Optimization Of Energy Resolution In The Digital Hadron Calorimeter Using Longitudinal Weights

Abstract

Particle physics seeks to describe matter in its most elementary structure. With lepton colliders; couplings of gauge bosons and heavy quarks, physics beyond the Standard Model, and properties of a recently discovered Higgs boson can be studied with very high precision. Particle Flow Algorithms (PFA), able to achieve necessary jet-energy and di-jet mass resolutions, require fine transverse and longitudinal segmentation from calorimeters. To validate digital imaging calorimetry in this context, a Digital Hadron Calorimeter (DHCAL) with single-bit (digital) readout on imbedded electronics from 1×1 cm<super>2</super> pads throughout its volume has been constructed and exposed to particle beams as a large prototype with 54 layers of Resistive Plate Chambers and nearly 500k readout channels. With this data, I report on a sophisticated statistical technique to improve the single particle energy resolution using weights that take advantage of correlations of the energy deposit between layers. Limitations to resolution improvement are identified and possible modifications are discussed. Simulation is used to verify particle identification techniques applied to the data.