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Planets and stars are expected to be compositionally linked because they accrete from the same material reservoir. However, astronomical observations revealed the existence of exoplanets whose bulk density is far higher than what is expected from host-stars' composition. A commonly-invoked theory is that these high-density exoplanets are the metallic cores of super-Earth-sized planets whose rocky mantles were stripped by giant impacts. Here, by combining orbital dynamics and impact physics, we show that mantle-stripping giant impacts between super-Earths are unlikely to occur at rates sufficient to explain the observed size and currently estimated abundance of the high-density exoplanets. We explain this as the interplay of two main factors: the parent super-Earths being in most cases smaller than 2 Earth radii; and the efficiency of mantle stripping decreasing with increasing planetary size. We conclude that most of the observed high-density exoplanets are unlikely to be metal-rich giant-impact remnants., Comment: 48 pages, 15 figures

We investigate the effect of focused-ion-beam (FIB) irradiation on spin waves with sub-micron wavelengths in Yttrium-Iron-Garnet (YIG) films. Time-resolved scanning transmission X-ray (TR-STXM) microscopy was used to image the spin waves in irradiated regions and deduce corresponding changes in the magnetic parameters of the film. We find that the changes of Ga$^+$ irradiation can be understood by assuming a few percent change in the effective magnetization $M_\mathrm{eff}$ of the film due to a trade-off between changes in anisotropy and effective film thickness. Our results demonstrate that FIB irradiation can be used to locally alter the dispersion relation and the effective refractive index $n_\textrm{eff}$ of the film, even for submicron wavelengths. To achieve the same change in $n_\textrm{eff}$ for shorter wavelengths, a higher dose is required, but no significant deterioration of spin wave propagation length in the irradiated regions was observed, even at the highest applied doses., Comment: 5 pages, 6 figures

The safe operation of automated vehicles depends on their ability to perceive the environment comprehensively. However, occlusion, sensor range, and environmental factors limit their perception capabilities. To overcome these limitations, collective perception enables vehicles to exchange information. However, fusing this exchanged information is a challenging task. Early fusion approaches require large amounts of bandwidth, while intermediate fusion approaches face interchangeability issues. Late fusion of shared detections is currently the only feasible approach. However, it often results in inferior performance due to information loss. To address this issue, we propose MR3D-Net, a dynamic multi-resolution 3D sparse voxel grid fusion backbone architecture for LiDAR-based collective perception. We show that sparse voxel grids at varying resolutions provide a meaningful and compact environment representation that can adapt to the communication bandwidth. MR3D-Net achieves state-of-the-art performance on the OPV2V 3D object detection benchmark while reducing the required bandwidth by up to 94% compared to early fusion. Code is available at https://github.com/ekut-es/MR3D-Net, Comment: Accepted at IEEE ITSC 2024

This work presents the use of graph learning for the prediction of multi-step experimental outcomes for applications across experimental research, including material science, chemistry, and biology. The viability of geometric learning for regression tasks was benchmarked against a collection of linear models through a combination of simulated and real-world data training studies. First, a selection of five arbitrarily designed multi-step surrogate functions were developed to reflect various features commonly found within experimental processes. A graph transformer network outperformed all tested linear models in scenarios that featured hidden interactions between process steps and sequence dependent features, while retaining equivalent performance in sequence agnostic scenarios. Then, a similar comparison was applied to real-world literature data on algorithm guided colloidal atomic layer deposition. Using the complete reaction sequence as training data, the graph neural network outperformed all linear models in predicting the three spectral properties for most training set sizes. Further implementation of graph neural networks and geometric representation of scientific processes for the prediction of experiment outcomes could lead to algorithm driven navigation of higher dimension parameter spaces and efficient exploration of more dynamic systems.

Gate-level clocking, typical in traditional approaches to Single Flux Quantum (SFQ) technology, makes the effective synthesis of superconducting circuits a significant engineering hurdle. This paper addresses this challenge by employing the recently introduced alternating SFQ (xSFQ) logic family. xSFQ leverages dual-rail alternating encoding to eliminate the clock dependency from the superconducting gate semantics. This obviates the need for ad hoc modifications to existing synthesis tools and avoids unnecessary circuit resource overheads, marking a significant advancement in superconducting circuit design automation. Our implementation results demonstrate an average reduction of over 80\% in the Josephson junction count for circuits from the ISCAS85, EPFL, and ISCAS89 benchmark suites., Comment: Accepted for publication at DAC '24

Comprehensive perception of the vehicle's environment and correct interpretation of the environment are crucial for the safe operation of autonomous vehicles. The perception of surrounding objects is the main component for further tasks such as trajectory planning. However, safe trajectory planning requires not only object detection, but also the detection of drivable areas and lane corridors. While first approaches consider an advanced safety evaluation of object detection, the evaluation of lane detection still lacks sufficient safety metrics. Similar to the safety metrics for object detection, additional factors such as the semantics of the scene with road type and road width, the detection range as well as the potential causes of missing detections, incorporated by vehicle speed, should be considered for the evaluation of lane detection. Therefore, we propose the Lane Safety Metric (LSM), which takes these factors into account and allows to evaluate the safety of lane detection systems by determining an easily interpretable safety score. We evaluate our offline safety metric on various virtual scenarios using different lane detection approaches and compare it with state-of-the-art performance metrics.

Slot machines can have fairly complex behaviour. Determining the RTP (return to player) can be involved, especially when a player has an influence on the course of the game. In this paper we model the behaviour of slot machines using probabilistic process specifications where the intervention of players is modelled using non-determinism. The RTP is formulated as a quantitative modal formula which can be evaluated fully automatically on the behavioural specifications of these slot machines. We apply the method on an actual slot machine provided by the company Err\`el Industries B.V. The most useful contribution of this paper is that we show how to describe the behaviour of slot machines both concisely and unequivocally. Using quantitative modal logics there is an extra bonus, as we can quite easily provide valuable insights by a.o. computing the exact RTP and obtaining the optimal player strategies.

Asynchronous Bayesian optimization is a recently implemented technique that allows for parallel operation of experimental systems and disjointed workflows. Contrasting with serial Bayesian optimization which individually selects experiments one at a time after conducting a measurement for each experiment, asynchronous policies sequentially assign multiple experiments before measurements can be taken and evaluate new measurements continuously as they are made available. This technique allows for faster data generation and therefore faster optimization of an experimental space. This work extends the capabilities of asynchronous optimization methods beyond prior studies by evaluating four additional policies that incorporate pessimistic predictions in the training data set. Combined with a conventional greedy policy, the five total policies were evaluated in a simulated environment and benchmarked with serial sampling. Under some conditions and parameter space dimensionalities, the pessimistic asynchronous policy reached optimum experimental conditions in significantly fewer experiments than equivalent serial policies and proved to be less susceptible to convergence onto local optima at higher dimensions. Without accounting for the faster sampling rate, the pessimistic asynchronous algorithm presented in this work could result in more efficient algorithm driven optimization of high-cost experimental spaces. Accounting for sampling rate, the presented asynchronous algorithm could allow for faster optimization in experimental spaces where multiple experiments can be run before results are collected.

The AAS Strategic Plan for 2021-26 called for the creation of a task force to identify how the AAS can meet the goals of the Paris Agreement. The AAS and its membership recognize the danger climate change represents to humanity and our world, and to astronomy -- as a profession, a hobby, and a cultural good. Our profession in general -- and the AAS in particular -- should work to make it possible for all astronomers to have an equal opportunity to be successful without needing to incur high carbon emissions, and to preserve astronomy for future generations. A study was completed of the carbon emissions associated with the AAS, finding that 84% of total AAS-related emissions are from in-person conferences. We also conducted a survey of AAS members to determine their attitudes about climate change. Respondents overwhelmingly (97%) think that the AAS should reduce its carbon footprint. Our task force created a list of fourteen recommendations, with two ranked as top priorities: The AAS should not schedule additional in-person meetings before 2030 and it should work to innovate the AAS conference model. Based upon our analysis it is clear that online interaction is the only way to increase participation while meaningfully decreasing emissions. Our recommendations are aligned with the Astro2020 Decadal Survey as well as AAS values to disseminate our scientific understanding of the universe, and to do our work in an ethically responsible way. Because of their other benefits -- particularly in making our society more welcoming to those who traditionally have been excluded -- we feel that these are sound decisions, worthy of implementation even if the AAS wasn't trying to reduce its carbon footprint. They simply make sense as steps towards a professional society that better serves a broader membership, as our profession evolves to be greener, more inclusive, and more productive., Comment: 46 pages

To ensure safe operation of autonomous vehicles in complex urban environments, complete perception of the environment is necessary. However, due to environmental conditions, sensor limitations, and occlusions, this is not always possible from a single point of view. To address this issue, collective perception is an effective method. Realistic and large-scale datasets are essential for training and evaluating collective perception methods. This paper provides the first comprehensive technical review of collective perception datasets in the context of autonomous driving. The survey analyzes existing V2V and V2X datasets, categorizing them based on different criteria such as sensor modalities, environmental conditions, and scenario variety. The focus is on their applicability for the development of connected automated vehicles. This study aims to identify the key criteria of all datasets and to present their strengths, weaknesses, and anomalies. Finally, this survey concludes by making recommendations regarding which dataset is most suitable for collective 3D object detection, tracking, and semantic segmentation., Comment: Accepted at IEEE IV 2024

Crystal plasticity finite element simulations are frequently employed to predict the plastic anisotropy of polycrystalline metals based on their crystallographic texture. However, precipitation in age-hardenable aluminium alloys is known to affect the texture-induced plastic anisotropy. A new modelling approach that incorporates this effect into crystal plasticity constitutive models is presented to address this issue. The approach is based on the hypothesis that precipitation occurs in a global preferred direction, resulting in direction-dependent hardening behaviour for the slip systems. The direction dependency is implemented into a conventional crystal plasticity constitutive model by modification of the hardening law using only one additional parameter. To demonstrate the applicability of the new modelling approach, it is applied to an AA6014-T4 age-hardenable aluminium alloy and compared against the current state-of-the-art crystal plasticity modelling approach, taking only the crystallographic texture into account. The results demonstrate that the predicted accuracy of the plastic anisotropy for the AA6014-T4 aluminium alloy studied is significantly improved by the proposed new modelling approach for incorporating precipitation-related effects., Comment: as submitted

The analysis of formal models that include quantitative aspects such as timing or probabilistic choices is performed by quantitative verification tools. Broad and mature tool support is available for computing basic properties such as expected rewards on basic models such as Markov chains. Previous editions of QComp, the comparison of tools for the analysis of quantitative formal models, focused on this setting. Many application scenarios, however, require more advanced property types such as LTL and parameter synthesis queries as well as advanced models like stochastic games and partially observable MDPs. For these, tool support is in its infancy today. This paper presents the outcomes of QComp 2023: a survey of the state of the art in quantitative verification tool support for advanced property types and models. With tools ranging from first research prototypes to well-supported integrations into established toolsets, this report highlights today's active areas and tomorrow's challenges in tool-focused research for quantitative verification.

Trans-Neptunian objects (TNOs) are small, icy bodies in the outer solar system. They are observed to have a complex orbital distribution that was shaped by the early dynamical history and migration of the giant planets. Comparisons between the different dynamical classes of modeled and observed TNOs can help constrain the history of the outer solar system. Because of the complex dynamics of TNOs, particularly those in and near mean motion resonances with Neptune, classification has traditionally been done by human inspection of plots of the time evolution of orbital parameters. This is very inefficient. The Vera Rubin Observatory's Legacy Survey of Space and Time (LSST) is expected to increase the number of known TNOs by a factor of $\sim$10, necessitating a much more automated process. In this chapter we present an improved supervised machine learning classifier for TNOs. Using a large and diverse training set as well as carefully chosen, dynamically motivated data features calculated from numerical integrations of TNO orbits, our classifier returns results that match those of a human classifier 98% of the time, and dynamically relevant classifications 99.7% of the time. This classifier is dramatically more efficient than human classification, and it will improve classification of both observed and modeled TNO data., Comment: Accepted chapter to appear in the Elsevier book "Machine Learning for Small Solar System Bodies", edited by Valerio Carruba, Evgeny Smirnov, and Dagmara Oszkiewicz

The discovery of the transient Jupiter co-orbital comet P/2019 LD2 (ATLAS) drew significant interest. Not only will LD2 transition between being a Centaur and a Jupiter Family Comet (JFC) in 2063, the first time this process can be observed as it happens, it is also very active for its large heliocentric distance. We present observations and orbital integrations of the newly discovered transient Jupiter co-orbital comet P/2023 V6 (PANSTARRS), the second such object known. Despite similar modern orbits, V6 is significantly (15 times) less active than LD2 and most JFCs as determined via Afrho measurements at the same heliocentric distance. We find V6 is co-orbital between 2020 and 2044, twice the duration of LD2, but it will not become a JFC soon. We interpret these differences in activity as evolutionary, with V6 having lost a significant fraction of its near-surface ice compared to LD2 by previously being warmer. While V6's pre-encounter orbit was somewhat warmer than LD2's, future thermal modeling will be needed to understand if this can explain their differences or if a more significant difference further into the past is required. This is more evidence that LD2 is a pristine and ice-rich object, and thus it may display very strong activity when it becomes a JFC. We sue the differences between V6 and LD2 to discuss the interpretation of cometary activity at large heliocentric distances as well as the small end of the crater record of the Galilean Satellites. Continuing observations of both objects are highly encouraged., Comment: Accepted for publication in the Astrophysical Journal: Letters on April 11, 2024. Four figures, fifteen pages

We present a stability analysis of a large set of simulated planetary systems of three or more planets based on architectures of multiplanet systems discovered by \textit{Kepler} and \textit{K2}. We propagated 21,400 simulated planetary systems up to 5 billion orbits of the innermost planet; approximately 13% of these simulations ended in a planet-planet collision within that timespan. We examined trends in dynamical stability based on dynamical spacings, orbital period ratios, and mass ratios of nearest-neighbor planets as well as the system-wide planet mass distribution and the spectral fraction describing the system's short-term evolution. We find that instability is more likely in planetary systems with adjacent planet pairs that have period ratios less than two and in systems of greater variance of planet masses. Systems with planet pairs at very small dynamical spacings (less than $\sim10-12$ mutual Hill radius) are also prone to instabilities, but instabilities also occur at much larger planetary separations. We find that a large spectral fraction (calculated from short integrations) is a reasonable predictor of longer-term dynamical instability; systems that have a large number of Fourier components in their eccentricity vectors are prone to secular chaos and subsequent eccentricity growth and instabilities., Comment: accepted for publication in AJ

This volume contains the proceedings of MARS 2024, the sixth workshop on Models for Formal Analysis of Real Systems, held as part of ETAPS 2024, the European Joint Conferences on Theory and Practice of Software. The MARS workshops bring together researchers from different communities who are developing formal models of real systems in areas where complex models occur, such as networks, cyber-physical systems, hardware/software co-design, biology, etc. The motivation and aim for MARS stem from the following two observations: (1) Large case studies are essential to show that specification formalisms and modelling techniques are applicable to real systems, whereas many research papers only consider toy examples or tiny case studies. (2) Developing an accurate model of a real system takes a large amount of time, often months or years. In most scientific papers, however, salient details of the model need to be skipped due to lack of space, and to leave room for formal verification methodologies and results. The MARS workshops aim at remedying these issues, emphasising modelling over verification, so as to retain lessons learnt from formal modelling, which are not usually discussed elsewhere.

Beyond-Moore computing technologies are expected to provide a sustainable alternative to the von Neumann approach not only due to their down-scaling potential but also via exploiting device-level functional complexity at the lowest possible energy consumption. The dynamics of the Mott transition in correlated electron oxides, such as vanadium dioxide, has been identified as a rich and reliable source of such functional complexity. However, its full potential in high-speed and low-power operation has been largely unexplored. We fabricated nanoscale VO$_{2}$ devices embedded in a broad-band test circuit to study the speed and energy limitations of their resistive switching operation. Our picosecond time-resolution, real-time resistive switching experiments and numerical simulations demonstrate that tunable low-resistance states can be set by the application of 20~ps long, $<$1.7~V amplitude voltage pulses at 15~ps incubation times and switching energies starting from a few femtojoule. Moreover, we demonstrate that at nanometer-scale device sizes not only the electric field induced insulator-to-metal transition, but also the thermal conduction limited metal-to-insulator transition can take place at timescales of 100's of picoseconds. These orders of magnitude breakthroughs open the route to the design of high-speed and low-power dynamical circuits for a plethora of neuromorphic computing applications from pattern recognition to numerical optimization.

We apply physics-informed neural networks to three-dimensional Rayleigh-B\'enard convection in a cubic cell with a Rayleigh number of Ra = 10^6 and a Prandtl number of Pr = 0.7 to assimilate the velocity vector field from given temperature fields and vice versa. With the respective ground truth data provided by a direct numerical simulation, we are able to evaluate the performance of the different activation functions applied (sine, hyperbolic tangent and exponential linear unit) and different numbers of neurons (32, 64, 128, 256) for each of the five hidden layers of the multi-layer perceptron. The main result is that the use of a periodic activation function (sine) typically benefits the assimilation performance in terms of the analyzed metrics, correlation with the ground truth and mean average error. The higher quality of results from sine-activated physics-informed neural networks is also manifested in the probability density function and power spectra of the inferred velocity or temperature fields. Regarding the two assimilation directions, the assimilation of temperature fields based on velocities appears to be more challenging in the sense that it exhibits a sharper limit on the number of neurons below which viable assimilation results can not be achieved., Comment: revision

We design a deterministic subexponential time algorithm that takes as input a multivariate polynomial $f$ computed by a constant-depth circuit over rational numbers, and outputs a list $L$ of circuits (of unbounded depth and possibly with division gates) that contains all irreducible factors of $f$ computable by constant-depth circuits. This list $L$ might also include circuits that are spurious: they either do not correspond to factors of $f$ or are not even well-defined, e.g. the input to a division gate is a sub-circuit that computes the identically zero polynomial. The key technical ingredient of our algorithm is a notion of the pseudo-resultant of $f$ and a factor $g$, which serves as a proxy for the resultant of $g$ and $f/g$, with the advantage that the circuit complexity of the pseudo-resultant is comparable to that of the circuit complexity of $f$ and $g$. This notion, which might be of independent interest, together with the recent results of Limaye, Srinivasan and Tavenas, helps us derandomize one key step of multivariate polynomial factorization algorithms - that of deterministically finding a good starting point for Newton Iteration for the case when the input polynomial as well as the irreducible factor of interest have small constant-depth circuits.

The valley degree of freedom in honeycomb crystals such as 2D semiconductors, graphene and bilayer graphene is a promising carrier of quantum information alongside spin and charge. This is all the more true since gate-controlled single-particle quantum dots (QDs) have been demonstrated in bilayer graphene (BLG), paving the way for the realisation of spin and valley qubits. Although long spin relaxation times have recently been reported in BLG QDs, nothing is known about single-particle valley lifetimes. Here we report single-particle valley relaxation times ($T_1$ times) exceeding several microseconds in electrostatically defined BLG QDs. The observed dependence of $T_1$ on the perpendicular magnetic field can be understood qualitatively and quantitatively by a model in which $T_1$ is limited by electron-phonon coupling. We identify coupling to acoustic phonons via the bond length change and via the deformation potential as the limiting mechanisms., Comment: 7 pages, 4 figures

We construct explicit pseudorandom generators that fool $n$-variate polynomials of degree at most $d$ over a finite field $\mathbb{F}_q$. The seed length of our generators is $O(d \log n + \log q)$, over fields of size exponential in $d$ and characteristic at least $d(d-1)+1$. Previous constructions such as Bogdanov's (STOC 2005) and Derksen and Viola's (FOCS 2022) had either suboptimal seed length or required the field size to depend on $n$. Our approach follows Bogdanov's paradigm while incorporating techniques from Lecerf's factorization algorithm (J. Symb. Comput. 2007) and insights from the construction of Derksen and Viola regarding the role of indecomposability of polynomials.

Neptune's external mean motion resonances play an important role in sculpting the observed population of transneptunian objects (TNOs). The population of scattering TNOs are known to 'stick' to Neptune's resonances while evolving in semimajor axis ($a$), though simulations show that resonance sticking is less prevalent at $a\gtrsim200-250$ au. Here we present an extensive numerical exploration of the strengths of Neptune's resonances for scattering TNOs with perihelion distances $q=33$ au. We show that the drop-off in resonance sticking for the large $a$ scattering TNOs is not a generic feature of scattering dynamics, but can instead be attributed to the specific configuration of Neptune and Uranus in our solar system. In simulations with just Uranus removed from the giant planet system, Neptune's resonances are strong in the scattering population out to at least $\sim300$ au. Uranus and Neptune are near a 2:1 period ratio, and the variations in Neptune's orbit resulting from this near resonance are responsible for destabilizing Neptune's resonances for high-$e$ TNO orbits beyond the $\sim20$:1 resonance at $a\approx220$ au. Direct interactions between Uranus and the scattering population are responsible for slightly weakening Neptune's closer-in resonances. In simulations where Neptune and Uranus are placed in their mutual 2:1 resonance, we see almost no stable libration of scattering particles in Neptune's external resonances. Our results have important implications for how the strengths of Neptune's distant resonances varied during the epoch of planet migration when the Neptune-Uranus period ratio was evolving. These strength variations likely affected the distant scattering, resonant, and detached TNO populations., Comment: Accepted for publication in the Planetary Science Journal

Labeled continuous-time Markov chains (CTMCs) describe processes subject to random timing and partial observability. In applications such as runtime monitoring, we must incorporate past observations. The timing of these observations matters but may be uncertain. Thus, we consider a setting in which we are given a sequence of imprecisely timed labels called the evidence. The problem is to compute reachability probabilities, which we condition on this evidence. Our key contribution is a method that solves this problem by unfolding the CTMC states over all possible timings for the evidence. We formalize this unfolding as a Markov decision process (MDP) in which each timing for the evidence is reflected by a scheduler. This MDP has infinitely many states and actions in general, making a direct analysis infeasible. Thus, we abstract the continuous MDP into a finite interval MDP (iMDP) and develop an iterative refinement scheme to upper-bound conditional probabilities in the CTMC. We show the feasibility of our method on several numerical benchmarks and discuss key challenges to further enhance the performance., Comment: Extended version (with appendix) of the paper accepted at TACAS 2024

We present a dynamical study of 39 active Centaurs and 17 high-perihelion (q$>$4.5 au) JFCs with a focus on investigating recent orbital changes as potential triggers for comet-like activity. We have identified a common feature in the recent dynamical histories of all active Centaurs and JFC in our sample that is not present in the history of the majority of inactive population members: a sharp decrease in semi-major axis and eccentricity occurring within the last several hundred years prior to observed activity. We define these rapid orbital changes as `a-jumps'. Our results indicate that these orbital reshaping events lead to shorter orbital periods and subsequently greater average per-orbit heating of Centaur nuclei. We suggest the a-jumps could therefore be a major trigger of cometary activity on Centaurs and JFCs. Our results further imply that analyses of the recent dynamical histories could be used to identify objects that are currently active or may become active soon, where we have identified three such Centaurs with recent a-jumps that should be considered high-priority targets for observational monitoring to search for activity., Comment: 19 pages, 4 figures, 1 table, accepted for publication in ApJL

High-dimensional imbalanced data poses a machine learning challenge. In the absence of sufficient or high-quality labels, unsupervised feature selection methods are crucial for the success of subsequent algorithms. Therefore, we introduce a Marginal Laplacian Score (MLS), a modification of the well known Laplacian Score (LS) tailored to better address imbalanced data. We introduce an assumption that the minority class or anomalous appear more frequently in the margin of the features. Consequently, MLS aims to preserve the local structure of the dataset's margin. We propose its integration into modern feature selection methods that utilize the Laplacian score. We integrate the MLS algorithm into the Differentiable Unsupervised Feature Selection (DUFS), resulting in DUFS-MLS. The proposed methods demonstrate robust and improved performance on synthetic and public datasets., Comment: 10 pages

We present an algorithm that mitigates the effects of charge migration due to the "brighter-fatter effect'' (BFE) that occurs for highly illuminated stars in the Teledyne HAWAII-2RG detectors used in the NIRCam, NIRISS, and NIRSpec science instruments aboard the James Webb Space Telescope (JWST). The impact of this effect is most significant for photometry and spectrophotometry of bright stars in data for which the point spread function (PSF) is undersampled, which is the case for several observing modes of the NIRISS instrument. The main impact of the BFE to NIRISS data is incorrect count rate determinations for pixels in the central regions of PSFs of bright stars due to jump detections that are caused by charge migration from peak pixels to surrounding pixels. The effect is especially significant for bright compact sources in resampled, distortion-free images produced by the drizzle algorithm: quantitatively, apparent flux losses of $>$ 50% can occur in such images due to the BFE. We describe the algorithm of the "charge_migration'' mitigation step that has been implemented in version 10.0 of the operational JWST calibration pipeline as of Dec 5, 2023. We illustrate the impact of this step in terms of the resulting improvements of the precision of imaging photometry of point sources. The algorithm renders the effects of the BFE on photometry and surface brightness measurements to stay within 1%., Comment: 21 pages, 12 figures, published in PASP

We present infrared aperture masking interferometry (AMI) observations of newly formed dust from the colliding winds of the massive binary system Wolf-Rayet (WR) 137 with JWST using the Near Infrared Imager and Slitless Spectrograph (NIRISS). NIRISS AMI observations of WR 137 and a point-spread-function calibrator star, HD~228337, were taken using the F380M and F480M filters in 2022 July and August as part of the Director's Discretionary Early Release Science (DD-ERS) program 1349. Interferometric observables (squared visibilities and closure phases) from the WR 137 "interferogram" were extracted and calibrated using three independent software tools: ImPlaneIA, AMICAL, and SAMpip. The analysis of the calibrated observables yielded consistent values except for slightly discrepant closure phases measured by ImPlaneIA. Based on all three sets of calibrated observables, images were reconstructed using three independent software tools: BSMEM, IRBis, and SQUEEZE. All reconstructed image combinations generated consistent images in both F380M and F480M filters. The reconstructed images of WR 137 reveal a bright central core with a $\sim300$ mas linear filament extending to the northwest. A geometric colliding-wind model with dust production constrained to the orbital plane of the binary system and enhanced as the system approaches periapsis provided a general agreement with the interferometric observables and reconstructed images. Based on a colliding-wind dust condensation analysis, we suggest that dust formation within the orbital plane of WR 137 is induced by enhanced equatorial mass-loss from the rapidly rotating O9 companion star, whose axis of rotation is aligned with that of the orbit., Comment: 18 pages, 8 figures, Accepted for publication in ApJ. Updated plotting error in Fig. 2

When utilizing the NIRISS/SOSS mode on JWST, the pupil wheel (tasked with orienting the GR700XD grism into the optical path) does not consistently settle into its commanded position resulting in a minor misalignment with deviations of a few fractions of a degree. These small offsets subsequently introduce noticeable changes in the trace positions of the NIRISS SOSS spectral orders between visits. This inconsistency, in turn, can lead to variations of the wavelength solution. In this report, we present the visit-to-visit characterization of the NIRISS GR700XD Wavelength Calibration for spectral orders 1 and 2. Employing data from Calibration Program 1512 (PI: Espinoza), which intentionally and randomly sampled assorted pupil wheel positions during observations of the A-star BD+60-1753, as well as data from preceding commissioning and calibration activities to model this effect, we demonstrate that the wavelength solution can fluctuate in a predictable fashion between visits by up to a few pixels. We show that via two independent polynomial regression models for spectral orders 1 and 2, respectively, using the measured x-pixel positions of known Hydrogen absorption features in the A-star spectra and pupil wheel positions as regressors, we can accurately predict the wavelength solution for a particular visit with an RMS error within a few tenths of a pixel. We incorporate these models in PASTASOSS, a Python package for predicting the GR700XD spectral traces, which now allows to accurately predict spectral trace positions and their associated wavelengths for any NIRISS/SOSS observation., Comment: 12 pages, package to predict wavelength solution for NIRISS/SOSS: https://github.com/spacetelescope/pastasoss

In this report, we present the results of our analysis of trace position changes during NIRISS/SOSS observations. We examine the visit-to-visit impact of the GR700XD pupil wheel (PW) position alignment on trace positions for spectral orders 1 and 2 using the data obtained to date. Our goal is to improve the wavelength solution by correlating the trace positions on the detector with the PW position angle. We find that there is a one-to-one correspondence between PW position and spectral trace rotation for both orders. This allowed us in turn to find an analytic model that is able to predict a trace position/shape as a function of PW position with sub-pixel accuracy of about ~0.1 pixels. Such a function can be used to predict the trace position in low signal-to-noise ratio cases, and/or as a template to track trace position changes as function of time in Time Series Observations (TSOs)., Comment: 9 pages, package to predict spectral traces for NIRISS/SOSS: https://github.com/spacetelescope/pastasoss

The human face is one of the most crucial parts in interhuman communication. Even when parts of the face are hidden or obstructed the underlying facial movements can be understood. Machine learning approaches often fail in that regard due to the complexity of the facial structures. To alleviate this problem a common approach is to fine-tune a model for such a specific application. However, this is computational intensive and might have to be repeated for each desired analysis task. In this paper, we propose to reconstruct obstructed facial parts to avoid the task of repeated fine-tuning. As a result, existing facial analysis methods can be used without further changes with respect to the data. In our approach, the restoration of facial features is interpreted as a style transfer task between different recording setups. By using the CycleGAN architecture the requirement of matched pairs, which is often hard to fullfill, can be eliminated. To proof the viability of our approach, we compare our reconstructions with real unobstructed recordings. We created a novel data set in which 36 test subjects were recorded both with and without 62 surface electromyography sensors attached to their faces. In our evaluation, we feature typical facial analysis tasks, like the computation of Facial Action Units and the detection of emotions. To further assess the quality of the restoration, we also compare perceptional distances. We can show, that scores similar to the videos without obstructing sensors can be achieved., Comment: VISAPP 2023 paper

We test the hypothesis that (3) Juno is a parent body of the H chondrites with dynamical modeling of an asteroid-family-forming impact and comparison to current observational data. Using a dynamical model that includes the Yarkovsky force on a simulated Juno family and a simplified cosmic ray exposure age model we examine the expected distribution of Juno family members in both the main belt and near-Earth orbits over 300 Myrs and the cosmic ray exposure distribution for fragments exiting the main belt via the 3:1J, 5:2J, and 8:3J mean motion resonances. We find that the smallest modeled ($D<$10 m) family members of (3) Juno cannot be directly responsible for the observed H chondrite flux and that the breakup of larger family members creates an CRE distribution that resembles the measured H chondrite CRE distribution but is still unable to adequately explain the significant number of H chondrites with CRE ages of 6-8 Myrs. A similar model was performed for the asteroid (6) Hebe, another parent body candidate, and produced a CRE age distribution that is inconsistent with the measured H chondrite CRE ages. We also find from our dynamical models that we can expect $<$7 km-scale Juno family members in near-Earth orbits in the present day, consistent with the recent discovery of the shock-darkened H chondrite-like asteroid (52768) 1998 OR$_{2}$., Comment: 16 pages, 9 figures

In this study, we investigated the flow dynamics in a fixed bed of hydrogel beads using Particle Tracking Velocimetry to compute the velocity field in the middle of the bed for moderate Reynolds numbers. We discovered that despite the overall stationarity of the flow and relatively low Reynolds number, it exhibits complex multiscale spatial dynamics reminiscent of those observed in classical turbulence. We found evidence of the presence of an inertial range and a direct energy cascade, and were able to obtain a value for a "porous" Kolmogorov constant of $C_2 = 3.1\pm 0.3$. This analogy with turbulence opens up new possibilities for understanding mixing and global transport properties in porous media., Comment: 9 pages, 11 figures, manuscript + supp mat

Turbulence is prevalent in nature and industry, from large-scale wave dynamics to small-scale combustion nozzle sprays. In addition to the multi-scale nonlinear complexity and both randomness and coherent structures in its dynamics, practical turbulence is often non-homogeneous and anisotropic, leading to great modeling challenges. In this letter, an efficient model is proposed to predict turbulent jet statistics with high accuracy. The model leverages detailed knowledge of readily available velocity signals from idealized homogeneous turbulence and transforms them into Lagrangian trajectories of a turbulent jet. The resulting spatio-temporal statistics are compared against experimental jet data showing remarkable agreement at all scales. In particular the intermittency phenomenon is accurately mapped by the model to this inhomogeneous situation, as observed by higher-order moments and velocity increment probability density functions. Crucial to the advancement of turbulence modeling, the transformation is simple to implement, with possible extensions to other inhomogeneous flows such as wind turbine wakes and canopy flows, to name a few.