Speaker
Description
Astronomical datasets include millions, sometimes billions of records, and in order to handle such volumes of data in the last 20 years astronomers actively use ML methods for various classification and characterization tasks. However, most of those applications utilize supervised ML, which requires large pre-existing training samples. Obtaining those training samples is a complicated task, and it often introduces poorly accounted biases.
One tantalizing possibility is to use unsupervised ML for developing data-driven classifications and, most interestingly, for searching extremely rare or even previously unseen types of objects and phenomena. While such a possibility has been discussed for decades, the achievements in this area are much less prominent than in applications of supervised ML. In this talk, I describe the current state of the field, outline the perspectives of using unsupervised ML for the anomaly and novelty detection in large-scale astronomical surveys, in particular in the upcoming LSST, discuss the problems arising on this path and possible solutions to them, and highlight some related issues that must be addressed by the astronomical community as a whole.
