Using cGANs for Anomaly Detection: Identifying Astronomical Anomalies in JWST Imaging

We present a proof of concept for mining JWST imaging data for anomalous galaxy populations using a conditional Generative Adversarial Network (cGAN). We train our model to predict long wavelength NIRcam fluxes (LW: F277W, F356W, F444W between 2.4 and 5.0 μm) from short wavelength fluxes (SW: F115W, F150W, F200W between 0.6 and 2.3 μm) in ~2000 galaxies. We test the cGAN on a population of 37 Extremely Red Objects (EROs) discovered by the CEERS JWST Team. Despite their red long wavelength colors, the EROs have blue short wavelength colors (F150W - F200W ~ 0 mag) indicative of bimodal SEDs. Surprisingly, given their unusual SEDs, we find that the cGAN accurately predicts the LW NIRcam fluxes of the EROs. However, it fails to predict LW fluxes for other rare astronomical objects, such as a merger between two galaxies, suggesting that the cGAN can be used to detect some anomalies. (The source code and trained model (Pearce-Casey 2023) are available at https://github.com/RubyPC/Anomaly_Detection_with_cGANs.)