Semisupervised Learning for Detecting Inverse Compton Emission in Galaxy Clusters

Inverse Compton (IC) emission associated with the nonthermal component of the intracluster medium (ICM) has been a long-sought phenomenon in cluster physics. Traditional spectral fitting often suffers from the degeneracy between the two-temperature thermal (2T) spectrum and the one-temperature plus IC power-law (1T+IC) spectrum. We present a semisupervised deep-learning approach to search for IC emission in galaxy clusters. We employ a conditional autoencoder (CAE), which is based on an autoencoder with latent representations trained to constrain the thermal parameters of the ICM. The algorithm is trained and tested using synthetic NuSTAR X-ray spectra with instrumental and astrophysical backgrounds included. The training data set only contains 2T spectra, which is more common than 1T+IC spectra. Anomaly detection is performed on the validation and test data sets consisting of 2T spectra as the normal set and 1T+IC spectra as anomalies. With a threshold anomaly score, chosen based on cross validation, our algorithm is able to identify spectra that contain an IC component in the test data set, with a balanced accuracy (BAcc) of 0.64, which outperforms traditional spectral fitting (BAcc = 0.55) and ordinary autoencoders (BAcc = 0.55). Traditional spectral fitting is better at identifying IC cases among true IC spectra (a better recall), while IC predictions made by CAE have a higher chance of being true IC cases (a better precision), demonstrating that they mutually complement each other.