Master Thesis Research Projects
The following Master thesis research projects are offered at Nikhef. If you are interested in one of these projects, please contact the coordinator listed with the project.
Projects with September 2020 start
Lepton Collider: Pixel TPC testbeam
In the Lepton Collider group at Nikhef we work on a tracking detector for a future Collider (e.g. the ILC in Japan). We are developing a gaseous Time Projection Chamber with a pixel readout. At Nikhef we have built an 8-quad GridPix module based on the Timepix3 chip, which is a detector of about 20 cm x 40 cm x 10 cm in size. In August 2020 we will test the device at the DESY particle accelerator in Hamburg. For the project you could work on preparations for the test beam (e.g. running the data acquisition, perform data monitoring using our set up in the lab). The next topics will be the participation in the data taking during the test beam at DESY, the analysis of the data using C++ and ROOT and - finally - publication of the results in a scientific journal.
Our latest paper can be found in https://www.nikhef.nl/~s01/quad_paper.pdf [www.nikhef.nl].
Contact: Peter Kluit and Kees Ligtenberg
ATLAS: The Next Generation
After the observation of the coupling of Higgs bosons to fermions of the third generation, the search for the coupling to fermions of the second generation is one of the next priorities for research at CERN's Large Hadron Collider. The search for the decay of the Higgs boson to two charm quarks is very new  and we see various opportunities for interesting developments. For this project we propose improvements in reconstruction (using exclusive decays), advanced analysis techiques (using deep learning methods) and expanding the theory interpretation. Another opportunity would be the development of the first statistical combination of results between the ATLAS and CMS experiment, which could significantly improve the discovery potentional.
ATLAS: The Most Energetic Higgs Boson
The production of Higgs bosons at the highest energies could give the first indications for deviations from the standard model of particle physics, but production energies above 500 GeV have not been observed yet . The LHC Run-2 dataset, collected during the last 4 years, might be the first opportunity to observe such processes, and we have various ideas for new studies. Possible developments include the improvement of boosted reconstruction techniques, for example using multivariate deep learning methods. Also, there are various opportunities for unexplored theory interpretations (using the MadGraph event generator), including effective field theory models (with novel ‘morphing’ techniques) and new interpretations of the newly observed boosted VZ(bb) process.
Contact: Tristan du Pree and Brian Moser
Dark Matter: XENON1T Data Analysis
The XENON collaboration has used the XENON1T detector to achieve the world’s most sensitive direct detection dark matter results and is currently building the XENONnT successor experiment. The detectors operate at the Gran Sasso underground laboratory and consist of so-called dual-phase xenon time-projection chambers filled with ultra-pure xenon. Our group has an opening for a motivated MSc student to do analysis with the data from the XENON1T detector. The work will consist of understanding the detector signals and applying machine learning tools such as deep neutral networks to improve the reconstruction performance in our Python-based analysis tool, following the approach described in arXiv:1804.09641. The final goal is to improve the energy and position reconstruction uncertainties for the dark matter search. There will also be opportunity to do data-taking shifts at the Gran Sasso underground laboratory in Italy.
Dark Matter: XAMS R&D Setup
The Amsterdam Dark Matter group operates an R&D xenon detector at Nikhef. The detector is a dual-phase xenon time-projection chamber and contains about 4kg of ultra-pure liquid xenon. We plan to use this detector for the development of new detection techniques (such as utilizing new photosensors) and to improve the understanding of the response of liquid xenon to various forms of radiation. The results could be directly used in the XENON experiment, the world’s most sensitive direct detection dark matter experiment at the Gran Sasso underground laboratory. We have several interesting projects for this facility. We are looking for someone who is interested in working in a laboratory on high-tech equipment, modifying the detector, taking data and analyzing the data him/herself. You will "own" this experiment.
Dark Matter: DARWIN Sensitivity Studies
DARWIN is the "ultimate" direct detection dark matter experiment, with the goal to reach the so-called "neutrino floor", when neutrinos become a hard-to-reduce background. The large and exquisitely clean xenon mass will allow DARWIN to also be sensitive to other physics signals such as solar neutrinos, double-beta decay from Xe-136, axions and axion-like particles etc. While the experiment will only start in 2025, we are in the midst of optimizing the experiment, which is driven by simulations. We have an opening for a student to work on the GEANT4 Monte Carlo simulations for DARWIN, as part of a simulation team together with the University of Freiburg and Zurich. We are also working on a "fast simulation" that could be included in this framework. It is your opportunity to steer the optimization of a large and unique experiment. This project requires good programming skills (Python and C++) and data analysis/physics interpretation skills.
The Modulation Experiment: Data Analysis
There exist a few measurements that suggest an annual modulation in the activity of radioactive sources. With a few groups from the XENON collaboration we have developed four sets of table-top experiments to investigate this effect on a few well known radioactive sources. The experiments are under construction in Purdue University (USA), a mountain top in Switzerland, a beach in Rio de Janeiro and the last one at Nikhef in Amsterdam. We urgently need a master student to (1) analyze the first big data set, and (2) contribute to the first physics paper from the experiment. We are looking for all-round physicists with interest in both lab-work and data-analysis. The student(s) will directly collaborate with the other groups in this small collaboration (around 10 people), and the goal is to have the first physics publication ready by the end of the project. During the 2018-2019 season there are positions for two MSc students.
Contact: Auke Colijn
Detector R&D: Laser Interferometer Space Antenna (LISA)
The space-based gravitational wave antenna LISA is, without a doubt, one of the most challenging space missions ever proposed. ESA plans to launch around 2030 three spacecraft that are separated by a few million kilometers to measure tiny variations in the distances between test-masses located in each satellite to detect the gravitational waves from sources such as supermassive black holes. The triangular constellation of the LISA mission is dynamic, requiring a constant fine-tuning related to the pointing of the laser links between the spacecraft and a simultaneous refocusing of the telescope. The noise sources related to the laser links expect to provide a dominant contribution to the LISA performance. An update and extension of the LISA science simulation software are needed to assess the hardware development for LISA at Nikhef, TNO, and SRON. A position is therefore available for a master student to study the impact of instrumental noise on the performance of LISA. Realistic simulations based on hardware (noise) characterization measurements performed at TNO will be carried out and compared to the expected tantalizing gravitational wave sources.
Theory: The Effective Field Theory Pathway to New Physics at the LHC
A promising framework to parametrise in a robust and model-independent way deviations from the Standard Model (SM) induced by new heavy particles is the Standard Model Effective Field Theory (SMEFT). In this formalism, beyond the SM effects are encapsulated in higher-dimensional operators constructed from SM fields respecting their symmetry properties. In this project, we aim to carry out a global analysis of the SMEFT from high-precision LHC data, including Higgs boson production, flavour observables, and low-energy measurements. This analysis will be carried out in the context of the recently developed SMEFiT approach  based on Machine Learning techniques to efficiently explore the complex theory parameter space. The ultimate goal is either to uncover glimpses of new particles or interactions at the LHC, or to derive the most stringent model-independent bounds to date on general theories of New Physics. Of particular interest are novel methods for charting the parameter space , the matching to UV-complete theories in explicit BSM scenarios , and the interplay between EFT-based model-independent searches for new physics and determinations of the proton structure from LHC data .
Contact: Juan Rojo
Theory: Charting the quark and gluon structure of protons and nuclei with Machine Learning
Deepening our knowledge of the partonic content of nucleons and nuclei  represents a central endeavour of modern high-energy and nuclear physics, with ramifications in related disciplines such as astroparticle physics. There are two main scientific drivers motivating these investigations of the partonic structure of hadrons. On the one hand, addressing fundamental open issues in our understanding in the strong interactions such as the origin of the nucleon mass, spin, and transverse structure; the presence of heavy quarks in the nucleon wave function; and the possible onset of novel gluon-dominated dynamical regimes. On the other hand, pinning down with the highest possible precision the substructure of nucleons and nuclei is a central component for theoretical predictions in a wide range of experiments, from proton and heavy ion collisions at the Large Hadron Collider to ultra-high energy neutrino interactions at neutrino telescopes. The goal of this project is to exploit Machine Learning and Artificial Intelligence tools [2,3] (neural networks trained by stochastic gradient descent) to pin down the quark and gluon substructure of protons and nuclei by using recent measurements from proton-proton and proton-lead collisions at the LHC. Topics of special interest are i) the strange content of protons and nuclei, ii) parton distributions at higher-orders in the QCD couplings for precision Higgs physics, iii) the interplay between jet, photon, and top quark production data to pin down the large-x gluon, and iv) charm quarks as a probe of gluon shadowing at small-x. The project also involves developing projects for the Electron-Ion Collider (EIC), a new lepton-nucleus experiment to start operations in the next years.
Contact: Juan Rojo