Publications

LensFlow: A Convolutional Neural Network in Search of Strong Gravitational Lenses (May 16, 2016)
Milad Pourrahmani, Hooshang Nayyeri, Asantha Cooray
Abstract:
We have entered the era of big data astronomy. Sky surveys such as the LSST, Euclid, and WFIRST will produce more imaging data than humans can ever analyze by eye. The challenges of designing such surveys are no longer merely instrumentational, but they also demand powerful data analysis and classification tools that can identify astronomical objects autonomously. To gradually prepare for the era of autonomous astronomy, we present our machine learning classification algorithm for identifying strong gravitational lenses from wide-area surveys using convolutional neural networks; LensFlow. We train and test the algorithm using a wide variety of strong gravitational lens configurations from simulations of lensing events. Images are processed through multiple convolutional layers which extract feature maps necessary to assign a lens probability to each image. LensFlow provides a ranking scheme for all sources which could be used to identify potential gravitational lens candidates by significantly reducing the number of images that have to be visually inspected. We further apply our algorithm to the HST/ACS i-band observations of the COSMOS field and present our sample of identified lensing candidates. The developed machine learning algorithm is much more computationally efficient than classical lens identification algorithms and is ideal for discovering such events across wide areas from current and future surveys such as LSST and WFIRST.