  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.
