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GEM Detector Fabrication and Characterization Facility at Panjab University
No full textThe Gas Electron Multiplier(GEM) detector is one of the most important Micro-PatternGaseous Detector (MPGD) first introduced in 1997 by F. Sauli at CERN. These detectorsutilize the principle of gas ionization, where an incoming particle interacts with the gas,ionizing it and creating electron-ion pairs. The core of the GEM detector is the GEM foil, athin insulating layer (usually made of kapton) sandwiched between two conductive layers,perforated with microscopic holes. GEM detectors are advanced gas-based detectors widelyused in particle physics experiments for particle tracking, imaging, and particleidentification. The fabrication and characterization facility of these GEM detectors isavailable in our Panjab University, Chandigarh. This paper present the overview of facilitiesalong with selected Quality Control Tests (QCs) of these GEM detectors like QC1(Visualinspection of GEM foils), QC2(Leakage Current Test), QC3(Gas Leak Test), QC4(I-VCharacteristic and Intrinsic Noise Rate Measurement)
EOS 2025 Workshop
No full textThis work presents an overview of the EOS operations at CERN, focusing on its role in supporting physics data processing and storage. EOS is a high-performance distributed storage system designed to handle the vast volumes of scientific data generated by CERN experiments. This study examines key performance metrics, recent achievements, and strategic objectives for the current year, emphasizing improvements in efficiency, reliability, and scalability. Special attention is given to the impact of EOS on physics workflows, ensuring seamless data access and analysis. By evaluating past accomplishments and future goals, this work highlights the continuous evolution of EOS to meet the growing demands of physics research at CERN
Searches for new physics in final states with multiple top quarks with the ATLAS detector and upgrade of the ATLAS tracking detector
No full textThe Standard Model's known limitations drive the search for new physics, a major focus of modern experimental particle physics. This thesis presents two searches for Beyond the Standard Model physics in the four-top-quark final state at the ATLAS experiment. The first search focuses on final states with a single lepton. The mass of the resonance is explicitly reconstructed by using the products of its fully-hadronic decay. The presence of the signal is investigated both for a simplified vector top-philic boson model, and without model dependence by identifying localized deviations in data compared to the background. The data was found to be compatible with the background-only hypothesis, and exclusion limits were set. The second search investigates final states with at least two reconstructed leptons with the same-sign electric charge. This thesis presents the algorithm for reconstruction of the resonance in this final state and shows its potential application for obtaining model-independent results for final states with multiple leptons. Before the next LHC phase, the current ATLAS tracking detector will be replaced by a new one, called the ITk. The outer part of the ITk is made using silicon strip sensors, and in the forward regions the tracking is performed in the endcaps. This thesis describes the work on production and quality control of ITk silicon strip endcap modules, the single sensitive units in the ITk strip endcaps. A tool for monitoring module production is presented, which allows to identify deviations in the production process in time and quickly mitigate them in order to ensure excellent quality of the produced modules. The most demanding module quality control step is thermal cycling, requiring a specific setup and dedicated software, both of which are described in this work
Extension and first application of the ABCDisCo method with LHC data: ABCDisCoTEC
No full textThe ``ABCD method'' provides a reliable framework to estimate backgrounds using observed data by partitioning events into one signal-enhanced region (A) and three background-enhanced control regions (B, C, and D) via two statistically independent variables. In practice, even slight correlations between the two variables can significantly undermine the method's performance. Thus, choosing appropriate variables by hand can present a formidable challenge, especially when background and signal differ only subtly. To address this issue, the ABCDisCo method (ABCD with distance correlation) was developed to construct two artificial variables from the output scores of a neural network trained to maximize signal-background discrimination while minimizing correlations using the distance correlation measure. However, relying solely on minimizing the distance correlation can introduce undesirable characteristics in the resulting distributions, which may compromise the validity of the background prediction obtained using this method. The ABCDisCo training enhanced with closure (ABCDisCoTEC) method is introduced to provide a novel solution to this issue by directly minimizing the nonclosure, expressed as a dedicated differentiable loss term. This extended method is applied to a data set of proton-proton collisions at a center-of-mass energy of 13 TeV recorded by the CMS detector at the CERN LHC. Additionally, given the complexity of the minimization problem with constraints on multiple loss terms, the modified differential method of multipliers is applied and shown to greatly improve the stability and robustness of the ABCDisCoTEC method, compared to grid search hyperparameter optimization procedures
Master integrals for semi-inclusive cuts of massless four-loop propagators
No full textWe present the analytic calculation of all master integrals for 3-, 4-, and 5-particle semi-inclusive cuts of four-loop massless propagators by means of differential equations. We fix the integration constants by reducing the semi-inclusive integrals to their fully inclusive counterparts with the Integration-By-Parts (IBP) method. We validate our results by calculating the next-to-next-to-next-to-leading order (N3LO) semi-inclusive cross-section and integrating directly over the differential variable. We provide the results in a machine-readable form. The presented integrals are essential for the direct calculation of NNLO time-like splitting functions and N3LO coefficient functions, which play a crucial role in precision QCD calculations
Inverted CERN School of Computing 2025
No full textMaximum likelihood fitting is central to many high-energy physics analyses, yet modern software makes it easy to use as a black box without understanding the underlying statistics.
The statistics lectures in the main CSC and the tCSC on ML introduce the topic of likelihood, exploring the concept and showing its importance in data analysis. However these lectures do not have the time to dive into the more practical aspects of working with likelihoods, including performing maximum likelihood fits. Therefore I believe, a lecture on this topic, picking up where the CSC lectures left off and going more in depth on the fitting procedure makes for a natural continuation of the school.
I propose a lecture, starting with a brief refresher on the topic of likelihoods, followed by an introduction to the concept of likelihood fitting and the underlying mathematics. Lastly I will get more specific on the topics of binned and profile likelihood fits. After the lecture the school participants are given a hands on exercise where they can perform a simple example of a maximum likelihood fit themselves. Through all of this I want to focus more on the underlying statistics and calculations, instead of relying on "out of the box" "plug and play" algorithms.
This session will help participants build both conceptual and practical skills in maximum likelihood fitting
Inverted CERN School of Computing 2025
No full textHigh energy physics (HEP) has historically favoured frequentist statistical methodologies, leading to the development of analysis workflows and tools optimized around this paradigm. Frequentist techniques, such as those embedded in the HistFactory statistical model, have become standard in particle physics, often utilizing asymptotic approximations for efficient parameter estimation and hypothesis testing. While these methods are highly effective, their assumptions may restrict flexibility in certain analyses, especially when asymptotic approximations break down or simply do not apply.
Bayesian approaches are starting to gain attention in HEP for their interpretative advantages, providing the full posterior distribution, which allows for flexible inference even when distributions deviate strongly from Gaussianity. Bayesian inference also handles cases with multiple parameters of interest more naturally and can incorporate prior information directly, without the need of auxiliary data. Additionally, Bayesian inference does not rely on asymptotic assumptions, making them well-suited for cases where such approximations may fail.
## Lecture plan
More concretely, the **lecture** (1 hour) could be structured as follows:
**What makes an analysis "frequentist" or "Bayesian"?**
- The likelihood as a fundamental object
- Priors vs. constraints
**Commonalities and differences in methodologies**
- Simplicity and speed of frequentist inference
- Robustness and computational cost of Bayesian analyses
- Non-Gaussian parameter distributions
- Asymptotics and multiple parameters of interest
- Bayesian updating
- Confidence vs. credible intervals
**MCMC sampling for posterior estimation**
- Introduction to essential MCMC algorithms (e.g., Metropolis-Hastings, Hamiltonian Monte Carlo)
- (Practical considerations in Bayesian computation and convergence diagnostics)
## Exercise plan
The **exercise session** (1 hour) could include:
**Bayesian analysis of a mass parameter estimation**
1. Select a simple frequentist analysis (e.g., mass scan yielding a non-Gaussian probability density function).
2. Construct the likelihood function for the mass parameter.
3. Implement or use an existing MCMC algorithm to sample from the posterior distribution.
4. Compute and visualize the posterior distribution of the mass parameter.
5. Derive the Bayesian credible interval and compare it to the frequentist confidence interval, discussing interpretational differences and implications