2004/02/13 by Michael D. Albrow
Engineering · Mathematics · Physics and Astronomy · #Algorithm #Artificial intelligence #Astrophysics #Astrophysics and Star Formation Studies #Bayesian probability #Computer science #Computer vision #Engineering #Event (particle physics) #Focus (optics) #Gamma-ray bursts and supernovae #Gravitational microlensing #Lens (geology) #Magnification #Mathematical optimization #Mathematics #Minification #Optics #Physics #Planet #Sensitivity (control systems) #Stars #Stellar, planetary, and galactic studies #astro-ph
paper · pdf · doi:10.1086/383565
published as Astrophys.J. 607 (2004) 821-827 · Accepted by ApJ. 19 pages, incl 7 figures and 2 tables
arxiv created 2004/02/13 · openalex publication_date 2004/05/19 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Gravitational microlensing events with high peak magnifications provide a much-enhanced sensitivity to the detection of planets around the lens star. However, estimates of peak magnification during the early stages of an event by means of χ 2 minimization frequently involve an overprediction, making observing campaigns with strategies that rely on these predictions inefficient. I show that a rudimentary Bayesian formulation, incorporating the known statistical characteristics of a detection system, produces much more accurate predictions of peak magnification than χ 2 minimization. Implementation of this system will allow efficient follow-up observing programs that focus solely on events that contribute to planetary abundance statistics.