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Register a new userA fast hybrid algorithm for exoplanetary transit searches
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Added by
Andrew Collier Cameron
Publication date
2006
fields
Physics
and research's language is
English
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We present a fast and efficient hybrid algorithm for selecting exoplanetary candidates from wide-field transit surveys. Our method is based on the widely-used SysRem and Box Least-Squares (BLS) algorithms. Patterns of systematic error that are common to all stars on the frame are mapped and eliminated using the SysRem algorithm. The remaining systematic errors caused by spatially localised flat-fielding and other errors are quantified using a boxcar-smoothing method. We show that the dimensions of the search-parameter space can be reduced greatly by carrying out an initial BLS search on a coarse grid of reduced dimensions, followed by Newton-Raphson refinement of the transit parameters in the vicinity of the most significant solutions. We illustrate the methods operation by applying it to data from one field of the SuperWASP survey, comprising 2300 observations of 7840 stars brighter than V=13.0. We identify 11 likely transit candidates. We reject stars that exhibit significant ellipsoidal variations indicative of a stellar-mass companion. We use colours and proper motions from the 2MASS and USNO-B1.0 surveys to estimate the stellar parameters and the companion radius. We find that two stars showing unambiguous transit signals pass all these tests, and so qualify for detailed high-resolution spectroscopic follow-up.
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Transmission spectra of exoplanetary atmospheres have been used to infer the presence of clouds/hazes. Such inferences are typically based on spectral slopes in the optical deviant from gaseous Rayleigh scattering or low-amplitude spectral features in the infrared. We investigate three observable metrics that could allow constraints on cloud properties from transmission spectra, namely, the optical slope, the uniformity of this slope, and condensate features in the infrared. We derive these metrics using model transmission spectra considering Mie extinction from a wide range of condensate species, particle sizes, and scale heights. Firstly, we investigate possible degeneracies among the cloud properties for an observed slope. We find, for example, that spectra with very steep optical slopes suggest sulphide clouds (e.g. MnS, ZnS, Na$_2$S) in the atmospheres. Secondly, (non)uniformities in optical slopes provide additional constraints on cloud properties, e.g., MnS, ZnS, TiO$_2$, and Fe$_2$O$_3$ have significantly non-uniform slopes. Thirdly, infrared spectra provide an additional powerful probe into cloud properties, with SiO$_2$, Fe$_2$O$_3$, Mg$_2$SiO$_4$, and MgSiO$_3$ bearing strong infrared features observable with the James Webb Space Telescope. We investigate observed spectra of eight hot Jupiters and discuss their implications. In particular, no single or composite condensate species considered here conforms to the steep and non-uniform optical slope observed for HD 189733b. Our work highlights the importance of the three above metrics to investigate cloud properties in exoplanetary atmospheres using high-precision transmission spectra and detailed cloud models. We make our Mie scattering data for condensates publicly available to the community.
There are different methods for finding exoplanets such as radial spectral shifts, astrometrical measurements, transits, timing etc. Gravitational microlensing (including pixel-lensing) is among the most promising techniques with the potentiality of detecting Earth-like planets at distances about a few astronomical units from their host star or near the so-called snow line with a temperature in the range $0-100^0$ C on a solid surface of an exoplanet. We emphasize the importance of polarization measurements which can help to resolve degeneracies in theoretical models. In particular, the polarization angle could give additional information about the relative position of the lens with respect to the source.
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Lucky imaging is a high-resolution astronomical image recovery technique with two classic implementation algorithms, i.e. image selecting, shifting and adding in image space and data selecting and image synthesizing in Fourier space. This paper proposes a novel lucky imaging algorithm where with space-domain and frequency-domain selection rates as a link, the two classic algorithms are combined successfully, making each algorithm a proper subset of the novel hybrid algorithm. Experimental results show that with the same experiment dataset and platform, the high-resolution image obtained by the proposed algorithm is superior to that obtained by the two classic algorithms. This paper also proposes a new lucky image selection and storage scheme, which can greatly save computer memory and enable lucky imaging algorithm to be implemented in a common desktop or laptop with small memory and to process astronomical images with more frames and larger size. Besides, through simulation analysis, this paper discusses the binary star detection limits of the novel lucky imaging algorithm and traditional ones under different atmospheric conditions.
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