Difference between revisions of "Master student Projects"

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=== Research projects for students in the Nikhef/UU ALICE group (Jan 2016) ===
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=== Research projects for students in the Nikhef/UU ALICE group (May 2021) ===
  
 
This is an overview of the student projects currently available in the  
 
This is an overview of the student projects currently available in the  

Latest revision as of 11:42, 11 May 2021

Research projects for students in the Nikhef/UU ALICE group (May 2021)

This is an overview of the student projects currently available in the ALICE group at Nikhef and Utrecht University. The research in ALICE focuses on the strong interactions. In particular, we use high-energy collisions of lead nuclei to study many-body systems in which the strong interaction is the main force. In these collisions, we expect to form a hot and dense system in which quarks and gluons are effectively deconfined: the Quark Gluon Plasma (QGP). The goal of the research is to understand the properties of the QGP, including transport properties such as viscosity and parton energy loss.

If you have your own research proposal, need more detailed information on the (availability) of individual proposals or would like to discuss about other available projects in the group you are always welcome to contact either the contact person for the project and/or the ALICE group leader, Raimond Snellings.


Projects with a 2021 start

ALICE: The next-generation multi-purpose detector at the LHC

This main goal of this project is to focus on the next-generation multi-purpose detector planned to be built at the LHC. Its core will be a nearly massless barrel detector consisting of truly cylindrical layers based on curved wafer-scale ultra-thin silicon sensors with MAPS technology, featuring an unprecedented low material budget of 0.05% X0 per layer, with the innermost layers possibly positioned inside the beam pipe. The proposed detector is conceived for studies of pp, pA and AA collisions at luminosities a factor of 20 to 50 times higher than possible with the upgraded ALICE detector, enabling a rich physics program ranging from measurements with electromagnetic probes at ultra-low transverse momenta to precision physics in the charm and beauty sector.

Contact: Panos Christakoglou and Alessandro Grelli and Marco van Leeuwen

ALICE: Searching for the strongest magnetic field in nature

In case of a non-central collision between two Pb ions, with a large value of impact parameter (b), the charged nucleons that do not participate in the interaction (called spectators) create strong magnetic fields. A back of the envelope calculation using the Biot-Savart law brings the magnitude of this filed close to 10^19Gauss in agreement with state of the art theoretical calculation, making it the strongest magnetic field in nature. The presence of this field could have direct implications in the motion of final state particles. The magnetic field, however, decays rapidly. The decay rate depends on the electric conductivity of the medium which is experimentally poorly constrained. Overall, the presence of the magnetic field, the main goal of this project, is so far not confirmed experimentally.

Contact: Panos Christakoglou

ALICE: Looking for parity violating effects in strong interactions

Within the Standard Model, symmetries, such as the combination of charge conjugation (C) and parity (P), known as CP-symmetry, are considered to be key principles of particle physics. The violation of the CP-invariance can be accommodated within the Standard Model in the weak and the strong interactions, however it has only been confirmed experimentally in the former. Theory predicts that in heavy-ion collisions, in the presence of a deconfined state, gluonic fields create domains where the parity symmetry is locally violated. This manifests itself in a charge-dependent asymmetry in the production of particles relative to the reaction plane, what is called the Chiral Magnetic Effect (CME). The first experimental results from STAR (RHIC) and ALICE (LHC) are consistent with the expectations from the CME, however further studies are needed to constrain background effects. These highly anticipated results have the potential to reveal exiting, new physics.

Contact: Panos Christakoglou

ALICE: Machine learning techniques as a tool to study the production of heavy flavour particles

There was recently a shift in the field of heavy-ion physics triggered by experimental results obtained in collisions between small systems (e.g. protons on protons). These results resemble the ones obtained in collisions between heavy ions. This consequently raises the question of whether we create the smallest QGP droplet in collisions between small systems. The main objective of this project will be to study the production of charm particles such as D-mesons and Λc-baryons in pp collisions at the LHC. This will be done with the help of a new and innovative technique which is based on machine learning (ML). The student will also extend the studies to investigate how this production rate depends on the event activity e.g. on how many particles are created after every collision.

Contact: Panos Christakoglou and Alessandro Grelli

ALICE: Energy Loss of Energetic Quarks and Gluons in the Quark-Gluon Plasma

One of the ways to study the quark-gluon plasma that is formed in high-energy nuclear collisions, is using high-energy partons (quarks or gluons) that are produced early in the collision and interact with the quark-gluon plasma as they propagate through it. There are several current open questions related to this topic, which can be explored in a Master's project. For example, we would like to use the new Monte Carlo generator framework JetScape to simulate collisions to see whether we can extract information about the interaction with the quark-gluon plasma. In the project you will collaborate with one of the PhD students or postdocs in our group to use the model to generate predictions of measurements and compare those to data analysis results. Depending on your interests, the project can focus more on the modeling aspects or on the analysis of experimental data from the ALICE detector at the LHC.

Contact: Marco van Leeuwen and Marta Verweij

ALICE: Extreme Rare Probes of the Quark-Gluon Plasma

The quark-gluon plasma is formed in high-energy nuclear collisions and also existed shortly after the big bang. With the large amount of data collected in recent years at the Large Hadron Collider at CERN, rare processes that previously were not accessible provide now new ways to study how the quark-gluon plasma emerges from the fundamental theory of strong interaction. One of such processes is the heavy W boson which in many cases decays to two quarks. The W boson itself doesn’t interact with the quark-gluon plasma because it doesn’t carry color, but the quark decay products do interact with the plasma and therefore provide an ideal tool to study the space-time evolution of this hot and dense medium. In this project you will use data from the ALICE detector at the LHC and simulated data from generators to study various physics mechanisms that could be happening in the real collisions.

Contact: Marta Verweij and Marco van Leeuwen

ALICE: Jet Quenching with Machine Learning

Machine learning applications are rising steadily as a vital tool in the field of data science but are relatively new in the particle physics community. In this project machine learning tools will be used to gain insights into the modification of a parton shower in the quark-gluon plasma (QGP). The QGP is created in high-energy nuclear collisions and only lives for a very short period of time. Highly energetic partons created in the same collisions interact with the plasma while they travers it and are observed as a collimated spray of particles, known as jets, in the detector. One of the key recent insights is that the internal structure of jets provides information about the evolution of the QGP. With data recorded by the ALICE experiment, you will use jet substructure techniques in combination with machine learning algorithms to dissect the structure of the QGP. Machine learning will be used to select the regions of radiation phase space that are affected by the presence of the QGP.

Contact: Marta Verweij and Marco van Leeuwen