  We present a deep learning model to predict the r-band bulge-to-total light
ratio (B/T) of nearby galaxies using their multi-band JPEG images alone. Our
Convolutional Neural Network (CNN) based regression model is trained on a large
sample of galaxies with reliable decomposition into the bulge and disk
components. The existing approaches to estimate the B/T use galaxy
light-profile modelling to find the best fit. This method is computationally
expensive, prohibitively so for large samples of galaxies, and requires a
significant amount of human intervention. Machine learning models have the
potential to overcome these shortcomings. In our CNN model, for a test set of
20000 galaxies, 85.7 per cent of the predicted B/T values have absolute error
(AE) less than 0.1. We see further improvement to 87.5 per cent if, while
testing, we only consider brighter galaxies (with r-band apparent magnitude <
17) with no bright neighbours. Our model estimates B/T for the 20000 test
galaxies in less than a minute. This is a significant improvement in inference
time from the conventional fitting pipelines, which manage around 2-3 estimates
per minute. Thus, the proposed machine learning approach could potentially save
a tremendous amount of time, effort and computational resources while
predicting B/T reliably, particularly in the era of next-generation sky surveys
such as the Legacy Survey of Space and Time (LSST) and the Euclid sky survey
which will produce extremely large samples of galaxies.
