  Astronomy is a discipline primarily reliant on visual data. However,
alternative data representation techniques are being explored, in particular
''sonification'', namely, the representation of data into sound. While there is
increasing interest in the astronomical community in using sonification in
research and educational contexts, its full potential is still to be explored.
This study measured the performance of astronomers and non-astronomers to
detect a transit-like feature in time series data (i.e., light curves), that
were represented visually or auditorily, adopting different data-to-sound
mappings. We also assessed the bias that participants exhibited in the
different conditions. We simulated the data of 160 light curves with different
signal-to-noise ratios (SNR). We represented them as visual plots or auditory
streams with different sound parameters to represent brightness: pitch,
duration, or the redundant duration & pitch. We asked the participants to
identify the presence of transit-like features in these four conditions in a
session that included an equal number of stimuli with and without transit-like
features. With auditory stimuli, participants detected transits with
performances above the chance level. However, visual stimuli led to overall
better performances compared to auditory stimuli and astronomers outperformed
non-astronomers. Visualisations led to a conservative response bias (reluctance
to answer ''yes, there is a transit''), whereas sonifications led to more
liberal responses (proneness to respond ''yes, there is a transit''). Overall,
this study contributes to understanding how different representations (visual
or auditory) and sound mappings (pitch, duration, duration & pitch) of time
series data affect detection accuracy and biases.
