This paper presents GPFC, a novel Graphics Processing Unit (GPU) Phase Folding and Convolutional Neural Network (CNN) system to detect exoplanets using the transit method. We devise a fast folding algorithm parallelized on a GPU to amplify low signal-to-noise ratio transit signals, allowing a search at high precision and speed. A CNN trained on two million synthetic light curves reports a score indicating the likelihood of a planetary signal at each period. While the GPFC method has broad applicability across period ranges, this research specifically focuses on detecting ultra-short-period planets with orbital periods less than one day. GPFC improves on speed by three orders of magnitude over the predominant Box-fitting Least Squares (BLS) method. Our simulation results show GPFC achieves $97%$ training accuracy, higher true positive rate at the same false positive rate of detection, and higher precision at the same recall rate when compared to BLS. GPFC recovers $100\%$ of known ultra-short-period planets in $\textit{Kepler}$ light curves from a blind search. These results highlight the promise of GPFC as an alternative approach to the traditional BLS algorithm for finding new transiting exoplanets in data taken with $\textit{Kepler}$ and other space transit missions such as K2, TESS and future PLATO and Earth 2.0.

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Nasa has announced that it has found evidence of flowing water on Mars. Scientists have long speculated that Recurring Slope Lineae -- or dark patches -- on Mars were made up of briny water but the new findings prove that those patches are caused by liquid water, which it has established by finding hydrated salts. Several hundred camped outside the London store in Covent Garden. The 6s will have new features like a vastly improved camera and a pressure-sensitive "3D Touch" display

The ideal Bayesian agent reasons from a global probability model, but real agents are restricted to simplified models which they know to be adequate only in restricted circumstances. Very little formal theory has been developed to help fallibly rational agents manage the process of constructing and revising small world models. The goal of this paper is to present a theoretical framework for analyzing model management approaches. For a probability forecasting problem, a search process over small world models is analyzed as an approximation to a larger-world model which the agent cannot explicitly enumerate or compute. Conditions are given under which the sequence of small-world models converges to the larger-world probabilities.

ECENT advances in photometric technologies have facilitated the proliferation of extensive astronomical surveys such as MACHO [1], OGLE [2], and recently Pan-STARRS [3]. A light curve is a time series in which the measured phenomenon corresponds to the brightness (magnitude or flux) of a stellar object. Light curves are the basic tool for the analysis of variable stars [4], whose brightness varies through time due to internal physical processes, or to external factors such as interactions with other astronomical objects. Some variable stars, such as eclipsing binaries (EB), cepheids, and RR Lyrae, exhibit periodic behaviors that are reflected on their corresponding light curves. For example, EB stars are systems composed of two stars, whose brightness shows periodic variations due to the mutual eclipses between them. The period of a light curve is a key parameter for classifying variable stars [5], [6], and estimating other parameters such as mass and distance to Earth [7]. Light curves are unevenly sampled due to constraints on the observation schedules: the day-night cycle, weather conditions, cali-Manuscript received February 02, 2011; revised March 28, 2011; accepted March 30, 2011.