Dynamic Scheduling of Gamma-ray Source Observations

The most exciting recent astrophysical events have involved transient phenomena. Notable examples
are the possible correlation of an astrophysical neutrino with a flaring gamma-ray source, and the
observation of gamma rays and gravitational waves produced by a Kilonova. The potential very-
high-energy gamma-ray emission from such events is of particular scientific interest, tracking extreme
acceleration processes. However, the occurrence of such transients can often only be anticipated on a
very short term and, hence, is not considered in traditional approaches to scheduling observations.
This project sets out to increase the coverage of transient and variable gamma-ray sources through
dynamic scheduling of observations. To this end, time series data representing light curves of gamma-
ray and further energy bands will be used for short-term forecasting of flares, e.g., based on outlier
detection or recurrent neural networks. Combining these forecasts with contextual information, such as
monitored weather conditions, source positions in the sky, and telescope permutations in the
participating array, the observing schedule is optimised.

Any realisation of the above idea, however, has to cope with scalability and responsiveness challenges:
Time series data are collected at high rates (several hundred Hertz for current telescope arrays) and
some gamma-ray sources are known to be variable at minute timescales. Hence, low-latency data
processing and efficient online decision making are of crucial importance for dynamic observation
scheduling. This project aims at providing the respective conceptual and technological foundations, by
answering the following research questions:

What are models for online decision making that combine approaches for flare forecasting with
contextual information for optimal observation scheduling? This includes questions related to
the expressiveness needed for the decision mechanism as well as the temporal and spatial
granularity considered in these models.

How can the online decision making be expressed in a computational model that is based on
streaming data? Here, important aspects are the required operator algebra and data correlation
mechanisms. Also, the notions of state to be maintained during processing is to be clarified.

How to optimise the latency of stream processing for online decision making? Directions to
answer this question are (i) prefetching of contextual information for low-latency assessment of
flare forecasts, (ii) state management for streaming operators, and (iii) approximate stream
processing using data sketches.

In sum, the results of this project will be a grounding of dynamic observation scheduling in models for
data stream processing along with algorithm for their efficient realisation.