In the most recent Age of Exploration, deep space exploration has become a pillar of the field of astronomy. The purpose of having a robust space program is simple: that we, as humans, should “undertake it for the most basic of reasons – our self-preservation as a creative, as opposed to stagnating, society .”
In recent years, the amount of data various spacecrafts and satellites have accumulated has overwhelmed the field of study. One way this data is being used by astronomers is in galaxy morphological classification, whose principal goal is to obtain insight into galaxy formation and evolution. Galaxy classification used to require human intervention, as the data received by spacecraft were photographic plates and required visual inspection and analysis. But as more and more data come online (The Sloan Digital Sky Survey alone will produce more than 50 million images of galaxies in the coming years), it has become unrealistic to devote human resources to the time-consuming and expensive process that is galaxy/planet/star classification . With the advent of machine learning, companies like NASA now have the opportunity to automate image analysis through neural network and data mining algorithms . Technological advances in data collection from space had not been matched by similar advances in data analysis (classification) creating a massive imbalance between the rates at which the data being collected was being processed; machine learning offers a way to correct this imbalance by allowing the rate of analysis to exponentially increase as computing power can take the place of human minds.
Neural networks have been one of the most popular machine learning algorithms deployed for morphological classification; in fact, scientists have attempted to use neural networks for classification of morphologies since the early 1990s, with limited degrees of success. But recently artificial neural network algorithms have become much more sophisticated and NASA has been able to see a significant increase in their classification accuracy. In 2017, for example, NASA discovered an eighth planet circling Kepler-90, tying the Kepler-90 solar system with our own for the most number of planets in a single solar system. The planet was found by feeding data from NASA’s Kepler Space Telescope into an artificial neural network programmed to identify exoplanets. To create the neural network, researchers trained the algorithm “using 15,000 previously-vetted signals from the Kepler exoplanet catalogue.” Once the neural network achieved a certain level of accuracy (96%), they applied the algorithm to a previously unanalyzed set of 670 star systems. With the success of this “first” application, NASA now plans to apply the algorithm to the full set of 150,000 star systems .
The success of neural networks in these instances has much broader implications for NASA; machine learning applied to astronomy has reached a level of accuracy and sophistication that NASA can more comfortably deploy these algorithms to process the massive backlog of current and archived astronomical data. These algorithms now have the capability to detect some of the weakest signals of morphology, signals that would have been missed entirely by human classification. NASA is not just realizing time and cost savings by no longer needing to utilize humans for classification; NASA can now analyze data they knew could never have been analyzed by humans in the first place.
One of the biggest challenges of applying machine learning to astronomy is the risk of creating “black box” applications that give little insight and questionable results . In the short term, NASA needs to invest resources in two things: 1) expanding the “known” datasets of galaxy catalogues so that neural networks have a larger variety of input data fed into them and 2) advancing a technique known as deep convolutional neural networks, which have shown to be “as good as humans at face recognition”, to mitigate the “black box” risks of these algorithms . In the longer term, NASA faces a major lack of funding, public apathy, and increased competition due to the commercialization of space. NASA must become more “network-oriented to develop and acquire the technologies it needs .” Relatedly, NASA must embrace open-source solutions, and support crowdsourced ideas; it is through these initiatives that many of the existing galaxy catalogues were developed in the first place. As the galaxy datasets will need to be greatly expanded to feed into machine learning algorithms, and quickly, NASA’s best option is to rely on amateur astronomers to build these datasets, so as not to impede the major progress being made with machine learning and morphological classification. (744 words)
 Blanton, M., et al, (2017). Sloan Digital Sky Survey IV: Mapping the Milky Way, Nearby Galaxies, and the Distant Universe. The Astronomical Journal,154(1), 28th ser., 35. doi:10.3847/1538-3881/aa7567
 Jorge De La Calleja, Olac Fuentes; Machine learning and image analysis for morphological galaxy classification, Monthly Notices of the Royal Astronomical Society, Volume 349, Issue 1, 21 March 2004, Pages 87–93, https://doi.org/10.1111/j.1365-2966.2004.07442.x
 Manda Banerji, Ofer Lahav, Chris J. Lintott, Filipe B. Abdalla, Kevin Schawinski, Steven P. Bamford, Dan Andreescu, Phil Murray, M. Jordan Raddick, Anze Slosar, Alex Szalay, Daniel Thomas, Jan Vandenberg; Galaxy Zoo: reproducing galaxy morphologies via machine learning, Monthly Notices of the Royal Astronomical Society, Volume 406, Issue 1, 21 July 2010, Pages 342–353, https://doi.org/10.1111/j.1365-2966.2010.16713.x