Student specification: For this task he/she will use C++ and/or Python code to analyse the output of the ATLAS reconstruction software suite. Therefore, the student should have interest in computing, but no deep prior knowledge in C++ or Python is required.

High-Level Tracking Triggers for ATLAS

The ATLAS Trigger system makes fast, real-time, decisions on whether to keep data from interesting proton-proton collision events to be studied later, or discard them. We can only keep about 1 in 100,000 collisions. The High-Level Trigger (HLT) includes fast software algorithms that process information from the Inner Detector to find charged particle tracks. Because of the huge number of particles produced in LHC collisions, the Inner Detector tracking software uses a lot of computing power - almost half of the HLT computing resources are used to reconstruct tracks in real time. ATLAS has developed custom-built electronics (called the Fast TracKer, FTK) to find tracks before the start of the HLT; this hardware will be deployed and operated for the first time this year. In this project, you will use the ROOT analysis package to perform validation of the tracks from FTK and commission new FTK-based triggers ready to be used online later in the year. 
Proposed dates of placement: 8 weeks in the period June to August.
Student specification: You should have an interest in computing with some experience of programming in C++ or a similar language. Some knowledge of ROOT would be helpful but is not essential.

Pixel Module Mounting for the ATLAS Tracker

The ATLAS Pixel detector will be replaced for the High-Luminosity Upgrade of the ATLAS Tracker. At RAL, we are responsible for mounting Pixel Modules on the carbon-fibre C-sections.

The student will be involved in:

• Making test assemblies/targets and comparing accuracy using the RAL module placement system and Smartscope.

• Qualification of different adhesives for module mounting as necessary.

• Measuring pot-life of adhesives to be used and programming the new glue dispenser to compensate.

• Mechanical drawings.

• DAQ and test setup for electrical testing of assembled half-rings.

Utilising Machine Learning to optimise Data Placement for the ATLAS experiment

Since the start of LHC data taking, the ATLAS experiments has produced hundreds of petabytes of data.  The data produced by ATLAS during LHC running is only a small fraction of the total as there are many derived data formats for specific types of analysis as well as a huge amount of Monte Carlo simulations.

The ATLAS data management system distributes this data across 120 sites around the world.  While the data movement is handle automatically, decisions about what data to replicate, archive or delete are still made by humans.  The aim of the project is to utilise machine learning techniques to analyse the data access patterns of ATLAS jobs in order to predict future usage.  This can then be used to optimise data placement to improve the rate at which data is processed as well as saving resources.

CMS

Particle Reconstruction Algorithms for the CMS Level-1 Trigger Upgrade

In 2026, after a series of upgrades, the Large Hadron Collider will be colliding protons at a significantly higher rate, in order to increase the sensitivity of the ATLAS and CMS detectors to evidence for new physics. The CMS detector's level-1 trigger system identifies the most interesting collisions within a few microseconds, by reconstructing the numerous particles produced in each collision using high-speed programmable electronics (FPGAs). From 2026, identifying the most interesting collisions will become significantly more difficult due to the increased rate of proton collisions, and so the level-1 trigger system will be upgraded to use state-of-the-art technologies. This is a very challenging project, and research is underway here to optimise our proposed solution.

The summer student will use C++ software running on simulated LHC collision events to develop algorithms that identify the particles produced from each collision - either reconstructing their trajectories within the tracking systems, or identifying particular types of particles by combining these reconstructed tracks with information from other detector components (such as calorimeters).

Since these algorithms must ultimately be run in FPGAs, the student will need to keep them as simple as possible, and understand the limitations and strengths of the electronics. Depending on how quickly the project progresses, the student may also be able to write firmware that implements the algorithms in FPGAs using a high-level synthesis language.

Proposed dates of placement: 8 weeks during a period to be agreed from June to August.

Student specification: You need to have a logical mind, and should be familiar with computer programming, and ideally with C++. An interest in particle physics or electronics would be a bonus.

Deep Neural Networks in CMS

The CMS experiment at the Large Hadron Collider is collecting large volumes of complex data. The data is being examined for evidence of physics beyond the standard model, such as new types of Higgs Boson. Traditional data analysis techniques are evolving to include using sophisticated machine learning algorithms. The increase in computing power is making recent developments such as Deep Neural Networks viable in the large and complex data environment at the LHC. The student will use neural networks to study their capability for identifying signatures of interesting events in the CMS detector. This will involve the use of GPUs and possibly FPGAs.

Student specification: The student should have familiarity with computer programming, and ideally with C++ and an interest in Particle Physics.

LZ

Simulation studies of the LUX-ZEPLIN Dark Matter experiment

The nature of dark matter is one of the open and fundamental questions in physics, and the LZ experiment is at the forefront of technology designed to pursue this question. The experiment will be using a two-phase Time Projection Chamber with the ability to measure both the scintillation light from the liquid and the electroluminescence light from the gas above the liquid. The success of the LZ experiment depends on a careful understanding of the response of the detector.
The summer student will use simulation to study the impact of a non-uniformity of the gas gap above the liquid on the electroluminescence light generation.

Proposed dates of placement: 8 weeks during June-August

Student Specification: You should have an interest in particle physics and computing, with some experience of programming in C . Some knowledge of ROOT and Finite Element Methods would be helpful but is not essential.