vestrand 2 nd Zwicky Workshop Thinking Telescopes, RAPTOR, and GRB Follow-up by Tom Vestrand Los Alamos National Laboratory

vestrand 2 nd Zwicky Workshop Machine Learning GENIE, ML Classifiers, Anomaly Detection Context Knowledge Record of Sky variability (Virtual Observatories), Massive Distributed Disk Array Robotic Hardware Wide-Field Sky Monitoring Rapid Response Telescopes, Real Time Pipeline Thinking Telescopes An Engine for Discovery in the Time Domain Goal is to Integrate Three Components

vestrand 2 nd Zwicky Workshop Thinking Telescope Objectives Find fast transients, distinguish foreground from celestial sources Monitoring of persistent sources for important changes in real time Machine learning merged with real time context information Anomaly detection, automated classification “find more like this” Learn to optimize telescope network response, respond in real time System Adaptability; Querying the Sky

vestrand 2 nd Zwicky Workshop

vestrand 2 nd Zwicky Workshop RAPTOR design approach Must be a full system capable of both finding and following-up transients Modular, scalable, techniques COTS (commercial off-the-shelf) components Distributed aperture approach Must be robust, not requiring optimal imaging and tuning

vestrand 2 nd Zwicky Workshop Raptor: Sky Monitoring with Both Eyes Open Wide-field imaging system monitors ~1300 square-deg with resolution ~35 arcsec and limiting magnitude of R~13 th in 60 seconds. ( like the rod cells of the retina ) Each array has a “fovea” telescope with limiting magnitude of R~16.5 (60 sec), resolution of ~7 arcsec and Gunn g (or r) filter. Provides color, better resolution, and faster cadence light curves (cone cells of fovea) Rapidly slewing mount places the “fovea” anywhere in the field in <3 seconds. (rapid eye movement). Two identical arrays are separated by ~38 km. (stereoscopic vision)

vestrand 2 nd Zwicky Workshop Memory and Context

vestrand 2 nd Zwicky Workshop Machine Learning Automated identification of artifacts and transients in direct and difference images. Automated classification of celestial objects based on temporal and spectral properties. Real time recognition of important deviations from normal behavior for persistent sources.

vestrand 2 nd Zwicky Workshop

vestrand 2 nd Zwicky Workshop

vestrand 2 nd Zwicky Workshop

vestrand 2 nd Zwicky Workshop

vestrand 2 nd Zwicky Workshop Taxonomy of GRB Optical Emission in three classes Prompt Optical Emission varying simultaneously with prompt gamma- rays. Early Afterglow Emission that may start during prompt gamma-rays, but persists after gamma-rays fade. Late Afterglow Emission that can last for hours to days.

vestrand 2 nd Zwicky Workshop In the Standard Theoretical Framework it makes sense to attribute the components to Prompt optical emission is generated by internal shocks in ejecta---driven by engine. Early afterglow is a reverse shock driven into ejecta by interaction with surroundings. Late afterglow is generated by forward external shocks driven into surrounding medium.

vestrand 2 nd Zwicky Workshop

vestrand 2 nd Zwicky Workshop

vestrand 2 nd Zwicky Workshop

vestrand 2 nd Zwicky Workshop Conclusions: What one needs to search for fast optical transients Ability to filter false positives robustly Real time follow-up Rank follow-up priorities Configure the response to optimize scientific yield, networking of telescopes All this must be autonomously without a human in the loop.

vestrand 2 nd Zwicky Workshop