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Superfast rotators (SFRs) are small solar system objects that rotate faster than generally possible for a cohesionless rubble pile. Their rotational characteristics allow us to make inferences about their interior structure and composition. Here, we present the methods and results from a preliminary search for SFRs in the DECam Ecliptic Exploration Project (DEEP) data set. We find three SFRs from a sample of 686 main-belt asteroids, implying an occurrence rate of 0.4 -0.3/+0.1 percent - a higher incidence rate than has been measured by previous studies. We suggest that this high occurrence rate is due to the small sub-kilometer size regime to which DEEP has access: the objects searched here were as small as 500 m. We compute the minimum required cohesive strength for each of these SFRs and discuss the implications of these strengths in the context of likely evolution mechanisms. We find that all three of these SFRs require strengths that are more than that of weak regolith but consistent with many cohesive asteroid strengths reported in the literature. Across the full DEEP data set, we have identified ~70,000 Main-Belt Asteroids and expect ~300 SFRs - a result that will be assessed in a future paper.

We have conducted extremely ultra-deep pencil beam observations for new satellites around both Uranus and Neptune. Tens of images on several different nights in 2021, 2022 and 2023 were obtained and shifted and added together to reach as faint as 26.9 and 27.2 magnitudes in the r-band around Uranus and Neptune, respectively. One new moon of Uranus, S/2023 U1, and two new moons of Neptune, S/2021 N1 and S/2002 N5, were found. S/2023 U1 was 26.6 mags, is about 7 km in diameter and has a distant, eccentric and inclined retrograde orbit similar to Caliban and Stephano, implying these satellites are fragments from a once larger parent satellite. S/2023 U1 almost completely overlaps Stephano in orbital phase space. S/2021 N1 was 26.9 mags, about 14 km in size and has a retrograde orbit similar to Neso and Psamathe, indicating they are a dynamical family. We find S/2021 N1 is in a Kozai-Lidov orbital resonance. S/2002 N5 was 25.9 mags, is about 23 km in size and it makes a family of distant prograde satellites with Sao and Laomedeia. All three new moons show for the first time dynamical groups of moons exist around both Uranus and Neptune. The creation of these groups likely produced dust that could be the source of red material seen on the leading hemispheres of some larger inner satellites like Titania, Oberon and Umbriel. We also detected all known outer moons of Uranus and Neptune on multiple nights. This survey mostly completes the outer satellites of Uranus to about 8 km and Neptune to about 14 km in diameter. The size distributions of satellite dynamical families around the giant planets shows a strong steepening in the power law size distribution smaller than 5 km in diameter. The satellites of a family become much more common smaller than 5 km and their size distribution is consistent with a collisional break-up of a once larger parent satellite., Comment: Accepted Astronomical Journal

Dimension reduction is often the first step in statistical modeling or prediction of multivariate spatial data. However, most existing dimension reduction techniques do not account for the spatial correlation between observations and do not take the downstream modeling task into consideration when finding the lower-dimensional representation. We formalize the closeness of approximation to the original data and the utility of lower-dimensional scores for downstream modeling as two complementary, sometimes conflicting, metrics for dimension reduction. We illustrate how existing methodologies fall into this framework and propose a flexible dimension reduction algorithm that achieves the optimal trade-off. We derive a computationally simple form for our algorithm and illustrate its performance through simulation studies, as well as two applications in air pollution modeling and spatial transcriptomics.

Selective plane illumination microscopy (SPIM) is an optical sectioning imaging approach based on orthogonal light pathways for excitation and detection. The excitation pathway has an inverse relation between the optical sectioning strength and the effective field of view (FOV). Multiple approaches exist to extend the effective FOV, and here we focus on remote focusing to axially scan the light sheet, synchronized with a CMOS camera's rolling shutter. A typical axially scanned SPIM configuration for imaging large samples utilizes a tunable optic for remote focusing, paired with air objectives focused into higher refractive index media. To quantitatively explore the effect of remote focus choices and sample space refractive index mismatch on light sheet intensity distributions, we developed a computational model integrating ray tracing and field propagation. We validate our model's performance against experimental light sheet profiles for various SPIM configurations. Our findings indicate that optimizing the position of the sample chamber relative to the excitation optics can enhance image quality by balancing aberrations induced by refractive index mismatch. We validate this prediction using a homebuilt, large sample axially scanned SPIM configuration and calibration samples.

Time is, figuratively and literally, becoming the new dimension for crystalline matter. As such, rapid recent progress on time-varying media gave rise to the notion of temporal and spatiotemporal crystals. Fundamentally rethinking the role of time, which, in contrast to space exhibits a unique unidirectionality often referred to as the arrow of time, promises a new dimension also for topological physics. Here, we enter the new realm of time and spacetime topology: Firstly, we implement a time-topological time interface state. Secondly, we propose and observe a spacetime-topological event and demonstrate unique features like its limited collapse under disorder and causality-suppressed coupling. The new paradigms of time and spacetime topology unveil a distinctive role of causality and non-Hermiticity in topology and pave the way towards topological spatiotemporal wave control with unique robustness.

Symbolic regression (SR) methods attempt to learn mathematical expressions that approximate the behavior of an observed system. However, when dealing with multivariate systems, they often fail to identify the functional form that explains the relationship between each variable and the system's response. To begin to address this, we propose an explainable neural SR method that generates univariate symbolic skeletons that aim to explain how each variable influences the system's response. By analyzing multiple sets of data generated artificially, where one input variable varies while others are fixed, relationships are modeled separately for each input variable. The response of such artificial data sets is estimated using a regression neural network (NN). Finally, the multiple sets of input-response pairs are processed by a pre-trained Multi-Set Transformer that solves a problem we termed Multi-Set Skeleton Prediction and outputs a univariate symbolic skeleton. Thus, such skeletons represent explanations of the function approximated by the regression NN. Experimental results demonstrate that this method learns skeleton expressions matching the underlying functions and outperforms two GP-based and two neural SR methods., Comment: Paper accepted at European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) 2024

Exposure assessment is fundamental to air pollution cohort studies. The objective is to predict air pollution exposures for study subjects at locations without data in order to optimize our ability to learn about health effects of air pollution. In addition to generating accurate predictions to minimize exposure measurement error, understanding the mechanism captured by the model is another crucial aspect that may not always be straightforward due to the complex nature of machine learning methods, as well as the lack of unifying notions of variable importance. This is further complicated in air pollution modeling by the presence of spatial correlation. We tackle these challenges in two datasets: sulfur (S) from regulatory United States national PM2.5 sub-species data and ultrafine particles (UFP) from a new Seattle-area traffic-related air pollution dataset. Our key contribution is a leave-one-out approach for variable importance that leads to interpretable and comparable measures for a broad class of models with separable mean and covariance components. We illustrate our approach with several spatial machine learning models, and it clearly highlights the difference in model mechanisms, even for those producing similar predictions. We leverage insights from this variable importance measure to assess the relative utilities of two exposure models for S and UFP that have similar out-of-sample prediction accuracies but appear to draw on different types of spatial information to make predictions.

Terrain classification is an important problem for mobile robots operating in extreme environments as it can aid downstream tasks such as autonomous navigation and planning. While RGB cameras are widely used for terrain identification, vision-based methods can suffer due to poor lighting conditions and occlusions. In this paper, we propose the novel use of Ground Penetrating Radar (GPR) for terrain characterization for mobile robot platforms. Our approach leverages machine learning for surface terrain classification from GPR data. We collect a new dataset consisting of four different terrain types, and present qualitative and quantitative results. Our results demonstrate that classification networks can learn terrain categories from GPR signals. Additionally, we integrate our GPR-based classification approach into a multimodal semantic mapping framework to demonstrate a practical use case of GPR for surface terrain classification on mobile robots. Overall, this work extends the usability of GPR sensors deployed on robots to enable terrain classification in addition to GPR's existing scientific use cases., Comment: Accepted to the 2024 Conference on Computer Vision and Pattern Recognition (CVPR) Perception Beyond the Visible Spectrum Workshop

Diffusive transport has implications for the long-term status of underground storage of hydrogen (H$_2$) fuel and carbon dioxide (CO$_2$), technologies which are being pursued to mitigate climate change and advance the energy transition. Once injected underground, CO$_2$ and H$_2$ will exist in multiphase fluid-water-rock systems: being partially-soluble, injected fluids can flow through the porous rack in a connected plume, become disconnected and trapped as ganglia surrounded by groundwater within the storage rock pore space, and also dissolve and migrate through the aqueous phase. Recent analyses have focused on the concentration gradients induced by differing capillary pressure between fluid ganglia which can drive diffusive transport ("Ostwald ripening"). However, studies have neglected or excessively simplified important factors; namely: the non-ideality of gases under geologic conditions, the opposing equilibrium state of dissolved CO$_2$ and H$_2$ driven by the partial molar density of dissolved solutes, and entropic and thermodiffusive effects resulting from geothermal gradients. We conduct an analysis from thermodynamic first principles and use this to provide numerical estimates at conditions relevant to underground storage reservoirs. We show that entropic contributions to the free energy are so significant as to cause a reversal in the direction of diffusive transport in systems with geothermal gradients. For CO$_2$, even geothermal gradients less than 10 C/km induce downwards diffusion at depths relevant to storage. Diffusive transport of H$_2$ is less affected, but still reverses direction under typical gradients. Contrary to previous studies, we find that in diffusion and convection will likely work in concert - both driving CO$_2$ downwards, and both driving H$_2$ upwards - for conditions representative of their respective storage reservoirs., Comment: 16 pages, 8 figures. Revision 2

In Precision Agriculture, the utilization of management zones (MZs) that take into account within-field variability facilitates effective fertilizer management. This approach enables the optimization of nitrogen (N) rates to maximize crop yield production and enhance agronomic use efficiency. However, existing works often neglect the consideration of responsivity to fertilizer as a factor influencing MZ determination. In response to this gap, we present a MZ clustering method based on fertilizer responsivity. We build upon the statement that the responsivity of a given site to the fertilizer rate is described by the shape of its corresponding N fertilizer-yield response (N-response) curve. Thus, we generate N-response curves for all sites within the field using a convolutional neural network (CNN). The shape of the approximated N-response curves is then characterized using functional principal component analysis. Subsequently, a counterfactual explanation (CFE) method is applied to discern the impact of various variables on MZ membership. The genetic algorithm-based CFE solves a multi-objective optimization problem and aims to identify the minimum combination of features needed to alter a site's cluster assignment. Results from two yield prediction datasets indicate that the features with the greatest influence on MZ membership are associated with terrain characteristics that either facilitate or impede fertilizer runoff, such as terrain slope or topographic aspect., Comment: Accepted to appear in the International Joint Conference on Neural Networks 2024

We present the Citizen Science program Active Asteroids and describe discoveries stemming from our ongoing project. Our NASA Partner program is hosted on the Zooniverse online platform and launched on 2021 August 31, with the goal of engaging the community in the search for active asteroids -- asteroids with comet-like tails or comae. We also set out to identify other unusual active solar system objects, such as active Centaurs, active quasi-Hilda asteroids, and Jupiter-family comets (JFCs). Active objects are rare in large part because they are difficult to identify, so we ask volunteers to assist us in searching for active bodies in our collection of millions of images of known minor planets. We produced these cutout images with our project pipeline that makes use of publicly available Dark Energy Camera (DECam) data. Since the project launch, roughly 8,300 volunteers have scrutinized some 430,000 images to great effect, which we describe in this work. In total we have identified previously unknown activity on 15 asteroids, plus one Centaur, that were thought to be asteroidal (i.e., inactive). Of the asteroids, we classify four as active quasi-Hilda asteroids, seven as JFCs, and four as active asteroids, consisting of one Main-belt comet (MBC) and three MBC candidates. We also include our findings concerning known active objects that our program facilitated, an unanticipated avenue of scientific discovery. These include discovering activity occurring during an orbital epoch for which objects were not known to be active, and the reclassification of objects based on our dynamical analyses., Comment: 35 pages, 5 figures, 3 tables

Following the Pluto fly-by of the New Horizons spacecraft, the mission provided a unique opportunity to explore the Kuiper Belt in-situ. The possibility existed to fly-by a Kuiper Belt object (KBO) as well as to observe additional objects at distances closer than are feasible from earth-orbit facilities. However, at the time of launch no KBOs were known about that were accessible by the spacecraft. In this paper we present the results of 10 years of observations and three uniquely dedicated efforts -- two ground-based using the Subaru Suprime Camera, the Magellan MegaCam and IMACS Cameras, and one with the Hubble Space Telescope -- to find such KBOs for study. In this paper we overview the search criteria and strategies employed in our work and detail the analysis efforts to locate and track faint objects in the galactic plane. We also present a summary of all of the KBOs that were discovered as part of our efforts and how spacecraft targetability was assessed, including a detailed description of our astrometric analysis which included development of an extensive secondary calibration network. Overall, these efforts resulted in the discovery of 89 KBOs including 11 which became objects for distant observation by New Horizons and (486958) Arrokoth which became the first post-Pluto fly-by destination., Comment: Accepted to PSJ. 40 pages, 10 figures, 10 tables

The task of multimedia geolocation is becoming an increasingly essential component of the digital forensics toolkit to effectively combat human trafficking, child sexual exploitation, and other illegal acts. Typically, metadata-based geolocation information is stripped when multimedia content is shared via instant messaging and social media. The intricacy of geolocating, geotagging, or finding geographical clues in this content is often overly burdensome for investigators. Recent research has shown that contemporary advancements in artificial intelligence, specifically computer vision and deep learning, show significant promise towards expediting the multimedia geolocation task. This systematic literature review thoroughly examines the state-of-the-art leveraging computer vision techniques for multimedia geolocation and assesses their potential to expedite human trafficking investigation. This includes a comprehensive overview of the application of computer vision-based approaches to multimedia geolocation, identifies their applicability in combating human trafficking, and highlights the potential implications of enhanced multimedia geolocation for prosecuting human trafficking. 123 articles inform this systematic literature review. The findings suggest numerous potential paths for future impactful research on the subject.

We design a motion planning algorithm to coordinate the movements of two robots along a figure eight track, in such a way that no collisions occur. We use a topological approach to robot motion planning that relates instabilities in motion planning algorithms to topological features of configuration spaces. The topological complexity of a configuration space is an invariant that measures the complexity of motion planning algorithms. We show that the topological complexity of our problem is 3 and construct an explicit algorithm with three continuous instructions., Comment: 25 pages, 45 figures, First published in PUMP Journal of Undergraduate Research. This research paper was completed under the supervision of Prof. Hellen Colman at Wilbur Wright College

Non-destructive nano-imaging of the internal structure of solid matter is only feasible using hard X-rays due to their high penetration. The highest resolution images are achieved at synchrotron radiation sources (SRF), offering superior spectral brightness and enabling methods such as X-ray ptychography delivering single-digit nm resolution. However the resolution or field of view is ultimately constrained by the available coherent flux. To address this, the beam's incoherent fraction can be exploited using multiple parallel beams in an approach known as X-ray multibeam ptychography (MBP). This expands the domain of X-ray ptychography to larger samples or more rapid measurements. Both qualities favor the study of complex composite or functional samples, such as catalysts, energy materials, or electronic devices. The challenges of performing ptychography at high energy and with many parallel beams must be overcome to extract the full advantages for extended samples while minimizing beam attenuation. Here, we report the application of MBP with up to 12 beams and at photon energies of 13 and 20 keV. We demonstrate performance for various samples: a Siemens star test pattern, a porous Ni/\ce{Al2O3} catalyst, a microchip, and gold nano-crystal clusters, exceeding the measurement limits of conventional hard X-ray ptychography without compromising image quality.

The 5:3 and 7:4 mean motion resonances of Neptune are at 42.3 and 43.7 au, respectively, and overlap with objects in the Classical trans-neptunian belt (Kuiper belt). We report the complete/partial lightcurves of 13 and 14 trans-Neptunian objects (TNOs) in the 5:3 and 7:4 resonances, respectively. We report a most likely contact binary in the 7:4 resonance, 2013 FR$_{28}$, with a periodicity of 13.97$\pm$0.04 h and a lightcurve amplitude of 0.94$\pm$0.02 mag. With a V-/U-shaped lightcurve, 2013 FR$_{28}$ has one of the largest well-sampled TNO amplitude observed with ground-based observations, comparable to the well-determined contact binary 2001 QG$_{298}$. 2013 FR$_{28}$ has a mass ratio q$\sim$1 with a density $\rho$$\sim$1 g cm$^{-3}$. We find several objects with large amplitudes and classify 2004 SC$_{60}$, 2006 CJ$_{69}$, and 2013 BN$_{82}$ as likely contact binaries, and 2001 QF$_{331}$, 2003 YW$_{179}$, and 2015 FP$_{345}$ as likely elongated objects. We observe the 17:9 resonant or classical object 2003 SP$_{317}$ that we classify as a likely contact binary. A lower estimate of 10-50% and 20-55% for the fraction of (nearly) equal-sized contact binaries is calculated in the 5:3 and 7:4 resonances, respectively. Surface colors of 2004 SC$_{60}$, 2013 BN$_{82}$, 2014 OL$_{394}$, and 2015 FP$_{345}$ have been obtained. Including these colors with ones from the literature reveals that elongated objects and contact binaries share the same ultra-red surface color, except Manw\"e-Thorondor and 2004 SC$_{60}$. Not only are the colors of the 7:4 and 5:3 TNOs similar to the Cold Classicals, but we demonstrate that the rotational properties of the 5:3 and 7:4 resonants are similar to those of the Cold Classicals, inferring a clear link between these sub-populations., Comment: Accepted for publication in Planetary Science Journal (PSJ)

Open-source benchmark datasets have been a critical component for advancing machine learning for robot perception in terrestrial applications. Benchmark datasets enable the widespread development of state-of-the-art machine learning methods, which require large datasets for training, validation, and thorough comparison to competing approaches. Underwater environments impose several operational challenges that hinder efforts to collect large benchmark datasets for marine robot perception. Furthermore, a low abundance of targets of interest relative to the size of the search space leads to increased time and cost required to collect useful datasets for a specific task. As a result, there is limited availability of labeled benchmark datasets for underwater applications. We present the AI4Shipwrecks dataset, which consists of 28 distinct shipwrecks totaling 286 high-resolution labeled side scan sonar images to advance the state-of-the-art in autonomous sonar image understanding. We leverage the unique abundance of targets in Thunder Bay National Marine Sanctuary in Lake Huron, MI, to collect and compile a sonar imagery benchmark dataset through surveys with an autonomous underwater vehicle (AUV). We consulted with expert marine archaeologists for the labeling of robotically gathered data. We then leverage this dataset to perform benchmark experiments for comparison of state-of-the-art supervised segmentation methods, and we present insights on opportunities and open challenges for the field. The dataset and benchmarking tools will be released as an open-source benchmark dataset to spur innovation in machine learning for Great Lakes and ocean exploration. The dataset and accompanying software are available at https://umfieldrobotics.github.io/ai4shipwrecks/., Comment: Project website link: https://umfieldrobotics.github.io/ai4shipwrecks/

High entropy alloys (HEAs) have attracted substantial interest in recent years. Thus far, most investigations have focused on their applications as structural materials rather than functional materials. In this paper, we show that FeMnNiAlCr HEAs can potentially be applied as both a structural and functional material for high-efficiency concentrated solar thermal power (CSP) systems working at >700 degrees C. The HEA itself would be used in high-temperature tubing to carry working fluids, while its surface oxide would act as a high-efficiency solar thermal absorber. These HEAs have demonstrated yield strengths 2-3x greater than that of stainless steel at 700 degrees C and a creep lifetime >800 h at 700 degrees C under a typical CSP tubing mechanical load of 35 MPa. Their Mn-rich surface oxides maintain a high optical-to-thermal conversion efficiency of ~87% under 1000x solar concentration for 20 simulated day-night thermal cycles between 750 degrees C and environmental temperature. These HEAs have also sustained immersion in unpurified bromide molten salts for 14 days at 750{\deg}C with <2% weight loss, in contrast to 70% weight loss from a 316 stainless steel reference. The simultaneous achievement of promising mechanical, optical, and thermochemical properties in this FeMnNiAlCr system opens the door to new applications of HEAs in solar energy harvesting.

Conducting a systematic literature review and comprehensive analysis, this paper surveys all 135 peer-reviewed articles published at the Digital Forensics Research Conference Europe (DFRWS EU) spanning the decade since its inaugural running (2014-2023). This comprehensive study of DFRWS EU articles encompasses sub-disciplines such as digital forensic science, device forensics, techniques and fundamentals, artefact forensics, multimedia forensics, memory forensics, and network forensics. Quantitative analysis of the articles' co-authorships, geographical spread and citation metrics are outlined. The analysis presented offers insights into the evolution of digital forensic research efforts over these ten years and informs some identified future research directions.

Developing accurate computational models of wildfire dynamics is increasingly important due to the substantial and expanding negative impacts of wildfire events on human health, infrastructure, and the environment. Wildfire spread and emissions depend on a number of factors, including fuel type, environmental conditions (moisture, wind speed, etc.) and terrain/location. However, there currently exist only a few experimental facilities that enable testing of the interplay of these factors at length scales <1 m with carefully controlled and characterized boundary conditions and advanced diagnostics. Experiments performed at such facilities are required for informing and validating computational models. Here we present the design and characterization of a novel tilting wind tunnel (the 'WindCline') for studying wildfire dynamics. The WindCline is unique in that the entire tunnel platform is constructed to pivot around a central axis, which enables sloping of the entire system without compromising the quality of the flow properties. In addition, this facility has a configurable design for the test section and diffuser to accommodate a suite of advanced diagnostics to aid in the characterization of 1) the parameters needed to establish boundary conditions and 2) flame properties and dynamics. The WindCline thus allows for measurement and control of several critical wildfire variables and boundary conditions, especially at the small length scales important to the development of high fidelity computational simulations (10 - 100 cm). Computational modeling frameworks developed and validated under these controlled conditions can expand understanding of fundamental combustion processes, promoting greater confidence when leveraging these processes in complex combustion environments., Comment: 27 pages, 12 figures + 6 in supplemental material, submitted to Review of Scientific Instruments

Using novel approaches to dataset development, the Biasly dataset captures the nuance and subtlety of misogyny in ways that are unique within the literature. Built in collaboration with multi-disciplinary experts and annotators themselves, the dataset contains annotations of movie subtitles, capturing colloquial expressions of misogyny in North American film. The dataset can be used for a range of NLP tasks, including classification, severity score regression, and text generation for rewrites. In this paper, we discuss the methodology used, analyze the annotations obtained, and provide baselines using common NLP algorithms in the context of misogyny detection and mitigation. We hope this work will promote AI for social good in NLP for bias detection, explanation, and removal., Comment: 8 pages, 2 figures

We have discovered two epochs of activity on quasi-Hilda 2009 DQ118. Small bodies that display comet-like activity, such as active asteroids and active quasi-Hildas, are important for understanding the distribution of water and other volatiles throughout the solar system. Through our NASA Partner Citizen Science project, Active Asteroids, volunteers classified archival images of 2009 DQ118 as displaying comet-like activity. By performing an in-depth archival image search, we found over 20 images from UT 2016 March 8--9 with clear signs of a comet-like tail. We then carried out follow-up observations of 2009 DQ118 using the 3.5 m Astrophysical Research Consortium Telescope at Apache Point Observatory, Sunspot, New Mexico, USA and the 6.5 m Magellan Baade Telescope at Las Campanas Observatory, Chile. These images revealed a second epoch of activity associated with the UT 2023 April 22 perihelion passage of 2009 DQ118. We performed photometric analysis of the tail and find that it had a similar apparent length and surface brightness during both epochs. We also explored the orbital history and future of 2009 DQ118 through dynamical simulations. These simulations show that 2009 DQ118 is currently a quasi-Hilda and that it frequently experiences close encounters with Jupiter. We find that 2009 DQ118 is currently on the boundary between asteroidal and cometary orbits. Additionally, it has likely been a Jupiter family comet or Centaur for much of the past 10 kyr and will be in these same regions for the majority of the next 10 kyr. Since both detected epochs of activity occurred near perihelion, the observed activity is consistent with sublimation of volatile ices. 2009 DQ118 is currently observable until ~mid-October 2023. Further observations would help to characterize the observed activity., Comment: 10 pages, 3 figures

We present the DECam Ecliptic Exploration Project (DEEP) survey strategy including observing cadence for orbit determination, exposure times, field pointings and filter choices. The overall goal of the survey is to discover and characterize the orbits of a few thousand Trans-Neptunian Objects (TNOs) using the Dark Energy Camera (DECam) on the Cerro Tololo Inter-American Observatory (CTIO) Blanco 4 meter telescope. The experiment is designed to collect a very deep series of exposures totaling a few hours on sky for each of several 2.7 square degree DECam fields-of-view to achieve a magnitude of about 26.2 using a wide VR filter which encompasses both the V and R bandpasses. In the first year, several nights were combined to achieve a sky area of about 34 square degrees. In subsequent years, the fields have been re-visited to allow TNOs to be tracked for orbit determination. When complete, DEEP will be the largest survey of the outer solar system ever undertaken in terms of newly discovered object numbers, and the most prolific at producing multi-year orbital information for the population of minor planets beyond Neptune at 30 au., Comment: 29 pages, 4 figures and 4 tables

We present the first set of trans-Neptunian objects (TNOs) observed on multiple nights in data taken from the DECam Ecliptic Exploration Project (DEEP). Of these 110 TNOs, 105 do not coincide with previously known TNOs and appear to be new discoveries. Each individual detection for our objects resulted from a digital tracking search at TNO rates of motion, using two to four hour exposure sets, and the detections were subsequently linked across multiple observing seasons. This procedure allows us to find objects with magnitudes $m_{VR} \approx 26$. The object discovery processing also included a comprehensive population of objects injected into the images, with a recovery and linking rate of at least $94\%$. The final orbits were obtained using a specialized orbit fitting procedure that accounts for the positional errors derived from the digital tracking procedure. Our results include robust orbits and magnitudes for classical TNOs with absolute magnitudes $H \sim 10$, as well as a dynamically detached object found at 76 au (semi-major axis $a\approx 77 \, \mathrm{au}$). We find a disagreement between our population of classical TNOs and the CFEPS-L7 three component model for the Kuiper belt., Comment: Accepted to AJ, companion paper do DEEP III. Objects will be released in the journal version (or contacting the authors)

We present a detailed study of the observational biases of the DECam Ecliptic Exploration Project's (DEEP) B1 data release and survey simulation software that enables direct statistical comparisons between models and our data. We inject a synthetic population of objects into the images, and then subsequently recover them in the same processing as our real detections. This enables us to characterize the survey's completeness as a function of apparent magnitudes and on-sky rates of motion. We study the statistically optimal functional form for the magnitude, and develop a methodology that can estimate the magnitude and rate efficiencies for all survey's pointing groups simultaneously. We have determined that our peak completeness is on average 80\% in each pointing group, and our magnitude drops to $25\%$ of this value at $m_{25} = 26.22$. We describe the freely available survey simulation software and its methodology. We conclude by using it to infer that our effective search area for objects at 40 au is $14.8\deg^2$, and that our lack of dynamically cold distant objects means that there at most $8\times 10^3$ objects with $60 < a < 80$ au and absolute magnitudes $H \leq 8$., Comment: Accepted to AJ, companion paper to DEEP VI

Autonomous terrain classification is an important problem in planetary navigation, whether the goal is to identify scientific sites of interest or to traverse treacherous areas safely. Past Martian rovers have relied on human operators to manually identify a navigable path from transmitted imagery. Our goals on Mars in the next few decades will eventually require rovers that can autonomously move farther, faster, and through more dangerous landscapes--demonstrating a need for improved terrain classification for traversability. Autonomous navigation through extreme environments will enable the search for water on the Moon and Mars as well as preparations for human habitats. Advancements in machine learning techniques have demonstrated potential to improve terrain classification capabilities for ground vehicles on Earth. However, classification results for space applications are limited by the availability of training data suitable for supervised learning methods. This paper contributes an open source automatic data processing pipeline that uses camera geometry to co-locate Curiosity and Perseverance Mastcam image products with Mars overhead maps via ray projection over a terrain model. In future work, this automated data processing pipeline will be leveraged for development of machine learning methods for terrain classification., Comment: Presented as a poster at IAC 2023

We present the methods and results from the discovery and photometric measurement of 26 bright (VR $>$ 24 trans-Neptunian objects (TNOs) during the first year (2019-20) of the DECam Ecliptic Exploration Project (DEEP). The DEEP survey is an observational TNO survey with wide sky coverage, high sensitivity, and a fast photometric cadence. We apply a computer vision technique known as a progressive probabilistic Hough transform to identify linearly-moving transient sources within DEEP photometric catalogs. After subsequent visual vetting, we provide a photometric and astrometric catalog of our TNOs. By modeling the partial lightcurve amplitude distribution of the DEEP TNOs using Monte Carlo techniques, we find our data to be most consistent with an average TNO axis ratio b/a $<$ 0.5, implying a population dominated by non-spherical objects. Based on ellipsoidal gravitational stability arguments, we find our data to be consistent with a TNO population containing a high fraction of contact binaries or other extremely non-spherical objects. We also discuss our data as evidence that the expected binarity fraction of TNOs may be size-dependent.

We present here the DECam Ecliptic Exploration Project (DEEP), a three year NOAO/NOIRLab Survey that was allocated 46.5 nights to discover and measure the properties of thousands of trans-Neptunian objects (TNOs) to magnitudes as faint as VR~27, corresponding to sizes as small as 20 km diameter. In this paper we present the science goals of this project, the experimental design of our survey, and a technical demonstration of our approach. The core of our project is "digital tracking," in which all collected images are combined at a range of motion vectors to detect unknown TNOs that are fainter than the single exposure depth of VR~23 mag. Through this approach we reach a depth that is approximately 2.5 magnitudes fainter than the standard LSST "wide fast deep" nominal survey depth of 24.5 mag. DEEP will more than double the number of known TNOs with observational arcs of 24 hours or more, and increase by a factor of 10 or more the number of known small (<50 km) TNOs. We also describe our ancillary science goals, including measuring the mean shape distribution of very small main belt asteroids, and briefly outline a set of forthcoming papers that present further aspects of and preliminary results from the DEEP program., Comment: AJ, in press. First in a series of papers