<i>Euclid</i>: Identification of asteroid streaks in simulated images using StreakDet software

Pöntinen, M.; Granvik, Mikael; Nucita, A. A.; Conversi, L.; Altiéri, B.; Auricchio, N.; Bodendorf, C.; Bonino, D.; Brescia, M.; Capobianco, V.; Carretero, J.; Carry, B.; Castellano, M.; Clédassou, R.; Congedo, G.; Corcione, L.; Cropper, Mark; Dusini, S.; Frailis, M.; Franceschi, E.; Fumana, M.; Garilli, B.; Grupp, F.; Hormuth, F.; Israel, Holger; Jahnke, K.; Kermiche, S.; Kitching, Thomas D.; Kohley, R.; Kubik, B.; Kunz, M.; Laureijs, R. J.; Lilje, P. B.; Lloro, I.; Maiorano, E.; Marggraf, O.; Massey, Richard; Meneghetti, M.; Meylan, G.; Moscardini, L.; Padilla, C.; Paltani, S.; Pasian, F.; Pires, S.; Polenta, G.; Raison, F.; Roncarelli, M.; Rossetti, E.; Saglia, R.; Schneider, Peter B.; Secroun, A.; Serrano, S.; Sirri, G.; Taylor, A. N.; Tereno, I.; Toledo-Moreo, R.; Valenziano, L.; Wang, Y.; Wetzstein, Michael E.; Zoubian, J.

doi:10.1051/0004-6361/202037765

A&A

2020

DOI: 10.1051/0004-6361/202037765

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Euclid: Identification of asteroid streaks in simulated images using StreakDet software

M. Pöntinen

Mikael Granvik

A. A. Nucita

et al.

Abstract: Context. The ESA Euclid space telescope could observe up to 150 000 asteroids as a side product of its primary cosmological mission. Asteroids appear as trailed sources, that is streaks, in the images. Owing to the survey area of 15 000 square degrees and the number of sources, automated methods have to be used to find them. Euclid is equipped with a visible camera, VIS (VISual imager), and a near-infrared camera, NISP (Near-Infrared Spectrometer and Photometer), with three filters. Aims. We aim to develop a p… Show more

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Cited by 3 publications

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Mining archival data from wide-field astronomical surveys in search of near-Earth objects

Saifollahi¹,

Kleijn²,

Williams³

et al. 2023

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Context. 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 life. The detection of serendipitous NEO appearances among the millions of archived exposures from large astronomical imaging surveys can provide a contribution which is complementary to NEO surveys. Aims. Using the ASTROWISE information system, this work aims to assess the detectability rate, the achieved recovery rate and the quality of astrometry when data mining the European Southern Observatory (ESO) archive for the OmegaCAM wide-field imager at the VLT Survey Telescope (VST). Methods. We developed an automatic pipeline that searches for NEO appearances inside the ASTROWISE environment. Throughout the recovery process the pipeline uses several public web tools (SSOIS, NEODyS, JPL Horizons) to identify possible images that overlap with the positions of NEOs, and acquires information on the NEOs’ predicted position and other properties (e.g. magnitude, rate, and direction of motion) at the time of observations. Considering these properties, the pipeline narrows down the search to potentially detectable NEOs, searches for streak-like objects across the images, and finds a matching streak for the NEOs. Results. We recovered 196 appearances of NEOs from a set of 968 appearances predicted to be recoverable. It includes appearances for three NEOs that were on the impact risk list at that point. These appearances occurred well before their discovery. The subsequent risk assessment using the extracted astrometry removes these NEOs from the risk list. More generally, we estimate a detectability rate of ~0.05 per NEO at a signal-to-noise ratio higher than 3 for NEOs in the OmegaCAM archive. Our automatic recovery rates are 40% and 20% for NEOs on the risk list and the full list, respectively. The achieved astrometric and photometric accuracy is on average 0.12″ and 0.1 mag. Conclusions. These results show the high potential of the archival imaging data of the ground-based wide-field surveys as useful instruments for the search, (p)recovery, and characterization of NEOs. Highly automated approaches, as possible using ASTROWISE, make this undertaking feasible.

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Mining archival data from wide-field astronomical surveys in search of near-Earth objects

Saifollahi¹,

Kleijn²,

Williams³

et al. 2023

A&A

View full text Add to dashboard Cite

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Hubble Asteroid Hunter

García-Martín,

Kruk,

Popescu

et al. 2024

A&A

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Determining the size distribution of asteroids is key to understanding the collisional history and evolution of the inner Solar System. We aim to improve our knowledge of the size distribution of small asteroids in the main belt by determining the parallaxes of newly detected asteroids in the Hubble Space Telescope (HST) archive and subsequently their absolute magnitudes and sizes. Asteroids appear as curved trails in HST images because of the parallax induced by the fast orbital motion of the spacecraft. Taking into account the trajectory of this latter, the parallax effect can be computed to obtain the distance to the asteroids by fitting simulated trajectories to the observed trails. Using distance, we can obtain the absolute magnitude of an object and an estimation of its size assuming an albedo value, along with some boundaries for its orbital parameters. In this work, we analyse a set of 632 serendipitously imaged asteroids found in the ESA HST archive. Images were captured with the ACS/WFC and WFC3/UVIS instruments. A machine learning algorithm (trained with the results of a citizen science project) was used to detect objects in these images as part of a previous study. Our raw data consist of 1,031 asteroid trails from unknown objects, not matching any entries in the Minor Planet Center (MPC) database using their coordinates and imaging time. We also found 670 trails from known objects (objects featuring matching entries in the MPC). After an accuracy assessment and filtering process, our analysed HST asteroid set consists of 454 unknown objects and 178 known objects. We obtain a sample dominated by potential main belt objects featuring absolute magnitudes (H) mostly between 15 and 22 mag. The absolute magnitude cumulative distribution $log N(H>H_0) confirms the previously reported slope change for $15<H<18$, from $ 0.56$ to $ 0.26$, maintained in our case down to absolute magnitudes of around $H 20$, and therefore expanding the previous result by approximately two magnitudes. HST archival observations can be used as an asteroid survey because the telescope pointings are statistically randomly oriented in the sky and cover long periods of time. They allow us to expand the current best samples of astronomical objects at no extra cost in regard to telescope time.

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Euclid: Identification of asteroid streaks in simulated images using deep learning

Pöntinen,

Granvik,

Nucita

et al. 2023

A&A

Self Cite

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The material composition of asteroids is an essential piece of knowledge in the quest to understand the formation and evolution of the Solar System. Visual to near-infrared spectra or multiband photometry is required to constrain the material composition of asteroids, but we currently have such data, especially in the near-infrared wavelengths, for only a limited number of asteroids. This is a significant limitation considering the complex orbital structures of the asteroid populations. Up to 150 000 asteroids will be visible in the images of the upcoming ESA Euclid space telescope, and the instruments of Euclid will offer multiband visual to near-infrared photometry and slitless near-infrared spectra of these objects. Most of the asteroids will appear as streaks in the images. Due to the large number of images and asteroids, automated detection methods are needed. A non-machine-learning approach based on the Streak Det software was previously tested, but the results were not optimal for short and/or faint streaks. We set out to improve the capability to detect asteroid streaks in Euclid images by using deep learning. We built, trained, and tested a three-step machine-learning pipeline with simulated Euclid images. First, a convolutional neural network (CNN) detected streaks and their coordinates in full images, aiming to maximize the completeness (recall) of detections. Then, a recurrent neural network (RNN) merged snippets of long streaks detected in several parts by the CNN. Lastly, gradient-boosted trees (XGBoost) linked detected streaks between different Euclid exposures to reduce the number of false positives and improve the purity (precision) of the sample. The deep-learning pipeline surpasses the completeness and reaches a similar level of purity of a non-machine-learning pipeline based on the StreakDet software. Additionally, the deep-learning pipeline can detect asteroids 0.25–0.5 magnitudes fainter than StreakDet. The deep-learning pipeline could result in a 50% increase in the number of detected asteroids compared to the StreakDet software. There is still scope for further refinement, particularly in improving the accuracy of streak coordinates and enhancing the completeness of the final stage of the pipeline, which involves linking detections across multiple exposures.

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Euclid: Identification of asteroid streaks in simulated images using StreakDet software

Cited by 3 publications

References 40 publications

Mining archival data from wide-field astronomical surveys in search of near-Earth objects

Mining archival data from wide-field astronomical surveys in search of near-Earth objects

Hubble Asteroid Hunter

Euclid: Identification of asteroid streaks in simulated images using deep learning

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