Related papers: Improving the discovery of near-Earth objects with…

Near Earth Asteroids (NEAs) and dead comets comprise the vast majority of the population of Near Earth Objects (NEOs) detected to date. Less is known of their physical properties than of the much larger population of main-belt asteroids.…

Although state-of-the-art object detection methods have shown compelling performance, models often are not robust to adversarial attacks and out-of-distribution data. We introduce a new dataset, Natural Adversarial Objects (NAO), to…

Near-Earth objects (NEOs) are excellent laboratories for testing processes that affect airless bodies, as well as informing us about Solar System history. Though most NEOs are nominally anhydrous because they formed inside the Solar System…

Near-Earth Objects (NEOs) are asteroids, comets and meteoroids in heliocentric orbits with perihelion below 1.3 au. Similarly to the population of the Main Asteroid Belt, NEOs are primordial bodies and their study can improve our…

Object detection remains as one of the most notorious open problems in computer vision. Despite large strides in accuracy in recent years, modern object detectors have started to saturate on popular benchmarks raising the question of how…

This report describes the methodology and results of an assessment study of the performance of the Large Synoptic Survey Telescope (LSST) in its planned efforts to detect and catalog near-Earth objects (NEOs). The baseline LSST survey…

Vetting of exoplanet candidates in transit surveys is a manual process, which suffers from a large number of false positives and a lack of consistency. Previous work has shown that Convolutional Neural Networks (CNN) provide an efficient…

Nongravitational accelerations in the absence of observed activity have recently been identified on NEOs, opening the question of the prevalence of anisotropic mass-loss in the near-Earth environment. Motivated by the necessity of…

The rapid expansion of exoplanet survey missions such as Kepler, TESS, and the upcoming PLATO mission has generated massive light-curve datasets that challenge traditional vetting pipelines. We introduce a hybrid deep-learning framework…

[Abriged] Astronomical Wide Field Imaging performed with new large format CCD detectors poses data reduction problems of unprecedented scale which are difficult to deal with traditional interactive tools. We present here NExt (Neural…

With manual searching processes, the rate at which scientists and astronomers discover exoplanets is slow because of inefficiencies that require an extensive time of laborious inspections. In fact, as of now there have been about only 5,000…

Increasing our knowledge of the orbits and compositions of Near-Earth Objects (NEOs) is important for a better understanding of the evolution of the Solar System and of life. The detection of serendipitous NEO appearances among the millions…

Wide Field Survey Telescope (WFST), with a powerful sky survey capability in the northern hemisphere, will play an important role in asteroid searching and monitoring. However, WFST is not a telescope dedicated to near-Earth asteroids…

Safe deployment of deep neural networks in high-stake real-world applications requires theoretically sound uncertainty quantification. Conformal prediction (CP) is a principled framework for uncertainty quantification of deep models in the…

We present a pipeline for searching for trans-Neptunian objects (TNOs) using data from the TESS mission, that includes a novel optimization-based framework for subtracting the effects of scattered light and pointing jitter. The background…

The existing open cluster membership determination algorithms are either prior dependent on some known parameters of clusters or are not automatable to large samples of clusters. In this paper, we present, ML-MOC, a new machine learning…

We present new results of the observing program which is a part of the NEOROCKS project aimed to improve knowledge on physical properties of near-Earth Objects (NEOs) for planetary defense. Photometric observations were performed using the…

Automated asteroid detection routines set requirements on the number of detections, signal-to-noise ratio, and the linearity of the expected motion in order to balance completeness, reliability, and time delay after data acquisition when…

Robust impact monitoring of near-Earth objects is an essential task of planetary defense. Current systems such as NASA's Sentry-II, the University of Pisa's CLOMON2, and ESA's Aegis have been highly successful, but independent approaches…

Thermal infrared measurements of near-Earth objects provide critical data for constraining their physical properties such as size. The NEOWISE mission has been conducting an all-sky infrared survey to gather such data and improve our…