The SNR of a transit

Author:

Kipping David1ORCID

Affiliation:

1. Department of Astronomy, Columbia University , 550 W 120th Street, New York, NY 10027, USA

Abstract

ABSTRACTAccurate quantification of the signal-to-noise ratio (SNR) of a given observational phenomenon is central to associated calculations of sensitivity, yield, completeness, and occurrence rate. Within the field of exoplanets, the SNR of a transit has been widely assumed to be the formula that one would obtain by assuming a boxcar light curve, yielding an SNR of the form $(\delta /\sigma _0) \sqrt{D}$. In this work, a general framework is outlined for calculating the SNR of any analytic function and it is applied to the specific case of a trapezoidal transit as a demonstration. By refining the approximation from boxcar to trapezoid, an improved SNR equation is obtained that takes the form $(\delta /\sigma _0) \sqrt{(T_{14}+2T_{23})/3}$. A solution is also derived for the case of a trapezoid convolved with a top-hat, corresponding to observations with finite integration time, where it is proved that SNR is a monotonically decreasing function of integration time. As a rule of thumb, integration times exceeding T14/3 lead to a 10 per cent loss in SNR. This work establishes that the boxcar transit is approximate and it is argued that efforts to calculate accurate completeness maps or occurrence rate statistics should either use the refined expression, or even better numerically solve for the SNR of a more physically complete transit model.

Publisher

Oxford University Press (OUP)

Subject

Space and Planetary Science,Astronomy and Astrophysics

Cited by 7 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3