arXiv subjects

Richard Cloete

Publications and source records attributed to Richard Cloete.

16 recordsLinked to original sources

arXiv2026

A High-Likelihood Polar Interstellar Meteor Candidate

We report a newly identified polar interstellar meteor candidate, labeled polarIM, detected on 2026-04-01 02:13:14 UTC at latitude $-41.9^\circ$, longitude $-54.7^\circ$, and altitude 90.5 km over the South Atlantic Ocean, east of Argentina. We transform the reported Earth-fixed velocity vector $(+3.6,\,-34.6,\,+59.8)~\mathrm{km\,s^{-1}}$ to an inertial geocentric state, remove Earth's gravitational acceleration with a two-body hyperbolic model, add the JPL Horizons heliocentric velocity of Earth, and test the resulting heliocentric orbit against solar escape speed. The final velocity component in the polar ($z$) direction of $+47.09~\mathrm{km\,s^{-1}}$ exceeds by itself the local solar escape speed $v_{\rm esc,\odot}=42.14~\mathrm{km\,s^{-1}}$. The full heliocentric speed is $v_{\rm hel}=51.73~\mathrm{km\,s^{-1}}$, corresponding to positive heliocentric specific energy $\varepsilon_\odot=+450.1~\mathrm{km^2\,s^{-2}}$, heliocentric excess speed $v_{\infty,\odot}=30.00~\mathrm{km\,s^{-1}}$, and a two-body inclination $i=89.4^\circ$. We propagate measurement uncertainty through 1,000,000 Monte Carlo realizations using the empirical post-2018 low-discrepancy CNEOS error model of Pena-Asensio et al. (2025), with $σ_v=0.55~\mathrm{km\,s^{-1}}$, $σ_{\rm RA}=1.35^\circ$, and $σ_{\rm Dec}=0.84^\circ$. No realization yields a bound heliocentric orbit, giving a statistical confidence on the interstellar fraction of $>99.9997\%$. The Monte Carlo margin above escape is $\langleΔ\rangle=9.60\pm0.75~\mathrm{km\,s^{-1}}$, corresponding to a $12.82σ$ margin-to-scatter ratio under the adopted perturbation model. The result identifies polarIM as the highest-margin post-2018 candidate in the CNEOS catalog.

astro-ph.EP↗

arXiv2026

Two Robust Interstellar Meteor Candidates in the Post-2018 CNEOS Fireball Database

We report the identification of two previously unrecognized interstellar meteor candidates in the NASA CNEOS fireball database. Using the empirically calibrated low-discrepancy uncertainty model of Peña-Asensio et al.\ (2025) for post-2018 CNEOS velocity accuracy (1$σ$: speed 0.55~km~s$^{-1}$, right ascension 1.35$^\circ$, declination 0.84$^\circ$), we transform CNEOS velocity vectors to heliocentric orbits and assess interstellar candidacy via $10^{6}$-draw Monte Carlo simulations. Two post-2018 events have heliocentric speeds robustly exceeding the Solar System escape speed. CNEOS-22 (2022-07-28; 6.0$^\circ$S, 86.9$^\circ$W; eastern tropical Pacific) has $v_{\rm hel}=46.98$~km~s$^{-1}$, exceeding escape by $Δ= 5.18 \pm 0.60$~km~s$^{-1}$ ($z_Δ=8.7σ$), with interstellar speed $v_{\infty,\odot}=21.5$~km~s$^{-1}$. CNEOS-25 (2025-02-12; 73.4$^\circ$N, 49.3$^\circ$E; Barents Sea) has $v_{\rm hel}=45.63$~km~s$^{-1}$, exceeding escape by $Δ= 3.22 \pm 0.58$~km~s$^{-1}$ ($z_Δ=5.5σ$), with $v_{\infty,\odot}=16.9$~km~s$^{-1}$. For both events, none of $10^{6}$ realizations yield a gravitationally bound orbit ($p_{\rm bound} < 3\times 10^{-6}$). The adopted error model would need to underestimate the true uncertainties by factors of 5--9 for either candidate to be marginally bound.

astro-ph.EP↗

arXiv2025

Upper Limit on the Non-Gravitational Acceleration and Lower Limits on the Nucleus Mass and Diameter of 3I/ATLAS

We use astrometric data on 3I/ATLAS compiled by the Minor Planet Center from May 15 to September 23, 2025, and derive an upper limit on any statistically significant deviation from the best-fit gravity-based trajectory. The residuals imply that the non-gravitational acceleration is smaller than $\sim 3 \times 10^{-10}\,{\rm au\,d^{-2}}$. Based on the total mass-loss rate and outflow speed inferred from JWST data on August 6, 2025, we derive lower limits on the mass and diameter of 3I/ATLAS of $3.3 \times 10^{16}\,{\rm g}$ and $5\,{\rm km}$, respectively.

astro-ph.EP↗

arXiv2025

Improving the discovery of near-Earth objects with machine-learning methods

We present a comprehensive analysis of the digest2 parameters for candidates of the Near-Earth Object Confirmation Page (NEOCP) that were reported between 2019 and 2024. Our study proposes methods for significantly reducing the inclusion of non-NEO objects on the NEOCP. Despite the substantial increase in near-Earth object (NEO) discoveries in recent years, only about half of the NEOCP candidates are ultimately confirmed as NEOs. Therefore, much observing time is spent following up on non-NEOs. Furthermore, approximately 11% of the candidates remain unconfirmed because the follow-up observations are insufficient. These are nearly 600 cases per year. To reduce false positives and minimize wasted resources on non-NEOs, we refine the posting criteria for NEOCP based on a detailed analysis of all digest2 scores. We investigated 30 distinct digest2 parameter categories for candidates that were confirmed as NEOs and non-NEOs. From this analysis, we derived a filtering mechanism based on selected digest2 parameters that were able to exclude 20% of the non-NEOs from the NEOCP while maintaining a minimal loss of true NEOs. We also investigated the application of four machine-learning (ML) techniques, that is, the gradient-boosting machine (GBM), the random forest (RF) classifier, the stochastic gradient descent (SGD) classifier, and neural networks (NN) to classify NEOCP candidates as NEOs or non-NEOs. Based on digest2 parameters as input, our ML models achieved a precision of approximately 95% in distinguishing between NEOs and non-NEOs. Results. Combining the digest2 parameter filter with an ML-based classification model, we demonstrate a significant reduction in non-NEOs on the NEOCP that exceeds 80%, while limiting the loss of NEO discovery tracklets to 5.5%. Importantly, we show that most follow-up tracklets of initially misclassified NEOs are later correctly identified as NEOs.

astro-ph.IM↗

arXiv2025

Is the "Dark Comet" 2005 VL1 the Venera 2 Spacecraft?

Recently, Seligman et. al. (2024) identified a population of near-Earth objects (NEOs) that exhibit statistically significant non-gravitational accelerations with no coma, and labeled them dark comets. Here, we show that one of these objects, 2005 VL1, was at closest approach to Earth in November 1965 when the Venera 2 spacecraft was launched to explore Venus. The observed H magnitude of 2005 VL1 is consistent with a high reflectance from the full surface of Venera 2 including its Solar panels. As known for Venera 2, 2005 VL1 arrived within a short distance from Venus in February 1966, a highly improbable coincidence (< 1%) for the orbital phase of a near-Earth object that does not target a close approach to Venus. Indeed, 2005 VL1's orbital parameters are very similar to the reported values for Venera 2. Given the area-to-mass ratio of Venera 2, we show that 2005 VL1's non-gravitational acceleration and negligible transverse acceleration match the values expected from Solar radiation pressure.

astro-ph.EP↗

arXiv2025

Commissioning An All-Sky Infrared Camera Array for Detection Of Airborne Objects

To date there is little publicly available scientific data on Unidentified Aerial Phenomena (UAP) whose properties and kinematics purportedly reside outside the performance envelope of known phenomena. To address this deficiency, the Galileo Project is designing, building, and commissioning a multi-modal ground-based observatory to continuously monitor the sky and conduct a rigorous long-term aerial census of all aerial phenomena, including natural and human-made. One of the key instruments is an all-sky infrared camera array using eight uncooled long-wave infrared FLIR Boson 640 cameras. Their calibration includes a novel extrinsic calibration method using airplane positions from Automatic Dependent Surveillance-Broadcast (ADS-B) data. We establish a first baseline for the system performance over five months of field operation, using a real-world dataset derived from ADS-B data, synthetic 3-D trajectories, and a hand-labelled real-world dataset. We report acceptance rates (e.g. viewable airplanes that are recorded) and detection efficiencies (e.g. recorded airplanes which are successfully detected) for a variety of weather conditions, range and aircraft size. We reconstruct $\sim$500,000 trajectories of aerial objects from this commissioning period. A toy outlier search focused on large sinuosity of the 2-D reconstructed trajectories flags about 16% of trajectories as outliers. After manual review, 144 trajectories remain ambiguous: they are likely mundane objects but cannot be elucidated at this stage of development without distance and kinematics estimation or other sensor modalities. Our observed count of ambiguous outliers combined with systematic uncertainties yields an upper limit of 18,271 outliers count for the five-month interval at a 95% confidence level. This likelihood-based method to evaluate significance is applicable to all of our future outlier searches.

astro-ph.IM↗

arXiv2024

Machine Learning Methods for Automated Interstellar Object Classification with LSST

The Legacy Survey of Space and Time, to be conducted with the Vera C. Rubin Observatory, is poised to revolutionize our understanding of the Solar System by providing an unprecedented wealth of data on various objects, including the elusive interstellar objects (ISOs). Detecting and classifying ISOs is crucial for studying the composition and diversity of materials from other planetary systems. However, the rarity and brief observation windows of ISOs, coupled with the vast quantities of data to be generated by LSST, create significant challenges for their identification and classification. This study aims to address these challenges by exploring the application of machine learning algorithms to the automated classification of ISO tracklets in simulated LSST data. We employed various machine learning algorithms, including random forests (RFs), stochastic gradient descent (SGD), gradient boosting machines (GBMs), and neural networks (NNs), to classify ISO tracklets in simulated LSST data. We demonstrate that GBM and RF algorithms outperform SGD and NN algorithms in accurately distinguishing ISOs from other Solar System objects. RF analysis shows that many derived Digest2 values are more important than direct observables in classifying ISOs from the LSST tracklets. The GBM model achieves the highest precision, recall, and F1 score, with values of 0.9987, 0.9986, and 0.9987, respectively. These findings lay the foundation for the development of an efficient and robust automated system for ISO discovery using LSST data, paving the way for a deeper understanding of the materials and processes that shape planetary systems beyond our own. The integration of our proposed machine learning approach into the LSST data processing pipeline will optimize the survey's potential for identifying these rare and valuable objects, enabling timely follow-up observations and further characterization.

astro-ph.EP↗

arXiv2023

Improvement of digest2 NEO Classification Code -- utilizing the Astrometry Data Exchange Standard

We describe enhancements to the digest2 software, a short-arc orbit classifier for heliocentric orbits. Digest2 is primarily used by the Near-Earth Object (NEO) community to flag newly discovered objects for a immediate follow-up and has been a part of NEO discovery process for more than 15 years. We have updated the solar system population model used to weight the digest2 score according to the 2023 catalog of known solar system orbits and extended the list of mean uncertainties for 140 observatory codes. Moreover, we have added Astrometry Data Exchange Standard (ADES) input format support to digest2, which provides additional information for the astrometry, such as positional uncertainties for each detection. The digest2 code was also extended to read the roving observer astrometric format as well as the ability to compute a new parameter from the provided astrometric uncertainties ($RMS'$) that can serve as an indicator of in-tracklet curvature when compared with tracklet's great-circle fit RMS. Comparison with the previous version of digest2 confirmed the improvement in accuracy of NEO identification and found that using ADES XML input significantly reduces the computation time of the digest2.

astro-ph.EP↗

arXiv2023

Discovery of Spherules of Likely Extrasolar Composition in the Pacific Ocean Site of the CNEOS 2014-01-08 (IM1) Bolide

We have conducted an extensive towed-magnetic-sled survey during the period 14-28 June, 2023, over the seafloor centered around the calculated path of the bolide CNEOS 2014-01-08 (IM1) about 85 km north of Manus Island, Papua New Guinea. We found about 700 spherules of diameter 0.05-1.3 millimeters in our samples, of which 57 were analyzed so far. The spherules were significantly concentrated along the expected meteor path. Mass spectrometry of 47 spherules near the high-yield regions along IM1's path reveals a distinct extra-solar abundance pattern for 5 of them, while background spherules have abundances consistent with a solar system origin. The unique spherules show an excess of Be, La and U, by up to three orders of magnitude relative to the solar system standard of CI chondrites. These "BeLaU"-type spherules, never seen before, also have very low refractory siderophile elements such as Re. Volatile elements, such as Mn, Zn, Pb, are depleted as expected from evaporation losses during a meteor's airburst. In addition, the mass-dependent variations in $^{57}$Fe/$^{54}$Fe and $^{56}$Fe/$^{54}$Fe are also consistent with evaporative loss of the light isotopes during the spherules' travel in the atmosphere. The "BeLaU" abundance pattern is not found in control regions outside of IM1's path and does not match commonly manufactured alloys or natural meteorites in the solar system. This evidence points towards an association of "BeLaU"-type spherules with IM1, supporting its interstellar origin independently of the high velocity and unusual material strength implied from the CNEOS data. We suggest that the "BeLaU" abundance pattern could have originated from a highly differentiated magma ocean of a planet with an iron core outside the solar system or from more exotic sources.

astro-ph.EP↗

arXiv2023

SkyWatch: A Passive Multistatic Radar Network for the Measurement of Object Position and Velocity

(Abridged) Quantitative three-dimensional (3D) position and velocity estimates obtained by passive radar will assist the Galileo Project in the detection and classification of aerial objects by providing critical measurements of range, location, and kinematics. These parameters will be combined with those derived from the Project{\textquoteright}s suite of electromagnetic sensors and used to separate known aerial objects from those exhibiting anomalous kinematics. SkyWatch, a passive multistatic radar system based on commercial broadcast FM radio transmitters of opportunity, is a network of receivers spaced at geographical scales that enables estimation of the 3D position and velocity time series of objects at altitudes up to 80km, horizontal distances up to 150km, and at velocities to {\textpm}2{\textpm}2km/s ({\textpm}6{\textpm}6Mach). The receivers are designed to collect useful data in a variety of environments varying by terrain, transmitter power, relative transmitter distance, adjacent channel strength, etc. In some cases, the direct signal from the transmitter may be large enough to be used as the reference with which the echoes are correlated. In other cases, the direct signal may be weak or absent, in which case a reference is communicated to the receiver from another network node via the internet for echo correlation. Various techniques are discussed specific to the two modes of operation and a hybrid mode. Delay and Doppler data are sent via internet to a central server where triangulation is used to deduce time series of 3D positions and velocities. A multiple receiver (multistatic) radar experiment is undergoing Phase 1 testing, with several receivers placed at various distances around the Harvard{\textendash}Smithsonian Center for Astrophysics (CfA), to validate full 3D position and velocity recovery.

astro-ph.IM↗

arXiv2023

The Scientific Investigation of Unidentified Aerial Phenomena (UAP) Using Multimodal Ground-Based Observatories

(Abridged) Unidentified Aerial Phenomena (UAP) have resisted explanation and have received little formal scientific attention for 75 years. A primary objective of the Galileo Project is to build an integrated software and instrumentation system designed to conduct a multimodal census of aerial phenomena and to recognize anomalies. Here we present key motivations for the study of UAP and address historical objections to this research. We describe an approach for highlighting outlier events in the high-dimensional parameter space of our census measurements. We provide a detailed roadmap for deciding measurement requirements, as well as a science traceability matrix (STM) for connecting sought-after physical parameters to observables and instrument requirements. We also discuss potential strategies for deciding where to locate instruments for development, testing, and final deployment. Our instrument package is multimodal and multispectral, consisting of (1) wide-field cameras in multiple bands for targeting and tracking of aerial objects and deriving their positions and kinematics using triangulation; (2) narrow-field instruments including cameras for characterizing morphology, spectra, polarimetry, and photometry; (3) passive multistatic arrays of antennas and receivers for radar-derived range and kinematics; (4) radio spectrum analyzers to measure radio and microwave emissions; (5) microphones for sampling acoustic emissions in the infrasonic through ultrasonic frequency bands; and (6) environmental sensors for characterizing ambient conditions (temperature, pressure, humidity, and wind velocity), as well as quasistatic electric and magnetic fields, and energetic particles. The use of multispectral instruments and multiple sensor modalities will help to ensure that artifacts are recognized and that true detections are corroborated and verifiable.

astro-ph.IM↗

arXiv2023

A Hardware and Software Platform for Aerial Object Localization

To date, there are little reliable data on the position, velocity and acceleration characteristics of Unidentified Aerial Phenomena (UAP). The dual hardware and software system described in this document provides a means to address this gap. We describe a weatherized multi-camera system which can capture images in the visible, infrared and near infrared wavelengths. We then describe the software we will use to calibrate the cameras and to robustly localize objects-of-interest in three dimensions. We show how object localizations captured over time will be used to compute the velocity and acceleration of airborne objects.

astro-ph.IM↗

arXiv2023

Multi-Band Acoustic Monitoring of Aerial Signatures

The Galileo Project's acoustic monitoring, omni-directional system (AMOS) aids in the detection and characterization of aerial phenomena. It uses a multi-band microphone suite spanning infrasonic to ultrasonic frequencies, providing an independent signal modality for validation and characterization of detected objects. The system utilizes infrasonic, audible, and ultrasonic systems to cover a wide range of sounds produced by both natural and man-made aerial phenomena. Sound signals from aerial objects can be captured given certain conditions, such as when the sound level is above ambient noise and isn't excessively distorted by its transmission path. Findings suggest that audible sources can be detected up to 1 km away, infrasonic sources can be detected over much longer distances, and ultrasonic at shorter ones. Initial data collected from aircraft recordings with spectral analysis will help develop algorithms and software for quick identification of known aircraft. Future work will involve multi-sensor arrays for sound localization, larger data sets analysis, and incorporation of machine learning and AI for detection and identification of more types of phenomena in all frequency bands.

astro-ph.IM↗

arXiv2022

Physical Considerations for an Intercept Mission to a 1I/'Oumuamua-like Interstellar Object

In this paper, we review some of the extant literature on the study of interstellar objects (ISOs). With the forthcoming Vera C. Rubin Telescope and Legacy Survey of Space and Time (LSST), we find that $0.38 - 84$ `Oumuamua-like interstellar objects are expected to be detected in the next 10 years, with 95\% confidence. The feasibility of a rendezvous trajectory has been demonstrated in previous work. In this paper, we investigate the requirements for a rendezvous mission with the primary objective of producing a resolved image of an interstellar object. We outline the rendezvous distances necessary as a function of resolution elements and object size. We expand upon current population synthesis models to account for the size dependency on the detection rates for reachable interstellar objects. We assess the trade-off between object diameter and occurrence rate, and conclude that objects with the size range between a third of the size and the size of `Oumuamua will be optimal targets for an imaging rendezvous. We also discuss expectations for surface properties and spectral features of interstellar objects, as well as the benefits of various spacecraft storage locations.

astro-ph.EP↗

arXiv2020

Monitoring Misuse for Accountable 'Artificial Intelligence as a Service'

AI is increasingly being offered 'as a service' (AIaaS). This entails service providers offering customers access to pre-built AI models and services, for tasks such as object recognition, text translation, text-to-voice conversion, and facial recognition, to name a few. The offerings enable customers to easily integrate a range of powerful AI-driven capabilities into their applications. Customers access these models through the provider's APIs, sending particular data to which models are applied, the results of which returned. However, there are many situations in which the use of AI can be problematic. AIaaS services typically represent generic functionality, available 'at a click'. Providers may therefore, for reasons of reputation or responsibility, seek to ensure that the AIaaS services they offer are being used by customers for 'appropriate' purposes. This paper introduces and explores the concept whereby AIaaS providers uncover situations of possible service misuse by their customers. Illustrated through topical examples, we consider the technical usage patterns that could signal situations warranting scrutiny, and raise some of the legal and technical challenges of monitoring for misuse. In all, by introducing this concept, we indicate a potential area for further inquiry from a range of perspectives.

cs.CY↗

arXiv2019

Measuring and simulating latency in interactive remote rendering systems

Background: The computationally intensive task of real-time rendering can be offloaded to remote cloud systems. However, due to network latency, interactive remote rendering (IRR) introduces the challenge of interaction latency (IL), which is the time between an action and response to that action. Objectives: to model sources of latency, measure it in a real-world network and to use this understanding to simulate latency so that we have a controlled platform for experimental work in latency management. Method: we present a seven-parameter model of latency for a typical IRR system; we describe new, minimally intrusive software methods for measuring latency in a 3D graphics environment and create a novel latency simulator tool in software. Results: We demonstrate our latency simulator is comparable to real-world behavior and confirm that real-world latency exceeds the interactive limit of 70ms over long distance connections. We also find that current approaches to measuring IL are not general enough for most situations and therefore propose a novel general-purpose solution. Conclusion: to ameliorate latency in IRR systems we need controllable simulation tools for experimentation. In addition to a new measurement technique, we propose a new approach that will be of interest to IRR researchers and developers when designing IL compensation techniques.

cs.NI↗