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Register a new userAnalytic Approximations for Transit Light Curve Observables, Uncertainties, and Covariances
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The light curve of an exoplanetary transit can be used to estimate the planetary radius and other parameters of interest. Because accurate parameter estimation is a non-analytic and computationally intensive problem, it is often useful to have analytic approximations for the parameters as well as their uncertainties and covariances. Here we give such formulas, for the case of an exoplanet transiting a star with a uniform brightness distribution. We also assess the advantages of some relatively uncorrelated parameter sets for fitting actual data. When limb darkening is significant, our parameter sets are still useful, although our analytic formulas underpredict the covariances and uncertainties.
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The light curve of an exoplanetary transit can be used to estimate the planetary radius and other parameters of interest. Because accurate parameter estimation is a non-analytic and computationally intensive problem, it is often useful to have analytic approximations for the parameters as well as their uncertainties and covariances. Here we give such formulas, for the case of an exoplanet transiting a star with a uniform brightness distribution. When limb darkening is significant, our parameter sets are still useful, although our analytic formulas underpredict the covariances and uncertainties.
In this paper a set of analytic formulae are presented with which the partial derivatives of the flux obscuration function can be evaluated -- for planetary transits and eclipsing binaries -- under the assumption of quadratic limb darkening. The knowledge of these partial derivatives is crucial for many of the data modeling algorithms and estimates of the light curve variations directly from the changes in the orbital elements. These derivatives can also be utilized to speed up some of the fitting methods. A gain of ~8 in computing time can be achieved in the implementation of the Levenberg-Marquardt algorithm, relative to using numerical derivatives.
New sum and product uncertainty relations, containing variances of three or four observables, but not containing explicitly their covariances, are derived. One of consequences is the new inequality, giving a nonzero lower bound for the product of two variances in the case of zero mean value of the commutator between the related operators. Moreover, explicit examples show that in some cases this new bound can be better than the known Robertson--Schrodinger one.
Photometric observations of exoplanet transits can be used to derive the orbital and physical parameters of an exoplanet. We analyzed several transit light curves of exoplanets that are suitable for ground-based observations whose complete information is available on the Exoplanet Transit Database (ETD). We analyzed transit data of planets including HAT-P-8 b, HAT-P-16 b, HAT-P-21 b, HAT-P-22 b, HAT-P-28 b and HAT-P-30 b using the AstroImageJ (AIJ) software package. In this paper, we investigated 82 transit light curves from ETD, deriving their physical parameters as well as computing their mid-transit times for future Transit Timing Variation (TTV) analyses. The Precise values of the parameters show that using AIJ as a fitting tool for follow-up observations can lead to results comparable to the values at the NASA Exoplanet Archive (the NEA). Such information will be invaluable considering the numbers of future discoveries from the ground and space-based exoplanet surveys.
We use simulated SN Ia samples, including both photometry and spectra, to perform the first direct validation of cosmology analysis using the SALT-II light curve model. This validation includes residuals from the light curve training process, systematic biases in SN Ia distance measurements, and the bias on the dark energy equation of state parameter w. Using the SN-analysis package SNANA, we simulate and analyze realistic samples corresponding to the data samples used in the SNLS3 analysis: 120 low-redshift (z < 0.1) SNe Ia, 255 SDSS SNe Ia (z < 0.4), and 290 SNLS SNe Ia (z <= 1). To probe systematic uncertainties in detail, we vary the input spectral model, the model of intrinsic scatter, and the smoothing (i.e., regularization) parameters used during the SALT-II model training. Using realistic intrinsic scatter models results in a slight bias in the ultraviolet portion of the trained SALT-II model, and w biases (winput - wrecovered) ranging from -0.005 +/- 0.012 to -0.024 +/- 0.010. These biases are indistinguishable from each other within uncertainty; the average bias on w is -0.014 +/- 0.007.
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