Online neural trigger for optimizing data acquisition during particle beam calibration tests with calorimeters

For LHC, the hadronic calorimetry of the ATLAS detector is performed by Tilecal, a scintillating tile calorimeter. For calibration purposes, a fraction of the Tilecal modules is placed in particle beam lines. Despite beam high quality, experimental beam contamination is observed and this masks the actual performance of the calorimeter. For optimizing the calibration task, an online neural particle classifier was developed for Tilecal. Envisaging a neural trigger for incoming particles, a neural process runs integrated to the data acquisition task and performs online training for particle identification. The neural classification performance is evaluated by correlating the neural response to classical methodology, confirming an ability for outsider identification at levels as high as 99.3%.