Session G18: Undergrad Research VI

Performance Tests of CMS Barrel Timing Layer Components for the MIP Timing Detector of the High Luminosity LHC

The Large Hadron Collider (LHC) and its experiments are preparing for major upgrades in preparation for the High-Luminosity LHC (HL-LHC) phase starting in 2029. The Compact Muon Solenoid (CMS) experiment will feature a precision timing layer called the Minimum Ionising Particle (MIP) Timing Detection Layer (MTD), comprising LYSO crystal bars with a 30-60 picosecond time resolution. Positioned just outside the tracker, the MTD aims to enhance particle reconstruction and vertex identification by mitigating the impact of up to 200 pileup events per crossing. It incorporates Thermoelectric coolers (TECs) attached to Silicon Photomultiplier (SiPM) modules, maintaining SiPM performance at -45º C and reducing dark current over the detector's run period. The TECs can be reverse biased for periodic annealing, optimizing SiPM functionality post radiation damage. Our project focused on assessing TEC performance at the CERN Tracker Integration Facility (TIF), confirming the achievement of the SiPM working point within TEC power limits. However, TEC efficiency in cooling cycles was slightly lower than expected, attributed to higher-than-anticipated TEC conductance opposing the thermoelectric effect. Further optimizations in thermal interfaces are planned to maximize TEC efficiency, ultimately expanding the operational margin of the BTL detector.   

A Search for Vector-Like Leptons: Compact Analysis

The Standard Model (SM) conveys our fundamental understanding of matter and its interactions in the universe, yet within the current Standard Model, there remain unresolved questions. Theoretical extensions of the Standard Model are being developed in hopes to resolve these conflicts. Several SM extensions predict the existence of a new type of particle, the vector-like lepton (VLL). In a proposed search for vector-like leptons, we make use of data produced by proton-proton collisions at the Large Hadron Collider and collected by the Compact Muon Solenoid (CMS). This analysis focuses the search for pair-produced vector-like leptons with two SM leptons in the final state. Various SM processes exist that have a similar final state, a few being ttbar production, DY+jets, Di-boson production. This analysis examines several kinematic variables to explore distinctive characteristics between signal and background with the intention to search for the absence or presence of vector-like lepton signal by analyzing Run II data. Finally, limits will be set on vector-like lepton experimental parameters.   

Determining the Size of Quark and Gluon Jets in CMS Open Data

The complex substructure of jets produced in proton-proton collisions at the Large Hadron Collider (LHC) is one of the keys to probe new physics. However, the shape and the size of jets are not fully understood. The focus of this study is to fit quark and gluon jets from CMS Open Data collected in 2011 into various shapes defined by the SHAPER optimal transport framework and determine the radii distributions of each shape. We first analyze the radii distributions in the dataset based on theoretical predictions and a simulation of the CMS detector. In processing the real data, a classifier is trained using the EnergyFlow machine learning framework to distinguish quark and gluon jets. The radii distributions in real data are then analyzed and compared with the simulation data. The results are the first analysis of the size of jets under this framework, which provides further insight into the jet substructure and can potentially expand our understanding of quantum chromodynamics.   

Improving Background Rejection with Normalizing Flows in Anomalous Signal Searches within Derived Spaces

After the discovery of the Higgs Boson, researchers have been attempting to find new particles that could explain the phenomena that cannot be explained by the particles in the Standard Model of physics, such as dark matter. Deep Learning approaches are a popular method to try to find anomalous events. We propose the Quasi Anomalous Knowledge (QUAK) semi-supervised deep learning approach which develops a 2D space (QUAK space) where the axes capture how signal-like or background-like an event is. The axes are based on Normalizing Flows (NF) where one is trained on simulated background and the other is trained on postulated beyond standard model scenarios – these give the likeness to background or signal for an event. The background, signals, and anomalies live in different areas of this QUAK space. By training an NF, a generative model, on the QUAK space, the NF can create a Probability Density Function which estimates the distribution of events in the space. The NF is conditionally trained on the invariant jet mass for different mass bins to capture the mass dependence of events. An interpolation scheme across generated mass bins allows for a background subtraction which creates a flat distribution where resonances are more apparent.     

Jet Calibration and Mixture Density Networks

The energy calibration for jets is important for many physics measurements at the Large Hadron Collider. Jets are sprays of particles in the detector originating from quarks and gluons. The ATLAS detector is composed of the inner charged particle tracking detector, electromagnetic and hadronic calorimeters, and a muon spectrometer. The information gained from these layers contributes to the process of jet reconstruction. This process is complicated by the large number of overlapping collisions known as pileup. The ATLAS detector will be upgraded for the High Luminosity Large Hadron Collider (HL-LHC) where the pileup will be much higher. An important step in the jet reconstruction is the Monte Carlo based calibration (MCJES) which corrects for overall jet energy scale. There is ongoing effort to replace this step with a machine learning (ML) regression that quickly learns and performs improved calibrations. In this talk, I will present a possible method using a mixture density network, a model that draws its predictions from a constructed probability density function, to perform a ML-based MCJES calibration for Run 3 and Simulated HL-LHC samples. There will be an overview of mixture density networks, performance compared to other deep learning networks, and future plans for the project.   

Jet Calibration: A Regression and Classification Model for the ATLAS Global Event Processor

Measurement of the Higgs self-coupling is one of the core objectives of the ATLAS experiment for the CERN laboratory’s High-Luminosity Large Hadron Collider (2029-2045). The di-Higgs process is sensitive to the jet momentum threshold available from the trigger system. One possible solution for lowering the threshold is to implement machine learning algorithms in the ATLAS Global Event Processor, part of the FPGA hardware trigger. These small model architectures allow for accurate and resource-efficient jet reconstruction on the scale of nanoseconds when run on FPGA hardware. In particular, this project uses a regression and classification boosted decision tree model to implement a jet calibration scheme using simulated events of the ATLAS detector.