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Sonification is the technique of representing data with sound, with potentialapplications 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, aprototype tool for sonification functionality within the Barbara A. MikulskiArchive for Space Telescopes (MAST). We created synthetic light curvescontaining zero, one, or two transit-like signals with a range ofsignal-to-noise ratios (SNRs=3-100) and applied the default mapping ofbrightness to pitch. We performed remote testing, asking participants to countsignals when presented with light curves as a sonification, visual plot, orcombination of both. We obtained 192 responses, of which 118 self-classified asexperts in astronomy and data analysis. For high SNRs (=30 and 100), expertsand non-experts performed well with sonified data (85-100% successful signalcounting). At low SNRs (=3 and 5) both groups were consistent with guessingwith sonifications. At medium SNRs (=7 and 10), experts performed no betterthan non-experts with sonifications but significantly better (factor of ~2-3)with visuals. We infer that sonification training, like that experienced byexperts for visual data inspection, will be important if this sonificationmethod is to be useful for moderate SNR signal detection within astronomicalarchives and broader research. Nonetheless, we show that even a very simple,and non-optimised, sonification approach allows users to identify high SNRsignals. A more optimised approach, for which we present ideas, would likelyyield higher success for lower SNR signals.

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