Searches for new phenomena in final states with 3rd generation quarks using the ATLAS detector

Many theories beyond the Standard Model predict new phenomena, such as heavy vectors or scalar, vector-like quarks, and leptoquarks in final states containing bottom or top quarks. Such final states offer great potential to reduce the Standard Model background, although with significant challenges in reconstructing and identifying the decay products and modelling the remaining background. The recent 13 TeV pp results, along with the associated improvements in identification techniques, will be reported

Exploring selections across channels in Dark Matter searches with top quarks at the ATLAS experiment of the LHC

Current estimates put Dark Matter to 26% of the energy-matter content of the universe, but very little is known about it other than its gravitational interactions. Eorts to learn more about Dark Matter include searching for it at high energy particle colliders. The lack of information about the nature of Dark Matter makes this a complicated task, and many searches are performed in dierent channels, and considering dierent theoretical models. In this thesis, I explore two such analyses, performed in the ATLAS collaboration using data from the ATLAS detector at the Large Hadron Collider at CERN: the tW+MET (missing transverse energy) nal state and the tt+MET nal state. I have made a generation-level study of the overlap between the signal regions used, and come to the conclusion that there is some. I have also compared the models used in these analyses, the 2HDM+a and the simplied spin-0 pseudoscalar model. Given the simplications made in my study, however, more sophisticated approaches should be used before anything conclusive can be said

25th International Conference on Computing in High Energy & Nuclear Physics

In recent years, machine learning methods have become increasingly important for the experiments of the Large Hadron Collider (LHC). They are utilized in everything from trigger systems to reconstruction to data analysis. The recent UCluster method is a general model providing unsupervised clustering of particle physics data, that can be easily modified for a variety of different tasks. In the current paper, we improve on the UCluster method by adding the option of training the model in a scalable and distributed fashion, which extends its usefulness even further. UCluster combines the graph-based neural network ABCnet with a clustering step, using a combined loss function to train. It was written in TensorFlow v1.14 and has previously been trained on a single GPU. It shows a clustering accuracy of 81% when applied to the problem of multiclass classification of simulated jet events. Our implementation adds the distributed training functionality by utilizing the Horovod distributed training framework, which necessitated a migration of the code to TensorFlow v2. Together with using parquet files for splitting data up between different nodes, the distributed training makes the model scalable to any amount of input data, something that will be essential for use with real LHC datasets. We find that the model is well suited for distributed training, with the training time decreasing in direct relation to the number of GPU's used

Exploring selections across channels in Dark Matter searches with top quarks at the ATLAS experiment of the LHC

Current estimates put Dark Matter to 26% of the energy-matter content of the universe, but very little is known about it other than its gravitational interactions. Eorts to learn more about Dark Matter include searching for it at high energy particle colliders. The lack of information about the nature of Dark Matter makes this a complicated task, and many searches are performed in dierent channels, and considering dierent theoretical models. In this thesis, I explore two such analyses, performed in the ATLAS collaboration using data from the ATLAS detector at the Large Hadron Collider at CERN: the tW+MET (missing transverse energy) nal state and the tt+MET nal state. I have made a generation-level study of the overlap between the signal regions used, and come to the conclusion that there is some. I have also compared the models used in these analyses, the 2HDM+a and the simplied spin-0 pseudoscalar model. Given the simplications made in my study, however, more sophisticated approaches should be used before anything conclusive can be said

Distributed training and scalability for the particle clustering method UCluster

In recent years, machine-learning methods have become increasingly important for the experiments at the Large Hadron Collider (LHC). They are utilised in everything from trigger systems to reconstruction and data analysis. The recent UCluster method is a general model providing unsupervised clustering of particle physics data, that can be easily modified to provide solutions for a variety of different decision problems. In the current paper, we improve on the UCluster method by adding the option of training the model in a scalable and distributed fashion, and thereby extending its utility to learn from arbitrarily large data sets. UCluster combines a graph-based neural network called ABCnet with a clustering step, using a combined loss function in the training phase. The original code is publicly available in TensorFlow v1.14 and has previously been trained on a single GPU. It shows a clustering accuracy of 81% when applied to the problem of multi-class classification of simulated jet events. Our implementation adds the distributed training functionality by utilising the Horovod distributed training framework, which necessitated a migration of the code to TensorFlow v2. Together with using parquet files for splitting data up between different compute nodes, the distributed training makes the model scalable to any amount of input data, something that will be essential for use with real LHC data sets. We find that the model is well suited for distributed training, with the training time decreasing in direct relation to the number of GPU’s used. However, further improvements by a more exhaustive and possibly distributed hyper-parameter search is required in order to achieve the reported accuracy of the original UCluster method

Search for new phenomena in three- or four-lepton events in pp collisions at root s=13 TeV with the ATLAS detector

A search with minimal model dependence for physics beyond the Standard Model in events featuring three or four charged leptons (3l and 4l, l = e, mu) is presented. The analysis aims to be sensitive to a wide range of potential new-physics theories simultaneously. This analysis uses data from pp collisions delivered by the Large Hadron Collider at a centre-of-mass energy of root s = 13 TeV and recorded with the ATLAS detector, corresponding to the full Run 2 dataset of 139 fb(-1). The 3l and 4l phase space is divided into 22 event categories according to the number of leptons in the event, the missing transverse momentum, the invariant mass of the leptons, and the presence of leptons originating from a Z-boson candidate. These event categories are analysed independently for the presence of deviations from the Standard Model. No statistically significant deviations from the Standard Model predictions are observed. Upper limits for all signal regions are reported in terms of the visible cross-section. Crown Copyright (C) 2021 Published by Elsevier B.V.For complete list of authors see http://dx.doi.org/10.1016/j.physletb.2021.136832</p

Search for chargino-neutralino pair production in final states with three leptons and missing transverse momentum in root s=13 TeV pp collisions with the ATLAS detector

A search for chargino-neutralino pair production in three-lepton final states with missing transverse momentum is presented. The study is based on a dataset of root s = 13 TeV pp collisions recorded with the ATLAS detector at the LHC, corresponding to an integrated luminosity of 139 fb(-1). No significant excess relative to the Standard Model predictions is found in data. The results are interpreted in simplified models of supersymmetry, and statistically combined with results from a previous ATLAS search for compressed spectra in two-lepton final states. Various scenarios for the production and decay of charginos ((chi) over tilde (+/-)(1)) and neutralinos ((chi) over tilde (0)(2)) are considered. For pure higgsino (chi) over tilde (+/-)(1)(chi) over tilde (0)(2) pair-production scenarios, exclusion limits at 95% confidence level are set on (chi) over tilde (0)(2) masses up to 210 GeV. Limits are also set for pure wino (chi) over tilde (+/-)(1)(chi) over tilde (0)(2) pair production, on (chi) over tilde (0)(2) masses up to 640 GeV for decays via on-shell W and Z bosons, up to 300 GeV for decays via off-shell W and Z bosons, and up to 190 GeV for decays via W and Standard Model Higgs bosons.For complete list of authors see http://dx.doi.org/10.1140/epjc/s10052-021-09749-7</p