The results of a search for pair production of the scalar partners of the bottom quarks in 4.7 fb1 of pp collisions at ps = 7 TeV using the ATLAS detector at the LHC are reported. Scalar bottoms are searched for in events with large missing transverse momentum and two jets identified as originating from a b-quark in the final state. In an R-parity conserving minimal supersymmetric scenario, assuming that the scalar bottom decays exclusively into a bottom quark and a neutralino, 95% confidence-level upper limits are obtained in the b1 c10 mass plane. Exclusions are first calculated using the frequentist method. For c 10 masses less than 150 GeV, b1 masses up to 500 GeV are excluded. The exclusion is extended up to 600 GeV by the use of machine learning methods.

Astrophysical observations implying the existence of Dark Matter and Dark Energy, which are not described by the Standard Model (SM) of particle physics, have led to extensions of the SM predicting new particles that could be directly produced at the Large Hadron Collider (LHC) at CERN. Based on 2015 and 2016 ATLAS proton-proton collision data, this thesis presents searches for the supersymmetric partner of the top quark, for Dark Matter, and for DarkEnergy, in signatures with jets and missing transverse energy. Muon detection is key to some of the most important LHC physics results, including the discovery of the Higgs boson and the measurement of its properties. The efficiency with which muons can be detected with the ATLAS detector is measured using Z boson decays. The performance of high-precision Monitored Drift Tube muon chambers under background rates similar to the ones expected for the High Luminosity-LHC is studied.

This concise primer reviews the latest developments in the field of jets. Jets are collinear sprays of hadrons produced in very high-energy collisions, e.g. at the LHC or at a future hadron collider. They are essential to and ubiquitous in experimental analyses, making their study crucial. At present LHC energies and beyond, massive particles around the electroweak scale are frequently produced with transverse momenta that are much larger than their mass, i.e., boosted. The decay products of such boosted massive objects tend to occupy only a relatively small and confined area of the detector and are observed as a single jet. Jets hence arise from many different sources and it is important to be able to distinguish the rare events with boosted resonances from the large backgrounds originating from Quantum Chromodynamics (QCD). This requires familiarity with the internal properties of jets, such as their different radiation patterns, a field broadly known as jet substructure. This set of notes begins by providing a phenomenological motivation, explaining why the study of jets and their substructure is of particular importance for the current and future program of the LHC, followed by a brief but insightful introduction to QCD and to hadron-collider phenomenology. The next section introduces jets as complex objects constructed from a sequential recombination algorithm. In this context some experimental aspects are also reviewed. Since jet substructure calculations are multi-scale problems that call for all-order treatments (resummations), the bases of such calculations are discussed for simple jet quantities. With these QCD and jet physics ingredients in hand, readers can then dig into jet substructure itself. Accordingly, these notes first highlight the main concepts behind substructure techniques and introduce a list of the main jet substructure tools that have been used over the past decade. Analytic calculations are then provided for several families of tools, the goal being to identify their key characteristics. In closing, the book provides an overview of LHC searches and measurements where jet substructure techniques are used, reviews the main take-home messages, and outlines future perspectives.

This state-of-the-art survey is dedicated to the memory of Emmanuil Markovich Braverman (1931-1977), a pioneer in developing machine learning theory. The 12 revised full papers and 4 short papers included in this volume were presented at the conference "Braverman Readings in Machine Learning: Key Ideas from Inception to Current State" held in Boston, MA, USA, in April 2017, commemorating the 40th anniversary of Emmanuil Braverman's decease. The papers present an overview of some of Braverman's ideas and approaches. The collection is divided in three parts. The first part bridges the past and the present and covers the concept of kernel function and its application to signal and image analysis as well as clustering. The second part presents a set of extensions of Braverman's work to issues of current interest both in theory and applications of machine learning. The third part includes short essays by a friend, a student, and a colleague.

Cosmology’s journey to the present day has been a long one. This book outlines the latest research on modern cosmology and related topics from world-class experts. Through it, readers will learn how multi-disciplinary approaches and technologies are used to search the unknown and how we arrived at the knowledge used and assumptions made by cosmologists today. The book is organized into four parts, each exploring a theme that has troubled humankind for centuries. Since the dawn of time, looking at the sky, humans have tried to understand their origin, the laws governing it, and what influence it all has on human life. In most ancient civilizations, astronomers embodied the power of knowledge. This knowledge was not compartmentalized, and scientists often found philosophical implications within their quests, many of which destroyed the borders between the natural sciences. Even now, as observers and scientists continue to use conjecture to generate theoretical assumptions and laws that then have to be confirmed experimentally, said theoretical and experimental searches are being linked to philosophical thinking and artistic representation, as they were up until the 18th century. This multi-disciplinary book will appeal to anyone with an interest in the fields of Astronomy, Cosmology or Physics.

The Higgs boson discovery at the Large Hadron Collider in 2012 relied on boosted decision trees. Since then, high energy physics (HEP) has applied modern machine learning (ML) techniques to all stages of the data analysis pipeline, from raw data processing to statistical analysis. The unique requirements of HEP data analysis, the availability of high-quality simulators, the complexity of the data structures (which rarely are image-like), the control of uncertainties expected from scientific measurements, and the exabyte-scale datasets require the development of HEP-specific ML techniques. While these developments proceed at full speed along many paths, the nineteen reviews in this book offer a self-contained, pedagogical introduction to ML models' real-life applications in HEP, written by some of the foremost experts in their area.

This book describes the first application at CMS of deep learning algorithms trained directly on low-level, “raw” detector data, or so-called end-to-end physics reconstruction. Growing interest in searches for exotic new physics in the CMS collaboration at the Large Hadron Collider at CERN has highlighted the need for a new generation of particle reconstruction algorithms. For many exotic physics searches, sensitivity is constrained not by the ability to extract information from particle-level data but by inefficiencies in the reconstruction of the particle-level quantities themselves. The technique achieves a breakthrough in the reconstruction of highly merged photon pairs that are completely unresolved in the CMS detector. This newfound ability is used to perform the first direct search for exotic Higgs boson decays to a pair of hypothetical light scalar particles H→aa, each subsequently decaying to a pair of highly merged photons a→yy, an analysis once thought impossible to perform. The book concludes with an outlook on potential new exotic searches made accessible by this new reconstruction paradigm.

Particle dark matter: the name of the game -- The thermal relic paradigm: zeroth-order lessons from cosmology -- The thermal relic paradigm: a closer look -- The art of WIMP direct detection -- Indirect dark matter searches -- Searching for dark matter with particle colliders -- Axions and axion-like particles as dark matter -- Sterile neutrinos as dark matter particles -- Bestiarium: a short, biased compendium of notable dark matter particle candidates and models