The SPectral Image Typer (SPIT)

Abstract

We present the Spectral Image Typer (SPIT), a convolutional neural network (CNN) built to classify spectral images. In contrast to traditional, rules-based algorithms which rely on meta data provided with the image (e.g. header cards), SPIT is trained solely on the image data. We have trained SPIT on 2,004 human-classified images taken with the Kast spectrometer at Lick Observatory with types of Bias, Arc, Flat, Science and Standard. We include several pre-processing steps (scaling, trimming) motivated by human practice and also expanded the training set to balance between image type and increase diversity. The algorithm achieved an accuracy of 98.7% on the held-out validation set and an accuracy of 98.7% on the test set of images. We then adopt a slightly modified classification scheme to improve robustness at a modestly reduced cost in accuracy (98.2%). The majority of mis-classifications are Science frames with very faint sources confused with Arc images (e.g. faint emission-line galaxies) or Science frames with very bright sources confused with Standard stars. These are errors that even a well-trained human is prone to make. Future work will increase the training set from Kast, will include additional optical and near-IR instruments, and may expand the CNN architecture complexity. We are now incorporating SPIT in the PYPIT data reduction pipeline (DRP) and are willing to facilitate its inclusion in other DRPs.