A simulation study to distinguish prompt photon from π^0 and beam halo in a granular calorimeter using deep networks

08/12/2018
by   Shamik Ghosh, et al.
0

In a hadron collider environment identification of prompt photons originating in a hard partonic scattering process and rejection of non-prompt photons coming from hadronic jets or from beam related sources, is the first step for study of processes with photons in final state. Photons coming from decay of π_0's produced inside a hadronic jet and photons produced in catastrophic bremsstrahlung by beam halo muons are two major sources of non-prompt photons. In this paper the potential of deep learning methods for separating the prompt photons from beam halo and π^0's in the electromagnetic calorimeter of a collider detector is investigated, using an approximate description of the CMS detector. It is shown that, using only calorimetric information as images with a Convolutional Neural Network, beam halo (and π^0) can be separated from photon with 99.96 efficiency which is much better than traditionally employed variables.

READ FULL TEXT
research
02/04/2022

Beam Management with Orientation and RSRP using Deep Learning for Beyond 5G Systems

Beam management (BM), i.e., the process of finding and maintaining a sui...
research
11/01/2018

Learning Beam Search Policies via Imitation Learning

Beam search is widely used for approximate decoding in structured predic...
research
10/08/2020

A two-stage approach for beam hardening artifact reduction in low-dose dental CBCT

This paper presents a two-stage method for beam hardening artifact corre...
research
09/29/2021

Vision-Aided Beam Tracking: Explore the Proper Use of Camera Images with Deep Learning

We investigate the problem of wireless beam tracking on mmWave bands wit...
research
10/24/2018

Dental pathology detection in 3D cone-beam CT

Cone-beam computed tomography (CBCT) is a valuable imaging method in den...
research
11/16/2018

Beam Search Decoding using Manner of Articulation Detection Knowledge Derived from Connectionist Temporal Classification

Manner of articulation detection using deep neural networks require a pr...
research
12/14/2016

Beam Search for Learning a Deep Convolutional Neural Network of 3D Shapes

This paper addresses 3D shape recognition. Recent work typically represe...

Please sign up or login with your details

Forgot password? Click here to reset