J-PLUS: Understanding outlier white dwarfs in the third data release via dimensionality reduction

Aims. We present the white dwarf catalog derived from the third data release of the Javalambre Photometric Local Universe Survey (J-PLUS DR3), which covers 3284 deg2 using 12 optical filters. A particular focus is given to the classification of outlier sources, those largely incompatible with the theoretical models used in the analysis, through dimensionality reduction techniques. Methods. We applied a Bayesian fitting process to the 12-band J-PLUS photometry of white dwarf candidates from Gaia EDR3. The derived parameters were effective temperature, surface gravity, and parallax. We used theoretical models from H- and He-dominated atmospheres, with priors applied to parallax and spectral type. From the posteriors, we derived the probability of an H-dominated atmosphere and of calcium absorption for each source. Outliers were identified as sources withχ2 ≥ 23.2, indicating significant deviations from the best-fitting model. We analyzed the residuals from the fits using the uniform manifold approximation and projection (<ns0:monospace>UMAP</ns0:monospace>) technique, which enables the classification of outliers into distinct categories. Results. The catalog includes 14 844 white dwarfs with r ≤ 20 mag and 1 ≤ ϖ < 100 mas, with 72% of the sources lacking spectroscopic (R ≳ 500) classification. The application of UMAP to the residuals identified three main types of outliers: random measurement fluctuations (391 sources), metal-polluted white dwarfs (98 sources), and two-component systems (282 sources). The last category also includes white dwarfs with strong carbon absorption lines. We validated the reliability of J-PLUS classifications by comparison with spectroscopy from the Sloan Digital Sky Survey and the Dark Energy Spectroscopic Instrument, and with Gaia BP/RP low-resolution spectra, confirming a one-to-one correspondence between J-PLUS photometric and spectroscopic classifications. Conclusions. The J-PLUS DR3 white dwarf catalog provides a robust dataset for statistical studies. The use of dimensionality reduction techniques enhances the identification of peculiar objects, making this catalog a valuable resource for the selection of interesting targets such as metal-polluted white dwarfs or binary systems.