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.

We show unsupervised machine learning techniques are a valuable tool for both visualizing and computationally accelerating the estimation of galaxy physical properties from photometric data. As a proof of concept, we use self organizing maps (SOMs) to visualize a spectral energy distribution (SED) model library in the observed photometry space. The resulting visual maps allow for a better understanding of how the observed data maps to physical properties and to better optimize the model libraries for a given set of observational data. Next, the SOMs are used to estimate the physical parameters of 14,000 z ∼ 1 galaxies in the COSMOS field and found to be in agreement with those measured with SED fitting. However, the SOM method is able to estimate the full probability distribution functions for each galaxy up to ∼ 10 6 times faster than direct model fitting. We conclude by discussing how this speed up and learning how the galaxy data manifold maps to physical parameter space and visualizing this mapping in lower dimensions helps overcome other challenges in galaxy formation and evolution.

SPHEREx, the Spectro-Photometer for the History of the Universe, Epoch of Reionization, and ices Explorer, is a NASA MIDEX mission planned for launch in 2024. SPHEREx will carry out the first all-sky spectral survey at wavelengths between 0.75µm and 5µm with spectral resolving power ∼40 between 0.75 and 3.8µm and ∼120 between 3.8 and 5µm At the end of its two-year mission, SPHEREx will provide 0.75-to-5µm spectra of each 6. 2×6. 2 pixel on the sky -14 billion spectra in all. This paper updates an earlier description of SPHEREx presenting changes made during the mission's Preliminary Design Phase, including a discussion of instrument integration and test flow and a summary of the data processing, analysis, and distribution plans.