Studies of cosmology, galaxy evolution, and galaxy clustering all critically depend on redshift measurements. However, spectroscopic redshifts can only be obtained for a small fraction of the galaxies detected in current and next-generation deep imaging surveys, so most redshifts must be estimated from the images alone. This project introduces a promising new neural network architecture, called a deep capsule network, which will leverage pixel-level information from imaging surveys to estimate photometric redshifts. Principal objectives are 1) achieving state-of-the-art accuracy on a common wide-field test data set; 2) extending these methods to higher redshifts using Legacy Survey imaging and Dark Energy Spectroscopic Instrument data; and 3) combining the resolved optical imaging with integrated multiwavelength photometry in the ultraviolet and infrared, focusing on very low redshift galaxies that might host gravitational wave sources. The work includes graduate and undergraduate research, and a summer research boot camp and weekly seminar series. All course materials from the bootcamp will be made publicly available with open access.
Estimating photometric redshifts is a well-posed problem for machine learning algorithms because spectroscopic redshifts can provide definitive measurements for training. The pooling operation that fueled the widespread success of convolutional neural networks throws away fine-grained spatial information and ultimately limits their ability to generalize to novel viewpoints and to parse blended objects. The deep capsule network overcomes these drawbacks of convolutional neural networks, and is much more easily interpreted. Projects to be carried out, and suitable for student research, include combining Sloan capsule network results with other photo-z methods to improve overall accuracy, and applying capsule networks to simulated data specifically to test performance on blended objects.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
