Speaker
Description
Large-area multi-band photometric surveys produce expansive catalogs of spectral energy distributions (SEDs) that frequently contain missing or compromised data points due to image artifacts. Standard machine learning architectures are brittle to incomplete sequences, while traditional template-fitting imputation scales poorly and systematically underestimates uncertainties. We present J-Patch, a Transformer encoder deep learning architecture designed for missing data imputation and anomaly detection in photometric SEDs. Bypassing predefined spectral templates, J-Patch treats an SED as an unordered set of measurements paired with a continuous wavelength positional encoding, utilizing self-attention mechanisms to capture non-linear dependencies between photometric bands. The model predicts both the missing flux and the corresponding total variance. Applying a leave-one-out imputation strategy to the J-PLUS DR4 catalog, we demonstrate that J-Patch flux predictions exhibit a systematically lower dispersion than the empirical scatter derived from duplicated observations. The predicted total variance accurately models the true residual variance across all magnitudes, enabling the detection of subtle systematics such as Eddington bias at the faint end. Furthermore, this predictive fidelity allows for robust artifact identification; extreme discrepancies (|f_pred - f_obs|/σ > 20) are almost exclusively driven by erroneous observed photometry. We also present a value-added catalog containing leave-one-out imputed photometry for 78.6 million unique J-PLUS DR4 sources up to magnitude r < 22.