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The continuing exploration of neighboring planetary systems is providing deeper insights into the relative prevalence of various system architectures, particularly with respect to the solar system. However, a full assessment of the dynamical feasibility of possible terrestrial planets within the Habitable Zones (HZ) of nearby stars requires detailed knowledge of the masses and orbital solutions of any known planets within these systems. Moreover, the presence of as yet undetected planets in or near the HZ will be crucial for providing a robust target list for future direct imaging surveys. In this work, we quantify the distribution of uncertainties on planetary masses and semi-major axes for 1062 confirmed planets, finding median uncertainties of 11.1% and 2.2%, respectively. We show the dependence of these uncertainties on stellar mass and orbital period, and discuss the effects of these uncertainties on dynamical analyses and the locations of mean motion resonance. We also calculate the expected radial velocity (RV) semi-amplitude for a Neptune-mass planet in the middle of the HZ for each of the proposed Habitable Worlds Observatory target stars. We find that for more than half of these stars, the RV semi-amplitude is less than 1.5 m/s, rendering them unlikely to be detected in archival RV data sets and highlighting the need for further observations to understand the dynamical viability of the HZ for these systems. We provide specific recommendations regarding stellar characterization and RV survey strategies that work toward the detection of presently unseen perturbers within the HZ., Comment: 13 pages, 3 figures, 1 table, accepted for publication in the Astronomical Journal

Wildfire frequency is increasing as the climate changes, and the resulting air pollution poses health risks. Just as people routinely use weather forecasts to plan their activities around precipitation, reliable air quality forecasts could help individuals reduce their exposure to air pollution. In the present work, we evaluate several existing forecasts of fine particular matter (PM2.5) within the continental United States in the context of individual decision-making. Our comparison suggests there is meaningful room for improvement in air pollution forecasting, which might be realized by incorporating more data sources and using machine learning tools. To facilitate future machine learning development and benchmarking, we set up a framework to evaluate and compare air pollution forecasts for individual decision making. We introduce a new loss to capture decisions about when to use mitigation measures. We highlight the importance of visualizations when comparing forecasts. Finally, we provide code to download and compare archived forecast predictions., Comment: 22 pages, 3 figures

A data analyst might worry about generalization if dropping a very small fraction of data points from a study could change its substantive conclusions. Finding the worst-case data subset to drop poses a combinatorial optimization problem. To overcome this intractability, recent works propose using additive approximations, which treat the contribution of a collection of data points as the sum of their individual contributions, and greedy approximations, which iteratively select the point with the highest impact to drop and re-run the data analysis without that point [Broderick et al., 2020, Kuschnig et al., 2021]. We identify that, even in a setting as simple as OLS linear regression, many of these approximations can break down in realistic data arrangements. Several of our examples reflect masking, where one outlier may hide or conceal the effect of another outlier. Based on the failures we identify, we provide recommendations for users and suggest directions for future improvements., Comment: 39 pages

Distributed quantum computing (DQC) is a new paradigm aimed at scaling up quantum computing via the interconnection of smaller quantum processing units (QPUs). Shared entanglement allows teleportation of both states and gates between QPUs. This leads to an attractive horizontal scaling of quantum processing power, which comes at the expense of the additional time and noise introduced by entanglement sharing protocols. Consequently, methods for partitioning quantum circuits across multiple QPUs should aim to minimise the amount of entanglement-based communication required between distributed QPUs. Existing protocols tend to focus primarily on optimising entanglement costs for gate teleportation or state teleportation to cover operations between QPUs, rather than both at the same time. The most general form of the problem should treat gate and state teleportation on the same footing, allowing minimal cost circuit partitions through a combination of the two. This work introduces a graph-based formulation which allows joint optimisation of gate and state teleportation cost, including extensions of gate teleportation which group gates together for distribution using common resources. The formulation permits low e-bit cost for a variety of circuit types. Using a basic genetic algorithm, improved performance over state-of-the-art methods is obtained in terms of both average e-bit cost and time scaling., Comment: 10 pages, 13 figures

Characterizing outer companions to hot Jupiters plays a crucial role in deciphering their origins. We present the discovery of a long-period giant planet, HD 118203 c ($m_{c}=11.79^{+0.69}_{-0.63}\ \mathrm{M_{J}}$, $a_{c}=6.28^{+0.10}_{-0.11}$ AU) exterior to a close-in eccentric hot Jupiter HD 118203 b ($P_{b}=6.135\ \mathrm{days}$, $m_{b}=2.14\pm{0.12}\ \mathrm{M_{J}}$, $r_{b}=1.14\pm{0.029}\ \mathrm{R_{J}}$, $e_{b}=0.31\pm{0.007}$) based on twenty-year radial velocities. Using Rossiter-McLaughlin (RM) observations from the Keck Planet Finder (KPF), we measured a low sky-projected spin-orbit angle $\lambda_{b}=-11^{\circ}.7^{+7.6}_{-10.0}$ for HD 118203 b and detected stellar oscillations in the host star, confirming its evolved status. Combining the RM observation with the stellar inclination measurement, we constrained the true spin-orbit angle of HD 118203 b as $\Psi_{b}<33^{\circ}.5\ (2\sigma)$, indicating the orbit normal of the hot Jupiter nearly aligned with the stellar spin axis. Furthermore, by combining radial velocities and Hipparcos-Gaia astrometric acceleration, we constrained the line-of-sight mutual inclination between the hot Jupiter and the outer planet to be $9^{\circ}.8^{+16.2}_{-9.3}$ at $2\sigma$ level. HD 118203 is one of first hot Jupiter systems where both the true spin-orbit angle of the hot Jupiter and the mutual inclination between inner and outer planets have been determined. Our results are consistent with a system-wide alignment, with low mutual inclinations between the outer giant planet, the inner hot Jupiter, and the host star. This alignment, along with the moderate eccentricity of HD 118203 c, implies that the system may have undergone coplanar high-eccentricity tidal migration. Under this framework, our dynamical analysis suggests an initial semi-major axis of 0.3 to 3.2 AU for the proto-hot Jupiter., Comment: 30 pages, 12 figures, accepted by AJ

This paper introduces a noise-aware distributed Quantum Approximate Optimization Algorithm (QAOA) tailored for execution on near-term quantum hardware. Leveraging a distributed framework, we address the limitations of current Noisy Intermediate-Scale Quantum (NISQ) devices, which are hindered by limited qubit counts and high error rates. Our approach decomposes large QAOA problems into smaller subproblems, distributing them across multiple Quantum Processing Units (QPUs) to enhance scalability and performance. The noise-aware strategy incorporates error mitigation techniques to optimize qubit fidelity and gate operations, ensuring reliable quantum computations. We evaluate the efficacy of our framework using the HamilToniQ Benchmarking Toolkit, which quantifies the performance across various quantum hardware configurations. The results demonstrate that our distributed QAOA framework achieves significant improvements in computational speed and accuracy, showcasing its potential to solve complex optimization problems efficiently in the NISQ era. This work sets the stage for advanced algorithmic strategies and practical quantum system enhancements, contributing to the broader goal of achieving quantum advantage.

The escalating impacts of climate change and the increasing demand for sustainable development and natural resource management necessitate innovative technological solutions. Quantum computing (QC) has emerged as a promising tool with the potential to revolutionize these critical areas. This review explores the application of quantum machine learning and optimization techniques for climate change prediction and enhancing sustainable development. Traditional computational methods often fall short in handling the scale and complexity of climate models and natural resource management. Quantum advancements, however, offer significant improvements in computational efficiency and problem-solving capabilities. By synthesizing the latest research and developments, this paper highlights how QC and quantum machine learning can optimize multi-infrastructure systems towards climate neutrality. The paper also evaluates the performance of current quantum algorithms and hardware in practical applications and presents realistic cases, i.e., waste-to-energy in anaerobic digestion, disaster prevention in flooding prediction, and new material development for carbon capture. The integration of these quantum technologies promises to drive significant advancements in achieving climate resilience and sustainable development.

Spatial variation in the intensity of magnetospheric and ionospheric fluctuation during solar storms creates ground-induced currents, of importance in both infrastructure engineering and geophysical science. This activity is currently measured using a network of ground-based magnetometers, typically consisting of extensive installations at established observatory sites. We show that this network can be enhanced by the addition of remote quantum magnetometers which combine high sensitivity with intrinsic calibration. These nodes utilize scalable hardware and run independently of wired communication and power networks. We demonstrate that optically pumped magnetometers, utilizing mass-produced and miniaturized components, offer a single scalable sensor with the sensitivity and stability required for space weather observation. We describe the development and deployment of an off-grid magnetic sensing node, powered by a solar panel, present observed data from periods of low and high geomagnetic activity, and compare it to existing geomagnetic observatories., Comment: 9 pages, 7 figures

The International Muon Collider Collaboration (IMCC) [1] was established in 2020 following the recommendations of the European Strategy for Particle Physics (ESPP) and the implementation of the European Strategy for Particle Physics-Accelerator R&D Roadmap by the Laboratory Directors Group [2], hereinafter referred to as the the European LDG roadmap. The Muon Collider Study (MuC) covers the accelerator complex, detectors and physics for a future muon collider. In 2023, European Commission support was obtained for a design study of a muon collider (MuCol) [3]. This project started on 1st March 2023, with work-packages aligned with the overall muon collider studies. In preparation of and during the 2021-22 U.S. Snowmass process, the muon collider project parameters, technical studies and physics performance studies were performed and presented in great detail. Recently, the P5 panel [4] in the U.S. recommended a muon collider R&D, proposed to join the IMCC and envisages that the U.S. should prepare to host a muon collider, calling this their "muon shot". In the past, the U.S. Muon Accelerator Programme (MAP) [5] has been instrumental in studies of concepts and technologies for a muon collider., Comment: This document summarises the International Muon Collider Collaboration (IMCC) progress and status of the Muon Collider R&D programme

The recent discoveries of Neptune-sized ultra-short period planets (USPs) challenge existing planet formation theories. It is unclear whether these residents of the Hot Neptune Desert have similar origins to smaller, rocky USPs, or if this discrete population is evidence of a different formation pathway altogether. We report the discovery of TOI-3261b, an ultra-hot Neptune with an orbital period $P$ = 0.88 days. The host star is a $V = 13.2$ magnitude, slightly super-solar metallicity ([Fe/H] $\simeq$ 0.15), inactive K1.5 main sequence star at $d = 300$ pc. Using data from the Transiting Exoplanet Survey Satellite and the Las Cumbres Observatory Global Telescope, we find that TOI-3261b has a radius of $3.82_{-0.35}^{+0.42}$ $R_{\oplus}$. Moreover, radial velocities from ESPRESSO and HARPS reveal a mass of $30.3_{-2.4}^{+2.2}$ $M_{\oplus}$, more than twice the median mass of Neptune-sized planets on longer orbits. We investigate multiple mechanisms of mass loss that can reproduce the current-day properties of TOI-3261b, simulating the evolution of the planet via tidal stripping and photoevaporation. Thermal evolution models suggest that TOI-3261b should retain an envelope potentially enriched with volatiles constituting $\sim$5% of its total mass. This is the second highest envelope mass fraction among ultra-hot Neptunes discovered to date, making TOI-3261b an ideal candidate for atmospheric follow-up observations., Comment: 20 pages, 11 figures, accepted to AJ

An important local vanishing theorem for the minimal model program is the fact that klt singularities in characteristic zero are Cohen-Macaulay. In contrast, even in the narrow setting of terminal singularities of dimension 3, we show that Cohen-Macaulayness can fail in characteristic $p$ or mixed characteristic $(0,p)$ for $p$ equal to 2, 3, or 5. This is optimal, by work of Arvidsson-Bernasconi-Lacini. The examples are quotients of regular schemes by the cyclic group $G$ of order $p$. In characteristic $p$ or mixed characteristic, such quotients can exhibit a wide range of behavior. Our key technical tool is a sufficient condition for quotients by $G$ to have only toric singularities., Comment: 49 pages, 6 figures; v2: references added

The microfabrication of cesium vapor cells for optically pumped magnetometry relies on optimization of buffer gas pressure in order to maximize atomic coherence time and sensitivity to external magnetic signals. We demonstrate post-bond nitrogen buffer gas pressure tuning through localized heating of an integrated micro-pill dispenser. We characterize the variation in the intrinsic longitudinal relaxation rate, $\gamma_{10}$, and magnetic sensitivity, as a function of the resulting nitrogen buffer gas pressure. Measurements are conducted through employing an optically pumped magnetometer operating in a free-induction-decay configuration. $\gamma_{10}$ is extracted across a range of nitrogen pressures between $\sim$~60~-~700~Torr, measuring a minimum of 140~Hz at 115~Torr. Additionally, we achieve sensitivities as low as 130 ~fT/$\sqrt{\text{Hz}}$ at a bias field amplitude of $\sim 50~\mu$T. With the optimal nitrogen buffer gas pressure now quantified and achievable post-fabrication, these mass-producible cells can be tailored to suit a variety of sensing applications, ensuring peak magnetometer performance., Comment: 7 pages (inc references), 3 figures

We present an updated ephemeris and physical parameters for the exoplanet WASP-77 A b. In this effort, we combine 64 ground- and space-based transit observations, 6 space-based eclipse observations, and 32 radial velocity observations to produce the most precise orbital solution to date for this target, aiding in the planning of James Webb Space Telescope (JWST) and Ariel observations and atmospheric studies. We report a new orbital period of 1.360029395 +- 5.7e-8 days, a new mid-transit time of 2459957.337860 +- 4.3e-5 BJDTDB (Barycentric Julian Date in the Barycentric Dynamical Time scale; arXiv:1005.4415) and a new mid-eclipse time of 2459956.658192 +- 6.7e-5 BJDTDB. Furthermore, the methods presented in this study reduce the uncertainties in the planet mass to 1.6654 +- 4.5e-3 Mjup and orbital period to 1.360029395 +- 5.7e-8 days by factors of 15.1 and 10.9, respectively. Through a joint fit analysis comparison of transit data taken by space-based and citizen science-led initiatives, our study demonstrates the power of including data collected by citizen scientists compared to a fit of the space-based data alone. Additionally, by including a vast array of citizen science data from ExoClock, Exoplanet Transit Database (ETD), and Exoplanet Watch, we can increase our observational baseline and thus acquire better constraints on the forward propagation of our ephemeris than what is achievable with TESS data alone., Comment: Updated a co-author name. Added a co-author. Added an acknowledgement

We present an updated characterization of the TOI-1685 planetary system, which consists of a P$_{\rm{b}}$ = 0.69\,day USP super-Earth planet orbiting a nearby ($d$ = 37.6\,pc) M2.5V star (TIC 28900646, 2MASS J04342248+4302148). This planet was previously featured in two contemporaneous discovery papers, but the best-fit planet mass, radius, and bulk density values were discrepant allowing it to be interpreted either as a hot, bare rock or a 50\% H$_{2}$O / 50\% MgSiO$_{3}$ water world. TOI-1685 b will be observed in three independent JWST cycle two programs, two of which assume the planet is a water world while the third assumes that it is a hot rocky planet. Here we include a refined stellar classification with a focus on addressing the host star's metallicity, an updated planet radius measurement that includes two sectors of TESS data and multi-color photometry from a variety of ground-based facilities, and a more accurate dynamical mass measurement from a combined CARMENES, IRD, and MAROON-X radial velocity data set. We find that the star is very metal-rich ([Fe/H] $\simeq$ +0.3) and that the planet is systematically smaller, lower mass, and higher density than initially reported, with new best-fit parameters of \Rpl = 1.468 $^{+0.050}_{-0.051}$ \Rearth\ and \Mpl = 3.03$^{+0.33}_{-0.32}$ \Mearth. These results fall in between the previously derived values and suggest that TOI-1685 b is a hot, rocky, planet with an Earth-like density (\Rhopl = 5.3 $\pm$ 0.8 g cm$^{-3}$, or 0.96 \rhoearth), high equilibrium temperature (T$_{\rm{eq}}$ = 1062 $\pm$ 27 K) and negligible volatiles, rather than a water world., Comment: 20 pages, 9 Figures, accepted for publication in ApJL. Datasets and software available via Zenodo and GitHub links found in the paper

We revisit the long-studied radial velocity (RV) target HD26965 using recent observations from the NASA-NSF 'NEID' precision Doppler facility. Leveraging a suite of classical activity indicators, combined with line-by-line RV analyses, we demonstrate that the claimed 45-day signal previously identified as a planet candidate is most likely an activity-induced signal. Correlating the bulk (spectrally-averaged) RV with canonical line activity indicators confirms a multi-day 'lag' between the observed activity indicator time series and the measured RV. When accounting for this lag, we show that much of the observed RV signal can be removed by a linear detrending of the data. Investigating activity at the line-by-line level, we find a depth-dependent correlation between individual line RVs and the bulk RVs, further indicative of periodic suppression of convective blueshift causing the observed RV variability, rather than an orbiting planet. We conclude that the combined evidence of the activity correlations and depth dependence is consistent with a radial velocity signature dominated by a rotationally-modulated activity signal at a period of $\sim$42 days. We hypothesize that this activity signature is due to a combination of spots and convective blueshift suppression. The tools applied in our analysis are broadly applicable to other stars, and could help paint a more comprehensive picture of the manifestations of stellar activity in future Doppler RV surveys., Comment: 25 pages, 13 figures. Accepted in AJ

Studies of micrometeorites in mid-Ordovician limestones and Earth's impact craters indicate that our planet witnessed a massive infall of ordinary L chondrite material 466 million years (My) ago (Heck et al. 2017, Schmieder & Kring 2020, Kenkmann 2021) that may have been at the origin of the first major mass extinction event (Schmitz et al. 2019). The breakup of a large asteroid in the main belt is the likely cause of this massive infall. In modern times, material originating from this breakup still dominates meteorite falls (>20% of all falls) (Swindle et al. 2014). Here, we provide spectroscopic observations and dynamical evidence that the Massalia collisional family is the only plausible source of this catastrophic event and of the most abundant class of meteorites falling on Earth today. It is suitably located in the inner belt, at low-inclination orbits, which corresponds to the observed distribution of L-chondrite-like near-Earth objects (NEOs) and of interplanetary dust concentrated at 1.4 degrees (Sykes 1990, Reach et al. 1997)., Comment: 35 pages, 11 pages, under revision

Spatial prediction tasks are key to weather forecasting, studying air pollution, and other scientific endeavors. Determining how much to trust predictions made by statistical or physical methods is essential for the credibility of scientific conclusions. Unfortunately, classical approaches for validation fail to handle mismatch between locations available for validation and (test) locations where we want to make predictions. This mismatch is often not an instance of covariate shift (as commonly formalized) because the validation and test locations are fixed (e.g., on a grid or at select points) rather than i.i.d. from two distributions. In the present work, we formalize a check on validation methods: that they become arbitrarily accurate as validation data becomes arbitrarily dense. We show that classical and covariate-shift methods can fail this check. We instead propose a method that builds from existing ideas in the covariate-shift literature, but adapts them to the validation data at hand. We prove that our proposal passes our check. And we demonstrate its advantages empirically on simulated and real data., Comment: 52 pages, 14 figures

We present a numerical computation, based on neural network techniques, of the physical Yukawa couplings in a heterotic string theory compactification on a smooth Calabi-Yau threefold with non-standard embedding. The model belongs to a large class of heterotic line bundle models that have previously been identified and whose low-energy spectrum precisely matches that of the MSSM plus fields uncharged under the Standard Model group. The relevant quantities for the calculation, that is, the Ricci-flat Calabi-Yau metric, the Hermitian Yang-Mills bundle metrics and the harmonic bundle-valued forms, are all computed by training suitable neural networks. For illustration, we consider a one-parameter family in complex structure moduli space. The computation at each point along this locus takes about half a day on a single twelve-core CPU. Our results for the Yukawa couplings are estimated to be within 10\% of the expected analytic result. We find that the effect of the matter field normalisation can be significant and can contribute towards generating hierarchical couplings. We also demonstrate that a zeroth order, semi-analytic calculation, based on the Fubini-Study metric and its counterparts for the bundle metric and the bundle-valued forms, leads to roughly correct results, about 25\% away from the numerical ones. The method can be applied to other heterotic line bundle models and generalised to other constructions, including to F-theory models., Comment: Dedicated to the memory of Graham G. Ross

We recently described a protocol for computing the potential energy in heterotic M-theory for the dilaton, complex structure and K\"ahler moduli. This included the leading order non-perturbative contributions to the complex structure, gaugino condensation and worldsheet instantons assuming a hidden sector that contains an anomalous U(1) structure group embedded in $E_8$. In this paper, we elucidate, in detail, the mathematical and computational methods required to utilize this protocol. These methods are then applied to a realistic heterotic M-theory model, the $B-L$ MSSM, whose observable sector is consistent with all particle physics requirements. Within this context, it is shown that the dilaton and universal moduli can be completely stabilized at values compatible with every phenomenological and mathematical constraint -- as well as with $\Lambda$CDM cosmology. We also show that the heterotic M-theory vacua are consistent with all well-supported Swampland conjectures based on considerations of string theory and quantum gravity, and we discuss the implications of dark energy theorems for compactified theories., Comment: 42 pages, 6 figures

With machine learning applications now spanning a variety of computational tasks, multi-user shared computing facilities are devoting a rapidly increasing proportion of their resources to such algorithms. Graph neural networks (GNNs), for example, have provided astounding improvements in extracting complex signatures from data and are now widely used in a variety of applications, such as particle jet classification in high energy physics (HEP). However, GNNs also come with an enormous computational penalty that requires the use of GPUs to maintain reasonable throughput. At shared computing facilities, such as those used by physicists at Fermi National Accelerator Laboratory (Fermilab), methodical resource allocation and high throughput at the many-user scale are key to ensuring that resources are being used as efficiently as possible. These facilities, however, primarily provide CPU-only nodes, which proves detrimental to time-to-insight and computational throughput for workflows that include machine learning inference. In this work, we describe how a shared computing facility can use the NVIDIA Triton Inference Server to optimize its resource allocation and computing structure, recovering high throughput while scaling out to multiple users by massively parallelizing their machine learning inference. To demonstrate the effectiveness of this system in a realistic multi-user environment, we use the Fermilab Elastic Analysis Facility augmented with the Triton Inference Server to provide scalable and high throughput access to a HEP-specific GNN and report on the outcome., Comment: 20 pages, 14 figures, submitted to "Computing and Software for Big Science"

We compute the potential energy for the dilaton, complex structure and Kahler moduli and search of realistic vacua of heterotic M-theory compactified on Calabi-Yau threefolds. We present a protocol for deriving the potential that combines the non-perturbative complex structure, gaugino condensate and worldsheet instanton superpotentials in theories in which the hidden sector contains an anomalous $U(1)$ structure group. The Green-Schwarz anomaly cancellation induces inhomogeneous axion transformations for the imaginary components of the dilaton and Kahler modulus. Using this protocol we obtain explicit examples in which potential has a global minimum at negative or zero vacuum density or a metastable minimum with positive vacuum density. In all three cases, the dilaton, Kahler modulus and associated axion moduli are completely stabilized. Finally, we show that, for any of these vacua, the potential energy satisfies the large scalar field Swampland conjecture., Comment: 39 pages, 1 figure, minor typos and modifications to figure and text

The HL-LHC run is anticipated to start at the end of this decade and will pose a significant challenge for the scale of the HEP software and computing infrastructure. The mission of the U.S. CMS Software & Computing Operations Program is to develop and operate the software and computing resources necessary to process CMS data expeditiously and to enable U.S. physicists to fully participate in the physics of CMS. We have developed a strategic plan to prioritize R&D efforts to reach this goal for the HL-LHC. This plan includes four grand challenges: modernizing physics software and improving algorithms, building infrastructure for exabyte-scale datasets, transforming the scientific data analysis process and transitioning from R&D to operations. We are involved in a variety of R&D projects that fall within these grand challenges. In this talk, we will introduce our four grand challenges and outline the R&D program of the U.S. CMS Software & Computing Operations Program., Comment: CHEP2023 proceedings, to be published in EPJ Web of Conferences