A Comparison of Machine Learning Algorithms in Identifying Higgs Boson Events from the Background

Overview

Since its detection in the Large Hadron Collider (LHC) in 2012, the Higgs boson has remained a key element in confirming the standard model (SM) of particle physics. Current research to find Higgs boson decay channels predicted by the SM requires being able to accurately identify Higgs bosons or signal events from a noisy background. Through use of machine learning, signal events can be accurately classified in offline reconstruction; however, there exist many classification models which might suit this purpose. This study compares three supervised classification models in their ability to accurately and efficiently identify signal events: boosted decision trees, support vector machine, and neural networks. With an accuracy of 83.88%, F1-score of 81.72% and a training time of 8.4 seconds using the simulated dataset from the 2014 ATLAS Higgs boson Machine Learning Challenge, a histogram gradient boosted decision tree was determined to be the most effective classification model for identifying Higgs boson events. The worst performing algorithm was the support vector machine with the lowest accuracy at 80.35%, F1-score of 77.12%, and the second lowest training time of 50 minutes. Identification of Co-Expressed Genes to BDNF and trk-Bas Major Depressive Disorder Related Biomarkers Using Microarray Data Jennifer Hu Nikola Tesla STEM High School, Redmond, WA Teacher Kate Allender, Nikola Tesla STEM High School Depression is a leading cause of death and disability with more than 264 million people of all ages suffering from the disorder worldwide. Understanding how brain function is altered in depressed patients is crucial for determining novel biological targets that can be used to prevent suicidal behavior. This project aims to identify potential biomarkers for major depressive disorder by finding co-expressed genes with brain- derived neurotrophic factor (BDNF) and receptor tyrosine kinase B (trk-B), two genes that contribute to the pathophysiology of depression through neuron growth and survival (neuroplasticity). 3055 probes for genes involved in nervous system development were screened from public microarray data in the Allen Human Brain Atlas. Average gene expression level and Pearsons correlation coefficient were calculated for each probe using Java programming and Excel to determine correlations between either BDNF or trk-Band a potential gene biomarker. A total of 93 and 27 coexpressed genes were identified for trk-Band BDNF respectively with a significant Pearsons correlation of above +0.5 (trk-B: 0.82>x>0.50, BDNF: 0.68>x>0.50). In addition, 72 biomarkers were further identified by gene function and trends in data as especially important to depression research. These biomarkers may provide new insight into genetic factors for vulnerability to depression involving brain neuroplasticity and can be used to create new therapies as drug targets or to improve the remission rates of existing therapies.

Competition history

• JSHS 2022 Category not listed

Resources

• National JSHS abstract book ↗

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