50%, respectively. A future extension of the method to stereoscopic analysis for telescope arrays will be the next important step., Comment: Accepted in A&A

Does the choice of programming language affect energy consumption? Previous highly visible studies have established associations between certain programming languages and energy consumption. A causal misinterpretation of this work has led academics and industry leaders to use or support certain languages based on their claimed impact on energy consumption. This paper tackles this causal question directly. It first corrects and improves the measurement methodology used by prior work. It then develops a detailed causal model capturing the complex relationship between programming language choice and energy consumption. This model identifies and incorporates several critical but previously overlooked factors that affect energy usage. These factors, such as distinguishing programming languages from their implementations, the impact of the application implementations themselves, the number of active cores, and memory activity, can significantly skew energy consumption measurements if not accounted for. We show -- via empirical experiments, improved methodology, and careful examination of anomalies -- that when these factors are controlled for, notable discrepancies in prior work vanish. Our analysis suggests that the choice of programming language implementation has no significant impact on energy consumption beyond execution time., Comment: 18 pages

Visualization design influences how people perceive data patterns, yet most research focuses on low-level analytic tasks, such as finding correlations. Existing work has criticized pie charts for their perceptual limitations. However, simpler visualizations like pie and bar charts are widely used for real-world decision-making, such as choosing schools or advisors. As a case study, we examine whether pie charts hurt high-level decisions compared to bar charts, using the website that presents academic data, CSRankings.org. By comparing the impact of pie charts versus bar charts on users' impressions of faculty productivity and projected workload, we found no significant differences in decisions among over 300 participants. Our findings challenge traditional views on visualization design, emphasizing the need for real-world use cases in evaluations.

The Polana-Eulalia Complex (PEC) is an Inner Main Belt, C-complex asteroid population that may be the source of the near-Earth asteroid spacecraft mission targets (101955) Bennu and (162173) Ryugu. Here, we report a size-based investigation of the visible (VIS; 0.47 -- 0.89 um) spectrophotometric slopes of the PEC's constituent families, the New Polana and Eulalia Families. Using two releases of the Sloan Digital Sky Survey's Moving Object Catalog as well as the 3rd data release of the Gaia catalog, we present evidence of size-based slope variability within each family. We find that Eulalia family members exhibit lower average slopes than Polana family members in all catalogs' samples, particularly for objects < 9 km in diameter. We are unable to conclude that VIS slope distinguishability between the families is statistically significant, but we explore a potential cause of the bulk slope differences between the PEC families, in addition to providing commentary on size-slope trends generally., Comment: 32 pages, 18 figures, 4 tables, Accepted by Icarus on 17 September 2024

To support and enhance educational attainment in the United States, the Lumina Foundation established a national goal of 60% of adults earning a high-quality credential by 2025. To achieve this goal, the Lumina Foundation expanded its funding to the adult learner population in 2017 by establishing the Adult Promise Initiative. The organization recognizes and values diversity, equity, and inclusion in its work and understands how minority-serving institutions, such as Historically Black Colleges and Universities (HBCUs), are integral to the proliferation of Black adult learners. Recognizing the tremendous impact HBCUs have on the educational attainment of Black Adult Learners, we must identify their curricular and co-curricular best practices that are critical to the academic success of this population. This report will provide an overview of these best practices for faculty, staff, and students of the five HBCUs participating in the Lumina Adult Learner Initiative grant. The organization of this report will include a brief review of the Adult Learner Initiative grant and its components, followed by the best practices frequently discussed among faculty, staff, and students participating in the grant and recommendations. The authors will then review Lumina's philanthropic approach to the grant. Lastly, how staff and administrators define capacity-building for the adult learner population will be examined.

This article presents a neural network approach for estimating the covariance function of spatial Gaussian random fields defined in a portion of the Euclidean plane. Our proposal builds upon recent contributions, expanding from the purely isotropic setting to encompass geometrically anisotropic correlation structures, i.e., random fields with correlation ranges that vary across different directions. We conduct experiments with both simulated and real data to assess the performance of the methodology and to provide guidelines to practitioners.

Since the dawn of scanning probe microscopy (SPM), tapping or intermittent contact mode has been one of the most widely used imaging modes. Manual optimization of tapping mode not only takes a lot of instrument and operator time, but also often leads to frequent probe and sample damage, poor image quality and reproducibility issues for new types of samples or inexperienced users. Despite wide use, optimization of tapping mode imaging is an extremely hard problem, ill-suited to either classical control methods or machine learning. Here we introduce a reward-driven workflow to automate the optimization of SPM in the tapping mode. The reward function is defined based on multiple channels with physical and empirical knowledge of good scans encoded, representing a sample-agnostic measure of image quality and imitating the decision-making logic employed by human operators. This automated workflow gives optimal scanning parameters for different probes and samples and gives high-quality SPM images consistently in the attractive mode. This study broadens the application and accessibility of SPM and opens the door for fully automated SPM., Comment: 20 pages, 6 figures

Physical imaging is a foundational characterization method in areas from condensed matter physics and chemistry to astronomy and spans length scales from atomic to universe. Images encapsulate crucial data regarding atomic bonding, materials microstructures, and dynamic phenomena such as microstructural evolution and turbulence, among other phenomena. The challenge lies in effectively extracting and interpreting this information. Variational Autoencoders (VAEs) have emerged as powerful tools for identifying underlying factors of variation in image data, providing a systematic approach to distilling meaningful patterns from complex datasets. However, a significant hurdle in their application is the definition and selection of appropriate descriptors reflecting local structure. Here we introduce the scale-invariant VAE approach (SI-VAE) based on the progressive training of the VAE with the descriptors sampled at different length scales. The SI-VAE allows the discovery of the length scale dependent factors of variation in the system. Here, we illustrate this approach using the ferroelectric domain images and generalize it to the movies of the electron-beam induced phenomena in graphene and topography evolution across combinatorial libraries. This approach can further be used to initialize the decision making in automated experiments including structure-property discovery and can be applied across a broad range of imaging methods. This approach is universal and can be applied to any spatially resolved data including both experimental imaging studies and simulations, and can be particularly useful for exploration of phenomena such as turbulence, scale-invariant transformation fronts, etc.

Li-containing argyrodites represent a promising family of Li-ion conductors with several derived compounds exhibiting room-temperature ionic conductivity > 1 mS/cm and making them attractive as potential candidates as electrolytes in solid-state Li-ion batteries. Starting from the parent phase Li7PS6, several cation and anion substitution strategies have been attempted to increase the conductivity of Li ions. Nonetheless, a detailed understanding of the thermodynamics of native defects and doping of Li argyrodite and their effect on the ionic conductivity of Li is missing. Here, we report a comprehensive computational study of defect chemistry of the parent phase Li7PS6 in both intrinsic and extrinsic regimes, using a newly developed workflow to automate the computations of several defect formation energies in a thermodynamically consistent framework. Our findings agree with known experimental findings, rule out several unfavorable aliovalent dopants, narrowing down the potential promising candidates that can be tested experimentally. We also find that cation-anion co-doping can provide a powerful strategy to further optimize the composition of argyrodite. In particular, Si-F co-doping is predicted to be thermodynamically favorable; this could lead to the synthesis of the first F-doped Li-containing argyrodite. Finally, using DeePMD neural networks, we have mapped the ionic conductivity landscape as function of the concentration of the most promising cation and anion dopants identified from the defect calculations, and identified the most promising region in the compositional space with high Li conductivity that can be explored experimentally.

(2060) Chiron is a large centaur that has been reported active on multiple occasions including during aphelion passage. Studies of Chirons coma during active periods have resulted in the detection of C(triple)N and CO outgassing. Significant work remains to be undertaken to comprehend the activation mechanisms on Chiron and the parent molecules of the gas phases detected. This work reports the study of the ices on Chirons surface and coma and seeks spectral indicators of volatiles associated with the activity. Additionally, we discuss how these detections could be related to the activation mechanism for Chiron and, potentially, other centaurs. In July 2023, the James Webb Space Telescope (JWST) observed Chiron when it was active near its aphelion. We present JWST/NIRSpec spectra from 0.97 to 5.27 microns with a resolving power of 1000, and compare them with laboratory data for identification of the spectral bands. We report the first detections on Chiron of absorption bands of several volatile ices, including CO2, CO, C2H6, C3H8, and C2H2. We also confirm the presence of water ice in its amorphous state. A key discovery arising from these data is the detection of fluorescence emissions of CH4, revealing the presence of a gas coma rich in this hyper-volatile molecule, which we also identify to be in non-local thermal equilibrium (nonLTE). CO2 gas emission is also detected in the fundamental stretching band at 4.27 microns. We argue that the presence of CH4 emission is the first proof of the desorption of CH4 due to a density phase transition of amorphous water ice at low temperature in agreement with the estimated temperature of Chiron during the JWST observations (61 K). Detection of photolytic and proton irradiation products of CH4 and CO2 on the surface, in the coma ice grains, or in the ring material is also detected via a forest of absorption features from 3.5 to 5.3 microns., Comment: 4 figures in paper plus 2 more in the appendix. Accepted as A&A Letter

Foams are versatile by nature and ubiquitous in a wide range of applications, including padding, insulation, and acoustic dampening. Previous work established that foams 3D printed via Viscous Thread Printing (VTP) can in principle combine the flexibility of 3D printing with the mechanical properties of conventional foams. However, the generality of prior work is limited due to the lack of predictable process-property relationships. In this work, we utilize a self-driving lab that combines automated experimentation with machine learning to identify a processing subspace in which dimensionally consistent materials are produced using VTP with spatially programmable mechanical properties. In carrying out this process, we discover an underlying self-stabilizing characteristic of VTP layer thickness, an important feature for its extension to new materials and systems. Several complex exemplars are constructed to illustrate the newly enabled capabilities of foams produced via VTP, including 1D gradient rectangular slabs, 2D localized stiffness zones on an insole orthotic and living hinges, and programmed 3D deformation via a cable driven humanoid hand. Predictive mapping models are developed and validated for both thermoplastic polyurethane (TPU) and polylactic acid (PLA) filaments, suggesting the ability to train a model for any material suitable for material extrusion (ME) 3D printing., Comment: 23 pages, 7 figures

Context: There are currently three pulsars firmly detected by imaging atmospheric Cherenkov telescopes (IACTs), two of them reaching TeV energies, challenging models of very-high-energy (VHE) emission in pulsars. More precise observations are needed to better characterize pulsar emission at these energies. The LST-1 is the prototype of the Large-Sized Telescope, that will be part of the Cherenkov Telescope Array Observatory (CTAO). Its improved performance over previous IACTs makes it well suited for studying pulsars. Aims: To study the Crab pulsar emission with the LST-1, improving and complementing the results from other telescopes. These observations can also be used to characterize the potential of the LST-1 to study other pulsars and detect new ones. Methods: We analyzed a total of $\sim$103 hours of gamma-ray observations of the Crab pulsar conducted with the LST-1 in the period from September 2020 to January 2023. The observations were carried out at zenith angles less than 50 degrees. A new analysis of the Fermi-LAT data was also performed, including $\sim$14 years of observations. Results: The Crab pulsar phaseogram, long-term light-curve, and phase-resolved spectra are reconstructed with the LST-1 from 20 GeV to 450 GeV for P1 and up to 700 GeV for P2. The pulsed emission is detected with a significance of 15.2$\sigma$. The two characteristic emission peaks of the Crab pulsar are clearly detected (>10$\sigma$), as well as the so-called bridge emission (5.7$\sigma$). We find that both peaks are well described by power laws, with spectral indices of $\sim$3.44 and $\sim$3.03 respectively. The joint analysis of Fermi-LAT and LST-1 data shows a good agreement between both instruments in the overlapping energy range. The detailed results obtained in the first observations of the Crab pulsar with LST-1 show the potential that CTAO will have to study this type of sources., Comment: Accepted by A&A

The asteroid (142) Polana is classified as a B-type asteroid located in the inner Main Belt. This asteroid is the parent of the New Polana family, which has been proposed to be the likely source of primitive near-Earth asteroids such as the B-type asteroid (101955) Bennu. To investigate the compositional correlation between Polana and Bennu at the 3-micron band and their aqueous alteration histories, we analyzed the spectra of Polana in the ~2.0-4.0-micron spectral range using the NASA Infrared Telescope Facility in Hawaii. Our findings indicate that Polana does not exhibit discernable 3-micron hydrated mineral absorption (within 2 sigma), which is in contrast to asteroid Bennu. Bennu displayed a significant 3-micron absorption feature similar to CM- and CI-type carbonaceous chondrites. This suggests two possibilities: either Bennu did not originate from the New Polana family parented by asteroid Polana or the interior of Bennu's parent body was not homogenous, with diverse levels of aqueous alteration. Several explanations support the latter possibility, including heating due to shock waves and pressure, which could have caused the current dehydrated state of Bennu's parent body.

In the current era of noisy intermediate-scale quantum (NISQ) technology, quantum devices present new avenues for addressing complex, real-world challenges including potentially NP-hard optimization problems. This work aims to showcase how the inherent noise in NISQ devices can be leveraged to solve such real-world problems effectively. Utilizing a D-Wave quantum annealer and IonQ's gate-based NISQ computers, we generate and analyze solutions for managing train traffic under stochastic disturbances. Our case study focuses on the Baltimore Light RailLink, which embodies the characteristics of both tramway and railway networks. We explore the feasibility of using NISQ technology to model the stochastic nature of disruptions in these transportation systems. Our research marks the inaugural application of both quantum computing paradigms to tramway and railway rescheduling, highlighting the potential of quantum noise as a beneficial resource in complex optimization scenarios.

Simon's algorithm was one of the first problems to demonstrate a genuine quantum advantage. The algorithm, however, assumes access to noise-free qubits. In our work we use Simon's algorithm to benchmark the error rates of devices currently available in the "quantum cloud." As a main result we obtain an objective comparison between the different physical platforms made available by IBM and IonQ. Our study highlights the importance of understanding the device architectures and chip topologies when transpiling quantum algorithms onto hardware. For instance, we demonstrate that two-qubit operations on spatially separated qubits on superconducting chips should be avoided.

The Lorentz Invariance Violation (LIV), a proposed consequence of certain quantum gravity (QG) scenarios, could instigate an energy-dependent group velocity for ultra-relativistic particles. This energy dependence, although suppressed by the massive QG energy scale $E_\mathrm{QG}$, expected to be on the level of the Planck energy $1.22 \times 10^{19}$ GeV, is potentially detectable in astrophysical observations. In this scenario, the cosmological distances traversed by photons act as an amplifier for this effect. By leveraging the observation of a remarkable flare from the blazar Mrk\,421, recorded at energies above 100 GeV by the MAGIC telescopes on the night of April 25 to 26, 2014, we look for time delays scaling linearly and quadratically with the photon energies. Using for the first time in LIV studies a binned-likelihood approach we set constraints on the QG energy scale. For the linear scenario, we set $95\%$ lower limits $E_\mathrm{QG}>2.7\times10^{17}$ GeV for the subluminal case and $E_\mathrm{QG}> 3.6 \times10^{17}$ GeV for the superluminal case. For the quadratic scenario, the $95\%$ lower limits for the subluminal and superluminal cases are $E_\mathrm{QG}>2.6 \times10^{10}$ GeV and $E_\mathrm{QG}>2.5\times10^{10}$ GeV, respectively.

The Super-Kamiokande and T2K collaborations present a joint measurement of neutrino oscillation parameters from their atmospheric and beam neutrino data. It uses a common interaction model for events overlapping in neutrino energy and correlated detector systematic uncertainties between the two datasets, which are found to be compatible. Using 3244.4 days of atmospheric data and a beam exposure of $19.7(16.3) \times 10^{20}$ protons on target in (anti)neutrino mode, the analysis finds a 1.9$\sigma$ exclusion of CP-conservation (defined as $J_{CP}=0$) and a preference for the normal mass ordering., Comment: 12 pages, 4 figures

The rapid development of computation power and machine learning algorithms has paved the way for automating scientific discovery with a scanning probe microscope (SPM). The key elements towards operationalization of automated SPM are the interface to enable SPM control from Python codes, availability of high computing power, and development of workflows for scientific discovery. Here we build a Python interface library that enables controlling an SPM from either a local computer or a remote high-performance computer (HPC), which satisfies the high computation power need of machine learning algorithms in autonomous workflows. We further introduce a general platform to abstract the operations of SPM in scientific discovery into fixed-policy or reward-driven workflows. Our work provides a full infrastructure to build automated SPM workflows for both routine operations and autonomous scientific discovery with machine learning., Comment: 16 pages, 7 figures

Quantum Darwinism is a paradigm to understand how classically objective reality emerges from within a fundamentally quantum universe. Despite the growing attention that this field of research as been enjoying, it is currently not known what specific properties a given Hamiltonian describing a generic quantum system must have to allow the emergence of classicality. Therefore, in the present work, we consider a broadly applicable generic model of an arbitrary finite-dimensional system interacting with an environment formed from an arbitrary collection of finite-dimensional degrees of freedom via an unspecified, potentially time-dependent Hamiltonian containing at most two-body interaction terms. We show that such models support quantum Darwinism if the set of operators acting on the system which enter the Hamiltonian satisfy a set of commutation relations with a pointer observable and with one other. We demonstrate our results by analyzing a wide range of example systems: a qutrit interacting with a qubit environment, a qubit-qubit model with interactions alternating in time, and a series of collision models including a minimal model of a quantum Maxwell demon., Comment: 24 pages, 11 figures

Primitive asteroids with low albedos and red slopes in the visible and near infrared (VNIR) are found in both the Main Belt and the Jupiter Trojan clouds. In order to determine whether the VNIR spectral similarities of primitive Main Belt asteroids and Jupiter Trojans are reflective of a true compositional similarity, we compare the mid-infrared silicate emission features of Main Belt and Jupiter Trojan asteroids. Using archival data from the Spitzer Space Telescope's IRS spectrograph and observations from the Stratospheric Observatory for Infrared Astronomy's (SOFIA) FORCAST instrument, we analyze the 5-40 micron spectra of thirteen primitive Main Belt asteroids and compare them to those of Jupiter Trojans in the literature. We find that while many primitive asteroids in the Main Belt resemble their Trojan counterparts with strong spectral signatures of olivine-rich high-porosity silicate regoliths, we identify (368) Haidea as a spectrally distinctive asteroid that lacks strong evidence of olivine in its MIR spectrum. Differences in silicate compositions among D-type asteroids imply a diversity of origins for primitive asteroids., Comment: 22 Pages, 9 figures, draft version accepted for publication in PSJ

This work addresses the problem of simulating Gaussian random fields that are continuously indexed over a class of metric graphs, termed graphs with Euclidean edges, being more general and flexible than linear networks. We introduce three general algorithms that allow to reconstruct a wide spectrum of random fields having a covariance function that depends on a specific metric, called resistance metric, and proposed in recent literature. The algorithms are applied to a synthetic case study consisting of a street network. They prove to be fast and accurate in that they reproduce the target covariance function and provide random fields whose finite-dimensional distributions are approximately Gaussian.