Related papers: The digest2 NEO Classification Code

We use the Robo-AO survey of Kepler planetary candidate host stars, the largest adaptive optics survey yet performed, to measure the recovery rate of close stellar binaries in Gaia DR2. We find that Gaia recovers binaries down to 1" at…

The rapid deployment of Low Earth Orbit (LEO) satellite constellations has enabled the emergence of in-orbit edge computing and data centers-interconnected satellites equipped with onboard computing capabilities and high-speed…

We estimate the total population of near-Earth objects (NEOs) in the Solar System, using an extensive, `Solar System to pixels' fake-asteroid simulation to debias detections of real NEOs by the ATLAS survey. Down to absolute magnitudes…

Context. To date, stellar activity is one of the main limitations in detecting small exoplanets via transit photometry. Since this activity is enhanced in young stars, traditional filtering algorithms may severely under-perform in detecting…

We introduce a continuous depth version of the Residual Network (ResNet) called Neural ordinary differential equations (NODE) for the purpose of galaxy morphology classification. We carry out a classification of galaxy images from the…

Near Earth Objects (NEOs) are a transient population of small bodies with orbits near or in the terrestrial planet region. They represent a mid-stage in the dynamical cycle of asteroids and comets, which starts with their removal from the…

We have performed a detailed survey simulation of the LSST performance with regards to near-Earth objects (NEOs) using the project's current baseline cadence. The survey shows that if the project is able to reliably generate linked sets of…

Using the Wide Field Camera for the United Kingdom Infrared Telescope, we measure the near-infrared colors of near-Earth objects (NEOs) in order to put constraints on their taxonomic classifications. The rapid-response character of our…

The Near-Earth Object (NEO) Surveyor mission is a NASA observatory designed to discover and characterize near-Earth asteroids and comets. The mission's primary objective is to find the majority of objects large enough to cause severe…

Known for their efficiency in analyzing large data sets, machine learning classifiers are widely used in wide-field sky surveys. The upcoming Vera C. Rubin Observatory Legacy of Time and Space Survey (LSST) will generate millions of alerts…

Our previous model (NEOMOD2) for the orbital and absolute magnitude distribution of Near Earth Objects (NEOs) was calibrated on the Catalina Sky Survey observations between 2013 and 2022. Here we extend NEOMOD2 to include visible albedo…

Missions to near-Earth objects (NEOs) are key destinations in NASA's new "Flexible Path" approach. NEOs are also of interest for science, for the hazards they pose, and for their resources. We emphasize the importance of ultra-low delta-v…

We have made an extensive search for grouping amongst the near Earth asteroids (NEAs). We used two D- functions and rigorous cluster analysis approach. We have found sev- eral new groups (associations) among the NEAs: the objects moving on…

In the last decade, over a million stars were monitored to detect transiting planets. Manual interpretation of potential exoplanet candidates is labor intensive and subject to human error, the results of which are difficult to quantify.…

[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…

The known near-Earth object (NEO) population consists of over 32,000 objects, with a yearly discovery rate of over 3000 NEOs per year. An essential component of the next generation of NEO surveys is an understanding of the population of…

The goal of this study is to present the development of a machine learning based approach that utilizes phase space alone to separate the Gaia DR2 stars into two categories: those accreted onto the Milky Way from those that are in situ.…

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…

The study of asteroid families has provided tremendous insight into the forces that sculpted the main belt and continue to drive the collisional and dynamical evolution of asteroids. The identification of asteroid families within the NEO…

This study investigate the effectiveness of using Deep Learning (DL) for the classification of planetary nebulae (PNe). It focusses on distinguishing PNe from other types of objects, as well as their morphological classification. We adopted…