Bachelor­arbeiten

Quarkonia are unstable hadrons made up of identical quark-antiquark pairs, which decay according to the electromagnetic, strong or weak interactions. This project will work on improving the modelling of these decays in the simulation program "Sherpa" by updating branching ratios from measured results and implementing matrix elements for the most relevant decay channels.

Effective field theory (EFT) allows us to make accurate predictions about phenomena beyond the Standard Model (SM) without requiring a complete understanding of the fundamental mechanisms involved. Vector boson scattering (VBS) processes are ideal for precision measurements within the SM. In this project, it will be working on final states consisting of two photons and two jets (yyjj) produced by VBS processes. Using EFT interpretations, it is aimed to make predictions for neutral gauge couplings that do not exist in the SM.

The production of four leptons from photons are a very sensitive probe of the electroweak gauge structure. Data recorded by the ATLAS Experiment in the LHC run-2 are used to quantify the agreement of the theoretical predictions. This is done by constraining higher-dimensional beyond-the-Standard-Model operators that generate purely neutral electroweak interaction vertices between photons and the Z boson.

The production of W boson pairs from photons are a very sensitive probe of the electroweak gauge structure. Data recorded by the ATLAS Experiment in the LHC run-2 are used to quantify the agreement of the theoretical predictions. This is done by constraining higher-dimensional beyond-the-Standard-Model operators that modify the γγWW vertex.

Tbd

Studying specific polarisation configurations in which W or Z bosons can be produced is interesting due to the connection of the longitudinal polarisation of massive vector bosons to the Higgs mechanism and electroweak symmetry breaking. This is often done in the leptonic decay modes of the W/Z boson, but the high rate of the hadronic decay mode makes it an attractive alternative final state. We want to study simulations for polarised hadronically decaying W/Z bosons and identify interesting observables for a future analysis of these with the ATLAS detector.

More information to follow.

The complexity of matrix element calculations for final states with a high multiplicity is so high, that their computational cost often limits theory simulations for the LHC. In this project we want to extend a machine learning based approach to simplify these calculations by approximating the matrix elements. The goal is to study various parameters for the architecture and training of deep neural networks in this approach.

The simulation of different polarisation states allows us to study effects from physics beyond the Standard Model in more detail. In this project we want to study, how effective-field-theory extensions of the Standard Model will affect different polarisations of W bosons in W±W± pair production at the LHC.

The simulation of different polarisation states allows us to study effects from physics beyond the Standard Model in more detail. In this project we want to study, how effective-field-theory extensions of the Standard Model will affect different polarisations of W and Z bosons in WZ pair production at the LHC.

Investigating the polarization of vector bosons (VB) in VB production processes provides tests for the Standard Model's gauge symmetry structure and the concrete mechanism of electroweak symmetry breaking, as well as sensitivity to new physics. The ATLAS VB polarisation analysis is currently trying to measure the contribution of longitudinally-polarised W-bosons in the scattering of two same-charged W-bosons. To enhance the measurement’s sensitivity, artificial neural networks (ANNs) are used to separate the different VB polarisation components. At the moment, the Monte-Carlo simulations used to train the ANNs are based on the matrix element generator MadGraph.

Since recently, the event generator Sherpa is also able to simulate polarized cross sections of VB production processes. In preparation for its application in the ATLAS VB polarisation analysis, a first proof of concept study has been performed to investigate different methods to train ANNs to separate the VB polarisations. However, these investigations are so far based on “truth-level” simulations, i.e. without the inclusion of detector effects. In addition to extending the existing study to more complex training procedures, the goal of this Bachelor thesis is to apply the collected knowledge to “reconstruction-level” samples that include the effects of the detector simulation and object reconstruction. This allows a direct comparison of the expected measurement sensitivity when using ANNs trained on MadGraph vs. Sherpa-samples.

Investigating the polarization of vector bosons (VB) in VB production processes provides tests for the Standard Model's gauge symmetry structure and the concrete mechanism of electroweak symmetry breaking, as well as sensitivity to new physics. As one of the VB production processes with a very clean signature due to four charged leptons in the final state, the ZZ electroweak production process is very interesting for this purpose. Some literature studies on this process already exist. Since recently, the Monte-Carlo-Eventgenerator Sherpa is able to simulate polarized cross section of vector boson production. The goal of this thesis is to test this new feature with this process. Furthermore, aspects of this process not yet studied, such as the contribution of particle showers, can be investigated.

Triboson production V(->ll)V(->ll)V(->qq) is a component of the signal definition in vector boson scattering, but is sometimes ignored or generated as a separate process. This thesis should study the impact of the triboson component. In particular the effect of dim-8 EFT operators in the quartic vertex should be studied to quantify the effect of ignoring the triboson production in an EFT study.

Bachelor Thesis, Research Studies Master, Master Thesis in Experimental Particle Physics

• Multivariate analysis, machine learning and artificial intelligence
• Optimisation of data analyses for particle searches and reconstruction of particle decays
• Application and development of software for statistical data analysis

One of the research activities of the ATLAS group at the Institute of Nuclear and Particle Physics is the search for new Higgs bosons in extensions of the Standard Model at the Large Hadron Collider (LHC). A large data set is available from ATLAS Run-2 and Run-3 with a total luminosity of 200 fb^-1, which can be analysed. More data are currently being recorded by ATLAS.

In many scenarios beyond the Standard Model, the decay of the new Higgs bosons is into a pair of tau leptons. We therefore study the hadronic decay of such tau leptons in detail, in particular, to obtain a high significance for the Higgs signal, and a large suppression of backgrounds.

In the data analyses, we exploit advanced statistical methods and very often apply methods of machine learning to optimize the selection algorithms or parameter settings.

The topics of the Bachelor and Master theses will be defined individually according to your interest and to the current state of research.

In the thesis project, you will learn software-based methods of data analysis and statistics, the application of machine learning tools, and the use of modern particle detectors.

You should bring a basic knowledge of particle physics. Programming skills would be an asset, but we will also provide introductory sessions to all necessary programming and analysis tools.

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Bachelor Thesis, Research Studies Master, Master Thesis in Experimental Particle Physics

• Machine learning und artificial neural networks
• Optimisation of the energy measurement of photons, electrons and hadronic jets with Liquid-Argon calorimeters
• Programming or simulation of electronic and digital signal processing
• Statistical analysis of measurement data

The Liquid-Argon Calorimeters (LAr Calorimeters) of the ATLAS detector at the LHC are going to be upgraded with new readout electronics for operating at highest LHC luminosities. The calorimeter signals shall be reconstructed with improved energy and spatial resolution so that particles produced in proton-proton collisions can be identified with higher precision. The goal is, for example, an improved detection of Higgs boson decays with the ATLAS detector.

The particle identification and energy reconstruction must be performed in real-time, and the time to provide the energy calculation in each detector cell must not take longer than 0.5 μs. For this reason, we use modern and fast programmable electronic circuits, so-called Field Programmable Gate Arrays (FPGAs) for signal reconstruction. We exploit deep learning methods and artificial intelligence to optimize our measurements.

In the Bachelor or Master thesis, the energy reconstruction of the ATLAS LAr Calorimeters shall be improved using machine learning approaches.

In the research project, you will learn the application of machine learning tools (e.g. keras), software-based data analysis, and the functioning of a modern particle detector and electronic readout systems.

If you are interested you may also learn about programming of FPGAs using the programming language VHDL. You can get involved in the application of machine learning algorithms or on advanced data processing procedures running on FPGA devices.

You should bring a basic knowledge of particle physics, and the motivation to learn more about machine learning and/or FPGA programming. Basic programming skills are an asset. We also provide introductory sessions to VHDL and the analysis tools needed for your work.

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