THE NONLINEAR SIGNAL DOMAIN SELECTION USING A NEW QUALITY FUNCTION INNEURAL-NET TRAINING

Applying cuts in the space of event parameters is the traditional tech nique for background rejection in high energy physics. Obtained by con sidering the simulation of signal and background events, particular va lues of these cuts are used to reach required balance between efficien cy of signal detection and background rejection. Modern experiments in high energy accelerator physics and astrophysics are operating with m ultidimensional parametric spaces. Thus, the problem of the best cut s election is of vital interest. Frequently used rectangular cuts are us ually too restrictive and can deteriorate the shape of selected multiv ariate signal distribution. With the aid of the proposed method, it is possible to obtain smooth nonlinear shape of signal cluster which opt imizes the ratio of signal to noise. The search of the best gamma-clus ter on the data files of Crab nebula detection by Atmospheric Cherenko v Telescope of Whipple collaboration proves the superiority of neural techniques upon traditional methods.