AAS2RTO: Automated Alert Streams to Real-Time Observations

Context. The upcoming Vera C. Rubin Legacy Survey of Space and Time (LSST) will discover tens of thousands of astrophysical transients per night, far outpacing available spectroscopic follow-up capabilities. Carefully prioritising candidates for follow-up observations will maximise the scientific return from small telescopes with a single-object spectrograph. Aims. We introduce AAS2RTO, an astrophysical transient candidate prioritisation tool written in Python. AAS2RTO is flexible in that any number of criteria that consider observed properties of transients can be implemented. The visibility of candidates from a given observing site is also considered. The prioritised list of candidates provided by AAS2RTO is continually updated when new transient data are made available. Therefore, it can be applied to observing campaigns with a wide variety of scientific motivations. Methods. AAS2RTO uses a greedy algorithm to prioritise candidates. Candidates are represented by a single numerical value, or ‘score’. Scores are computed by constructing simple numerical factors that individually consider the competing facets of a candidate that make it suitable for follow-up observation. AAS2RTO is currently configured to work primarily with photometric data from the Zwicky Transient Facility (ZTF), distributed by certified LSST community brokers. Results. We provide an example of how AAS2RTO can be used by defining a set of criteria to prioritise observations of type Ia supernovae (SNe Ia) close to peak brightness, in preparation for observations with the spectrograph at the Danish-1.54 m telescope. Using a sample of archival alerts from ZTF, we evaluate the criteria we have designed to estimate the number of SNe Ia that we will be able to observe with a 1.5 m telescope. Finally, we evaluate the performance of our criteria when applied to mock LSST observations of SNe Ia.