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The Hubble Space Telescope (HST) archives hide many unexpected treasures, such as trails of asteroids, showing a characteristic curvature due to the parallax induced by the orbital motion of the spacecraft. We have explored two decades of HST data for serendipitously observed asteroid trails with a deep learning algorithm on Google Cloud, called AutoML, trained on classifications from the Hubble Asteroid Hunter (www.asteroidhunter.org) citizen science project. The project was set up as a collaboration between the ESAC Science Data Centre, Zooniverse, and engineers at Google as a proof of concept to valorize the rich data in the ESA archives. I will present the first results from the project, finding 1,700 asteroid trails in the HST archives (Kruk et al., 2022). Their distribution on the sky is shown in Figure 1. The majority of the asteroid trails (1,031) we found are faint (typically > 21 mag, see Figure 2) and do not match any entries in the Minor Planet Center database, thus likely correspond to previously unidentified asteroids (see a few examples in Figure 3). We will argue that a combination of AI and crowdsourcing is an efficient way of exploring increasingly large datasets by taking full advantage of the intuition of the human brain and the processing power of machines. The second part of this project aims to analyze in detail these potentially new asteroids and use them to improve our current understanding of the size distribution of small-sized asteroids, and thus help constrain models of the evolution of our Solar System. Taking into account Hubble’s motion around the Earth, the parallax effect can be computed to obtain the distance to the asteroids by fitting simulated trajectories to the observed trails and obtaining the best fit (Evans et al. 1998). We show one example of a curve fit to the observed trail in Figure 4. Once we know the distance to the asteroids, we are able to obtain their absolute magnitudes and, combined with an assumed albedo, we can obtain their sizes. This method is also able to estimate an envelope for the asteroid's main orbital parameters. Given Hubble’s resolution and capability of reaching faint magnitudes, we expect that many of the new asteroids to be small-sized Main Belt asteroids (diameter This project may serve in the future as a “proof of concept” for an automated detection and analysis pipeline in large astronomical archives or surveys. Figure 1: Distribution on the sky of the Solar System Objects (SSOs) identified in the HST images in Mollweide projection. The blue stars show the identified, known asteroids. The orange circles show the location of objects for which we did not find any associations with SSOs. The ecliptic is shown with red. The two gaps in this plot correspond to the Galactic plane, which was not observed by HST. Figure 2: Distribution of apparent magnitudes for the SSOs identified in the HST images. The measured magnitudes for the identified objects (blue bars) and for the objects for which we did not find any associations with known SSOs (orange bars). Figure 3: Examples of unidentified trails in HST observations. The HST observation IDs, clockwise, from the top left, are: j8pv03020, jds47w010, j9bk75010, icphg2010, jdrz23010, and jcng06010. Figure 4: A trail fitting example using the parallax method. The distance solution yielding the best fit for a simulated parallax using HST trajectory is shown in red, in blue the observed trail.

Large and publicly available astronomical archives open up new possibilities to search and study Solar System objects. However, advanced techniques are required to deal with the large amounts of data. These unbiased surveys can be used to constrain the size distribution of minor bodies, which represents a piece of the puzzle for the formation models of the Solar System. We aim to identify asteroids in archival images from the ESA Hubble Space Telescope (HST) Science data archive using data mining. We developed a citizen science project on the Zooniverse platform, Hubble Asteroid Hunter (www.asteroidhunter.org) asking members of the public to identify asteroid trails in archival HST images. We used the labels provided by the volunteers to train an automated deep learning model built with Google Cloud AutoML Vision to explore the entire HST archive to detect asteroids crossing the field-of-view. We report the detection of 1701 new asteroid trails identified in archival HST data via our citizen science project and the subsequent machine learning exploration of the ESA HST science data archive. We detect asteroids to a magnitude of 24.5, which are statistically fainter than the populations of asteroids identified from ground-based surveys. The majority of asteroids are distributed near the ecliptic plane, as expected, where we find an approximate density of 80 asteroids per square degree. We match 670 trails (39% of the trails found) with 454 known Solar System objects in the Minor Planet Center database, however, no matches are found for 1031 (61%) trails. The unidentified asteroids are faint, being on average 1.6 magnitudes fainter than the asteroids we succeeded to identify. They probably correspond to previously unknown objects. This work demonstrates that citizen science and machine learning are useful techniques for the systematic search of SSOs in existing astronomy science archives., Comment: 15 pages, 16 figures, 2 tables. Replaced to match the A&A journal version, https://www.aanda.org/10.1051/0004-6361/202142998

We identify spectral similarities between asteroids and meteorites. We identify spectral matches between 500 asteroid spectra and over 1,000 samples of RELAB meteorite spectra over 0.45-2.5 microns. We reproduce many major and previously known meteorite-asteroid connections and find possible new, more rare or less-established connections. Well-established connections include: ordinary chondrites (OC) with S-complex asteroids; pristine CM carbonaceous chondrites with Ch-type asteroids and heated CMs with C-type asteroids; HED meteorites with V-types; enstatite chondrites with Xc-type asteroids; CV meteorites with K-type asteroids; Brachinites, Pallasites and R chondrites with olivine-dominated A-type asteroids. We find a trend from Q, Sq, S, Sr to Sv correlates with LL to H, with Q-types matching predominately to L and LL ordinary chondrites, and Sr and Sv matching predominantly with L and H ordinary chondrites. Ordinary chondrite samples that match to the X-complex, all measurements of slabs and many labeled as dark or black (shocked) OCs. We find carbonaceous chondrite samples having spectral slopes large enough to match D-type asteroid spectra. In many cases the asteroid type to meteorite type links are not unique. While there are well established matches between an asteroid class and meteorite class, there are less common but still spectrally compatible matches between many asteroid types and meteorite types. This result emphasizes the diversity of asteroid and meteorite compositions and highlights the degeneracy of classification by spectral features alone. Recent and upcoming spacecraft missions will shed light on the compositions of many of the asteroid classes, particularly those without diagnostic features, (C-, B-, X-, and D-types), with measurements of Ceres, Ryugu, Bennu, Psyche, and C-, P-, and D-types as part of the Lucy mission., Comment: Accepted for publication in Icarus. 38 pages, 8 figures

In thirty-one years of observations, the Hubble Space Telescope (HST) has produced a vast archive of thousands of targeted observations. This includes galaxies, clusters of galaxies, and gravitational lenses. Occasionally, closer objects such as Solar System bodies or artificial satellites cross the telescope's field of view during the observations, leaving trails in the images. On one hand, these trails can impact the observations. The standard data processing pipeline (DrizzlePac) cleans cosmic rays artifacts (Hoffmann et al., 2021), also removing asteroid trails, but it leaves residuals in the combined images. On the other hand, this is a great opportunity for the Solar System small bodies science, considering the already existing images from the huge HST Archive, containing more than 100 Tb of data and spanning three decades. Our project is focused on studying serendipitous asteroid trails appearing in archival HST images. We used images from two instruments, namely the Advanced Camera for Surveys and Wide Field Camera 3, the ultraviolet and visible channels. These images were acquired between 2002 and 2021. We built an online citizen science project on the Zooniverse platform, Hubble Asteroid Hunter (www.asteroidhunter.org), launched on International Asteroid Day 2019, to identify the asteroid trails in the images (Kruk et al., in prep.). This project involved more than 11,000 people in search for asteroids, providing 2 million classifications for 150,000 images over a period of one year. The labels provided by the volunteers were used to train an automated classifier based on a deep learning algorithm, Google Cloud AutoML Vision. We recovered 2,400 trails in the HST images in total. The asteroid trails appear curved as viewed by HST, because of Hubble’s motion around the Earth every 90 minutes. One example is asteroid 2001 SE101 passing in front of the Crab Nebula in a rare cosmic coincidence, discovered by citizen scientist Melina Thévenot and shown in Figure 1. The project also contributed to other serendipitous discoveries, such as new strong gravitational lenses in the background of some famous HST targets.Figure 1: Trail of asteroid 2001 SE101 passing in front of the Crab Nebula, M1 in 2005. The trail appears curved because of the motion of HST around the Earth. ESA Image of the Week: http://www.esa.int/ESA_Multimedia/Images/2019/10/Foreground_asteroid_passing_the_Crab_Nebula. Credit: ESA/Hubble & NASA, M. Thévenot. We further analysed the asteroid trails in order to obtain their astrometry and photometry with a customised algorithm. We validated the trails visually, finding 1,700 trails presumably of Solar System objects. Their distribution in the sky is shown in Figure 2. We extracted each trail from the images by using a fixed-width aperture, which was moved along the trail. The position and corresponding flux were obtained for each point along the trail. The calibration was performed using the WCS (World Coordinate System) information stored in the header. As a by-product of this algorithm, we were able to derive partial light curves. The apparent magnitude of the corresponding Solar System object was obtained by integrating all the flux along the trail.We used the SkyBoT service provided by IMCCE/Paris Observatory and the JPL HORIZONS online solar system data and ephemeris for identifying the known objects. We computed the ephemerides taking into account the position of HST. Despite using the largest databases of minor bodies, we only matched 300 trails with already known asteroids, taking into account the orbital uncertainties and their apparent motion. Therefore, our data contains 1,400 unknown objects or objects with very large orbital uncertainties. This is not surprising, since most of the apparent magnitudes of our trails (Figure 3) are fainter than magnitude 22, which is the approximate limit for the asteroid discovery surveys performed with ground-based telescopes. Most of these objects will correspond to main-belt objects with sizes This project demonstrates the power of combining novel tools such as citizen science and artificial intelligence to efficiently explore archival images and obtain important results, with the invaluable help of Zooniverse volunteers, beyond the original scope of the Hubble observations. Figure 2: Sky distribution of the asteroids detected in HST observations. The vast majority of asteroids are in the Ecliptic plane (denoted with red). The two gaps are due to the lack of HST images in the Galactic Plane. Figure 3: The apparent magnitude distribution of the Solar System objects identified in HST observations. The majority of the identified asteroids have magnitudes >22, fainter than the detection capabilities of many ground-based surveys. References:Hoffmann, S. L., Mack, J., et al., 2021, “The DrizzlePac Handbook”, Version 2.0, (Baltimore: STScI).

Binary asteroids represent a natural laboratory to gather crucial information on small bodiesof the Solar System, providing an overview of the formation and evolution mechanisms of these objects. Their physical characterization can constrain the processes that took part in the formation and evolution of planetessimals in the Solar System. The characteristics assessed in this work are: mass, size, shape, spin, density, surface composition, and taxonomy.One of the most important characteristics that can be obtained of binaries -if the system can be resolved- is their mass through their mutual gravitational interaction. From the mass and the size of the asteroid we determine its density, which provides insight on its internal structure.For this purpose, data mining has been done for high-angular resolution images from HST and ground-based telescopes equipped with adaptive optics (VLT/NACO, VLT/SPHERE, Gemini/NIRI, Keck/NIRC2) in the visible and near infrared. Having reduced the images and determined the satellite positions for over many epochs, the genetic algorithm Genoid algorithm is used to determine the orbit of the companion, and mass of the primary. This improves the ephemerides of binary companions, which in turn allows to stellar occultations by asteroids for future occultation campaigns.The occultation technique is the most fruitfulfor observing small diameter Solar System objects. As for the size and shape determination, KOALA multidata inversion algorithm is used.Concerning photometry, light curves and SDSS colors have been obtained for binary asteroids from T1M at Pic du Midi & 1.20m telescope at Haute Provence Observatory, aiming at determining and refining their properties. I remotely acquired spectra of binary asteroids using Spex/IRTF system based on 3m at Mauna Kea (Hawaii), to determine their taxonomic class for the first time. Additionally, I collected spectra of small binaries from the SMASS collaborationdatabase, modelled it, and found their taxonomy. I compare the now larger sample of classified binaries to the population of NEAs and Mars Crossers, and found a predominance of Q/S types. This is in agreement with a formation by YORP spin-up and rotational disruption.Finally, I developed a taxonomic classification for asteroids in general, based on infrared large band photometry, and applied it to 30,000 asteroids from VHS survey at the ESO’s telescope VISTA.; Les astéroïdes binaires représentent un laboratoire naturel pour recueillir des informations cruciales sur les petits corps du Système Solaire, fournissant un aperçu des mécanismes de formation et d’évolution de ces objets. Leur caractérisation physique nous aide à comprendre les processus qui ont pris part à la formation et l’évolution des planétésimaux dans le Système Solaire. Les caractéristiques qui sont évaluées dans ce travail sont : la masse, la taille, la forme, la rotation, la densité, la composition et la taxonomie. L’une des plus importantes caractéristiques que l’on puisse obtenir avec les objets binaires -si le système peut être angulairement résolu- est leur masse grâce à l’interaction gravitationnelle mutuelle. Avec la masse et la taille du corps, nous pouvons déterminer sa densité, qui peut nous donner un aperçu de sa structure interne.A cet effet, l’exploration de données a été faite à partir d’images à haute résolution angulaire du télescope spatial Hubble et les télescopes au sol avec optique adaptative (VLT/NACO, VLT/SPHERE, Gemini/NIRI, Keck/NIRC2) dans le visible et proche infrarouge. Ayant réduit les images et mesuré les positions des satellites à de nombreuses époques, l’algorithme génétique Genoid est utilisé pour déterminer l’orbite de compagnons et la masse du corps central. Ceci est utile pour améliorer les éphémérides des satellites des binaires, qui à leur tour seront utiles pour prédire des occultations stellaires pour les futures campagnes d’occultation ; la technique d’occultation étant la plus fructueuse pour l’observation des objets de faible diamètre du Système Solaire. En ce qui concerne la taille et la détermination de la forme, l’algorithme KOALA d’inversion multidonnées est utilisé. En ce qui concerne la photométrie, courbes de lumière et couleurs SDSS ont été obtenues depuis le télescope de 1m au Pic du Midi et de 1.20 m de l’observatoire de Haute Provence dans le but de déterminer et affiner leurs propriétés. J’ai également acquis à distance des spectres d’astéroïdes binaires en utilisant le spectrographe Spex sur le télescope IRTF de 3m au Mauna Kea (Hawaii), afin de déterminer leur classe taxonomique pour la première fois. De plus, j’ai fait le modélisation de spectres de binaires sans taxonomie dans la base de données du SMASS collaboration. Ce plus grand échantillon, que j’ai comparé avec la population du NEAs et de Mars Crossers, en trouvant une prédominance dans le taxonomie Q/S. Cela est consistant avec la formation de binaires petits par effet YORP et perturbation rotationnelle. Finalement, j’ai développé une classification taxonomique générale, basée sur la photométrie large bande dans l’infrarouge, et je l’ai appliquée aux données de 30,000 astéroïdes provenant du survey VHS conduit par le télescope VISTA de l’ESO.