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

Of dark mesons and novel methods : A dark sector search in ATLAS data and development of new techniques for challenging final states

Studies of the interactions of elementary particles at high energies have been carried out at the Large Hadron Collider (LHC) at CERN for over a decade. Different quantities from the Standard Model (SM) of particle physics have been measured with increasing accuracy without substantial deviations from predictions. Searches for physics beyond the SM are similarly carried out, motivated by the existence of phenomena not yet described by it, such as dark matter. This thesis presents one such search in proton-proton collision data recorded by the ATLAS detector. The search is guided by a new, proposed addition to the SM, where the dark matter candidate arises as a composite particle of a new sector. If this were realised in nature, the same sector would give rise to other composite particles, dark mesons, that would be produced in proton-proton collisions and decay promptly to SM particles. This new model is largely free of previous constraints from searches and measurements. The full analysis targeting pair produced dark pions decaying to top and bottom quarks, tttb or ttbb, in the 1-lepton channel is described. It is carried out in the full Run 2 dataset of 140 fb−1 of proton-proton collisions at √s = 13 TeV center-of-mass energy. The analysis is sensitive to large parts of the parameter space of the model, and no significant excess was seen over SM predictions. Based on this, limits on the production cross-section of dark pions were set. By comparing with the theoretical cross sections of the model, these rule out dark pion masses up to 943 GeV in the most sensitive configuration. Further, several novel techniques that could aid with searches in similar phase-spaces are presented. First, the Extrapolation Engine fast simulation of the inner tracker for the high luminosity upgrade of ATLAS was used in the study of a proposed hardware track trigger (HTT). This could be crucial to retaining efficiency in similar phase-spaces in the extreme conditions at the high luminosity LHC (HL-LHC). Second, the fully scalable multi-dimensional density estimate in SparkDensityTrees was applied on background and signal similar to those in the dark meson analysis and was shown to efficiently find signal-enriched regions. Third, the unsupervised clustering algorithm UCluster which can be trained with any clustering objective, such as signal extraction, anomaly detection or jet tagging was developed to run on multiple cores for arbitrary scalability. Lastly, a Boosted Decision Tree (BDT) was applied for signal and background discrimination in the dark meson analysis, yielding promising results for future iterations of it.

Of dark mesons and novel methods : A dark sector search in ATLAS data and development of new techniques for challenging final states

Studies of the interactions of elementary particles at high energies have been carried out at the Large Hadron Collider (LHC) at CERN for over a decade. Different quantities from the Standard Model (SM) of particle physics have been measured with increasing accuracy without substantial deviations from predictions. Searches for physics beyond the SM are similarly carried out, motivated by the existence of phenomena not yet described by it, such as dark matter. This thesis presents one such search in proton-proton collision data recorded by the ATLAS detector. The search is guided by a new, proposed addition to the SM, where the dark matter candidate arises as a composite particle of a new sector. If this were realised in nature, the same sector would give rise to other composite particles, dark mesons, that would be produced in proton-proton collisions and decay promptly to SM particles. This new model is largely free of previous constraints from searches and measurements. The full analysis targeting pair produced dark pions decaying to top and bottom quarks, tttb or ttbb, in the 1-lepton channel is described. It is carried out in the full Run 2 dataset of 140 fb−1 of proton-proton collisions at √s = 13 TeV center-of-mass energy. The analysis is sensitive to large parts of the parameter space of the model, and no significant excess was seen over SM predictions. Based on this, limits on the production cross-section of dark pions were set. By comparing with the theoretical cross sections of the model, these rule out dark pion masses up to 943 GeV in the most sensitive configuration. Further, several novel techniques that could aid with searches in similar phase-spaces are presented. First, the Extrapolation Engine fast simulation of the inner tracker for the high luminosity upgrade of ATLAS was used in the study of a proposed hardware track trigger (HTT). This could be crucial to retaining efficiency in similar phase-spaces in the extreme conditions at the high luminosity LHC (HL-LHC). Second, the fully scalable multi-dimensional density estimate in SparkDensityTrees was applied on background and signal similar to those in the dark meson analysis and was shown to efficiently find signal-enriched regions. Third, the unsupervised clustering algorithm UCluster which can be trained with any clustering objective, such as signal extraction, anomaly detection or jet tagging was developed to run on multiple cores for arbitrary scalability. Lastly, a Boosted Decision Tree (BDT) was applied for signal and background discrimination in the dark meson analysis, yielding promising results for future iterations of it.

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