Evaluating the efficacy of sonification for signal detection in univariate, evenly sampled light curves using astronify

Abstract

Sonification is the technique of representing data with sound, with potential applications in astronomy research for aiding discovery and accessibility. Several astronomy-focused sonification tools have been developed; however, efficacy testing is extremely limited. We performed testing of astronify, a prototype tool for sonification functionality within the Barbara A. Mikulski Archive for Space Telescopes (MAST). We created synthetic light curves containing zero, one, or two transit-like signals with a range of signal-to-noise ratios (SNRs=3-100) and applied the default mapping of brightness to pitch. We performed remote testing, asking participants to count signals when presented with light curves as a sonification, visual plot, or combination of both. We obtained 192 responses, of which 118 self-classified as experts in astronomy and data analysis. For high SNRs (=30 and 100), experts and non-experts performed well with sonified data (85-100% successful signal counting). At low SNRs (=3 and 5) both groups were consistent with guessing with sonifications. At medium SNRs (=7 and 10), experts performed no better than non-experts with sonifications but significantly better (factor of ~2-3) with visuals. We infer that sonification training, like that experienced by experts for visual data inspection, will be important if this sonification method is to be useful for moderate SNR signal detection within astronomical archives and broader research. Nonetheless, we show that even a very simple, and non-optimised, sonification approach allows users to identify high SNR signals. A more optimised approach, for which we present ideas, would likely yield higher success for lower SNR signals.