ﻻ يوجد ملخص باللغة العربية
Bayesian data analysis techniques, together with suitable statistical models, can be used to obtain much more information from noisy data than the traditional frequentist methods. For instance, when searching for periodic signals in noisy data, the Bayesian techniques can be used to define exact detection criteria for low-amplitude signals - the most interesting signals that might correspond to habitable planets. We present an overview of Bayesian techniques and present detailed analyses of the HARPS-TERRA velocities of HD 40307, a nearby star observed to host a candidate habitable planet, to demonstrate in practice the applicability of Bayes rule to astronomical data.
The abilities of radial velocity exoplanet surveys to detect the lowest-mass extra-solar planets are currently limited by a combination of instrument precision, lack of data, and jitter. Jitter is a general term for any unknown features in the noise,
The hunt for Earth analogue planets orbiting Sun-like stars has forced the introduction of novel methods to detect signals at, or below, the level of the intrinsic noise of the observations. We present a new global periodogram method that returns mor
Exoplanet detection with precise radial velocity (RV) observations is currently limited by spurious RV signals introduced by stellar activity. We show that machine learning techniques such as linear regression and neural networks can effectively remo
GJ667C is the least massive component of a triple star system which lies at a distance of about 6.8 pc (22.1 light-years) from Earth. GJ667C has received much attention recently due to the claims that it hosts up to seven planets including three supe
Planetary rotation rates and obliquities provide information regarding the history of planet formation, but have not yet been measured for evolved extrasolar planets. Here we investigate the theoretical and observational perspective of the Rossiter-M