The SNR of idealized radial velocity signals
ABSTRACT One of the most basic quantities relevant to planning observations and assessing detection bias is the signal-to-noise ratio (SNR). Remarkably, the SNR of an idealized radial velocity (RV) signal has not been previously derived beyond scaling behaviours and ignoring orbital eccentricity. In...
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Veröffentlicht in: | Monthly notices of the Royal Astronomical Society 2024-07, Vol.532 (1), p.604-612 |
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Sprache: | eng |
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Zusammenfassung: | ABSTRACT
One of the most basic quantities relevant to planning observations and assessing detection bias is the signal-to-noise ratio (SNR). Remarkably, the SNR of an idealized radial velocity (RV) signal has not been previously derived beyond scaling behaviours and ignoring orbital eccentricity. In this work, we derive the RV SNR for three relevant cases to observers. First, we consider a single mass orbiting a star, revealing the expected result that $\mathrm{SNR}\propto K \sqrt{T}$, where T is the observing window, but an additional dependence on eccentricity and argument of periastron. We show that the RV method is biased towards companions with their semimajor axes aligned to the observer, which is physically intuitive, but also less obviously that the marginalized bias to eccentricity is negligible until one reaches very high eccentricities. Secondly, we derive the SNR necessary to discriminate eccentric companions from 2:1 resonance circular orbits, although our result is only valid for eccentricities $e\lesssim 0.3$. We find that the discriminatory SNR is $\tfrac{9}{8} e^2 (1-e^2)^{-1/2}$ times that of the eccentric planet solution’s SNR, and is thus typically an order of magnitude less. Finally, we have obtained a semi-empirical expression for the SNR of the idealized Rossiter–McLaughlin (RM) effect, revealing the bias with respect to spin–orbit alignment angle. Our formula is valid to within 10 per cent accuracy in 95.45 per cent of the training samples used (for $b\le 0.8$), but larger deviations occur when comparing to different RM models. |
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ISSN: | 0035-8711 1365-2966 |
DOI: | 10.1093/mnras/stae1423 |