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A novel multispacecraft technique applied to Magnetospheric Multiscale (MMS) mission data collected in the Earth's magnetosheath enables evaluation of the energy cascade rate solving the full Yaglom's equation in a turbulent space plasma. The method differs from existing approaches in that (i) it is inherently three-dimensional; (ii) it provides a statistically significant number of estimates from a single data stream; and (iii) it allows for a direct visualization of energy flux in turbulent plasmas. This new technique will ultimately provide a realistic, comprehensive picture of the turbulence process in plasmas.

Significance: Diffuse in-vivo Flow Cytometry (DiFC) is an emerging fluorescence sensing method to non-invasively detect labeled circulating cells in-vivo. However, due to Signal-to-Noise Ratio (SNR) constraints largely attributed to background tissue autofluorescence, DiFC's measurement depth is limited. multiplies Aim: The Dual-Ratio (DR) / dual-slope is a new optical measurement method that aims to suppress noise and enhance SNR to deep tissue regions. We aim to investigate the combination of DR and Near-InfraRed (NIR) DiFC to improve circulating cells' maximum detectable depth and SNR. Approach: Phantom experiments were used to estimate the key parameters in a diffuse fluorescence excitation and emission model. This model and parameters were implemented in Monte-Carlo to simulate DR DiFC while varying noise and autofluorescence parameters to identify the advantages and limitations of the proposed technique. Results: Two key factors must be true to give DR DiFC an advantage over traditional DiFC; first, the fraction of noise that DR methods cannot cancel cannot be above the order of 10% for acceptable SNR. Second, DR DiFC has an advantage, in terms of SNR, if the distribution of tissue autofluorescence contributors is surface-weighted. Conclusions: DR cancelable noise may be designed for (e.g. through the use of source multiplexing), and indications point to the autofluorescence contributors' distribution being truly surface-weighted in-vivo. Successful and worthwhile implementation of DR DiFC depends on these considerations, but results point to DR DiFC having possible advantages over traditional DiFC.

We describe and benchmark a new quantum charge-coupled device (QCCD) trapped-ion quantum computer based on a linear trap with periodic boundary conditions, which resembles a race track. The new system successfully incorporates several technologies crucial to future scalability, including electrode broadcasting, multi-layer RF routing, and magneto-optical trap (MOT) loading, while maintaining, and in some cases exceeding, the gate fidelities of previous QCCD systems. The system is initially operated with 32 qubits, but future upgrades will allow for more. We benchmark the performance of primitive operations, including an average state preparation and measurement error of 1.6(1)$\times 10^{-3}$, an average single-qubit gate infidelity of $2.5(3)\times 10^{-5}$, and an average two-qubit gate infidelity of $1.84(5)\times 10^{-3}$. The system-level performance of the quantum processor is assessed with mirror benchmarking, linear cross-entropy benchmarking, a quantum volume measurement of $\mathrm{QV}=2^{16}$, and the creation of 32-qubit entanglement in a GHZ state. We also tested application benchmarks including Hamiltonian simulation, QAOA, error correction on a repetition code, and dynamics simulations using qubit reuse. We also discuss future upgrades to the new system aimed at adding more qubits and capabilities., Comment: 24 pages, 24 figures. Made some minor edits and added several more authors

We search for the presence of cool cores in optically-selected galaxy clusters from the Dark Energy Survey (DES) and investigate their prevalence as a function of redshift and cluster richness. Clusters were selected from the redMaPPer analysis of three years of DES observations that have archival Chandra X-ray observations, giving a sample of 99 clusters with a redshift range of $0.11 < z < 0.87$ and a richness range of $25 < \lambda < 207$. Using the X-ray data, the core temperature was compared to the outer temperature to identify clusters where the core temperature is a factor of 0.7 or less than the outer temperature. We found a cool core fraction of approximately 20% with no significant trend in the cool core fraction with either redshift or richness., Comment: shortened version accepted to RNAAS

Jupiter's satellite auroral footprints are a consequence of the interaction between the Jovian magnetic field with co-rotating iogenic plasma and the Galilean moons. The disturbances created near the moons propagate as Alfv\'en waves along the magnetic field lines. The position of the moons is therefore "Alfv\'enically" connected to their respective auroral footprint. The angular separation from the instantaneous magnetic footprint can be estimated by the so-called lead angle. That lead angle varies periodically as a function of orbital longitude, since the time for the Alfv\'en waves to reach the Jovian ionosphere varies accordingly. Using spectral images of the Main Alfv\'en Wing auroral spots collected by Juno-UVS during the first forty-three orbits, this work provides the first empirical model of the Io, Europa and Ganymede equatorial lead angles for the northern and southern hemispheres. Alfv\'en travel times between the three innermost Galilean moons to Jupiter's northern and southern hemispheres are estimated from the lead angle measurements. We also demonstrate the accuracy of the mapping from the Juno magnetic field reference model (JRM33) at the completion of the prime mission for M-shells extending to at least 15RJ . Finally, we shows how the added knowledge of the lead angle can improve the interpretation of the moon-induced decametric emissions., Comment: 20 pages, 8 figures, Accepted for publication in Journal of Geophysical Research: Space Physics on 20 April 2023

We cross-match and compare characteristics of galaxy clusters identified in observations from two sky surveys using two completely different techniques. One sample is optically selected from the analysis of three years of Dark Energy Survey observations using the redMaPPer cluster detection algorithm. The second is X-ray selected from XMM observations analysed by the XMM Cluster Survey. The samples comprise a total area of 57.4 deg$^2$, bounded by the area of 4 contiguous XMM survey regions that overlap the DES footprint. We find that the X-ray selected sample is fully matched with entries in the redMaPPer catalogue, above $\lambda>$20 and within 0.1$< z <$0.9. Conversely, only 38\% of the redMaPPer catalogue is matched to an X-ray extended source. Next, using 120 optically clusters and 184 X-ray selected clusters, we investigate the form of the X-ray luminosity-temperature ($L_{X}-T_{X}$), luminosity-richness ($L_{X}-\lambda$) and temperature-richness ($T_{X}-\lambda$) scaling relations. We find that the fitted forms of the $L_{X}-T_{X}$ relations are consistent between the two selection methods and also with other studies in the literature. However, we find tentative evidence for a steepening of the slope of the relation for low richness systems in the X-ray selected sample. When considering the scaling of richness with X-ray properties, we again find consistency in the relations (i.e., $L_{X}-\lambda$ and $T_{X}-\lambda$) between the optical and X-ray selected samples. This is contrary to previous similar works that find a significant increase in the scatter of the luminosity scaling relation for X-ray selected samples compared to optically selected samples., Comment: Accepted for publication to MNRAS

Development of process-structure-property relationships in materials science is an important and challenging frontier which promises improved materials and reduced time and cost in production. Refractory high entropy alloys (RHEAs) are a class of materials that are capable of excellent hightemperature properties. However, due to their multi-component nature, RHEAs have a vast composition space which presents challenges for traditional experimental exploration. Here, quantitative models of compressive yield strength and room-temperature plasticity are developed through a deep learning approach. Uncertainty quantification is performed through a variety of statistical validation techniques. Model predictions are experimentally validated through collection of recent literature and the synthesis and experimental characterization of two new, unreported RHEAs: AlMoTaTiZr and Al0.239Mo0.123Ta0.095Ti0.342Zr0.201. Finally, through the application of model interpretability, features having the greatest impact on both the mechanical property and uncertainty of the deep learning models are revealed, and shown to agree well with current physics and materials science theory.

ESA's Jupiter Icy Moons Explorer (JUICE) will provide a detailed investigation of the Jovian system in the 2030s, combining a suite of state-of-the-art instruments with an orbital tour tailored to maximise observing opportunities. We review the Jupiter science enabled by the JUICE mission, building on the legacy of discoveries from the Galileo, Cassini, and Juno missions, alongside ground- and space-based observatories. We focus on remote sensing of the climate, meteorology, and chemistry of the atmosphere and auroras from the cloud-forming weather layer, through the upper troposphere, into the stratosphere and ionosphere. The Jupiter orbital tour provides a wealth of opportunities for atmospheric and auroral science: global perspectives with its near-equatorial and inclined phases, sampling all phase angles from dayside to nightside, and investigating phenomena evolving on timescales from minutes to months. The remote sensing payload spans far-UV spectroscopy (50-210 nm), visible imaging (340-1080 nm), visible/near-infrared spectroscopy (0.49-5.56 $\mu$m), and sub-millimetre sounding (near 530-625\,GHz and 1067-1275\,GHz). This is coupled to radio, stellar, and solar occultation opportunities to explore the atmosphere at high vertical resolution; and radio and plasma wave measurements of electric discharges in the Jovian atmosphere and auroras. Cross-disciplinary scientific investigations enable JUICE to explore coupling processes in giant planet atmospheres, to show how the atmosphere is connected to (i) the deep circulation and composition of the hydrogen-dominated interior; and (ii) to the currents and charged particle environments of the external magnetosphere. JUICE will provide a comprehensive characterisation of the atmosphere and auroras of this archetypal giant planet., Comment: 83 pages, 24 figures, accepted to Space Science Reviews special issue on ESA's JUICE mission

We present mid-infrared spectroscopy of Jupiter's mid-to-high latitudes using Gemini-North/TEXES (Texas Echelon Cross Echelle Spectrograph) on March 17-19, 2017. These observations capture Jupiter's hydrocarbon auroral emissions before, during and after the arrival of a solar wind compression on March 18th, which highlights the coupling between the polar stratosphere and external space environment. In comparing observations on March 17th and 19th, we observe a brightening of the CH$_4$, C$_2$H$_2$ and C$_2$H$_4$ emissions in regions spatially coincident with the northern, duskside main auroral emission (henceforth, MAE). In inverting the spectra to derive atmospheric information, we determine that the duskside brightening results from an upper stratospheric (p < 0.1 mbar/z > 200 km) heating (e.g. $\Delta T$ = 9.1 $\pm$ 2.1 K at 9 $\mu$bar at 67.5$^\circ$N, 162.5$^\circ$W) with negligible heating at deeper pressures. Our interpretation is that the arrival of the solar wind enhancement drove magnetospheric dynamics through compression and/or viscous interactions on the flank. These dynamics accelerated currents and/or generated higher Poynting fluxes, which ultimately warmed the atmosphere through Joule heating and ion-neutral collisions. Poleward of the southern MAE, temperature retrievals demonstrate that auroral-related heating penetrates as deep as the 10-mbar level, in contrast to poleward of the northern MAE, where heating is only observed as deep as $\sim$3 mbar. We suggest this results from the south having higher Pedersen conductivities, and therefore stronger currents and acceleration of the neutrals, as well as the poleward heating overlapping with the apex of Jupiter's circulation thereby inhibiting efficient horizontal mixing/advection.

We report the discovery of GRB 221009A, the highest flux gamma-ray burst ever observed by the Fermi Gamma-ray Burst Monitor (GBM). This GRB has continuous prompt emission lasting more than 600 seconds which smoothly transitions to afterglow visible in the GBM energy range (8 keV--40 MeV), and total energetics higher than any other burst in the GBM sample. By using a variety of new and existing analysis techniques we probe the spectral and temporal evolution of GRB 221009A. We find no emission prior to the GBM trigger time (t0; 2022 October 9 at 13:16:59.99 UTC), indicating that this is the time of prompt emission onset. The triggering pulse exhibits distinct spectral and temporal properties suggestive of the thermal, photospheric emission of shock-breakout, with significant emission up to $\sim$15 MeV. We characterize the onset of external shock at t0+600 s and find evidence of a plateau region in the early-afterglow phase which transitions to a slope consistent with Swift-XRT afterglow measurements. We place the total energetics of GRB 221009A in context with the rest of the GBM sample and find that this GRB has the highest total isotropic-equivalent energy ($\textrm{E}_{\gamma,\textrm{iso}}=1.0\times10^{55}$ erg) and second highest isotropic-equivalent luminosity ($\textrm{L}_{\gamma,\textrm{iso}}=9.9\times10^{53}$ erg/s) based on redshift of z = 0.151. These extreme energetics are what allowed us to observe the continuously emitting central engine of GBM from the beginning of the prompt emission phase through the onset of early afterglow., Comment: 26 pages 7 figures - accepted for publication in ApJL

PHENIX presents a simultaneous measurement of the production of direct $\gamma$ and $\pi^0$ in $d$$+$Au collisions at $\sqrt{s_{_{NN}}}=200$ GeV over a $p_T$ range of 7.5 to 18 GeV/$c$ for different event samples selected by event activity, i.e. charged-particle multiplicity detected at forward rapidity. Direct-photon yields are used to empirically estimate the contribution of hard-scattering processes in the different event samples. Using this estimate, the average nuclear-modification factor $R_{d\rm Au,EXP}^{\gamma^{\rm dir}}$ is $0.925{\pm}0.023({\rm stat}){\pm}0.15^{\rm (scale)}$, consistent with unity for minimum-bias (MB) $d$$+$Au events. For event classes with moderate event activity, $R_{d\rm Au,EXP}^{\gamma^{\rm dir}}$ is consistent with the MB value within 5\% uncertainty. These results confirm that the previously observed enhancement of high-$p_T$ $\pi^0$ production found in small-system collisions with low event activity is a result of a bias in interpreting event activity within the Glauber framework. In contrast, for the top 5\% of events with the highest event activity, $R_{d\rm Au,EXP}^{\gamma^{\rm dir}}$ is suppressed by 20\% relative to the MB value with a significance of $4.5\sigma$, which may be due to final-state effects., Comment: 279 authors from 69 institutions, 8 pages, 3 figures, v1 is version submitted to Physical Review Letters. HEPdata tables for the points plotted in figures for this and previous PHENIX publications are (or will be) publicly available at http://www.phenix.bnl.gov/papers.html

Reported here are transverse single-spin asymmetries ($A_{N}$) in the production of charged hadrons as a function of transverse momentum ($p_T$) and Feynman-$x$ ($x_F$) in polarized $p^{\uparrow}$+$p$, $p^{\uparrow}$+Al, and $p^{\uparrow}$+Au collisions at $\sqrt{s_{_{NN}}}=200$ GeV. The measurements have been performed at forward and backward rapidity ($1.4<|\eta|<2.4$) over the range of $1.50$) in $p^{\uparrow}$+$p$ collisions, whereas the $p^{\uparrow}$+Al and $p^{\uparrow}$+Au results show smaller asymmetries. This finding provides new opportunities to investigate the origin of transverse single-spin asymmetries and a tool to study nuclear effects in $p$+$A$ collisions., Comment: 322 authors from 70 institutions, 13 pages, 9 figures, 13 tables, one appendix, 2015 data. v2 is version accepted for publication in Phys. Rev. D. HEPData tables for the points plotted in figures for this and previous PHENIX publications are (or will be) publicly available at http://www.phenix.bnl.gov/papers.html

Presented are the first measurements of the transverse single-spin asymmetries ($A_N$) for neutral pions and eta mesons in $p$+Au and $p$+Al collisions at $\sqrt{s_{_{NN}}}=200$ GeV in the pseudorapidity range $|\eta|<$0.35 with the PHENIX detector at the Relativistic Heavy Ion Collider. The asymmetries are consistent with zero, similar to those for midrapidity neutral pions and eta mesons produced in $p$+$p$ collisions. These measurements show no evidence of additional effects that could potentially arise from the more complex partonic environment present in proton-nucleus collisions., Comment: 322 authors from 70 institutions, 8 pages, 2 figures, 1 table, 2015 data. v2 is version accepted for publication in Phys. Rev. D. HEPData tables for the points plotted in figures for this and previous PHENIX publications are (or will be) publicly available at http://www.phenix.bnl.gov/papers.html

Machine learning techniques are effective for building predictive models because they identify patterns in large datasets. Development of a model for complex real-life problems often stop at the point of publication, proof of concept or when made accessible through some mode of deployment. However, a model in the medical domain risks becoming obsolete as patient demographics, systems and clinical practices change. The maintenance and monitoring of predictive model performance post-publication is crucial to enable their safe and effective long-term use. We will assess the infrastructure required to monitor the outputs of a machine learning algorithm, and present two scenarios with examples of monitoring and updates of models, firstly on a breast cancer prognosis model trained on public longitudinal data, and secondly on a neurodegenerative stratification algorithm that is currently being developed and tested in clinic., Comment: 12 pages, 3 figures

We present a prototype hybrid prediction market and demonstrate the avenue it represents for meaningful human-AI collaboration. We build on prior work proposing artificial prediction markets as a novel machine-learning algorithm. In an artificial prediction market, trained AI agents buy and sell outcomes of future events. Classification decisions can be framed as outcomes of future events, and accordingly, the price of an asset corresponding to a given classification outcome can be taken as a proxy for the confidence of the system in that decision. By embedding human participants in these markets alongside bot traders, we can bring together insights from both. In this paper, we detail pilot studies with prototype hybrid markets for the prediction of replication study outcomes. We highlight challenges and opportunities, share insights from semi-structured interviews with hybrid market participants, and outline a vision for ongoing and future work.

Good figure captions help paper readers understand complex scientific figures. Unfortunately, even published papers often have poorly written captions. Automatic caption generation could aid paper writers by providing good starting captions that can be refined for better quality. Prior work often treated figure caption generation as a vision-to-language task. In this paper, we show that it can be more effectively tackled as a text summarization task in scientific documents. We fine-tuned PEGASUS, a pre-trained abstractive summarization model, to specifically summarize figure-referencing paragraphs (e.g., "Figure 3 shows...") into figure captions. Experiments on large-scale arXiv figures show that our method outperforms prior vision methods in both automatic and human evaluations. We further conducted an in-depth investigation focused on two key challenges: (i) the common presence of low-quality author-written captions and (ii) the lack of clear standards for good captions. Our code and data are available at: https://github.com/Crowd-AI-Lab/Generating-Figure-Captions-as-a-Text-Summarization-Task., Comment: Accepted by INLG-2023

Reflected sunlight observations from the Ultraviolet Spectrograph (UVS) on the Juno spacecraft were used to study the distribution of acetylene (C$_2$H$_2$) at Jupiter's south pole. We find that the shape of the C$_2$H$_2$ absorption feature varies significantly across the polar region, and this can be used to infer spatial variability in the C$_2$H$_2$ abundance. There is a localized region of enhanced C$_2$H$_2$ absorption which coincides with the location of Jupiter's southern polar aurora; the C$_2$H$_2$ abundance poleward of the auroral oval is a factor of 3 higher than adjacent quiescent, non-auroral longitudes. This builds on previous infrared studies which found enhanced C$_2$H$_2$ abundances within the northern auroral oval. This suggests that Jupiter's upper-atmosphere chemistry is being strongly influenced by the influx of charged auroral particles and demonstrates the necessity of developing ion-neutral photochemical models of Jupiter's polar regions., Comment: Accepted in JGR: Planets

In turbulence, nonlinear terms drive energy transfer from large-scale eddies into small scales through the so-called energy cascade. Turbulence often relaxes toward states that minimize energy; typically these states are considered globally. However, turbulence can also relax toward local quasi-equilibrium states, creating patches or cells where the magnitude of nonlinearity is reduced and energy cascade is impaired. We show, for the first time, compelling observational evidence that this ``cellularization'' of turbulence can occur due to local relaxation in a strongly turbulent natural environment such as the Earth's magnetosheath.

Most existing large-scale academic search engines are built to retrieve text-based information. However, there are no large-scale retrieval services for scientific figures and tables. One challenge for such services is understanding scientific figures' semantics, such as their types and purposes. A key obstacle is the need for datasets containing annotated scientific figures and tables, which can then be used for classification, question-answering, and auto-captioning. Here, we develop a pipeline that extracts figures and tables from the scientific literature and a deep-learning-based framework that classifies scientific figures using visual features. Using this pipeline, we built the first large-scale automatically annotated corpus, ACL-Fig, consisting of 112,052 scientific figures extracted from ~56K research papers in the ACL Anthology. The ACL-Fig-Pilot dataset contains 1,671 manually labeled scientific figures belonging to 19 categories. The dataset is accessible at https://huggingface.co/datasets/citeseerx/ACL-fig under a CC BY-NC license., Comment: 6 pages, 4 figures, accepted by the AAAI-23 Workshop on Scientific Document Understanding

The gold standard in the treatment of ischaemic stroke is set by evidence from randomized controlled trials. Yet the manifest complexity of the brain's functional, connective, and vascular architectures introduces heterogeneity in treatment susceptibility that violates the underlying statistical premisses, potentially leading to substantial errors at both individual and population levels. The counterfactual nature of therapeutic inference has made quantifying the impact of this defect difficult. Combining large-scale meta-analytic connective, functional, genetic expression, and receptor distribution data with high-resolution maps of 4 119 acute ischaemic lesions, here we conduct a comprehensive series of semi-synthetic virtual interventional trials, quantifying the fidelity of the traditional approach in inferring individual treatment effects against biologically plausible, empirically informed ground truths, across 103 628 800 distinct simulations. Combining deep generative models expressive enough to capture the observed lesion heterogeneity with flexible causal modelling, we find that the richness of the lesion representation is decisive in determining individual-level fidelity, even where freedom from treatment allocation bias cannot be guaranteed. Our results indicate that complex modelling with richly represented lesion data is critical to individualized prescriptive inference in ischaemic stroke., Comment: 131 pages

In recent years novel inference techniques have been developed based on the construction of non-linear summary statistics with neural networks by minimising inferencemotivated losses. One such technique is inferno (P. de Castro and T. Dorigo, Comp. Phys. Comm. 244 (2019) 170) which was shown on toy problems to outperform classical summary statistics for the problem of confidence interval estimation in the presence of nuisance parameters. In order to test and benchmark the algorithm in a real world application, a full, systematics-dominated analysis produced by the CMS experiment, "Measurement of the top-antitop production cross section in the tau+jets channel in pp collisions at sqrt(s) = 7 TeV" (CMS Collaboration, The European Physical Journal C, 2013) is reproduced with CMS Open Data. The application of the inferno-powered neural network architecture to this analysis demonstrates the potential to reduce the impact of systematic uncertainties in real LHC analyses. This work also exemplifies the extent to which LHC analyses can be reproduced with open data., Comment: 19 pages, 8 figures

The valuation of over-the-counter derivatives is subject to a series of valuation adjustments known as xVA, which pose additional risks for financial institutions. Associated risk measures, such as the value-at-risk of an underlying valuation adjustment, play an important role in managing these risks. Monte Carlo methods are often regarded as inefficient for computing such measures. As an example, we consider the value-at-risk of the Credit Valuation Adjustment (CVA-VaR), which can be expressed using a triple nested expectation. Traditional Monte Carlo methods are often inefficient at handling several nested expectations. Utilising recent developments in multilevel nested simulation for probabilities, we construct a hierarchical estimator of the CVA-VaR which reduces the computational complexity by 3 orders of magnitude compared to standard Monte Carlo., Comment: 35 pages, 2 figures

We present SOFIA+ALMA continuum and spectral-line polarisation data on the massive molecular cloud BYF 73, revealing important details about the magnetic field morphology, gas structures, and energetics in this unusual massive star formation laboratory. The 154$\mu$m HAWC+ polarisation map finds a highly organised magnetic field in the densest, inner 0.55$\times$0.40 pc portion of the cloud, compared to an unremarkable morphology in the cloud's outer layers. The 3mm continuum ALMA polarisation data reveal several more structures in the inner domain, including a pc-long, $\sim$500 M$_{\odot}$ "Streamer" around the central massive protostellar object MIR 2, with magnetic fields mostly parallel to the east-west Streamer but oriented north-south across MIR 2. The magnetic field orientation changes from mostly parallel to the column density structures to mostly perpendicular, at thresholds $N_{\rm crit}$ = 6.6$\times$10$^{26}$ m$^{-2}$, $n_{\rm crit}$ = 2.5$\times$10$^{11}$ m$^{-3}$, and $B_{\rm crit}$ = 42$\pm$7 nT. ALMA also mapped Goldreich-Kylafis polarisation in $^{12}$CO across the cloud, which traces in both total intensity and polarised flux, a powerful bipolar outflow from MIR 2 that interacts strongly with the Streamer. The magnetic field is also strongly aligned along the outflow direction; energetically, it may dominate the outflow near MIR 2, comprising rare evidence for a magnetocentrifugal origin to such outflows. A portion of the Streamer may be in Keplerian rotation around MIR 2, implying a gravitating mass 1350$\pm$50 M$_{\odot}$ for the protostar+disk+envelope; alternatively, these kinematics can be explained by gas in free fall towards a 950$\pm$35 M$_{\odot}$ object. The high accretion rate onto MIR 2 apparently occurs through the Streamer/disk, and could account for $\sim$33% of MIR 2's total luminosity via gravitational energy release., Comment: 33 pages, 32 figures, accepted by ApJ. Line-Integral Convolution (LIC) images and movie versions of Figures 3b, 7, and 29 are available at https://gemelli.spacescience.org/~pbarnes/research/champ/papers/