We report on the observation and measurement of astrometry, photometry, morphology, and activity of the interstellar object 3I/ATLAS, also designated C/2025 N1 (ATLAS), with the NSF-DOE Vera C. Rubin Observatory. The third interstellar object, comet 3I/ATLAS, was first discovered on UT 2025 July 1. Serendipitously, the Rubin Observatory collected imaging in the area of the sky inhabited by the object during regular commissioning activities. We successfully recovered object detections from Rubin visits spanning UT 2025 June 21 (10 days before discovery) to UT 2025 July 7. Facilitated by Rubin's high resolution and large aperture, we report on the detection of cometary activity as early as June 21st, and observe it throughout. We measure the location and magnitude of the object on 37 Rubin images in r, i, and z bands, with typical precision of about 20 mas (100 mas, systematic) and about 10 mmag, respectively. We use these to derive improved orbit solutions, and to show there is no detectable photometric variability on hourly timescales. We derive a V-band absolute magnitude of H_V = (13.7 +/- 0.2) mag, and an equivalent effective nucleus radius of around (5.6 +/- 0.7) km. These data represent the earliest observations of this object by a large (8-meter class) telescope reported to date, and illustrate the type of measurements (and discoveries) Rubin's Legacy Survey of Space and Time (LSST) will begin to provide once operational later this year.

Artificial Intelligence (AI) is now firmly at the center of evidence-based medicine. Despite many success stories that edge the path of AI's rise in healthcare, there are comparably many reports of significant shortcomings and unexpected behavior of AI in deployment. A major reason for these limitations is AI's reliance on association-based learning, where non-representative machine learning datasets can amplify latent bias during training and/or hide it during testing. To unlock new tools capable of foreseeing and preventing such AI bias issues, we present G-AUDIT. Generalized Attribute Utility and Detectability-Induced bias Testing (G-AUDIT) for datasets is a modality-agnostic dataset auditing framework that allows for generating targeted hypotheses about sources of bias in training or testing data. Our method examines the relationship between task-level annotations (commonly referred to as ``labels'') and data properties including patient attributes (e.g., age, sex) and environment/acquisition characteristics (e.g., clinical site, imaging protocols). G-AUDIT quantifies the extent to which the observed data attributes pose a risk for shortcut learning, or in the case of testing data, might hide predictions made based on spurious associations. We demonstrate the broad applicability of our method by analyzing large-scale medical datasets for three distinct modalities and machine learning tasks: skin lesion classification in images, stigmatizing language classification in Electronic Health Records (EHR), and mortality prediction for ICU tabular data. In each setting, G-AUDIT successfully identifies subtle biases commonly overlooked by traditional qualitative methods, underscoring its practical value in exposing dataset-level risks and supporting the downstream development of reliable AI systems.

Production of photorealistic, navigable 3D site models requires a large volume of carefully collected images that are often unavailable to first responders for disaster relief or law enforcement. Real-world challenges include limited numbers of images, heterogeneous unposed cameras, inconsistent lighting, and extreme viewpoint differences for images collected from varying altitudes. To promote research aimed at addressing these challenges, we have developed the first public benchmark dataset for 3D reconstruction and novel view synthesis based on multiple calibrated ground-level, security-level, and airborne cameras. We present datasets that pose real-world challenges, independently evaluate calibration of unposed cameras and quality of novel rendered views, demonstrate baseline performance using recent state-of-practice methods, and identify challenges for further research.

We obtained New Horizons LORRI images to measure the cosmic optical background (COB) intensity integrated over $0.4\lesssim\lambda\lesssim0.9{~\rm\mu m}.$ The survey comprises 16 high Galactic-latitude fields selected to minimize scattered diffuse Galactic light (DGL) from the Milky Way galaxy, as well as scattered light from bright stars. This work supersedes an earlier analysis based on observations of one of the present fields. Isolating the COB contribution to the raw total sky levels measured in the fields requires subtracting the remaining scattered light from bright stars and galaxies, intensity from faint stars within the fields fainter than the photometric detection-limit, and the DGL foreground. DGL is estimated from Planck HFI $350 {~\rm\mu m}$ and $550 {~\rm\mu m}$ intensities, using a new self-calibrated indicator based on the 16 fields augmented with eight additional DGL calibration fields obtained as part of the survey. The survey yields a highly significant detection ($6.8\sigma$) of the COB at ${\rm 11.16\pm 1.65~(1.47~sys,~0.75~ran) ~nW ~m^{-2} ~sr^{-1}}$ at the LORRI pivot wavelength of 0.608 $\mu$m. The estimated integrated intensity from background galaxies, ${\rm 8.17\pm 1.18 ~nW ~m^{-2} ~sr^{-1}},$ can account for the great majority of this signal. The rest of the COB signal, ${\rm 2.99\pm2.03~ (1.75~sys,~1.03~ran) ~nW ~m^{-2} ~sr^{-1}},$ is formally classified as anomalous intensity but is not significantly different from zero. The simplest interpretation is that the COB is completely due to galaxies.

As NASA's New Horizons spacecraft exits the Solar System bound for interstellar space, it has traveled so far that the nearest stars have shifted markedly from their positions seen from Earth. We demonstrated this by imaging the Proxima Centauri and Wolf 359 fields from Earth and New Horizons on 2020 April 23, when the spacecraft was 47.1 au distant. The observed parallaxes for Proxima Centauri and Wolf 359 are $32.4''$ and $15.7'',$ respectively. These measurements are not of research grade, but directly seeing large stellar parallaxes between two widely separated simultaneous observers is vividly educational. Using the New Horizons positions of the two stars alone, referenced to the three-dimensional model of the solar neighborhood constructed from Gaia DR3 astrometry, further provides the spacecraft spatial position relative to nearby stars with 0.44 au accuracy. The range to New Horizons from the Solar System barycenter is recovered to 0.27 au accuracy, and its angular direction to $0.4^\circ$ accuracy, when compared to the precise values from NASA Deep Space Network tracking. This is the first time optical stellar astrometry has been used to determine the three-dimensional location of a spacecraft with respect to nearby stars, and the first time any method of interstellar navigation has been demonstrated for a spacecraft on an interstellar trajectory. We conclude that the best astrometric approach to navigating spacecraft on their departures to interstellar space is to use a single pair of the closest stars as references, rather than a large sample of more distant stars.

3D data derived from satellite images is essential for scene modeling applications requiring large-scale coverage or involving locations not accessible by airborne lidar or cameras. Measuring the resolution of this data is important for determining mission utility and tracking improvements. In this work, we consider methods to evaluate the resolution of point clouds, digital surface models, and 3D mesh models. We describe 3D metric evaluation tools and workflows that enable automated evaluation based on high-resolution reference airborne lidar, and we present results of analyses with data of varying quality.

The increasingly common use of incidental satellite images for stereo reconstruction versus rigidly tasked binocular or trinocular coincident collection is helping to enable timely global-scale 3D mapping; however, reliable stereo correspondence from multi-date image pairs remains very challenging due to seasonal appearance differences and scene change. Promising recent work suggests that semantic scene segmentation can provide a robust regularizing prior for resolving ambiguities in stereo correspondence and reconstruction problems. To enable research for pairwise semantic stereo and multi-view semantic 3D reconstruction with incidental satellite images, we have established a large-scale public dataset including multi-view, multi-band satellite images and ground truth geometric and semantic labels for two large cities. To demonstrate the complementary nature of the stereo and segmentation tasks, we present lightweight public baselines adapted from recent state of the art convolutional neural network models and assess their performance.

The Laser Interferometer Lunar Antenna (LILA) is a next-generation gravitational-wave (GW) facility on the Moon. By harnessing the Moon's unique environment, LILA fills a critical observational gap in the mid-band GW spectrum ($0.1 - 10$ Hz) between terrestrial detectors (LIGO, Virgo, KAGRA) and the future space mission LISA. Observations enabled by LILA will fundamentally transform multi-messenger astrophysics and GW probes of fundamental physics. LILA will measure the lunar deep interior better than any existing planetary seismic instruments. The LILA mission is designed for phased development aligned with capabilities of the U.S.'s Commercial Lunar Payload Services and Artemis programs. LILA is a unique collaboration between universities, space industries, U.S. government laboratories, and international partners.

Asteroids with diameters less than about 5 km have complex histories because they are small enough for radiative torques, YORP, to be a notable factor in their evolution. (152830) Dinkinesh is a small asteroid orbiting the Sun near the inner edge of the Main Asteroid Belt with a heliocentric semimajor axis of 2.19 AU; its S type spectrum is typical of bodies in this part of the Main Belt. Here we report observations by the Lucy spacecraft as it passed within 431 km of Dinkinesh. Lucy revealed Dinkinesh, which has an effective diameter of only $\sim$720 m, to be unexpectedly complex. Of particular note is the presence of a prominent longitudinal trough overlain by a substantial equatorial ridge, and the discovery of the first confirmed contact binary satellite, now named (152830) Dinkinesh I Selam. Selam consists of two near-equal sized lobes with diameters of $\sim$210 m and $\sim$230 m. It orbits Dinkinesh at a distance of 3.1 km with an orbital period of about 52.7 hr, and is tidally locked. The dynamical state, angular momentum, and geomorphologic observations of the system lead us to infer that the ridge and trough of Dinkinesh are probably the result of mass failure resulting from spin-up by YORP followed by the partial reaccretion of the shed material. Selam probably accreted from material shed by this event.

The large-scale magnetic structure of interplanetary coronal mass ejections (ICMEs) has been shown to cause decreases in the galactic cosmic ray (GCR) flux measured in situ by spacecraft, known as Forbush decreases (Fds). We use measurements of the GCR count rate obtained by MESSENGER during its orbital phase around Mercury to identify such Fds related to the passage of ICMEs and characterize their structure. Of the 42 ICMEs with corresponding high-quality GCR data, 79% are associated with a Fd. Thus a total of 33 ICME-related Fds were identified, 24 of which (73%) have a two-step structure. We use a superposed epoch analysis to build an average Fd profile at MESSENGER and find that despite the variability of individual events, a two-step structure is produced and is directly linked with the magnetic boundaries of the ICME. By using results from previous studies at Earth and Mars, we also address whether two-step Fds are more commonly observed closer to the Sun; we found that although likely, this is not conclusive when comparing to the wide range of results of previous studies conducted at Earth. Finally, we find that the percentage decrease in GCR flux of the Fd is greater at MESSENGER on average than at Earth and Mars, decreasing with increasing heliocentric distance. The relationship between the percentage decrease and maximum hourly decrease is also in agreement with previous studies, and follows trends relating to the expansion of ICMEs as they propagate through the heliosphere.

Recent advances in miniaturization and commercial availability of critical satellite subsystems and detector technology have made small satellites (SmallSats, including CubeSats) an attractive, low-cost potential solution for space weather research and operational needs. Motivated by the 1st International Workshop on SmallSats for Space Weather Research and Forecasting, held in Washington, DC on 1-4 August 2017, we discuss the need for advanced space weather measurement capabilities, driven by analyses from the World Meteorological Organization (WMO), and how SmallSats can efficiently fill these measurement gaps. We present some current, recent missions and proposed/upcoming mission concepts using SmallSats that enhance space weather research and provide prototyping pathways for future operational applications; how they relate to the WMO requirements; and what challenges remain to be overcome to meet the WMO goals and operational needs in the future. With additional investment from cognizant funding agencies worldwide, SmallSats -- including standalone missions and constellations -- could significantly enhance space weather research and, eventually, operations, by reducing costs and enabling new measurements not feasible from traditional, large, monolithic missions.

The quantum approximate optimization algorithm (QAOA) is a hybrid quantum-classical algorithm that seeks to achieve approximate solutions to optimization problems by iteratively alternating between intervals of controlled quantum evolution. Here, we examine the effect of analog precision errors on QAOA performance both from the perspective of algorithmic training and canonical state- and observable-dependent QAOA-relevant metrics. Leveraging cumulant expansions, we recast the faulty QAOA as a control problem in which precision errors are expressed as multiplicative control noise and derive bounds on the performance of QAOA. We show using both analytical techniques and numerical simulations that errors in the analog implementation of QAOA circuits hinder its performance as an optimization algorithm. In particular, we find that any fixed precision implementation of QAOA will be subject to an exponential degradation in performance dependent upon the number of optimal QAOA layers and magnitude of the precision error. Despite this significant reduction, we show that it is possible to mitigate precision errors in QAOA via digitization of the variational parameters, therefore at the cost of increasing circuit depth. We illustrate our results via numerical simulations and analytic and empirical error bounds as a comparison. While focused on precision errors, our approach naturally lends itself to more general noise scenarios and the calculation of error bounds on QAOA performance and broader classes of variational quantum algorithms.

Scallop-shell stars, a recently discovered class of young M dwarfs, show complex optical light curves that are characterized by periodic dips as well as other features that are stable over tens to hundreds of rotation cycles. The origin of these features is not well-understood. 2MASS J05082729$-$2101444 is a $\sim$25 Myr old scallop-shell star that was identified using TESS data; it has a photometric period of 6.73h that has been attributed to rotation. Of the $\sim$50 recently confirmed scallop-shell stars, it is one of the few detected at radio frequencies between 1 and 8 GHz. We observed this rare system with the upgraded Giant Meterwave Radio Telescope at 575--720 MHz, covering 88% of the photometric period in each of the two observations scheduled almost a month apart in 2023. We detected $\sim$millijansky emission from the target in both epochs, with a significant circular polarization fraction: $|V/I|\sim$20--50%. The 3.5-min phase-folded light curves reveal unique variability in circular polarization, showing an $\sim$hour-long helicity reversal in both epochs, similar in amplitude, length, and (possibly) phase. These results suggest two emission components: The first is a persistent, moderately polarized component possibly ascribable to gyro-synchrotron emission driven by centrifugal breakout events. The second is a highly polarized, short burst-like component, likely due to an electron cyclotron maser (ECM), indicative of auroral emission and potentially responsible for the helicity reversal. To explain this, we discuss the different origins of the plasma responsible for the radio emission, including the possibility that the occulting material is acting as a plasma source. Future coordinated multifrequency radio and optical observations can further constrain the underlying scenario, as well as the magnetic geometry of the system, if we assume an ECM-like auroral emission.

The Effective Acceleration Model (EAM) predicts the Time-of-Arrival (ToA) of the Coronal Mass Ejection (CME) driven shock and the average speed within the sheath at 1 AU. The model is based on the assumption that the ambient solar wind interacts with the interplanetary CME (ICME) resulting in constant acceleration or deceleration. The upgraded version of the model (EAMv3), presented here, incorporates two basic improvements: (a) a new technique for the calculation of the acceleration (or deceleration) of the ICME from the Sun to 1 AU and (b) a correction for the CME plane-of-sky speed. A validation of the upgraded EAM model is performed via comparisons to predictions from the ensemble version of the Drag-Based model (DBEM) and the WSA-ENLIL+Cone ensemble model. A common sample of 16 CMEs/ICMEs, in 2013-2014, is used for the comparison. Basic performance metrics such as the mean absolute error (MAE), mean error (ME) and root mean squared error (RMSE) between observed and predicted values of ToA are presented. MAE for EAM model was 8.7$\pm$1.6 hours while for DBEM and ENLIL was 14.3$\pm$2.2 and 12.8$\pm$1.7 hours, respectively. ME for EAM was -1.4$\pm$2.7 hours in contrast with -9.7$\pm$3.4 and -6.1$\pm$3.3 hours from DBEM and ENLIL. We also study the hypothesis of stronger deceleration in the interplanetary (IP) space utilizing the EAMv3 and DBEM models. In particularly, the DBEM model perform better when a greater value of drag parameter, of order of a factor of 3, is used in contrast to previous studies. EAMv3 model shows a deceleration of ICMEs at greater distances, with a mean value of 0.72 AU.

This study provides a detailed analysis of fourteen distant interplanetary shocks observed by the Solar Wind Around Pluto (SWAP) instrument onboard New Horizons. These shocks were observed with a pickup ion data cadence of approximately 30 minutes, covering a heliocentric distance range of ~52-60 au. All the shocks observed within this distance range are fast-forward shocks, and the shock compression ratios vary between ~1.2 and 1.9. The shock transition scales are generally narrow, and the SW density compressions are more pronounced compared to the previous study of seven shocks by McComas et al. (2022). A majority (64%) of these shocks have upstream sonic Mach numbers greater than one. In addition, all high-resolution measurements of distant interplanetary shocks analyzed here show that the shock transition scale is independent of the shock compression ratio. However, the shock transition scale is strongly anti-correlated with the shock speed in the upstream plasma frame, meaning that faster shocks generally yield sharper transitions.

Calibration of instrumental polarization is critical for measuring polarized radio emissions from astrophysical sources to extract the magnetic field information in astrophysical, heliospheric, and terrestrial plasmas. At meter wavelengths, calibration of radio polarimetric observations is particularly challenging because of the scarcity of bright polarized sources due to significant Faraday depolarization. Here, we present a novel formalism for polarization calibration using an unpolarized sky model. The formalism is specifically designed for wide-field, low-frequency instruments like the Murchison Widefield Array (MWA), the LOw Frequency ARray (LOFAR), New Extension in Nan\c{c}ay Upgrading LoFAR (NenuFAR), Owens Valley Radio Observatory - Long Wavelength Array (OVRO-LWA), low-frequency telescope of the Square Kilometre Array Observatory (SKAO-low), etc. By leveraging the apparent polarization of the unpolarized sky induced by the polarized primary beam of the radio telescope, this method avoids dependence on bright polarized calibrators. It is also immune to ionospheric Faraday rotation. The validation of the approach via MWA observations confirms the accuracy of the method. This formalism provides a robust framework for low-frequency polarization calibration. It addresses the longstanding calibration challenges and advances the field of low-frequency polarimetry by enabling polarization studies of astrophysical radio sources.

Robustness audits of deep neural networks (DNN) provide a means to uncover model sensitivities to the challenging real-world imaging conditions that significantly degrade DNN performance in-the-wild. Such conditions are often the result of multiple interacting factors inherent to the environment, sensor, or processing pipeline and may lead to complex image distortions that are not easily categorized. When robustness audits are limited to a set of isolated imaging effects or distortions, the results cannot be (easily) transferred to real-world conditions where image corruptions may be more complex or nuanced. To address this challenge, we present a new alternative robustness auditing method that uses causal inference to measure DNN sensitivities to the factors of the imaging process that cause complex distortions. Our approach uses causal models to explicitly encode assumptions about the domain-relevant factors and their interactions. Then, through extensive experiments on natural and rendered images across multiple vision tasks, we show that our approach reliably estimates causal effects of each factor on DNN performance using only observational domain data. These causal effects directly tie DNN sensitivities to observable properties of the imaging pipeline in the domain of interest towards reducing the risk of unexpected DNN failures when deployed in that domain.

Image quality plays an important role in the performance of deep neural networks (DNNs) and DNNs have been widely shown to exhibit sensitivity to changes in imaging conditions. Large-scale datasets often contain images under a wide range of conditions prompting a need to quantify and understand their underlying quality distribution in order to better characterize DNN performance and robustness. Aligning the sensitivities of image quality metrics and DNNs ensures that estimates of quality can act as proxies for image/dataset difficulty independent of the task models trained/evaluated on the data. Conventional image quality assessment (IQA) seeks to measure and align quality relative to human perceptual judgments, but here we seek a quality measure that is not only sensitive to imaging conditions but also well-aligned with DNN sensitivities. We first ask whether conventional IQA metrics are also informative of DNN performance. In order to answer this question, we reframe IQA from a causal perspective and examine conditions under which quality metrics are predictive of DNN performance. We show theoretically and empirically that current IQA metrics are weak predictors of DNN performance in the context of classification. We then use our causal framework to provide an alternative formulation and a new image quality metric that is more strongly correlated with DNN performance and can act as a prior on performance without training new task models. Our approach provides a means to directly estimate the quality distribution of large-scale image datasets towards characterizing the relationship between dataset composition and DNN performance.

We report the discovery and characterization of TOI-2005b, a warm Jupiter on an eccentric (e~0.59), 17.3-day orbit around a V_mag = 9.867 rapidly rotating F-star. The object was detected as a candidate by TESS and the planetary nature of TOI-2005b was then confirmed via a series of ground-based photometric, spectroscopic, and diffraction-limited imaging observations. The planet was found to reside in a low sky-projected stellar obliquity orbit (lambda = 4.8 degrees) via a transit spectroscopic observation using the Magellan MIKE spectrograph.TOI-2005b is one of a few planets known to have a low-obliquity, high-eccentricity orbit, which may be the result of high-eccentricity coplanar migration. The planet has a periastron equilibrium temperature of ~ 2100 K, similar to some highly irradiated hot Jupiters where atomic metal species have been detected in transmission spectroscopy, and varies by almost 1000 K during its orbit. Future observations of the atmosphere of TOI-2005b can inform us about its radiative timescales thanks to the rapid heating and cooling of the planet.

We used existing data from the New Horizons LORRI camera to measure the optical-band ($0.4\lesssim\lambda\lesssim0.9{\rm\mu m}$) sky brightness within seven high galactic latitude fields. The average raw level measured while New Horizons was 42 to 45 AU from the Sun is $33.2\pm0.5{\rm ~nW ~m^{-2} ~sr^{-1}}.$This is$\sim10\times$ darker than the darkest sky accessible to the {\it Hubble Space Telescope}, highlighting the utility of New Horizons for detecting the cosmic optical background (COB). Isolating the COB contribution to the raw total requires subtracting scattered light from bright stars and galaxies, faint stars below the photometric detection-limit within the fields, and diffuse Milky Way light scattered by infrared cirrus. We remove newly identified residual zodiacal light from the IRIS $100\mu$m all sky maps to generate two different estimates for the diffuse galactic light (DGL). Using these yields a highly significant detection of the COB in the range ${\rm 15.9\pm 4.2\ (1.8~stat., 3.7~sys.) ~nW ~m^{-2} ~sr^{-1}}$to${\rm 18.7\pm 3.8\ (1.8~stat., 3.3 ~sys.)~ nW ~m^{-2} ~sr^{-1}}$ at the LORRI pivot wavelength of 0.608 $\mu$m. Subtraction of the integrated light of galaxies (IGL) fainter than the photometric detection-limit from the total COB level leaves a diffuse flux component of unknown origin in the range ${\rm 8.8\pm4.9\ (1.8 ~stat., 4.5 ~sys.) ~nW ~m^{-2} ~sr^{-1}}$to${\rm 11.9\pm4.6\ (1.8 ~stat., 4.2 ~sys.) ~nW ~m^{-2} ~sr^{-1}}$. Explaining it with undetected galaxies requires the galaxy-count faint-end slope to steepen markedly at V>24 or that existing surveys are missing half the galaxies with V< 30.