J-PLUS: Identifying Outlier Spectral Energy Distributions in the Fourth Data Release Using Unsupervised Machine Learning
Abstract
The selection of candidates belonging to rare or underrepresented stellar populations, as well as novel objects, is essential for spectroscopic follow-up. In this work, we demonstrate how combining broad- and narrow-band photometric information with machine-learning techniques enables this selection. Using photometric data in twelve filters from the Javalambre Photometric Local Universe Survey (J-PLUS) as input, we applied an anomaly detection technique based on autoencoders to identify spectral energy distributions (SEDs) that deviate from the dominant pattern in the J-PLUS objects sample. Subsequently, we applied the t-distributed stochastic neighbor embedding (t-SNE) algorithm to the J-PLUS colors of the outlier SEDs population. Combining information from the SIMBAD database and other astronomical surveys to classify the outliers identified by the autoencoder, we are able to characterize over-densities in the t-SNE map. Our study specifically focuses on well-defined islands in the t-SNE space, corresponding to specific populations. We identified a large sample of white dwarfs and low-mass stars, including a small subset of polluted white dwarfs, and report potential candidates for spectroscopic follow-up, including 19 carbon-rich stars, 11 cataclysmic variables and 170 white-dwarfs-main-sequence binaries, all previously unreported in the literature.
Versions
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BibTeX
@misc{quispehuaynasi2026jplus,
title = {{J-PLUS: Identifying Outlier Spectral Energy Distributions in the Fourth Data Release Using Unsupervised Machine Learning}},
author = {F. Quispe-Huaynasi and F. Roig and V. Loaiza-Tacuri and V. M. Placco and V. Marra and F. Jiménez-Esteban and A. del Pino and B. Anguiano and J. A. Alzate-Trujillo and S. Daflon and C. López-Sanjuan and P. Cruz and A. Alvarez-Candal and E. Machado-Pereira and J. Alcaniz and R. E. Angulo and J. Cenarro and D. Cristóbal-Hornillos and R. Dupke and A. Ederoclite and A. Hernán-Caballero and C. Hernández-Monteagudo and A. Marin-Franch and M. Moles and L. Sodré Jr and J. Varela and H. Vázquez-Ramió and J. Zaragoza-Cardiel},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.27252},
eprint = {2610.27252},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.27252},
}
RIS
TY - UNPB
TI - J-PLUS: Identifying Outlier Spectral Energy Distributions in the Fourth Data Release Using Unsupervised Machine Learning
AU - F. Quispe-Huaynasi
AU - F. Roig
AU - V. Loaiza-Tacuri
AU - V. M. Placco
AU - V. Marra
AU - F. Jiménez-Esteban
AU - A. del Pino
AU - B. Anguiano
AU - J. A. Alzate-Trujillo
AU - S. Daflon
AU - C. López-Sanjuan
AU - P. Cruz
AU - A. Alvarez-Candal
AU - E. Machado-Pereira
AU - J. Alcaniz
AU - R. E. Angulo
AU - J. Cenarro
AU - D. Cristóbal-Hornillos
AU - R. Dupke
AU - A. Ederoclite
AU - A. Hernán-Caballero
AU - C. Hernández-Monteagudo
AU - A. Marin-Franch
AU - M. Moles
AU - L. Sodré Jr
AU - J. Varela
AU - H. Vázquez-Ramió
AU - J. Zaragoza-Cardiel
PY - 2026
DA - 2026/10/19
PB - arXiv
DO - 10.48550/arxiv.2610.27252
UR - https://arxiv.org/abs/2610.27252
AB - The selection of candidates belonging to rare or underrepresented stellar populations, as well as novel objects, is essential for spectroscopic follow-up. In this work, we demonstrate how combining broad- and narrow-band photometric information with machine-learning techniques enables this selection. Using photometric data in twelve filters from the Javalambre Photometric Local Universe Survey (J-PLUS) as input, we applied an anomaly detection technique based on autoencoders to identify spectral energy distributions (SEDs) that deviate from the dominant pattern in the J-PLUS objects sample. Subsequently, we applied the t-distributed stochastic neighbor embedding (t-SNE) algorithm to the J-PLUS colors of the outlier SEDs population. Combining information from the SIMBAD database and other astronomical surveys to classify the outliers identified by the autoencoder, we are able to characterize over-densities in the t-SNE map. Our study specifically focuses on well-defined islands in the t-SNE space, corresponding to specific populations. We identified a large sample of white dwarfs and low-mass stars, including a small subset of polluted white dwarfs, and report potential candidates for spectroscopic follow-up, including 19 carbon-rich stars, 11 cataclysmic variables and 170 white-dwarfs-main-sequence binaries, all previously unreported in the literature.
ER -