Optimising the global detection of solar-like oscillations. Tuning the frequency range for asteroseismic detection predictions and searches

A well-established method exists for predicting the detectability of solar-like oscillations that has seen extensive application supporting target selection strategies for space-based photometric missions. The method assesses the probability of making an asteroseismic detection based on the expected global signal-to-noise ratio (SNR) of the observed signal due to the oscillations against the broadband background due to shot noise and granulation. Known stellar parameters are used to compute the expected oscillation and granulation signal, while instrumental specifications and the apparent brightness of the target are used to compute the expected shot noise. We explore whether there is an optimal choice for the range in frequency, W, over which the global SNR is determined. The observed power in solar-like oscillations is assumed to follow a Gaussian-like envelope of the full width at half maximum Γ_̊m env, centred on the frequency of maximum oscillation power. It has been common practice to set W ≃ 2Γ_ ̊m env when predicting detections. We make numerical predictions of the global SNR and resulting detection probabilities for a range of underlying stellar and observational parameters, adopting different choices for the width using W =α Γ_ ̊m env , where α is a multiplicative coefficient that controls the width. We also explored the effect of this choice on detection yields across an ensemble of targets, using a sample of bright solar-like oscillators observed by TESS as a representative example. We found that the commonly adopted value of α ≃ 2 is a sub-optimal choice and that adopting a range with α ≃ 1.2 maximises the detection probability. There can also be a substantial effect on the predicted detection yield across a sample of stars. In summary, we recommend the adoption of a range W ≃ 1.2Γ_ ̊m env , not only in computations of the detection probabilities, but also in actual searches for oscillations in real data based on testing the significance of excess mode power, since its adoption will also optimise the probability of making robust detections.