arXiv subjects

M. Pöntinen

Publications and source records attributed to M. Pöntinen.

7 recordsLinked to original sources

arXiv2026

Euclid: Asteroid rotation periods from the Euclid Ecliptic Survey

The Euclid Ecliptic Survey was conducted during the calibration phase of the mission, 23-31 December 2023, as a campaign to study Solar System objects. We used data from this survey to analyse more than 23 000 appeareances of 2321 known asteroids. Due to their high apparent angular motion relative to the background stars (5-$60^{\prime\prime}\,\mathrm{h}^{-1}$), these objects appear as streaks in VIS long-exposure images. We set out to estimate their spin periods, since only $7\%$ of them have periods published in the literature. We used multiple apertures along each streak to increase the time resolution of our light curves. Our method combines a Lomb-Scargle approach with a Markov chain Monte Carlo (MCMC) algorithm to characterise the posterior distributions. Some asteroids show multimodality in the MCMC search, indicating period aliases; in these cases, we report all aliases and their likelihoods. We validate our pipeline by comparing our fitted periods with 48 published periods, including period harmonics. We find that $44\%$ of our periods are within $1\%$ of those published and $98\%$ are within $15\%$, and we establish that with $98\%$ confidence the best solution can be found among the first three aliases. All reliable periods reported agree with our current understanding of the spin-period distribution for asteroids. We find 16 periods below the spin barrier of 2.2 h with absolute magnitudes below 19, and thus 16 candidate super-fast rotators. We provide light curves for all 2321 objects observed and 889 high-quality periods in an open-access catalogue. The asteroids with reported periods include five Mars crossers, four Cybeles, four Hildas, three Hungarias, and 877 asteroids in other regions of the main belt. Our results represent the first batch of spin periods extracted from Euclid light curves and include the first-ever period measurements for $93\%$ of the objects.

astro-ph.EP↗

arXiv2026

Machine Learning Assisted NEO Discovery and Polarimetric Characterisation with Astronomical Surveys

We are a group of over two dozen astronomers, computer scientists, data scientists and digital Big Data research platform experts at 11 universities and research institutes in South Africa and Europe. We study Near-Earth Objects (NEOs) for Planetary Defence and scientific purposes. We present our research and development programme for algorithms and digital data analysis platforms for machine learning-assisted NEO discovery and polarimetric characterisation in astronomical surveys. Typically, this is serendipitous because these surveys are designed for galactic and extragalactic science.

astro-ph.IM↗

arXiv2025

Euclid: A machine-learning search for dual and lensed AGN at sub-arcsec separations

Cosmological models of hierarchical structure formation predict the existence of a widespread population of dual accreting supermassive black holes (SMBHs) on kpc-scale separations, corresponding to projected distances < 0".8 at redshifts higher than 0.5. However, close companions to known active galactic nuclei (AGN) or quasars (QSOs) can also be multiple images of the object itself, strongly lensed by a foreground galaxy, as well as foreground stars in a chance superposition. Thanks to its large sky coverage, sensitivity, and high spatial resolution, Euclid offers a unique opportunity to obtain a large, homogeneous sample of dual/lensed AGN candidates with sub-arcsec projected separations. Here we present a machine learning approach, in particular a Convolutional Neural Network (CNN), to identify close companions to known QSOs down to separations of $\sim\,$0".15 comparable to the Euclid VIS point spread function (PSF). We studied the effectiveness of the CNN in identifying dual AGN and demonstrated that it outperforms traditional techniques. Applying our CNN to a sample of $\sim\,$6000 QSOs from the Q1 Euclid data release, we find a fraction of about 0.25% dual AGN candidates with separation $\sim\,$0".4 (corresponding to $\sim$3 kpc at z=1). Estimating the foreground contamination from stellar objects, we find that most of the pair candidates with separation higher than 0".5 are likely contaminants, while below this limit, contamination is expected to be less than 20%. For objects at higher separation (>0".5, i.e. 4 kpc at z=1), we performed PSF subtraction and used colour-colour diagrams to constrain their nature. We present a first set of dual/lensed AGN candidates detected in the Q1 Euclid data, providing a starting point for the analysis of future data releases.

astro-ph.GA↗

arXiv2025

Euclid: Detecting Solar System objects in Euclid images and classifying them using Kohonen self-organising maps

The ESA Euclid mission will survey more than 14,000 deg$^2$ of the sky in visible and near-infrared wavelengths, mapping the extra-galactic sky to constrain our cosmological model of the Universe. Although the survey focusses on regions further than 15 deg from the ecliptic, it should allow for the detection of more than about $10^5$ Solar System objects (SSOs). After simulating the expected signal from SSOs in Euclid images acquired with the visible camera (VIS), we describe an automated pipeline developed to detect moving objects with an apparent velocity in the range of 0.1-10 arcsec/h, typically corresponding to sources in the outer Solar System (from Centaurs to Kuiper-belt objects). In particular, the proposed detection scheme is based on Sourcextractor software and on applying a new algorithm capable of associating moving objects amongst different catalogues. After applying a suite of filters to improve the detection quality, we study the expected purity and completeness of the SSO detections. We also show how a Kohonen self-organising neural network can be successfully trained (in an unsupervised fashion) to classify stars, galaxies, and SSOs. By implementing an early-stopping method in the training scheme, we show that the network can be used in a predictive way, allowing one to assign the probability of each detected object being a member of each considered class.

astro-ph.IM↗

arXiv2024

Object classification with Convolutional Neural Networks: from KiDS to Euclid

Large-scale imaging surveys have grown about 1000 times faster than the number of astronomers in the last 3 decades. Using Artificial Intelligence instead of astronomer's brains for interpretative tasks allows astronomers to keep up with the data. We give a progress report on using Convolutional Neural Networks (CNNs) to classify three classes of rare objects (galaxy mergers, strong gravitational lenses and asteroids) in the Kilo-Degree Survey (KiDS) and the Euclid Survey.

astro-ph.IM↗

arXiv2023

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

Up to 150000 asteroids will be visible in the images of the ESA Euclid space telescope, and the instruments of Euclid offer multiband visual to near-infrared photometry and slitless spectra of these objects. Most 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 StreakDet 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.

astro-ph.EP↗

arXiv2020

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

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. We aim to develop a pipeline to detect fast-moving objects in Euclid images, with both high completeness and high purity. We tested the StreakDet software to find asteroids from simulated Euclid images. We optimized the parameters of StreakDet to maximize completeness, and developed a post-processing algorithm to improve the purity of the sample of detected sources by removing false-positive detections. StreakDet finds 96.9% of the synthetic asteroid streaks with apparent magnitudes brighter than 23rd magnitude and streak lengths longer than 15 pixels ($10\,{\rm arcsec\,h^{-1}}$), but this comes at the cost of finding a high number of false positives. The number of false positives can be radically reduced with multi-streak analysis, which utilizes all four dithers obtained by Euclid. StreakDet is a good tool for identifying asteroids in Euclid images, but there is still room for improvement, in particular, for finding short (less than 13 pixels, corresponding to 8$\,{\rm arcsec\,h^{-1}}$) and/or faint streaks (fainter than the apparent magnitude of 23).

astro-ph.EP↗