Within the framework of the SUNRISE project, the research units at Unisalento were tasked with developing and testing algorithms for simulating Euclid-like images and for “Euclidizing” pre-existing astrophysical images. The primary motivation for providing such software was to enable the evaluation of available detection methods, such as those implemented in Source Extractor or DAOphot, using sources with known properties.
The team focused on the development and maintenance of two codes, LSBsim and BLOB_DET, designed for the simulation and detection of faint sources in ESA/Euclid VIS images.
a) LSBsim
LSBsim is a Python-based tool developed to inject low surface brightness (LSB) galaxies, dwarf galaxies, and globular clusters—including their nuclei—into real VIS images. Given a calibrated Euclid VIS single exposure, LSBsim takes a list of galactic parameters (right ascension, declination, total magnitude, position angle, effective radius, ellipticity, and Sérsic index) to simulate sources.
As an example, LSBsim has been used to simulate globular clusters, modeled with King profiles, using parameters such as right ascension, declination, total magnitude, ellipticity, core radius, and tidal radius (Euclid Collaboration: Urbano et al., 2025, in press, Astronomy & Astrophysics). The code scans all 144 quadrants of an image and injects these sources using the GalSim Python package, convolving them with the instrument PSF. It has been extensively employed to test detection software’s capability to recover the injected parameters. This workflow, particularly suitable for studies of Local Universe dwarf galaxies, also serves as a benchmark for data reduction pipelines and as a validation tool for other deep surveys.
LSBsim is currently available for download at this link, though access is restricted. Interested users must contact the development team to obtain permission.
b) BLOB_DET (SDET)
Part of the effort was dedicated to developing a suite of Python scripts (SDET) available here, with restricted access requiring developer approval. The pipeline relies heavily on the Python package scikit-image and implements three object detection methods:
Laplacian of Gaussians (LoG)
Difference of Gaussians (DoG)
Determinant of the Hessian (DoH)
In this context, a “blob” is defined as a group of connected pixels sharing a common property, such as intensity. Blobs can appear either as bright regions on a dark background or vice versa. LoG and DoG are based on convolving the image with kernels at different spatial scales, while DoH exploits second derivatives of pixel intensities to locate local maxima and minima.
The Rome node, in collaboration with Unisalento, performed initial tests of BLOB_DET (Strafella, Testa, Nucita, et al., in preparation) to identify low surface brightness objects in EUCLID VIS and NIR images. Tests were carried out on both simulated and real mission images. Real images were pre-processed using a background subtraction and uniformization procedure developed within the SWG-LU working group (Dimauro et al., in preparation) to maximize detection of LSB sources and minimize false positives due to suboptimal standard pipeline reduction. Work is ongoing to integrate photometry and surface brightness measurements of candidates into the Python-based software.
c) Convolutional Neural Networks for LSB Detection
The team also explored the use of convolutional neural networks (CNNs) for detecting LSB objects in VIS images. Extensive testing on simulated datasets (produced with LSBsim) demonstrated the CNN’s ability to recognize faint galaxies with high precision, outperforming traditional detection methods. The model achieved an 84.5% success rate for galaxies with integrated magnitudes between 22 and 24, with improved performance for brighter sources. Detection performance, however, declines significantly for magnitudes 24–26, where background noise dominates.
This work formed the basis of the Master’s thesis of Dr. G. Donatiello, defended in 2025 at the University of Salento.
Publications Supported by the Project
Euclid Collaboration; Urbano et al., 2025, A&A, in press
Franco A. et al., 2023, New Astronomy, 103, 10204
Franco A. et al., 2024, New Astronomy, 108, 102174
Franco et al., 2025, ApJS, 279, 20
Franco et al., 2025, ApJS, 279, 33