SPARKESX: Single-dish PARKES data sets for finding the uneXpected -- A data challenge

TL;DR

SPARKESX is a comprehensive dataset of real and simulated high-time resolution radio observations designed to facilitate the development of algorithms capable of detecting a wide range of known and unknown astronomical signals.

Contribution

It introduces the SPARKESX dataset, combining real and simulated data to challenge and improve algorithms for discovering diverse and unexpected astronomical sources.

Findings

Standard pulsar search software detects some injected signals.

The dataset reveals limitations of current algorithms in identifying unknown signals.

SPARKESX enables testing and development of more versatile detection methods.

Abstract

New classes of astronomical objects are often discovered serendipitously. The enormous data volumes produced by recent high-time resolution, radio-telescope surveys imply that efficient algorithms are required for a discovery. Such algorithms are usually tuned to detect specific, known sources. Existing data sets therefore likely contain unknown astronomical sources, which will remain undetected unless algorithms are developed that can detect a more diverse range of signals. We present the Single-dish PARKES data challenge for finding the uneXpected (SPARKESX), a compilation of real and simulated high-time resolution observations. SPARKESX comprises three mock surveys from the Parkes "Murriyang" radio telescope. A broad selection of simulated and injected expected signals (such as pulsars, fast radio bursts), poorly characterised signals (plausible flare star signatures) and unknown unknowns are generated for each survey. The goal of this challenge is to aid in the development of new algorithms that can detect a wide-range of source types. We show how successful a typical pipeline based on the standard pulsar search software, PRESTO, is at finding the injected signals. The dataset is publicly available at https://doi.org/10.25919/fd4f-0g20.