arXiv subjects

Belén Yu Irureta-Goyena

Publications and source records attributed to Belén Yu Irureta-Goyena.

2 recordsLinked to original sources

arXiv2026

Streak detection in the VST/OmegaCAM archive using deep learning

Ground-based astronomical surveys inadvertently capture streaks from satellites and space debris crossing their fields of view. These incidental observations from wide-field instruments such as OmegaCAM on the VST offer valuable opportunities to characterise resident space objects without the need for dedicated observing time. We developed an automated deep-learning pipeline to detect and classify streaks in the OmegaCAM archive, enabling large-scale analyses of space object populations and their impact on astronomical data. The pipeline combines an adapted Hough transform lookup-based convolutional neural network (HT-LCNN) for initial streak detection on raw images with a VGG6-based CNN classifier to reject false positives. We augmented a manually annotated dataset of 384 000 patches from archive images with physically simulated streaks. Following a detection, we applied astrometric calibration and cross-matched the results with the space-track catalogue. We find the detector achieves F1-scores of 0.966 (validation) and 0.958 (test) on the augmented dataset, detecting > 95% of artificial streaks with a signal-to-noise ratio of S/N > 4. On real 2023 data, the precision drops to 0.783 due to image variability, but the classifier boosts it to 0.990, while retaining 97% of true positives and rejecting > 96% of false positives. Applied to one year of VST observations (1 246 048 OmegaCAM CCD frames), the pipeline identified 25 335 streaks, including more than 20% uncorrelated with catalogue entries; finally, 16.9% of images revealed some level of contamination. The pipeline demonstrates robust performance on real archival data and successfully uncovers faint uncatalogued objects, highlighting the potential of survey archives for debris monitoring.

astro-ph.IM↗

arXiv2025

Deep learning to improve the discovery of near-Earth asteroids in the Zwicky Transient Facility

We present a novel pipeline that uses a convolutional neural network (CNN) to improve the detection capability of near-Earth asteroids (NEAs) in the context of planetary defense. Our work aims to minimize the dependency on human intervention of the current approach adopted by the Zwicky Transient Facility (ZTF). The target NEAs have a high proper motion of up to tens of degrees per day and thus appear as streaks of light in the images. We trained our CNNs to detect these streaks using three datasets: a set with real asteroid streaks, a set with synthetic (i.e., simulated) streaks and a mixed set, and tested the resultant models on real survey images. The results achieved were almost identical across the three models: $0.843\pm0.005$ in completeness and $0.820\pm0.025$ in precision. The bias on streak measurements reported by the CNNs was $1.84\pm0.03$ pixels in streak position, $0.817\pm0.026$ degrees in streak angle and $-0.048\pm0.003$ in fractional bias in streak length (computed as the absolute length bias over the streak length, with the negative sign indicating an underestimation). We compared the performance of our CNN trained with a mix of synthetic and real streaks to that of the ZTF human scanners by analyzing a set of 317 streaks flagged as valid by the scanners. Our pipeline detected $80~\%$ of the streaks found by the scanners and 697 additional streaks that were subsequently verified by the scanners to be valid streaks. These results suggest that our automated pipeline can complement the work of the human scanners at no cost for the precision and find more objects than the current approach. They also prove that the synthetic streaks were realistic enough to be used for augmenting training sets when insufficient real streaks are available or exploring the simulation of streaks with unusual characteristics that have not yet been detected.

astro-ph.IM↗