Welcome! "Time-Domain astronomy and informatics" is newly emerging discipline involving astronomers, statisticians, and computer scientists. At the most basic level, we are interested in extracting optimal (and novel) information from a finite dataset of time-series data in a computational-constrained environment. Put another way, we wish to understand the huge landscape of variable stars and transient events in the Universe, using computers (and in particular, machine-learning) to help us do this efficiently.
Please explore our site---the science, the techniques, and the data---and let us know what you think!
Opening up truly new vistas on the dynamic universe requires both rapid data processing & quick decisions about what available resources (e.g., telescopes) must be marshalled to study newly discovered phenomena. This necessitates an intelligent “real-time” machine-based decision or “classification” framework that should be able to deal with incomplete (and in some cases spurious) information.
This collaboration will produce a framework for extracting novel science from large amounts of data in an environment where the computational needs vastly outweigh the available facilities, and intelligent (and dynamic) resource allocation is required. New statistical theory will be developed that will allow current machine learning paradigms to scale to large parallel computing environments. The core result is the production, for projects generating thousands of gigabytes of new data a night (such as the proposed Large Synoptic Survey Telescope), of calibrated probabilistic statements about the physical nature of astronomical events. Uncovering anomalous events that do not fit easily into a currently accepted classification taxonomy - events that may lead to completely new scientific discoveries - is particularly emphasized in our work.
Our collaboration is sponsored by an NSF-CDI grant (award #0941742) "Real-time Classification of Massive Time-series Data Streams" (PI: Bloom).