The use of adaptive predictor filter as a trigger mechanism in simulated cosmic rays radio signals corrupted with Gaussian noise

Adaptive filtering belongs to the realm of learning algorithms, widely used in our daily life in the context of machine learning, artificial intelligence, pattern recognition, etc. It is formally defined as a self-designing device with time-varying parameters that are adjusted recursively in accordance with the input data. The trigger mechanism is a central task in experiments using antennas to detect cosmic rays as it selects a cosmic- ray induced signal among all the voltages traces events that reach the antennas. This work presents the efficiency of a trigger mechanism developed using the adaptive predictor filter technique, whose capability is well known for time series prediction usage. This technique is independent of an external detector, using only the online temporal field recorded by the antennas in a simulated data set and Gaussian noise.