There’s no question that the space industry is undergoing drastic change, but how do you create the proper incentives for success with so many interdependent business models within the space sector? Professor Matt Weinzierl offers his advice using the classic game theory model the “stag hunt.”
Machine learning is a popular topic in most industries these days, and it will come as no surprise that this is true for astronomy and astrophysics as well. University of Colorado PhD student Avery Schiff explores how a deep learning algorithm is being used to classify galaxy morphologies in this article from graduate student astronomy journal Astrobites.
The receiver operating characteristic (ROC) curve for the three questions. As the probability threshold is increased, there are fewer false negatives so the TPR increases. However, the FPR rate also increases. The best algorithm will therefore achieve a high TPR without also generating a high FPR. Figure 2 in the paper.
The search for extraterrestrial life is bound to lead one place — underwater. To prepare for this reality, scientists from Harvard and the Woods Hole Oceanographic Institution are narrowing in on autonomous ocean-floor vehicles equipped with cutting edge cameras and sophisticated sensors that can wirelessly alert researchers hundreds of miles away when something of note happens. Pretty cool stuff? We’d say so.
David Latham of the Harvard-Smithsonian Center for Astrophysics is tasked with an incredible mission — overseeing the selection and study of planets that are viable candidates for life as captured by TESS (the Transiting Exoplanet Survey Satellite). In this interview with the Harvard Gazette, Latham discusses everything from TESS’s ground-breaking camera technology to the conditions required to foster life as we do (or don’t) know it.
