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The NANOGrav 15-year data provides compelling evidence for a stochastic gravitational-wave (GW) background at nanohertz frequencies. The simplest model-independent approach to characterizing the frequency spectrum of this signal consists in a simple power-law fit involving two parameters: an amplitude A and a spectral index \gamma. In this paper, we consider the next logical step beyond this minimal spectral model, allowing for a running (i.e., logarithmic frequency dependence) of the spectral index, \gamma_run(f) = \gamma + \beta \ln(f/f_ref). We fit this running-power-law (RPL) model to the NANOGrav 15-year data and perform a Bayesian model comparison with the minimal constant-power-law (CPL) model, which results in a 95% credible interval for the parameter \beta consistent with no running, \beta \in [-0.80,2.96], and an inconclusive Bayes factor, B(RPL vs. CPL) = 0.69 +- 0.01. We thus conclude that, at present, the minimal CPL model still suffices to adequately describe the NANOGrav signal; however, future data sets may well lead to a measurement of nonzero \beta. Finally, we interpret the RPL model as a description of primordial GWs generated during cosmic inflation, which allows us to combine our results with upper limits from big-bang nucleosynthesis, the cosmic microwave background, and LIGO-Virgo-KAGRA., Comment: 17 pages, 4 figures, 2 tables

This full paper in the research track evaluates the usage of data logged from cybersecurity exercises in order to predict students who are potentially at risk of performing poorly. Hands-on exercises are essential for learning since they enable students to practice their skills. In cybersecurity, hands-on exercises are often complex and require knowledge of many topics. Therefore, students may miss solutions due to gaps in their knowledge and become frustrated, which impedes their learning. Targeted aid by the instructor helps, but since the instructor's time is limited, efficient ways to detect struggling students are needed. This paper develops automated tools to predict when a student is having difficulty. We formed a dataset with the actions of 313 students from two countries and two learning environments: KYPO CRP and EDURange. These data are used in machine learning algorithms to predict the success of students in exercises deployed in these environments. After extracting features from the data, we trained and cross-validated eight classifiers for predicting the exercise outcome and evaluated their predictive power. The contribution of this paper is comparing two approaches to feature engineering, modeling, and classification performance on data from two learning environments. Using the features from either learning environment, we were able to detect and distinguish between successful and struggling students. A decision tree classifier achieved the highest balanced accuracy and sensitivity with data from both learning environments. The results show that activity data from cybersecurity exercises are suitable for predicting student success. In a potential application, such models can aid instructors in detecting struggling students and providing targeted help. We publish data and code for building these models so that others can adopt or adapt them., Comment: To appear for publication in the FIE 2024 conference proceedings

Jupiter's moon Io is a highly compelling target for future exploration that offers critical insight into tidal dissipation processes and the geology of high heat flux worlds, including primitive planetary bodies, such as the early Earth, that are shaped by enhanced rates of volcanism. Io is also important for understanding the development of volcanogenic atmospheres and mass-exchange within the Jupiter System. However, fundamental questions remain about the state of Io's interior, surface, and atmosphere, as well as its role in the evolution of the Galilean satellites. The Io Volcano Observer (IVO) would address these questions by achieving the following three key goals: (A) Determine how and where tidal heat is generated inside Io; (B) Understand how tidal heat is transported to the surface of Io; and (C) Understand how Io is evolving. IVO was selected for Phase A study through the NASA Discovery program in 2020 and, in anticipation of a New Frontiers 5 opportunity, an enhanced IVO-NF mission concept was advanced that would increase the Baseline mission from 10 flybys to 20, with an improved radiation design; employ a Ka-band communications to double IVO's total data downlink; add a wide angle camera for color and stereo mapping; add a dust mass spectrometer; and lower the altitude of later flybys to enable new science. This study compares and contrasts the mission architecture, instrument suite, and science objectives for Discovery (IVO) and New Frontiers (IVO-NF) missions to Io, and advocates for continued prioritization of Io as an exploration target for New Frontiers., Comment: Submitted to The Planetary Science Journal for peer-review on 14 August 2024

Pulsar-timing-array experiments have reported evidence for a stochastic background of nanohertz gravitational waves consistent with the signal expected from a population of supermassive--black-hole binaries. Those analyses assume power-law spectra for intrinsic pulsar noise and for the background, as well as a Hellings--Downs cross-correlation pattern among the gravitational-wave--induced residuals across pulsars. These assumptions are idealizations that may not be realized in actuality. We test them in the NANOGrav 15 yr data set using Bayesian posterior predictive checks: after fitting our fiducial model to real data, we generate a population of simulated data-set replications, and use them to assess whether the optimal-statistic significance, inter-pulsar correlations, and spectral coefficients assume extreme values for the real data when compared to the replications. We confirm that the NANOGrav 15 yr data set is consistent with power-law and Hellings--Downs assumptions. We also evaluate the evidence for the stochastic background using posterior-predictive versions of the frequentist optimal statistic and of Bayesian model comparison, and find comparable significance (3.2\ $\sigma$ and 3\ $\sigma$ respectively) to what was previously reported for the standard statistics. We conclude with novel visualizations of the reconstructed gravitational waveforms that enter the residuals for each pulsar. Our analysis strengthens confidence in the identification and characterization of the gravitational-wave background as reported by NANOGrav., Comment: 20 pages, 14 Figures

Based on the rate of change of its orbital period, PSR J2043+1711 has a substantial peculiar acceleration of 3.5 $\pm$ 0.8 mm/s/yr, which deviates from the acceleration predicted by equilibrium Milky Way models at a $4\sigma$ level. The magnitude of the peculiar acceleration is too large to be explained by disequilibrium effects of the Milky Way interacting with orbiting dwarf galaxies ($\sim$1 mm/s/yr), and too small to be caused by period variations due to the pulsar being a redback. We identify and examine two plausible causes for the anomalous acceleration: a stellar flyby, and a long-period orbital companion. We identify a main-sequence star in \textit{Gaia} DR3 and Pan-STARRS DR2 with the correct mass, distance, and on-sky position to potentially explain the observed peculiar acceleration. However, the star and the pulsar system have substantially different proper motions, indicating that they are not gravitationally bound. However, it is possible that this is an unrelated star that just happens to be located near J2043+1711 along our line of sight (chance probability of 1.6\%). Therefore, we also constrain possible orbital parameters for a circumbinary companion in a hierarchical triple system with J2043+1711; the changes in the spindown rate of the pulsar are consistent with an outer object that has an orbital period of 80 kyr, a companion mass of 0.3 $M_\odot$ (indicative of a white dwarf or low-mass star), and a semi-major axis of 2000 AU. Continued timing and/or future faint optical observations of J2043+1711 may eventually allow us to differentiate between these scenarios.

In magnetic topological insulators, spontaneous time-reversal symmetry breaking by intrinsic magnetic order can open an energy gap in the topological surface spectrum. In the resulting state, exotic properties like axion electrodynamics, the quantum anomalous Hall effect, and other topological magnetoelectric responses are expected to emerge. A detailed understanding of the magnetic order and its coupling to the topological surface states is essential to harness and tune these properties. Here, we leverage near-resonant electric quadrupole optical second harmonic generation to probe magnetic fluctuations in the candidate axion insulator EuSn$_2$(As,P)$_2$ across its antiferromagnetic phase boundary. We observe a pronounced dimensional crossover in the quantum decoherence induced by magnetic fluctuations, where two-dimensional in-plane ferromagnetic correlations at high temperatures give way to three-dimensional long-range order at the N\'eel temperature. We also observe the breaking of rotational symmetry within the long-range-ordered antiferromagnetic state and map out the resulting spatial domain structure. More generally, we demonstrate the unique capabilities of nonlinear optical spectroscopy to study quantum coherence and fluctuations in magnetic quantum materials.

Estimating win probability is one of the classic modeling tasks of sports analytics. Many widely used win probability estimators use machine learning to fit the relationship between a binary win/loss outcome variable and certain game-state variables. To illustrate just how difficult it is to accurately fit such a model from noisy and highly correlated observational data, in this paper we conduct a simulation study. We create a simplified random walk version of football in which true win probability at each game-state is known, and we see how well a model recovers it. We find that the dependence structure of observational play-by-play data substantially inflates the bias and variance of estimators and lowers the effective sample size. This makes it essential to quantify uncertainty in win probability estimates, but typical bootstrapped confidence intervals are too narrow and don't achieve nominal coverage. Hence, we introduce a novel method, the fractional bootstrap, to calibrate these intervals to achieve adequate coverage. Our findings are not unique to the particular application of estimating win probability; they are broadly applicable across sports analytics, as myriad other sports datasets are clustered into groups of observations that share the same outcome.

The transformation of time between the surface of the Earth, the solar system barycenter, and the surface of the Moon involves relativistic corrections. For solar system Barycentric Dynamical Time (TDB), we also require that there be no rate difference between Terrestrial Time (TT) and TDB. The IAU has addressed these transformations with several resolutions. A series of robotic and crewed landings on the Moon are planned. The analogous transformation between TDB and time on the surface of the Moon (TL) needs a review and discussion. In this paper, we compute the rate terms involved in that transformation. We also present the TDB-compatible spatial scale and Lorentz contraction of Moon-centered positional coordinates. These transformations have been implemented in the JPL programs used to generate ephemerides of the Moon and planets. Finally, we provide expressions that can be used to synchronize TT and TL using either TDB or TT. The relevant transformations contain a small secular drift between the two time scales, along with additional small periodic terms that can be numerically evaluated using the solar system and lunar ephemerides., Comment: 17 pages, 3 tables

Pulsar timing array experiments have recently uncovered evidence for a nanohertz gravitational wave background by precisely timing an ensemble of millisecond pulsars. The next significant milestones for these experiments include characterizing the detected background with greater precision, identifying its source(s), and detecting continuous gravitational waves from individual supermassive black hole binaries. To achieve these objectives, generating accurate and precise times of arrival of pulses from pulsar observations is crucial. Incorrect polarization calibration of the observed pulsar profiles may introduce errors in the measured times of arrival. Further, previous studies (e.g., van Straten 2013; Manchester et al. 2013) have demonstrated that robust polarization calibration of pulsar profiles can reduce noise in the pulsar timing data and improve timing solutions. In this paper, we investigate and compare the impact of different polarization calibration methods on pulsar timing precision using three distinct calibration techniques: the Ideal Feed Assumption (IFA), Measurement Equation Modeling (MEM), and Measurement Equation Template Matching (METM). Three NANOGrav pulsars-PSRs J1643$-$1224, J1744$-$1134, and J1909$-$3744-observed with the 800 MHz and 1.5 GHz receivers at the Green Bank Telescope (GBT) are utilized for our analysis. Our findings reveal that all three calibration methods enhance timing precision compared to scenarios where no polarization calibration is performed. Additionally, among the three calibration methods, the IFA approach generally provides the best results for timing analysis of pulsars observed with the GBT receiver system. We attribute the comparatively poorer performance of the MEM and METM methods to potential instabilities in the reference noise diode coupled to the receiver and temporal variations in the profile of the reference pulsar, respectively., Comment: 21 pages, 6 figures, 1 table, submitted to Astrophysical Journal

We use the `Middle Ages Galaxy Properties with Integral field spectroscopy' (MAGPI) survey to investigate whether galaxies have evolved in the distribution of their stellar angular momentum in the past 3-4 Gyr, as probed by the observational proxy for spin, $\lambda_{R}$. We use 2D stellar kinematics to measure $\lambda_{R}$ along with detailed photometric models to estimate galaxy ellipticity. The combination of these measurements quantifies the kinematic classes of `fast rotators' and the rarer `slow rotators', which show no regular rotation in their line-of-sight velocity fields. We compare 51 MAGPI galaxies with $\log_{10} (M_{\star}/\mathrm{M}_\odot) > 10$ to carefully drawn samples of MaNGA galaxies in the local Universe, selected to represent possible descendants of the MAGPI progenitors. The EAGLE simulations are used to identify possible evolutionary pathways between the two samples, explicitly accounting for progenitor bias in our results and the varied evolutionary pathways a galaxy might take between the two epochs. We find that the occurrence of slow rotating galaxies is unchanged between the MAGPI ($z \sim 0.3$) and MaNGA ($z \sim 0$) samples, suggesting the massive slow rotator population was already in place $\sim 4$ Gyr ago and has not accumulated since. There is a hint of the MAGPI sample having an excess of high $\lambda_{R}$ galaxies compared to the MaNGA sample, corresponding to more ordered rotation, but statistically the samples are not significantly different. The large-scale stellar kinematics, as quantified through the $\lambda_{R}$ parameter, of galaxies at $z \sim 0.3$ have already evolved into the diversity of structures seen today in the local Universe., Comment: 10 pages, 7 figures, accepted for publication in MNRAS

Pulsar timing arrays (PTAs) are designed to detect low-frequency gravitational waves (GWs). GWs induce achromatic signals in PTA data, meaning that the timing delays do not depend on radio-frequency. However, pulse arrival times are also affected by radio-frequency dependent "chromatic" noise from sources such as dispersion measure (DM) and scattering delay variations. Furthermore, the characterization of GW signals may be influenced by the choice of chromatic noise model for each pulsar. To better understand this effect, we assess if and how different chromatic noise models affect achromatic noise properties in each pulsar. The models we compare include existing DM models used by NANOGrav and noise models used for the European PTA Data Release 2 (EPTA DR2). We perform this comparison using a subsample of six pulsars from the NANOGrav 15 yr data set, selecting the same six pulsars as from the EPTA DR2 six-pulsar dataset. We find that the choice of chromatic noise model noticeably affects the achromatic noise properties of several pulsars. This is most dramatic for PSR J1713+0747, where the amplitude of its achromatic red noise lowers from $\log_{10}A_{\text{RN}} = -14.1^{+0.1}_{-0.1}$ to $-14.7^{+0.3}_{-0.5}$, and the spectral index broadens from $\gamma_{\text{RN}} = 2.6^{+0.5}_{-0.4}$ to $\gamma_{\text{RN}} = 3.5^{+1.2}_{-0.9}$. We also compare each pulsar's noise properties with those inferred from the EPTA DR2, using the same models. From the discrepancies, we identify potential areas where the noise models could be improved. These results highlight the potential for custom chromatic noise models to improve PTA sensitivity to GWs.

Algorithmic bias is a major issue in machine learning models in educational contexts. However, it has not yet been studied thoroughly in Asian learning contexts, and only limited work has considered algorithmic bias based on regional (sub-national) background. As a step towards addressing this gap, this paper examines the population of 5,986 students at a large university in the Philippines, investigating algorithmic bias based on students' regional background. The university used the Canvas learning management system (LMS) in its online courses across a broad range of domains. Over the period of three semesters, we collected 48.7 million log records of the students' activity in Canvas. We used these logs to train binary classification models that predict student grades from the LMS activity. The best-performing model reached AUC of 0.75 and weighted F1-score of 0.79. Subsequently, we examined the data for bias based on students' region. Evaluation using three metrics: AUC, weighted F1-score, and MADD showed consistent results across all demographic groups. Thus, no unfairness was observed against a particular student group in the grade predictions., Comment: Published in proceedings of the 17th Educational Data Mining Conference (EDM 2024)

The mergers of double neutron stars (DNSs) systems are believed to drive the majority of short $\gamma$-ray bursts (SGRBs), while also serving as production sites of heavy r-process elements. Despite being key to i) confirming the nature of the extragalactic SGRBs, ii) addressing the poorly-understood r-process enrichment in the ultra-faint dwarf galaxies (UFDGs), and iii) probing the formation process of DNS systems, the space velocity distribution of DNSs is still poorly constrained due to the small number of DNSs with well-determined astrometry. In this work, we determine new proper motions and parallaxes of two Galactic DNSs -- PSR J0509+3801 and PSR J1930-1852, using the Very Long Baseline Array, and estimate the transverse velocities $v_\perp$ of all the 11 isolated Galactic DNSs having proper motion measurements in a consistent manner. Our correlation analysis reveals that the DNS $v_\perp$ is tentatively correlated with three parameters: spin period, orbital eccentricity, and companion mass. With the preliminary $v_\perp$ distribution, we obtain the following findings. Firstly, the refined $v_\perp$ distribution is confirmed to agree with the observed displacements of the localized SGRBs from their host galaxy birth sites. Secondly, we estimate that around 11% and 25% of DNSs remain gravitationally bound to UFDGs with escape velocities of 15$\mathrm{~km~s^{-1}}$ and 25$\mathrm{~km~s^{-1}}$, respectively. Hence, the retained DNSs might indeed be responsible for the r-process enrichment confirmed so far in a few UFDGs. Finally, we discuss how a future ensemble of astrometrically determined DNSs may probe the multimodality of the $v_\perp$ distribution., Comment: 17 pages, 3 figures, 4 tables, accepted for publication in ApJ

The cystic fibrosis transmembrane conductance regulator (CFTR) is an important membrane protein in vertebrates. The function of CFTR is to transport chloride ions across the cell membrane, which is known to require adenosine triphosphate (ATP). Whereas most conventional wisdom suggests that ATP interacts with CFTR purely through random collisions via diffusion, we investigate electrostatic interactions between CFTR and ATP at the mesoscale (10s of Angstroms). We use molecular dynamics to simulate CFTR-ATP interactions in cases where CFTR is bound/unbound from ATP, and we demonstrate an electrostatic potential gradient towards CFTR when ATP is unbound. We additionally compute electrostatic interactions between ATP and the solvent and membrane, which are simulated explicitly.

The cosmic merger history of supermassive black hole binaries (SMBHBs) is expected to produce a low-frequency gravitational wave background (GWB). Here we investigate how signs of the discrete nature of this GWB can manifest in pulsar timing arrays through excursions from, and breaks in, the expected $f_{\mathrm{GW}}^{-2/3}$ power-law of the GWB strain spectrum. To do this, we create a semi-analytic SMBHB population model, fit to NANOGrav's 15 yr GWB amplitude, and with 1,000 realizations we study the populations' characteristic strain and residual spectra. Comparing our models to the NANOGrav 15 yr spectrum, we find two interesting excursions from the power-law. The first, at $2 \; \mathrm{nHz}$, is below our GWB realizations with $p$-value significance $p = 0.05$ to $0.06$ ($\approx 1.8 \sigma - 1.9 \sigma$). The second, at $16 \; \mathrm{nHz}$, is above our GWB realizations with $p = 0.04$ to $0.15$ ($\approx 1.4 \sigma - 2.1 \sigma$). We explore the properties of a loud SMBHB which could cause such an excursion. Our simulations also show that the expected number of SMBHBs decreases by three orders of magnitude, from $\sim 10^6$ to $\sim 10^3$, between $2\; \mathrm{nHz}$ and $20 \; \mathrm{nHz}$. This causes a break in the strain spectrum as the stochasticity of the background breaks down at $26^{+28}_{-19} \; \mathrm{nHz}$, consistent with predictions pre-dating GWB measurements. The diminished GWB signal from SMBHBs at frequencies above the $26$~nHz break opens a window for PTAs to detect continuous GWs from individual SMBHBs or GWs from the early universe., Comment: 10 pages, 8 figures, 1 appendix, submitted to ApJ

Recently, ACM FAccT published an article by Kwegyir-Aggrey and colleagues (2023), critiquing the use of AUC ROC in predictive analytics in several domains. In this article, we offer a critique of that article. Specifically, we highlight technical inaccuracies in that paper's comparison of metrics, mis-specification of the interpretation and goals of AUC ROC, the article's use of the accuracy metric as a gold standard for comparison to AUC ROC, and the article's application of critiques solely to AUC ROC for concerns that would apply to the use of any metric. We conclude with a re-framing of the very valid concerns raised in that article, and discuss how the use of AUC ROC can remain a valid and appropriate practice in a well-informed predictive analytics approach taking those concerns into account. We conclude by discussing the combined use of multiple metrics, including machine learning bias metrics, and AUC ROC's place in such an approach. Like broccoli, AUC ROC is healthy, but also like broccoli, researchers and practitioners in our field shouldn't eat a diet of only AUC ROC.

Programming courses can be challenging for first year university students, especially for those without prior coding experience. Students initially struggle with code syntax, but as more advanced topics are introduced across a semester, the difficulty in learning to program shifts to learning computational thinking (e.g., debugging strategies). This study examined the relationships between students' rate of programming errors and their grades on two exams. Using an online integrated development environment, data were collected from 280 students in a Java programming course. The course had two parts. The first focused on introductory procedural programming and culminated with exam 1, while the second part covered more complex topics and object-oriented programming and ended with exam 2. To measure students' programming abilities, 51095 code snapshots were collected from students while they completed assignments that were autograded based on unit tests. Compiler and runtime errors were extracted from the snapshots, and three measures -- Error Count, Error Quotient and Repeated Error Density -- were explored to identify the best measure explaining variability in exam grades. Models utilizing Error Quotient outperformed the models using the other two measures, in terms of the explained variability in grades and Bayesian Information Criterion. Compiler errors were significant predictors of exam 1 grades but not exam 2 grades; only runtime errors significantly predicted exam 2 grades. The findings indicate that leveraging Error Quotient with multiple error types (compiler and runtime) may be a better measure of students' introductory programming abilities, though still not explaining most of the observed variability., Comment: Published in ACM ITiCSE 2024 conference proceedings, see https://doi.org/10.1145/3649217.3653563

We examined the efficacy of AI-assisted learning in an introductory programming course at the university level by using a GPT-4 model to generate personalized hints for compiler errors within a platform for automated assessment of programming assignments. The control group had no access to GPT hints. In the experimental condition GPT hints were provided when a compiler error was detected, for the first half of the problems in each module. For the latter half of the module, hints were disabled. Students highly rated the usefulness of GPT hints. In affect surveys, the experimental group reported significantly higher levels of focus and lower levels of confrustion (confusion and frustration) than the control group. For the six most commonly occurring error types we observed mixed results in terms of performance when access to GPT hints was enabled for the experimental group. However, in the absence of GPT hints, the experimental group's performance surpassed the control group for five out of the six error types., Comment: Accepted at ITiCSE 2024

Numerous studies demonstrate the importance of self-regulation during learning by problem-solving. Recent work in learning analytics has largely examined students' use of SRL concerning overall learning gains. Limited research has related SRL to in-the-moment performance differences among learners. The present study investigates SRL behaviors in relationship to learners' moment-by-moment performance while working with intelligent tutoring systems for stoichiometry chemistry. We demonstrate the feasibility of labeling SRL behaviors based on AI-generated think-aloud transcripts, identifying the presence or absence of four SRL categories (processing information, planning, enacting, and realizing errors) in each utterance. Using the SRL codes, we conducted regression analyses to examine how the use of SRL in terms of presence, frequency, cyclical characteristics, and recency relate to student performance on subsequent steps in multi-step problems. A model considering students' SRL cycle characteristics outperformed a model only using in-the-moment SRL assessment. In line with theoretical predictions, students' actions during earlier, process-heavy stages of SRL cycles exhibited lower moment-by-moment correctness during problem-solving than later SRL cycle stages. We discuss system re-design opportunities to add SRL support during stages of processing and paths forward for using machine learning to speed research depending on the assessment of SRL based on transcription of think-aloud data., Comment: Full paper accepted to Learning Analytics and Knowledge (LAK 2024)

Culture fundamentally shapes people's reasoning, behavior, and communication. As people increasingly use generative artificial intelligence (AI) to expedite and automate personal and professional tasks, cultural values embedded in AI models may bias people's authentic expression and contribute to the dominance of certain cultures. We conduct a disaggregated evaluation of cultural bias for five widely used large language models (OpenAI's GPT-4o/4-turbo/4/3.5-turbo/3) by comparing the models' responses to nationally representative survey data. All models exhibit cultural values resembling English-speaking and Protestant European countries. We test cultural prompting as a control strategy to increase cultural alignment for each country/territory. For recent models (GPT-4, 4-turbo, 4o), this improves the cultural alignment of the models' output for 71-81% of countries and territories. We suggest using cultural prompting and ongoing evaluation to reduce cultural bias in the output of generative AI.

Jupiter's atmosphere-interior is a coupled fluid dynamical system strongly influenced by the rapid background rotation. While the visible atmosphere features east-west zonal winds on the order of 100 m/s (Tollefson et al. 2017), zonal flows in the dynamo region are significantly slower, on the order of 1 cm/s or less, according to the latest magnetic secular variation analysis (Bloxham et al. 2022). The vertical profile of the zonal flows and the underlying mechanism remain elusive. The latest Juno radio tracking measurements afforded the derivation of Jupiter's gravity field to spherical harmonic degree 40. Here, we use the latest gravity solution to reconstruct Jupiter's deep zonal winds without a priori assumptions about their latitudinal profile. The pattern of our reconstructed deep zonal winds strongly resembles that of the surface wind within $\pm$ 35 degrees latitude from the equator, in particular the northern off-equatorial jet (NOEJ) and the southern off-equatorial jet (SOEJ) (Kulowski et al. 2021). The reconstruction features larger uncertainties in the southern hemisphere due to the north south asymmetric nature of Juno's trajectory. Amplitude of the reconstructed deep NOEJ matches that of the surface wind when the wind is truncated at a depth around 2500 km, and becomes twice that of the surface wind if the truncation depth is reduced to about 1500 km. Our analysis supports the physical picture in which prominent part of the surface zonal winds extends into Jupiter's interior significantly deeper than the water cloud layer., Comment: Accepted for publication in ApJ, 11 pages, 6 figures

We report a resource estimation pipeline that explicitly compiles quantum circuits expressed using the Clifford+T gate set into a surface code lattice surgery instruction set. The cadence of magic state requests from the compiled circuit enables the optimization of magic state distillation and storage requirements in a post-hoc analysis. To compile logical circuits into lattice surgery operations, we build upon the open-source Lattice Surgery Compiler. The revised compiler operates in two stages: the first translates logical gates into an abstract, layout-independent instruction set; the second compiles these into local lattice surgery instructions that are allocated to hardware tiles according to a specified resource layout. The second stage retains logical parallelism while avoiding resource contention in the fault-tolerant layer, aiding realism. Additionally, users can specify dedicated tiles at which magic states are replenished, enabling resource costs from the logical computation to be considered independently from magic state distillation and storage. We demonstrate the applicability of our pipeline to large, practical quantum circuits by providing resource estimates for the ground state estimation of molecules. We find that variable magic state consumption rates in real circuits can cause the resource costs of magic state storage to dominate unless production is varied to suit., Comment: 28 pages, 10 figures

We introduce the Trapped-Ion Surface Code Compiler (TISCC), a software tool that generates circuits for a universal set of surface code patch operations in terms of a native trapped-ion gate set. To accomplish this, TISCC manages an internal representation of a trapped-ion system where a repeating pattern of trapping zones and junctions is arranged in an arbitrarily large rectangular grid. Surface code operations are compiled by instantiating surface code patches on the grid and using methods to generate transversal operations over data qubits, rounds of error correction over stabilizer plaquettes, and/or lattice surgery operations between neighboring patches. Beyond the implementation of a basic surface code instruction set, TISCC contains corner movement functionality and a patch translation that is implemented using ion movement alone. Except in the latter case, all TISCC functionality is extensible to alternative grid-like hardware architectures. TISCC output has been verified using the Oak Ridge Quasi-Clifford Simulator (ORQCS)., Comment: 10 pages, 5 figures. Software to be released at https://github.com/ORNL-QCI/TISCC

The standard mathematical approach to fourth-down decision making in American football is to make the decision that maximizes estimated win probability. Win probability estimates arise from machine learning models fit from historical data. These models attempt to capture a nuanced relationship between a noisy binary outcome variable and game-state variables replete with interactions and non-linearities from a finite dataset of just a few thousand games. Thus, it is imperative to knit uncertainty quantification into the fourth-down decision procedure; we do so using bootstrapping. We find that uncertainty in the estimated optimal fourth-down decision is far greater than that currently expressed by sports analysts in popular sports media.

Multi-species BGK models describe the dynamics of rarefied gases with constituent particles of different elements or compounds with potentially non-trivial velocity distributions. In this paper, moment equations for the bulk velocities, energies, and temperatures of a spatially homogeneous multi-species BGK model are examined. A key challenge in analyzing these equations is the fact that the collision frequencies are allowed to depend on the species temperatures, which allows for more realistic simulations of dilute gas flow. Therefore, a positive lower bound is established for the species temperatures. With this lower bound, a global existence and uniqueness of solutions to the coupled velocity-energy ODE system is established. The lower bound also enables a proof of exponential decay to a unique steady-state solution. Numerical results are presented to demonstrate how the bulk velocities and temperatures relax for large times., Comment: 21 pages, 3 figures

Recently we found compelling evidence for a gravitational wave background with Hellings and Downs (HD) correlations in our 15-year data set. These correlations describe gravitational waves as predicted by general relativity, which has two transverse polarization modes. However, more general metric theories of gravity can have additional polarization modes which produce different interpulsar correlations. In this work we search the NANOGrav 15-year data set for evidence of a gravitational wave background with quadrupolar Hellings and Downs (HD) and Scalar Transverse (ST) correlations. We find that HD correlations are the best fit to the data, and no significant evidence in favor of ST correlations. While Bayes factors show strong evidence for a correlated signal, the data does not strongly prefer either correlation signature, with Bayes factors $\sim 2$ when comparing HD to ST correlations, and $\sim 1$ for HD plus ST correlations to HD correlations alone. However, when modeled alongside HD correlations, the amplitude and spectral index posteriors for ST correlations are uninformative, with the HD process accounting for the vast majority of the total signal. Using the optimal statistic, a frequentist technique that focuses on the pulsar-pair cross-correlations, we find median signal-to-noise-ratios of 5.0 for HD and 4.6 for ST correlations when fit for separately, and median signal-to-noise-ratios of 3.5 for HD and 3.0 for ST correlations when fit for simultaneously. While the signal-to-noise-ratios for each of the correlations are comparable, the estimated amplitude and spectral index for HD are a significantly better fit to the total signal, in agreement with our Bayesian analysis., Comment: 11 pages, 5 figures

Hera represents the European Space Agency's inaugural planetary defense space mission and plays a pivotal role in the Asteroid Impact and Deflection Assessment international collaboration with NASA DART mission that performed the first asteroid deflection experiment using the kinetic impactor techniques. With the primary objective of conducting a detailed post-impact survey of the Didymos binary asteroid following the DART impact on its small moon called Dimorphos, Hera aims to comprehensively assess and characterize the feasibility of the kinetic impactor technique in asteroid deflection while conducting an in-depth investigation of the asteroid binary, including its physical and compositional properties as well as the effect of the impact on the surface and shape of Dimorphos. In this work, we describe the Hera radio science experiment, which will allow us to precisely estimate critical parameters, including the mass, which is required to determine the momentum enhancement resulting from the DART impact, mass distribution, rotational states, relative orbits, and dynamics of the asteroids Didymos and Dimorphos. Through a multi-arc covariance analysis, we present the achievable accuracy for these parameters, which consider the full expected asteroid phase and are based on ground radiometric, Hera optical images, and Hera to CubeSats InterSatellite Link radiometric measurements. The expected formal uncertainties for Didymos and Dimorphos GM are better than 0.01% and 0.1%, respectively, while their J2 formal uncertainties are better than 0.1% and 10%, respectively. [...]