Flux of muon component of secondary cosmic rays is affected by varying conditions in the atmosphere. Dominant effects are barometric and temperature effect which reflect variations of atmospheric pressure and atmospheric temperature respectively. Precise modeling and correction for these meteorological effects significantly increases sensitivity of Earth-based muon detectors to variations of primary cosmic ray flux.
We are presenting two recently developed empirical methods for correction of meteorological effects on cosmic ray muons. First method is based on principal component analysis, while second employs multivariate analysis using machine learning techniques. Both methods are applied for correction of barometric and temperature effects, but can easily be generalized to take more atmospheric parameters into account.
We apply these corrections to muon count rates measured by Belgrade cosmic ray station and study their effect on sensitivity of detection of periodic and aperiodic flux variations of primary cosmic rays. Comparison with the most widely used method for correction of meteorological effects – integral method, as well as with neutron monitor data, demonstrates very high effectiveness of presented methods.
cosmic ray muons; meteorological effects; atmospheric correction; principal component analysis; machine learning
|Subcategory||Experimental Methods & Instrumentation|