Context. The ESA Euclid mission has been designed to map the geometry of the dark Universe. Scheduled for launch in 2020, it will conduct a six-year visible and near-infrared imaging and spectroscopic survey over 15 000 deg2 down to V AB ~ 24.5. Although the survey will avoid ecliptic latitudes below 15°, the survey pattern in repeated sequences of four broadband filters seems well-adapted to detect and characterize solar system objects (SSOs). Aims. We aim at evaluating the capability of Euclid of discovering SSOs and of measuring their position, apparent magnitude, and spectral energy distribution. We also investigate how the SSO orbits, morphology (activity and multiplicity), physical properties (rotation period, spin orientation, and 3D shape), and surface composition can be determined based on these measurements. Methods. We used the current census of SSOs to extrapolate the total amount of SSOs that will be detectable by Euclid , that is, objects within the survey area and brighter than the limiting magnitude. For each different population of SSO, from neighboring near-Earth asteroids to distant Kuiper-belt objects (KBOs) and including comets, we compared the expected Euclid astrometry, photometry, and spectroscopy with the SSO properties to estimate how Euclid will constrain the SSOs dynamical, physical, and compositional properties. Results. With the current survey design, about 150 000 SSOs, mainly from the asteroid main-belt, should be observable by Euclid . These objects will all have high inclination, which is a difference to many SSO surveys that focus on the ecliptic plane. Euclid may be able to discover several 104 SSOs, in particular, distant KBOs at high declination. The Euclid observations will consist of a suite of four sequences of four measurements and will refine the spectral classification of SSOs by extending the spectral coverage provided by Gaia and the LSST, for instance, to 2 microns. Combined with sparse photometry such as measured by Gaia and the LSST, the time-resolved photometry will contribute to determining the SSO rotation period, spin orientation, and 3D shape model. The sharp and stable point-spread function of Euclid will also allow us to resolve binary systems in the Kuiper belt and detect activity around Centaurs. Conclusions. The depth of the Euclid survey (V AB ~ 24.5), its spectral coverage (0.5 to 2.0 μ m), and its observation cadence has great potential for solar system research. A dedicated processing for SSOs is being set up within the Euclid consortium to produce astrometry catalogs, multicolor and time-resolved photometry, and spectral classification of some 105 SSOs, which will be delivered as Legacy Science.

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Solar system science with ESA Euclid

We present a high-accuracy, low false-alarm rate, and low computational-cost methodology for removing stars and noise and detecting space debris with low signal-to-noise ratio (SNR) in optical image sequences. First, time-index filtering and bright star intensity enhancement are implemented to remove stars and noise effectively. Then, a multistage quasi-hypothesis-testing method is proposed to detect the pieces of space debris with continuous and discontinuous trajectories. For this purpose, a time-index image is defined and generated. Experimental results show that the proposed method can detect space debris effectively without any false alarms. When the SNR is higher than or equal to 1.5, the detection probability can reach 100%, and when the SNR is as low as 1.3, 1.2, and 1, it can still achieve 99%, 97%, and 85% detection probabilities, respectively. Additionally, two large sets of image sequences are tested to show that the proposed method performs stably and effectively.

Space debris detection in optical image sequences.

The ESA Euclid mission has been designed to map the geometry of the dark Universe. Scheduled for launch in 2020, it will conduct a six-years visible and NIR imaging and spectroscopic survey over 15,000 deg 2 down to mag~24.5. Although the survey will avoid low ecliptic latitudes, the survey pattern in repeated sequences of four broad-band filters seems well-adapted to Solar System objects (SSOs) detection and characterization. We aim at evaluating Euclid capability to discover SSOs, and measure their position, apparent magnitude, and SED. Also, we investigate how these measurements can lead to the determination of their orbits, morphology, physical properties, and surface composition. We use current census of SSOs to estimate the number of SSOs detectable by Euclid. Then we estimate how Euclid will constrain the SSOs dynamical, physical, and compositional properties. With current survey design, about 150,000 SSOs, mainly from the asteroid main-belt, should be observed by Euclid. These objects will all have high inclination. There is a potential for discovery of several 10,000 SSOs, in particular KBOs at high declination. Euclid observations will refine the spectral classification of SSOs by extending the spectral coverage provided by, e.g. Gaia and the LSST to 2 microns. The time-resolved photometry, combined with sparse photometry will contribute to the determination of SSO rotation period, spin orientation, and shape model. The sharp and stable point-spread function of Euclid will also allow to resolve KBO binary systems and detect activity around Centaurs. The depth of Euclid survey, its spectral coverage, and observation cadence has great potential for Solar System research. A dedicated processing for SSOs is being set in place to produce catalogs of astrometry, multi-color and time-resolved photometry, and spectral classification of some 10$^5$ SSOs, delivered as Legacy Science.

/pdf/solar-system-science-with-esa-euclid-1d6g5mx0b7.pdf

Solar System Science with ESA Euclid

Space debris detection is important in space situation awareness and space asset protection. In this article, we propose a method to detect space debris using feature learning of candidate regions. The acquired optical image sequences are first processed to remove hot pixels and flicker noise, and the nonuniform background information is removed by the proposed one dimensional mean iteration method. Then, the feature learning of candidate regions (FLCR) method is proposed to extract the candidate regions and to detect space debris. The candidate regions of space debris are precisely extracted, and then classified by a trained deep learning network. The feature learning model is trained using a large number of simulated space debris with different signal to noise ratios (SNRs) and motion parameters, instead of using real space debris, which make it difficult to extract a sufficient number of real space debris with diverse parameters in optical image sequences. Finally, the candidate regions are precisely placed in the optical image sequences. The experiment is performed using the simulated data and acquired image sequences. The results show that the proposed method has good performance when estimating and removing background, and it can detect low SNR space debris with high detection probability.

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Space Debris Detection Using Feature Learning of Candidate Regions in Optical Image Sequences

Long exposure time and wide field can effectively improve the ability of a space surveillance telescope to detect faint space targets. However, complicated situations pose challenges for space target detection. Background star images usually manifest a rotated streak, and target trajectories can be crossed, discontinuous, or nonlinear. This paper presents an accurate and robust space target detection method, namely, spatiotemporal pipeline multistage hypothesis testing (SPMHT), to overcome the issues. Specifically, the method includes the following two stages: First, in the spatiotemporal pipeline filtering step, Spatiotemporal-related Intersection over Union (SrIoU) is used to calculate the IoU score instead of the traditional method. Benefiting from the differences between motion characteristics of targets and stars and the insensitivity of the SrIoU score to the noise, the spatiotemporal pipeline filtering can effectively remove the streak images of background stars and obtain candidate points. Second, a series of candidate points is further organized into a tree structure. We pruned in the tree structure combined with these candidate trajectories by using velocity and direction feature of moving objects. Furthermore, in the search step, fast adaptive sequence region search is used to reduce the computational cost. The experimental results for two datasets, simulated image datasets and real captured image datasets, demonstrate the effectiveness in addressing the difficulties of space target detection in complicated situations.

/pdf/space-target-detection-in-complicated-situations-for-wide-4cmog4eltg.pdf

Space Target Detection in Complicated Situations for Wide-Field Surveillance

Streak detection and analysis pipeline for space-debris optical images

Context. We assess statistical inversion of asteroid rotation periods, pole orientations, shapes, and phase curve parameters from photometric lightcurve observations, here sparse data from the ESA Gaia space mission (Data Release 2) or dense and sparse data from ground-based observing programs.Aims. Assuming general convex shapes, we develop inverse methods for characterizing the Bayesian a posteriori probability density of the parameters (unknowns). We consider both random and systematic uncertainties (errors) in the observations, and assign weights to the observations with the help of Bayesian a priori probability densities.Methods. For general convex shapes comprising large numbers of parameters, we developed a Markov-chain Monte Carlo sampler (MCMC) with a novel proposal probability density function based on the simulation of virtual observations giving rise to virtual least-squares solutions. We utilized these least-squares solutions to construct a proposal probability density for MCMC sampling. For inverse methods involving triaxial ellipsoids, we update the uncertainty model for the observations.Results. We demonstrate the utilization of the inverse methods for three asteroids with Gaia photometry from Data Release 2: (21) Lutetia, (26) Proserpina, and (585) Bilkis. First, we validated the convex inverse methods using the combined ground-based and Gaia data for Lutetia, arriving at rotation and shape models in agreement with those derived with the help of Rosetta space mission data. Second, we applied the convex inverse methods to Proserpina and Bilkis, illustrating the potential of the Gaia photometry for setting constraints on asteroid light scattering as a function of the phase angle (the Sun-object-observer angle). Third, with the help of triaxial ellipsoid inversion as applied to Gaia photometry only, we provide additional proof that the absolute Gaia photometry alone can yield meaningful photometric slope parameters. Fourth, for (585) Bilkis, we report, with 1-σ uncertainties, a refined rotation period of (8.5750559 ± 0.0000026) h, pole longitude of 320.6° ± 1.2°, pole latitude of − 25.6° ± 1.7°, and the first shape model and its uncertainties from convex inversion.Conclusions. We conclude that the inverse methods provide realistic uncertainty estimators for the lightcurve inversion problem and that the Gaia photometry can provide an asteroid taxonomy based on the phase curves.

/pdf/asteroid-lightcurve-inversion-with-bayesian-inference-20mjk4eefw.pdf

Asteroid lightcurve inversion with Bayesian inference

An asteroid's lightcurve, i.e., its observed diskintegrated brightness as a function of time, depends on the shape and spin state of the asteroid, as well as its surface scattering properties. It follows that these properties can be estimated from the observations, to an extent allowed by a given data set. The phase curve of the asteroid refers to the dependence of diskintegrated brightness on the phase angle (the SunObject-Observer angle). In the present work, we study reference phase curves that are extrapolated to equatorial illumination and observation of the given asteroid. We show that the reference phase curves, with realistic error bars, can be efficiently derived through statistical lightcurve inversion. These phase curves can have substantial value in asteroid taxonomy.

Asteroid lightcurve inversion with Bayesian inference: Reference phase curves

Streak detection and analysis pipeline for optical images

Luonnossa esiintyvät kolmiulotteiset muodot ovat epäsäännöllisiä. Muotojen teoreettiseen mallintamiseen voidaan käyttää tilastollisia menetelmiä, joissa pyritään mahdollisimman vähäiseen vapaiden parametrien lukumäärään. Asteroidien ja perunan mukuloiden kolmiulotteisten muotojen samankaltaisuus on tiedostettu tähtitieteessä ja kasvipatologiassa jo toistakymmentä vuotta. Tässä työssä laajennetaan asteroidien muodon mallinnukseen kehitettyjä menetelmiä perunoiden muotojen mallinnukseen. Vastavuoroisesti havaitaan, että uusi perunan muotomalli edesauttaa asteroidien muotojen mallinnusta. Perunan mukulan muoto on tärkeä geneettisesti määräytyvä lajikkeen ominaisuus, johon vaikuttavat myös ympäristötekijät. Tässä tutkimuksessa määrätään kahden perunalajikkeen (Van Gogh ja Yukon Gold) pääsääntöinen muoto ellipsoidimallin avulla. Yksityiskohtaisemmat piirteet kuvataan palloharmonisten funktioiden sarjakehitelmän avulla. Tämän jälkeen menetelmää sovelletaan neljän muun lajikkeen mukuloiden muodon määritykseen (Bellona, Lady Rosetta, Pito ja Sabina). Tilastollisen muotomallin sovellus perunan mukuloihin osoittaa, että ellipsoidi kuvaa pääsääntöistä muotoa hyvin suurella suhteellisella tarkkuudella ja että palloharmoninen osa on alustavasti tilastollisessa mielessä samankaltainen kaikilla lajikkeilla. Uusi perunan mukulan tilastollinen muotomalli voi osoittautua hyödylliseksi muotoerojen luokittelussa, muotoeroihin vaikuttavien geneettisten ja ympäristötekijöiden vaikutusten kvantifioinnissa sekä konenäköön perustuvissa sovelluksissa.

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J. Torppa

Papers

Streak detection and analysis pipeline for space-debris optical images

Asteroid lightcurve inversion with Bayesian inference

Asteroid lightcurve inversion with Bayesian inference: Reference phase curves

Streak detection and analysis pipeline for optical images

Kolmiulotteisen muodon mallinnuksesta