Prototype of LSST Transient Broker Available

Current and next-generation time-domain surveys will produce more data than ever before.  The Zwicky Transient Facility generates up to 400,000 alerts per night, while the LSST is expected to produce 10 or 100 times as many. There are significant computational challenges in processing these data into a digestible format, in near real-time, in order to identify interesting transients and trigger follow-up such as spectroscopy and observations in other wavelength regimes. To this end, we present Lasair, the transient alerts broker for the LSST:UK collaboration. In preparation for LSST's data stream, Lasair ingests the ZTF public alert stream into a relational database, assimilates the alerts into objects, and produces lightcurves and reliable cross-matches to star and galaxy catalogues. Lasair is a web application, and we provide streams of interesting objects, as well as access to a full SQL search engine. Registration to the website is optional, free, and open to all.

There are several options to access the data. The tables can be searched and joined using normal SQL SELECT queries, with an SQL form builder supplied. Useful SQL queries can be stored by registered users, and either kept private or made public. The latter queries are called "streams" - useful substreams of the data that the Lasair team and users have provided. Often, a scientist would like to keep track of a set of sources, and is most interested in any transient activity associated with these (e.g. to monitor specific AGN activity). Lasair users can input lists of up to a few thousand sources (called watchlists), which are stored with their account, and ask for a crossmatch at any future time -like a multiple cone search. Watchlist owner alerts are under development, reducing to minutes the time from observation to astronomer alert. Experienced users can perform more advanced analysis on the results of their queries using Jupyter notebooks, with several examples provided. They can, for example, combine ZTF data with external resources such as machine-learning analysis of light curves.

Lasair is a collaboration of the University of Edinburgh and Queen's University Belfast funded through the LSST:UK Science Centre Phase A programme. Further information can be found in Smith et al (2019).