11 papers
Tabular foundation models for the estimation of probabilistic quasar photometric redshifts in S-PLUS
Raquel R. Valença, Lilianne Nakazono, Rafael Izbicki +6
We assess whether tabular foundation models can be used as off-the-shelf probabilistic photometric-redshift estimators for quasars in the 12-band S-PLUS DR6 survey, where colour-re…
The S-PLUS Fifth Data-Release: Over 4500 square degrees of the Southern Sky and a multicolor view of the Hydra and Antlia galaxy clusters
Erik Vinicius Rodrigues de Lima, Gustavo Bernhard Oliveira Schwarz, Fábio Rafael Herpich +55
We present the 5th data release (DR5) of the Southern Photometric Local Universe Survey (S-PLUS), covering 4592 square degrees across 2491 fields. Observations were conducted with…
S-PLUS Clusters And Large-scale Environments (SCALE): II. PZWav versus redMaPPer identification of eRosita groups
L. Doubrawa, A. Finoguenov, E. S. Cypriano +10
We present the construction and characterization of a multi-wavelength catalog of galaxy groups and clusters by matching optical detections from the Southern Photometric Local Univ…
S-PLUS Clusters And Large-scale Environments (SCALE): I. A catalog of known clusters and groups in DR5 and a pilot study of Abell 4038
C. Mendes de Oliveira, N. M. Cardoso, P. A. A. Lopes +26
Within the framework of the Southern Photometric Local Universe Survey (S-PLUS), we introduce -PLUS lusters nd arge-scale nvironments (…
SAGUI: SED-based Segmentation of Multi-band Galaxy Images -- Application to JADES in GOODS-South
Rafael S. de Souza, Andressa Wille, Shravya Shenoy +11
We present sagui, a modular framework for the analysis of multi-band imaging data in spatially resolved galaxies, with synergies to integral-field spectroscopy (IFS). Building on t…
OJALÃ: Optimizing J-PAS Astronomy for Large-scale Analysis. A foundation model for the SED of galaxies, QSOs and stars
G. MartÃnez-Solaeche, R. M. González Delgado, R. GarcÃa-Benito +37
The advent of large-scale surveys requires efficient ML techniques to exploit the information of massive datasets. We present OJALA, a transformer-based autoregressive foundation m…