Use Case

Transient Detection in Radio Astronomy Search Data

J. Cordes (Cornell) with M. McLaughlin (West Virginia University), V. Kaspi (McGill University), B. Allen (Albert Einstein Institute), and PALFA collaborators located at the National Radio Observatory, Max Planck Institute for Radio Astronomy, University of British Columbia-Vancouver, University of Texas at Brownville, Naval Research Laboratory, Franklin and Marshall College, University of Wisconsin-Milwaukee, and Columbia University


  • Low-latency/on-demand searching of time-varying, including one-time signals in radio astronomy data
  • Builds on existing Cornell/CAC collaboration

Why Aristotle?

  • Bursting on-demand to analyze new data
  • High-speed transport of very large data sets into and around the Federation


  • Developed a new containerized solution that features a full suite of all relevant pulsar-processing software, Python Conda distributions, the core PRESTO pulsar search package to perform transient search using the Spitler modulation index method, and the decimation code.
  • Implemented an improved reproduction of the PALFA2 pipeline for detecting single pulse candidates that may be a Fast Radio Burst (FRB) source; this pipeline includes PRESO functionality, modulation index calculation, parameter customization, and the production of graphic data output.
  • Built a new flexible framework for running radio astronomy searches; data are read from their native format into NumPy arrays, and the pipeline‚Äôs routines are selected from a configuration file and includes a friends-of-friends search, and also allows the running of the pipeline of Laura Spitler who discovered FRB 1211102.


  • Test several detection algorithms independently, including friend-of-friends and other two dimensional searchers, and the PRESTO-based (PulsR Exploration and Search TOolkit) detection method used for the Spitler discovery of Fast Radio Burst (FRB) 1211102, running in the new pipeline framework.
  • Combine these methods to evaluate the improvement in signal detection sensitivity.
  • Build a classification scheme for signals (particularly the RFI signals).
  • Make the Docker container publicly available and document the pipeline to be installed on it.
  • Perform a production run on 10s of TBs of raw search data with cross federation resources and NSF Jetstream if allocation available.


On demand access to new survey data will provide astronomers and the public with more opportunities to make discoveries
On demand access to new survey data will provide astronomers and the public with more opportunities to make discoveries

Next   Learn more