Application of Deep Learning techniques in the search for BSM Higgs bosons in the μμ\mu\mu final state in CMS

The Standard Model (SM) of particle physics predicts the existence of a Higgs field responsible for the generation of particles' mass. However, some aspects of this theory remain unsolved, supposing the presence of new physics Beyond the Standard Model (BSM) with the production of new particles at a higher energy scale compared to the current experimental limits. The search for additional Higgs bosons is, in fact, predicted by theoretical extensions of the SM including the Minimal Supersymmetry Standard Model (MSSM). In the MSSM, the Higgs sector consists of two Higgs doublets, resulting in five physical Higgs particles: two charged bosons $H^{\pm}$, two neutral scalars $h$ and $H$, and one pseudoscalar $A$. The work presented in this thesis is dedicated to the search of neutral non-Standard Model Higgs bosons decaying to two muons in the model independent MSSM scenario. Proton-proton collision data recorded by the CMS experiment at the CERN LHC at a center-of-mass energy of 13 TeV are used, corresponding to an integrated luminosity of $35.9\ \text{fb}^{-1}$. Such search is sensitive to neutral Higgs bosons produced either via gluon fusion process or in association with a $\text{b}\bar{\text{b}}$ quark pair. The extensive usage of Machine and Deep Learning techniques is a fundamental element in the discrimination between signal and background simulated events. A new network structure called parameterised Neural Network (pNN) has been implemented, replacing a whole set of single neural networks trained at a specific mass hypothesis value with a single neural network able to generalise well and interpolate in the entire mass range considered. The results of the pNN signal/background discrimination are used to set a model independent 95\% confidence level expected upper limit on the production cross section times branching ratio, for a generic $\phi$ boson decaying into a muon pair in the 130 to 1000 GeV range

An Approach to Guide Users Towards Less Revealing Internet Browsers

When browsing the Internet, HTTP headers enable both clients and servers send extra data in their requests or responses such as the User-Agent string. This string contains information related to the sender’s device, browser, and operating system. Previous research has shown that there are numerous privacy and security risks result from exposing sensitive information in the User-Agent string. For example, it enables device and browser fingerprinting and user tracking and identification. Our large analysis of thousands of User-Agent strings shows that browsers differ tremendously in the amount of information they include in their User-Agent strings. As such, our work aims at guiding users towards using less exposing browsers. In doing so, we propose to assign an exposure score to browsers based on the information they expose and vulnerability records. Thus, our contribution in this work is as follows: first, provide a full implementation that is ready to be deployed and used by users. Second, conduct a user study to identify the effectiveness and limitations of our proposed approach. Our implementation is based on using more than 52 thousand unique browsers. Our performance and validation analysis show that our solution is accurate and efficient. The source code and data set are publicly available and the solution has been deployed

Mapping Diffusion Properties in Living Cells

The function of living cells is based on chemical reactions. It has been shown that the velocity of these reactions is limited by the molecular transport in the cell. Therefore also the spatial organization of a cell plays a major role. In order to investigate such transport processes, fluorescence correlation spectroscopy (FCS) is often used in combination with fluorescently labeled proteins. In FCS a small subvolume of the cell (~1µm³) is observed with a laser-based microscope. The fluctuations of the fluorescence, emitted from this subvolume, are acquired. An autocorrelation analysis of these fluctuations reveals the concentrations and diffusion coefficients of the labeled particles. Usually, FCS is implemented using a confocal microscope, which can observe only a single spot at any time. For this thesis, FCS was extended to an imaging method, by combining it with light sheet fluorescence microscopy (SPIM). This relatively new widefield microscopy technique allows to observe an arbitrarily positionable, thin plane (diameter: 1-3µm) in the cell. By using a fast electron-multiplying charge-coupled device camera, the combination of SPIM and FCS allowed to map the motion also of relatively small autofluorescent proteins in living cells. At first, the setup of a light sheet microscope is described. This microscope was designed and optimized for SPIM-FCS measurements in living cells. Several test measurements show the applicability of SPIM-FCS to in vitro samples and to all larger compartments of a living cell (nucleus, cytoplasm, cellular membrane). Afterwards, the usability of several commercially available cameras as image sensor for SPIM-FCS measurements is assessed. At the time of writing, EM-CCD cameras offer the best trade-off between photosensitivity and achievable temporal resolution (~ 500µs). In addition to these linear cameras, also the use of single-photon avalanche diode (SPAD) arrays is investigated. These offer a significantly better temporal resolution (1-10µs) than current EM-CCD cameras, which would render them the ideal image sensor for SPIM-FCS. However, they do not yet reach the photo-sensitivity of EM-CCDs. Two different SPAD arrays were characterized in detail and first successful SPIM-FCS measurements of solute fluorescent molecules could be demonstrated. In a second step, SPIM-FCS was extended by a cross-correlation analysis (SPIM-FCCS), which allowed for the first time to map the interactions of differently labeled cytosolic molecules in living cells. For this purpose, the cross-correlation function between the fluorescence fluctuations from two different color channels is analyzed. A non-zero amplitude of this cross-correlation function is found only, if the differently labeled molecules interact and move together. Finally, the methods developed during this project were applied to different cellular systems. The mapping of the mobility of inert tracer molecules of different sizes allowed to measure the viscosity of the cytoplasm in different cells. A position-dependence of this mobility could only be found in the nucleoli. In addition, an important step in the remodelling cycle of the keratin intermediate filament system was investigated. As a third application, SPIM-F(C)CS measurements of different chromatin-associated proteins demonstrated the dynamics in the cellular nucleus. Mobility maps of labeled histone proteins revealed the organization of chromatin in interphase nuclei. In addition, the activity of the nuclear receptor RXR and a transcription factor were mapped