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The recent launch of low Earth orbit satellite constellations is creating a growing threat for astronomical observations with ground-based telescopes1–10 that has alarmed the astronomical community 11–16. Observations affected by artificial satellites can become unusable for scientific research, wasting a growing fraction of the research budget on costly infrastructures and mitigation efforts. Here we report the first measurements, to our knowledge, of artificial satellite contamination on observations from a low Earth orbit made with the Hubble Space Telescope. With the help of volunteers on a citizen science project (www.asteroidhunter.org) and a deep learning algorithm, we scanned the archive of Hubble Space Telescope images taken between 2002 and 2021. We find that a fraction of 2.7% of the individual exposures with a typical exposure time of 11 minutes are crossed by satellites and that the fraction of satellite trails in the images increases with time. This fraction depends on the size of the field of view, exposure time, filter used and pointing. With the growing number of artificial satellites currently planned, the fraction of Hubble Space Telescope images crossed by satellites will increase in the next decade and will need further close study and monitoring.

The vast amount of data in astronomy archives presents an opportunity for new discoveries. Deep learning combined with crowdsourcing provides an efficient way to explore this data using the intuition of the human brain and the processing power of machines.

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.

The European Space Agency’s Planetary Science Archive (PSA) is the home for all scientific data from ESA’s planetary missions. Adopting the NASA PDS standard (version 3 and 4) it is designed to make the data, meta-data and knowledge on how to use them available to the scientific community. As a multi-mission archive, the PSA supports (or will soon support) over ten missions and their associated instruments, with this number expected to grow significantly in the coming years. The PSA has a long legacy of successfully preserving and distributing mission data to the community, and offers several services to fulfil this, including tabular, image-based and map-based interfaces, several APIs and traditional FTP. However, the entry barrier for new users is quite high, and moving forward there are new data access requirements coming from scientists wanting to perform more complex queries, run machine learning algorithms and so on. This presentation will describe the current infrastructure, recent updates and plans for the next few years which will try to address these changing needs. In particular, the following key developments are foreseen: implementation of a new user interface, with a streamlined and more user-friendly design, which will also work well on mobile, and touchscreen displays, improvements to APIs to include more data (specifically instrument geometry), and to incorporate the new PDS API which will allow access to any meta-data in the data products, leveraging the full value of the effort put in by instrument teams and archive scientists to curate them, integration with ESA DataLabs, a project designed to “bring the code to the data” and allow data processing and analysis to be done in an interactive online environment hosted close to the data repository and allowing big data workflows without having to download the products, publication of data tutorials based on open-source tools and libraries, to give new users a “quick start guide” to using data from a given instrument, and a much higher frequency release cadence, responding to the needs of the scientific community in a timely manner. Finally, community input is sought on other improvements which could be made, and which use cases are not fulfilled by the current infrastructure.

Context. The Hubble Space Telescope (HST) archives constitute a rich dataset of high-resolution images to mine for strong gravitational lenses. While many HST programmes specifically target strong lenses, they can also be present by coincidence in other HST observations. Aims. Our aim is to identify non-Targeted strong gravitational lenses, without any prior selection on the lens properties, in almost two decades of images from the ESA HST archive (eHST). Methods. We used crowdsourcing on the Hubble Asteroid Hunter (HAH) citizen science project to identify strong lenses, along with asteroid trails, in publicly available large field-of-view HST images. We visually inspected 2354 objects tagged by citizen scientists as strong lenses to clean the sample and identify the genuine lenses. Results. We report the detection of 252 strong gravitational lens candidates, which were not the primary targets of the HST observations. A total of 198 of them are new, not previously reported by other studies, consisting of 45 A grades, 74 B grades and 79 C grades. The majority are galaxy-galaxy configurations. The newly detected lenses are, on average, 1.3 magnitudes fainter than previous HST searches. This sample of strong lenses with high-resolution HST imaging is ideal to follow up with spectroscopy for lens modelling and scientific analyses. Conclusions. This paper presents the unbiased search of lenses that enabled us to find a wide variety of lens configurations, including exotic lenses. We demonstrate the power of crowdsourcing in visually identifying strong lenses and the benefits of exploring large archival datasets. This study shows the potential of using crowdsourcing in combination with artificial intelligence for the detection and validation of strong lenses in future large-scale surveys such as ESA's Euclid mission or in James Webb Space Telescope (JWST) archival images., Astronomy & Astrophysics, 667, ISSN:0004-6361, ISSN:1432-0746

Comets are generally considered to be relatively pristine objects, having spent most of their existence at large heliocentric distances where there is not enough energy to significantly transform these objects. Comets, therefore, offer a window into the early Solar System; their physical and chemical properties reflect the conditions in their formation environment [1]. Yet, most comets that have been visited by spacecraft are small-period comets that circle the Sun in 10 years or less, meaning that their surface is no longer as pristine as we would need to investigate their origins, having been modified by several processes such as impacts, sublimation, dust deposition and explosive outbursts over many orbits around the Sun. To learn about the early Solar System, we need to understand these evolution processes and recover the original conditions, and this requires building an exhaustive catalog of all types of changes that may have taken place, as well as the timeline of this evolution. ESA's Rosetta mission at comet 67P provides the perfect data set for such a task [2]. Having monitored the comet's surface for two years, across perihelion, Rosetta witnessed a handful of large-scale changes such as cliff retreat, the deflation of smooth terrains and the transport of large size blocks. A whole lot more took place on smaller scales: a careful examination of selected high-resolution images has shown thousands of changes on a 1-10 meter scale, including the formation of small pits, impacts, rolling and bouncing boulders. The most significant changes have been presented in several publications [3-9], as well as the connection between morphological evolution and activity [10,11]. However, more than 5 years after the end of the mission, we still lack a complete description of changes at all scales. This is a challenging task, because most transformations are small (meter-size or less), which means the associated surface features occupy only a few pixels in the high-resolution images returned by Rosetta (OSIRIS NAC [12]). Over the last years, we have started to systematically analyze images from different epochs, and developed specialized algorithms to assist in the detection of surface changes [13]. The results are promising, but only a subset of the data was analyzed, as the algorithm requires images to be co-aligned, a much time consuming step. In order to speed up the process and analyze the full surface, we have enlisted the help of thousands of comet enthusiasts through a citizen science project steered by ESA and Zooniverse. Volunteers are viewing pairs of OSIRIS images of the same region of the comet, taken before and after the perihelion passage, and we ask them to identify whether they see significant modifications between the two images, marking the areas that display changes in the two images with purposely-designed tools. Volunteers are also asked to label the type of change in the images. This will produce maps of changes and active areas on the comet’s surface, with labels for each type of change, from the visual inspection of many volunteers, enabling us to associate activity with surface modifications and thus develop new models linking the physics of comet activity to observed changes like lifted boulders and collapsed cliffs. The database created from this citizen science project will also be used to verify the results given by the change detection algorithm, and will provide an excellent training set for potentially new machine learning efforts. The project launched on the 5th of May 2022 and several thousands of classifications have already been performed by the first volunteers. We will monitor this collaborative work over the coming months and report on the first results at EPSC in September 2022. Rosetta Zoo project: https://www.zooniverse.org/projects/ellenjj/rosetta-zoo/ References: [1] Weissman et al, SSR (2020); [2] Taylor et al, MNRAS (2017); [3] Groussin et al, A&A (2015); [4] El Maarry et al, Science (2017); [5] Vincent et al, MNRAS (2017); [6] Birch et al, MNRAS (2017); [7] Pajola et al, NatAstro (2017); [8] Birch et al, GRL (2019); [9] Vincent et al, EPSC (2019); [10] El Maarry et al, ISSI/SSR (2020); [11] Vincent et al, ISSI/SSR (2020); [12] Keller et al, SSR (2007); [13] Vincent et al, EPSC (2021)

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 introduce in this work the Solar System Object Search Service (SSOSS), a service aimed at providing the scientific community with a search service for all potential detections of SSOs among the ESA astronomy archival imaging data. We illustrate its functionalities using the case of asteroid (16) Psyche, for which no information in the far-IR (70-500 {\mu}m) has previously been reported, to derive its thermal properties in preparation for the upcoming NASA Psyche mission. This service performs a geometrical cross-match of the orbital path of each object with respect to the public high-level imaging footprints stored in the ESA archives. For this first release, three missions were chosen: XMM-Newton, the Hubble Space Telescope (HST), and Herschel Observatory. We present a catalog listing all potential detections of asteroids within estimated limiting magnitude or flux limit in Herschel, XMM-Newton, and HST archival imaging data, including 909 serendipitous detections in Herschel images, 985 in XMM-Newton Optical Monitor camera images, and over 32,000 potential serendipitous detections in HST images. We also present a case study: the analysis of the thermal properties of Psyche from four serendipitous Herschel detections, combined with previously published thermal IR measurements. We see strong evidence for an unusual drop in (hemispherical spectral) emissivity, from 0.9 at 100 {\mu}m down to about 0.6 at 350 {\mu}m, followed by a possible but not well-constrained increase towards 500 {\mu}m, comparable to what was found for Vesta. The combined thermal data set puts a strong constraint on Psyche's thermal inertia (between 20 to 80$J m^{-2} s^{-1/2} K^{-1}$) and favours an intermediate to low level surface roughness (below 0.4 for the rms of surface slopes)., Comment: 21 pages, 17 figures