A Search for a New Particle Decaying to Pairs of Weak Gauge Bosons with the ATLAS Detector

A search for new heavy resonances decaying to boson pairs (WZ, WW or ZZ) using 20.3 inverse femtobarns of proton-proton collision data at a center of mass energy of 8 TeV is presented. The data were recorded by the ATLAS detector at the Large Hadron Collider (LHC) in 2012. The analysis combines several search channels with the leptonic, semi-leptonic and fully hadronic final states. The diboson invariant mass spectrum is studied for local excesses above the Standard Model background prediction, and no significant excess is observed for the combined analysis. 95$\%$ confidence limits are set on the cross section times branching ratios for three signal models: an extended gauge model with a heavy W boson, a bulk Randall-Sundrum model with a spin-2 graviton, and a simplified model with a heavy vector triplet. Among the individual search channels, the fully-hadronic channel is predominantly presented where boson tagging technique and jet substructure cuts are used. Local excesses are found in the dijet mass distribution around 2 TeV, leading to a global significance of 2.5 standard deviations. This deviation from the Standard Model prediction results in many theory explanations, and the possibilities could be further explored using the LHC Run 2 data.

Search for Pair-Produced Supersymmetric Top Quark Partners with the ATLAS Experiment

Searches for the supersymmetric partner of the top quark (stop) are motivated by natural supersymmetry, where the stop has to be light to cancel the large radiative corrections to the Higgs boson mass. This thesis presents three different searches for the stop at √s = 8 TeV and √s = 13 TeV using data from the ATLAS experiment at CERN’s Large Hadron Collider. The thesis also includes a study of the primary vertex reconstruction performance in data and simulation at √s = 7 TeV using tt and Z events. All stop searches presented are carried out in final states with a single lepton, four or more jets and large missing transverse energy. A search for direct stop pair production is conducted with 20.3 fb−1 of data at a center-of-mass energy of √s = 8 TeV. Several stop decay scenarios are considered, including those to a top quark and the lightest neutralino and to a bottom quark and the lightest chargino. The sensitivity of the analysis is also studied in the context of various phenomenological MSSM models in which more complex decay scenarios can be present. Two different analyses are carried out at √s = 13 TeV. The first one is a search for both gluino-mediated and direct stop pair production with 3.2 fb−1 of data while the second one is a search for direct stop pair production with 13.2 fb−1 of data in the decay scenario to a bottom quark and the lightest chargino. The results of the analyses show no significant excess over the Standard Model predictions in the observed data. Consequently, exclusion limits are set at 95% CL on the masses of the stop and the lightest neutralino.

Lambda_c detection using a weighted Bayesian PID approach

Lo scopo della tesi è di stimare le prestazioni del rivelatore ALICE nella rivelazione del barione Lambda_c nelle collisioni PbPb usando un approccio innovativo per l'identificazione delle particelle. L'idea principale del nuovo approccio è di sostituire l'usuale selezione della particella, basata su tagli applicati ai segnali del rivelatore, con una selezione che usi le probabilità derivate dal teorema di Bayes (per questo è chiamato "pesato Bayesiano"). Per stabilire quale metodo è il più efficiente , viene presentato un confronto con altri approcci standard utilizzati in ALICE. Per fare ciò è stato implementato un software di simulazione Monte Carlo "fast", settato con le abbondanze di particelle che ci si aspetta nel nuovo regime energetico di LHC e con le prestazioni osservate del rivelatore. E' stata quindi ricavata una stima realistica della produzione di Lambda_c, combinando i risultati noti da esperimenti precedenti e ciò è stato usato per stimare la significatività secondo la statistica al RUN2 e RUN3 dell'LHC. Verranno descritti la fisica di ALICE, tra cui modello standard, cromodinamica quantistica e quark gluon plasma. Poi si passerà ad analizzare alcuni risultati sperimentali recenti (RHIC e LHC). Verrà descritto il funzionamento di ALICE e delle sue componenti e infine si passerà all'analisi dei risultati ottenuti. Questi ultimi hanno mostrato che il metodo risulta avere una efficienza superiore a quella degli usuali approcci in ALICE e che, conseguentemente, per quantificare ancora meglio le prestazioni del nuovo metodo si dovrebbe eseguire una simulazione "full", così da verificare i risultati ottenuti in uno scenario totalmente realistico.

Data Science Seminar: The data science revolution in Physics and Astronomy

Abstract Heading into the 2020s, Physics and Astronomy are undergoing experimental revolutions that will reshape our picture of the fabric of the Universe. The Large Hadron Collider (LHC), the largest particle physics project in the world, produces 30 petabytes of data annually that need to be sifted through, analysed, and modelled. In astrophysics, the Large Synoptic Survey Telescope (LSST) will be taking a high-resolution image of the full sky every 3 days, leading to data rates of 30 terabytes per night over ten years. These experiments endeavour to answer the question why 96% of the content of the universe currently elude our physical understanding. Both the LHC and LSST share the 5-dimensional nature of their data, with position, energy and time being the fundamental axes. This talk will present an overview of the experiments and data that is gathered, and outlines the challenges in extracting information. Common strategies employed are very similar to industrial data! Science problems (e.g., data filtering, machine learning, statistical interpretation) and provide a seed for exchange of knowledge between academia and industry. Speaker Biography Professor Mark Sullivan Mark Sullivan is a Professor of Astrophysics in the Department of Physics and Astronomy. Mark completed his PhD at Cambridge, and following postdoctoral study in Durham, Toronto and Oxford, now leads a research group at Southampton studying dark energy using exploding stars called "type Ia supernovae". Mark has many years' experience of research that involves repeatedly imaging the night sky to track the arrival of transient objects, involving significant challenges in data handling, processing, classification and analysis.

Searches for Beyond Standard Model Higgs Bosons in pp collisions at sqrt(s) = 8 and 13 TeV with the ATLAS detector

Thesis (Ph.D.)--University of Washington, 2016-08

Laser induced mechanisms controlling the size distribution of metallic nanoparticles

International audience

Using the Higgs to Probe Naturalness

The extreme sensitivity of the mass of the Higgs boson to quantum corrections from high mass states, makes it 'unnaturally' light in the standard model. This 'hierarchy problem' can be solved by symmetries, which predict new particles related, by the symmetry, to standard model fields. The Large Hadron Collider (LHC) can potentially discover these new particles, thereby finding the solution to the hierarchy problem. However, the dynamics of the Higgs boson is also sensitive to this new physics. We show that in many scenarios the Higgs can be a complementary and powerful probe of the hierarchy problem at the LHC and future colliders. If the top quark partners carry the color charge of the strong nuclear force, the production of Higgs pairs is affected. This effect is tightly correlated with single Higgs production, implying that only modest enhancements in di-Higgs production occur when the top partners are heavy. However, if the top partners are light, we show that di-Higgs production is a useful complementary probe to single Higgs production. We verify this result in the context of a simplified supersymmetric model. If the top partners do not carry color charge, their direct production is greatly reduced. Nevertheless, we show that such scenarios can be revealed through Higgs dynamics. We find that many color neutral frameworks leave observable traces in Higgs couplings, which, in some cases, may be the only way to probe these theories at the LHC. Some realizations of the color neutral framework also lead to exotic decays of the Higgs with displaced vertices. We show that these decays are so striking that the projected sensitivity for these searches, at hadron colliders, is comparable to that of searches for colored top partners. Taken together, these three case studies show the efficacy of the Higgs as a probe of naturalness.

L1 track finding for a time multiplexed trigger

At the HL-LHC, proton bunches will cross each other every 25. ns, producing an average of 140 pp-collisions per bunch crossing. To operate in such an environment, the CMS experiment will need a L1 hardware trigger able to identify interesting events within a latency of 12.5. μs. The future L1 trigger will make use also of data coming from the silicon tracker to control the trigger rate. The architecture that will be used in future to process tracker data is still under discussion. One interesting proposal makes use of the Time Multiplexed Trigger concept, already implemented in the CMS calorimeter trigger for the Phase I trigger upgrade. The proposed track finding algorithm is based on the Hough Transform method. The algorithm has been tested using simulated pp-collision data. Results show a very good tracking efficiency. The algorithm will be demonstrated in hardware in the coming months using the MP7, which is a μTCA board with a powerful FPGA capable of handling data rates approaching 1. Tb/s.

Studies of CMS data access patterns with machine learning techniques

This thesis presents a study of the Grid data access patterns in distributed analysis in the CMS experiment at the LHC accelerator. This study ranges from the deep analysis of the historical patterns of access to the most relevant data types in CMS, to the exploitation of a supervised Machine Learning classification system to set-up a machinery able to eventually predict future data access patterns - i.e. the so-called dataset “popularity” of the CMS datasets on the Grid - with focus on specific data types. All the CMS workflows run on the Worldwide LHC Computing Grid (WCG) computing centers (Tiers), and in particular the distributed analysis systems sustains hundreds of users and applications submitted every day. These applications (or “jobs”) access different data types hosted on disk storage systems at a large set of WLCG Tiers. The detailed study of how this data is accessed, in terms of data types, hosting Tiers, and different time periods, allows to gain precious insight on storage occupancy over time and different access patterns, and ultimately to extract suggested actions based on this information (e.g. targetted disk clean-up and/or data replication). In this sense, the application of Machine Learning techniques allows to learn from past data and to gain predictability potential for the future CMS data access patterns. Chapter 1 provides an introduction to High Energy Physics at the LHC. Chapter 2 describes the CMS Computing Model, with special focus on the data management sector, also discussing the concept of dataset popularity. Chapter 3 describes the study of CMS data access patterns with different depth levels. Chapter 4 offers a brief introduction to basic machine learning concepts and gives an introduction to its application in CMS and discuss the results obtained by using this approach in the context of this thesis.

Supporting Scientific Research Through Machine and Deep Learning: Fluorescence Microscopy and Operational Intelligence Use Cases

Although the debate of what data science is has a long history and has not reached a complete consensus yet, Data Science can be summarized as the process of learning from data. Guided by the above vision, this thesis presents two independent data science projects developed in the scope of multidisciplinary applied research. The first part analyzes fluorescence microscopy images typically produced in life science experiments, where the objective is to count how many marked neuronal cells are present in each image. Aiming to automate the task for supporting research in the area, we propose a neural network architecture tuned specifically for this use case, cell ResUnet (c-ResUnet), and discuss the impact of alternative training strategies in overcoming particular challenges of our data. The approach provides good results in terms of both detection and counting, showing performance comparable to the interpretation of human operators. As a meaningful addition, we release the pre-trained model and the Fluorescent Neuronal Cells dataset collecting pixel-level annotations of where neuronal cells are located. In this way, we hope to help future research in the area and foster innovative methodologies for tackling similar problems. The second part deals with the problem of distributed data management in the context of LHC experiments, with a focus on supporting ATLAS operations concerning data transfer failures. In particular, we analyze error messages produced by failed transfers and propose a Machine Learning pipeline that leverages the word2vec language model and K-means clustering. This provides groups of similar errors that are presented to human operators as suggestions of potential issues to investigate. The approach is demonstrated on one full day of data, showing promising ability in understanding the message content and providing meaningful groupings, in line with previously reported incidents by human operators.

Application of Deep Learning techniques in the search for BSM Higgs bosons in the $\mu\mu$ final state in CMS

The Standard Model (SM) of particle physics predicts the existence of a Higgs field responsible for the generation of particles' mass. However, some aspects of this theory remain unsolved, supposing the presence of new physics Beyond the Standard Model (BSM) with the production of new particles at a higher energy scale compared to the current experimental limits. The search for additional Higgs bosons is, in fact, predicted by theoretical extensions of the SM including the Minimal Supersymmetry Standard Model (MSSM). In the MSSM, the Higgs sector consists of two Higgs doublets, resulting in five physical Higgs particles: two charged bosons $H^{\pm}$, two neutral scalars $h$ and $H$, and one pseudoscalar $A$. The work presented in this thesis is dedicated to the search of neutral non-Standard Model Higgs bosons decaying to two muons in the model independent MSSM scenario. Proton-proton collision data recorded by the CMS experiment at the CERN LHC at a center-of-mass energy of 13 TeV are used, corresponding to an integrated luminosity of $35.9\ \text{fb}^{-1}$. Such search is sensitive to neutral Higgs bosons produced either via gluon fusion process or in association with a $\text{b}\bar{\text{b}}$ quark pair. The extensive usage of Machine and Deep Learning techniques is a fundamental element in the discrimination between signal and background simulated events. A new network structure called parameterised Neural Network (pNN) has been implemented, replacing a whole set of single neural networks trained at a specific mass hypothesis value with a single neural network able to generalise well and interpolate in the entire mass range considered. The results of the pNN signal/background discrimination are used to set a model independent 95\% confidence level expected upper limit on the production cross section times branching ratio, for a generic $\phi$ boson decaying into a muon pair in the 130 to 1000 GeV range.

Machine learning as a service for high energy physics (MLaaS4HEP): a service for ML-based data analyses

With the CERN LHC program underway, there has been an acceleration of data growth in the High Energy Physics (HEP) field and the usage of Machine Learning (ML) in HEP will be critical during the HL-LHC program when the data that will be produced will reach the exascale. ML techniques have been successfully used in many areas of HEP nevertheless, the development of a ML project and its implementation for production use is a highly time-consuming task and requires specific skills. Complicating this scenario is the fact that HEP data is stored in ROOT data format, which is mostly unknown outside of the HEP community. The work presented in this thesis is focused on the development of a ML as a Service (MLaaS) solution for HEP, aiming to provide a cloud service that allows HEP users to run ML pipelines via HTTP calls. These pipelines are executed by using the MLaaS4HEP framework, which allows reading data, processing data, and training ML models directly using ROOT files of arbitrary size from local or distributed data sources. Such a solution provides HEP users non-expert in ML with a tool that allows them to apply ML techniques in their analyses in a streamlined manner. Over the years the MLaaS4HEP framework has been developed, validated, and tested and new features have been added. A first MLaaS solution has been developed by automatizing the deployment of a platform equipped with the MLaaS4HEP framework. Then, a service with APIs has been developed, so that a user after being authenticated and authorized can submit MLaaS4HEP workflows producing trained ML models ready for the inference phase. A working prototype of this service is currently running on a virtual machine of INFN-Cloud and is compliant to be added to the INFN Cloud portfolio of services.

Diffusive models and chaos indicators for non-linear betatron motion in circular hadron accelerators

Understanding the complex dynamics of beam-halo formation and evolution in circular particle accelerators is crucial for the design of current and future rings, particularly those utilizing superconducting magnets such as the CERN Large Hadron Collider (LHC), its luminosity upgrade HL-LHC, and the proposed Future Circular Hadron Collider (FCC-hh). A recent diffusive framework, which describes the evolution of the beam distribution by means of a Fokker-Planck equation, with diffusion coefficient derived from the Nekhoroshev theorem, has been proposed to describe the long-term behaviour of beam dynamics and particle losses. In this thesis, we discuss the theoretical foundations of this framework, and propose the implementation of an original measurement protocol based on collimator scans in view of measuring the Nekhoroshev-like diffusive coefficient by means of beam loss data. The available LHC collimator scan data, unfortunately collected without the proposed measurement protocol, have been successfully analysed using the proposed framework. This approach is also applied to datasets from detailed measurements of the impact on the beam losses of so-called long-range beam-beam compensators also at the LHC. Furthermore, dynamic indicators have been studied as a tool for exploring the phase-space properties of realistic accelerator lattices in single-particle tracking simulations. By first examining the classification performance of known and new indicators in detecting the chaotic character of initial conditions for a modulated Hénon map and then applying this knowledge to study the properties of realistic accelerator lattices, we tried to identify a connection between the presence of chaotic regions in the phase space and Nekhoroshev-like diffusive behaviour, providing new tools to the accelerator physics community.

Test vector generation for the phase II ATLAS event filter trigger upgrade

In the near future, the LHC experiments will continue to be upgraded as the LHC luminosity will increase from the design 1034 to 7.5 × 1034, with the HL-LHC project, to reach 3000 × f b−1 of accumulated statistics. After the end of a period of data collection, CERN will face a long shutdown to improve overall performance by upgrading the experiments and implementing more advanced technologies and infrastructures. In particular, ATLAS will upgrade parts of the detector, the trigger, and the data acquisition system. It will also implement new strategies and algorithms for processing and transferring the data to the final storage. This PhD thesis presents a study of a new pattern recognition algorithm to be used in the trigger system, which is a software designed to provide the information necessary to select physical events from background data. The idea is to use the well-known Hough Transform mathematical formula as an algorithm for detecting particle trajectories. The effectiveness of the algorithm has already been validated in the past, independently of particle physics applications, to detect generic shapes in images. Here, a software emulation tool is proposed for the hardware implementation of the Hough Transform, to reconstruct the tracks in the ATLAS Trigger and Data Acquisition system. Until now, it has never been implemented on electronics in particle physics experiments, and as a hardware implementation it would provide overall latency benefits. A comparison between the simulated data and the physical system was performed on a Xilinx UltraScale+ FPGA device.

Study of charm hadronisation in pp collisions at $\sqrt{s}$ = 13 TeV with the ALICE experiment

In high-energy hadron collisions, the production at parton level of heavy-flavour quarks (charm and bottom) is described by perturbative Quantum Chromo-dynamics (pQCD) calculations, given the hard scale set by the quark masses. However, in hadron-hadron collisions, the predictions of the heavy-flavour hadrons eventually produced entail the knowledge of the parton distribution functions, as well as an accurate description of the hadronisation process. The latter is taken into account via the fragmentation functions measured at e$^+$e$^-$ colliders or in ep collisions, but several observations in LHC Run 1 and Run 2 data challenged this picture. In this dissertation, I studied the charm hadronisation in proton-proton collision at $\sqrt{s}$ = 13 TeV with the ALICE experiment at the LHC, making use of a large statistic data sample collected during LHC Run 2. The production of heavy-flavour in this collision system will be discussed, also describing various hadronisation models implemented in commonly used event generators, which try to reproduce experimental data, taking into account the unexpected results at LHC regarding the enhanced production of charmed baryons. The role of multiple parton interaction (MPI) will also be presented and how it affects the total charm production as a function of multiplicity. The ALICE apparatus will be described before moving to the experimental results, which are related to the measurement of relative production rates of the charm hadrons $\Sigma_c^{0,++}$ and $\Lambda_c^+$, which allow us to study the hadronisation mechanisms of charm quarks and to give constraints to different hadronisation models. Furthermore, the analysis of D mesons ($D^{0}$, $D^{+}$ and $D^{*+}$) as a function of charged-particle multiplicity and spherocity will be shown, investigating the role of multi-parton interactions. This research is relevant per se and for the mission of the ALICE experiment at the LHC, which is devoted to the study of Quark-Gluon Plasma.