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تسجيل مستخدم جديدThree microlensing planets with no caustic-crossing features
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We search for microlensing planets with signals exhibiting no caustic-crossing features, considering the possibility that such signals may be missed due to their weak and featureless nature. For this purpose, we reexamine the lensing events found by the KMTNet survey before the 2019 season. From this investigation, we find two new planetary lensing events, KMT-2018-BLG-1976 and KMT-2018-BLG-1996. We also present the analysis of the planetary event OGLE-2019-BLG-0954, for which the planetary signal was known, but no detailed analysis has been presented before. We identify the genuineness of the planetary signals by checking various interpretations that can generate short-term anomalies in lensing light curves. From Bayesian analyses conducted with the constraint from available observables, we find that the host and planet masses are $(M_1, M_2)sim (0.65~M_odot, 2~M_{rm J})$ for KMT-2018-BLG-1976L, $sim (0.69~M_odot, 1~M_{rm J})$ for KMT-2018-BLG-1996L, and $sim (0.80~M_odot, 14~M_{rm J})$ for OGLE-2019-BLG-0954L. The estimated distance to OGLE-2019-BLG-0954L, $3.63^{+1.22}_{-1.64}$~kpc, indicates that it is located in the disk, and the brightness expected from the mass and distance matches well the brightness of the blend, indicating that the lens accounts for most of the blended flux. The lens of OGLE-2019-BLG-0954 could be resolved from the source by conducting high-resolution follow-up observations in and after 2024.
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We conducted a project of reinvestigating the 2017--2019 microlensing data collected by the high-cadence surveys with the aim of finding planets that were missed due to the deviations of planetary signals from the typical form of short-term anomalies
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Aims: Caustic-crossing binary-lens microlensing events are important anomalous events because they are capable of detecting an extrasolar planet companion orbiting the lens star. Fast and robust modelling methods are thus of prime interest in helping
to decide whether a planet is detected by an event. Cassan (2008) introduced a new set of parameters to model binary-lens events, which are closely related to properties of the light curve. In this work, we explain how Bayesian priors can be added to this framework, and investigate on interesting options. Methods: We develop a mathematical formulation that allows us to compute analytically the priors on the new parameters, given some previous knowledge about other physical quantities. We explicitly compute the priors for a number of interesting cases, and show how this can be implemented in a fully Bayesian, Markov chain Monte Carlo algorithm. Results: Using Bayesian priors can accelerate microlens fitting codes by reducing the time spent considering physically implausible models, and helps us to discriminate between alternative models based on the physical plausibility of their parameters.
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