17 citations · 17 across the 3 of their papers we have counts for
4 papers
Euclid Quick Data Release (Q1) Euclid spectroscopy of quasars. 2. Physical properties from spectral fitting
Euclid Collaboration, J. Calhau, G. Calderone +312
A substantial volume of spectroscopic data has become available with the Euclid Data Release (Q1), which represents a unique opportunity to study quasars (QSOs) in the Euclid Deep…
Galaxy mergers classification using CNNs trained on Sérsic models, residuals and raw images
D. M. Chudy, W. J. Pearson, A. Pollo +5
Galaxy mergers are crucial for understanding galaxy evolution, and with large upcoming datasets, automated methods such as Convolutional Neural Networks (CNNs) are essential for ef…
Galaxy merger challenge: A comparison study between machine learning-based detection methods
B. Margalef-Bentabol, L. Wang, A. La Marca +14
Various galaxy merger detection methods have been applied to diverse datasets. However, it is difficult to understand how they compare. We aim to benchmark the relative performance…
Object classification with Convolutional Neural Networks: from KiDS to Euclid
G. A. Verdoes Kleijn, C. A. Marocico, Y. Mzayek +11
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 fo…