fix typo

chapters/Abstract.tex

@@ -6,7 +6,7 @@ \chapter*{Abstract}
 The CMS Collaboration has searched for signals of a dark matter model via the Emerging Jets analysis group.
 As with all experiments in High Energy physics, acquiring high quality of data is paramount to achieve groundbreaking science. The CMS experiment achieves the collection of it's high quality data through the triggering and data acquisition systems put in place, but require manual labor to certify.
 In this work I present trigger efficiency studies relevant to the Emerging Jets analysis. Moreover, I present my work
-to improve the process of data certification in the DQM workflow implemented at the CMS Tracker DQM group. This work adds the automation of a new web application called the Machine Learning playground designed to improve DQM shifter efficiency in data certification.
+to improve the process of data certification in the DQM workflow implemented at the CMS Tracker DQM group. This work adds the automation of a new web application called the Machine Learning Playground designed to improve DQM shifter efficiency in data certification.
 
 
 % The Data Quality Monitoring (DQM) of CMS is a key asset to deliver high-quality data for physics analysis and it is used both in the online and offline environment. The current paradigm of the quality assessment is labor intensive and it is based on the scrutiny of a large number of histograms by detector experts comparing them with a reference. This project aims at applying recent progress in Machine Learning techniques to the automation of the DQM scrutiny. In particular the use of convolutional neural networks to spot problems in the acquired data is presented with particular attention to semi-supervised models (e.g. autoencoders) to define a classification strategy that doesn’t assume previous knowledge of failure modes. Real data from the hadron calorimeter of CMS are used to demonstrate the effectiveness of the proposed approach.