Abstract A machine-learning–based method is developed to identify objects with unusual stellar spectra. The method employs an autoencoder, a neural network trained to compress spectral data into a low-dimensional representation and subsequently reconstruct it. Spectra that deviate significantly from the dominant patterns in the training dataset are identified using the reconstruction error as an anomaly score. The models are applied to selected datasets from the MaNGA Stellar Library, an empirical library of stellar spectra. Several spectra are flagged as anomalous: an object with likely instrumental and/or reduction issues, two carbon stars, and an oxygen-rich thermally pulsating asymptotic giant branch star. The sources of the large reconstruction errors are examined, and the effectiveness and limitations of autoencoder-based approaches for detecting anomalous stellar spectra are discussed.
Akihiro Suzuki (Mon,) studied this question.