Search For Flavour Changing Neutral Currents In Processes With A Single Top Quark In Association With A Photon Using A Deep Neural Network At The Atlas Experiment At S

Machine Learning under Resource Constraints - Discovery in Physics

Machine Learning under Resource Constraints addresses novel machine learning algorithms that are challenged by high-throughput data, by high dimensions, or by complex structures of the data in three volumes. Resource constraints are given by the relation between the demands for processing the data and the capacity of the computing machinery. The resources are runtime, memory, communication, and energy. Hence, modern computer architectures play a significant role. Novel machine learning algorithms are optimized with regard to minimal resource consumption. Moreover, learned predictions are executed on diverse architectures to save resources. It provides a comprehensive overview of the novel approaches to machine learning research that consider resource constraints, as well as the application of the described methods in various domains of science and engineering. Volume 2 covers machine learning for knowledge discovery in particle and astroparticle physics. Their instruments, e.g., particle detectors or telescopes, gather petabytes of data. Here, machine learning is necessary not only to process the vast amounts of data and to detect the relevant examples efficiently, but also as part of the knowledge discovery process itself. The physical knowledge is encoded in simulations that are used to train the machine learning models. At the same time, the interpretation of the learned models serves to expand the physical knowledge. This results in a cycle of theory enhancement supported by machine learning.

Search for Flavour-changing Neutral Currents in Processes with a Single Top Quark in Association with a Photon Using a Deep Neural Network at the ATLAS Experiment at √s

A Search for Flavour Changing Neutral Currents in Top-quark Decays in Pp Collision Data Collected with the ATLAS Detector at {u221A}s

A search for flavour changing neutral current (FCNC) processes in top-quark decays by the ATLAS Collaboration is presented. Data collected from pp collisions at the LHC at a centre-of-mass energy of √s = 7 TeV during 2011, corresponding to an integrated luminosity of 2.1 fb−1, were used. A search was performed for top-quark pair-production events, with one top quark decaying through the t → Zq FCNC (q = u, c) channel, and the other through the Standard Model dominant mode t → Wb. Only the decays of the Z boson to charged leptons and leptonic W-boson decays were considered as signal. Consequently, the final-state topology is characterised by the presence of three isolated charged leptons, at least two jets and missing transverse momentum from the undetected neutrino. No evidence for an FCNC signal was found. An upper limit on the t → Zq branching ratio of BR(t → Zq)