TAU Identification Improvements for ATLAS Phase-1 Upgrades in L1Calo Trigger System

ATLAS is one of the biggest detectors at the Large Hadron Collider (LHC) at CERN. It is designed to study high-energy particle collisions. Within the detector, there are two levels in the trigger system, Level 1 and High Level Trigger (HLT). The main objective of this project is to study the tau candidate particles in L1Calo to improve the tau identification in the L1Calo trigger system for the phase-1 upgrades in ATLAS. This is done by exploring the matching efficiency algorithm and studying the rCore energy

Improving the efficiency of xalign software using nosql database

The study compares the efficiency of file based XAlign software to align the peaks from different file samples with the efficiency of a database based XAlign. The time taken to generate output in XAlign is the major idea of the study. The time taken by the current implementation of file based XAlign, of different sample sizes, is used as a benchmark for comparison with the time taken for XAlign, when the input is retrieved from a NoSQL database. The study also compares the performance of XAlign when database is hosted on a single node with the performance of XAlign when the database is hosted on two, four and eight nodes. Statistics for some test runs on twelve-node database are collected to understand the performance of XAlign when the number of nodes is increased. All the factors including network latency and I/O criteria are considered to discuss the outcome of the study. The overall results help in understanding the role databases can play in the process of XAlign

Deep Learning Classifies Low- and High-Grade Glioma Patients with High Accuracy, Sensitivity, and Specificity Based on Their Brain White Matter Networks Derived from Diffusion Tensor Imaging

Classifying low-grade glioma (LGG) patients from high-grade glioma (HGG) is one of the most challenging tasks in planning treatment strategies for brain tumor patients. Previous studies derived several handcrafted features based on the tumor’s texture and volume from magnetic resonance images (MRI) to classify LGG and HGG patients. The accuracy of classification was moderate. We aimed to classify LGG from HGG with high accuracy using the brain white matter (WM) network connectivity matrix constructed using diffusion tensor tractography. We obtained diffusion tensor images (DTI) of 44 LGG and 48 HGG patients using routine clinical imaging. Fiber tractography and brain parcellation were performed for each patient to obtain the fractional anisotropy, axial diffusivity, radial diffusivity, and mean diffusivity weighted connectivity matrices. We used a deep convolutional neural network (DNN) for classification and the gradient class activation map (GRAD-CAM) technique to identify the neural connectivity features focused on by the DNN. DNN could classify both LGG and HGG with 98% accuracy. The sensitivity and specificity values were above 0.98. GRAD-CAM analysis revealed a distinct WM network pattern between LGG and HGG patients in the frontal, temporal, and parietal lobes. Our results demonstrate that glioma affects the WM network in LGG and HGG patients differently