Measurement of Wγγ Cross Section and Limits on Anomalous Quartic Gauge Couplings in Proton-Proton Collisions at 8 TeV with the CMS detector

Present the measurement of a rare Standard Model processes, pp →W±γγ for the leptonic decays of the W±. The measurement is made with 19.4 fb−1 of 8 TeV data collected in 2012 by the CMS experiment. The measured cross section is consistent with the Standard Model prediction and has a significance of 2.9σ. Limits are placed on dimension-8 Effective Field Theories of anomalous Quartic Gauge Couplings. The analysis has particularly sensitivity to the fT,0 coupling and a 95% confidence limit is placed at −35.9 < fT,0/Λ4< 36.7 TeV−4. Studies of the pp →Zγγ process are also presented. The Zγγ signal is in strict agreement with the Standard Model and has a significance of 5.9σ.

Higgs production in association with top quarks at the LHC

Since it has been found that the MadGraph Monte Carlo generator offers superior flavour-matching capability as compared to Alpgen, the suitability of MadGraph for the generation of ttb¯ ¯b events is explored, with a view to simulating this background in searches for the Standard Model Higgs production and decay process ttH, H ¯ → b ¯b. Comparisons are performed between the output of MadGraph and that of Alpgen, showing that satisfactory agreement in their predictions can be obtained with the appropriate generator settings. A search for the Standard Model Higgs boson, produced in association with the top quark and decaying into a b ¯b pair, using 20.3 fb−1 of 8 TeV collision data collected in 2012 by the ATLAS experiment at CERN’s Large Hadron Collider, is presented. The GlaNtp analysis framework, together with the RooFit package and associated software, are used to obtain an expected 95% confidence-level limit of 4.2 +4.1 −2.0 times the Standard Model expectation, and the corresponding observed limit is found to be 5.9; this is within experimental uncertainty of the published result of the analysis performed by the ATLAS collaboration. A search for a heavy charged Higgs boson of mass mH± in the range 200 ≤ mH± /GeV ≤ 600, where the Higgs mediates the five-flavour beyond-theStandard-Model physics process gb → tH± → ttb, with one top quark decaying leptonically and the other decaying hadronically, is presented, using the 20.3 fb−1 8 TeV ATLAS data set. Upper limits on the product of the production cross-section and the branching ratio of the H± boson are computed for six mass points, and these are found to be compatible within experimental uncertainty with those obtained by the corresponding published ATLAS analysis.

LHC main dipole magnet circuits: sustaining near-nominal beam energies

Crossing the Franco-Swiss border, the Large Hadron Collider (LHC), designed to collide 7 TeV proton beams, is the world's largest and most powerful particle accelerator the operation of which was originally intended to commence in 2008. Unfortunately, due to an interconnect discontinuity in one of the main dipole circuit's 13 kA superconducting busbars, a catastrophic quench event occurred during initial magnet training, causing significant physical system damage. Furthermore, investigation into the cause found that such discontinuities were not only present in the circuit in question, but throughout the entire LHC. This prevented further magnet training and ultimately resulted in the maximum sustainable beam energy being limited to approximately half that of the design nominal, 3.5-4 TeV, for the first three years of operation (Run 1, 2009-2012) and a major consolidation campaign being scheduled for the first long shutdown (LS 1, 2012-2014). Throughout Run 1, a series of studies attempted to predict the amount of post-installation training quenches still required to qualify each circuit to nominal-energy current levels. With predictions in excess of 80 quenches (each having a recovery time of 8-12+ hours) just to achieve 6.5 TeV and close to 1000 quenches for 7 TeV, it was decided that for Run 2, all systems be at least qualified for 6.5 TeV operation. However, even with all interconnect discontinuities scheduled to be repaired during LS 1, numerous other concerns regarding circuit stability arose. In particular, observations of an erratic behaviour of magnet bypass diodes and the degradation of other potentially weak busbar sections, as well as observations of seemingly random millisecond spikes in beam losses, known as unidentified falling object (UFO) events, which, if persist at 6.5 TeV, may eventually deposit sufficient energy to quench adjacent magnets. In light of the above, the thesis hypothesis states that, even with the observed issues, the LHC main dipole circuits can safely support and sustain near-nominal proton beam energies of at least 6.5 TeV. Research into minimising the risk of magnet training led to the development and implementation of a new qualification method, capable of providing conclusive evidence that all aspects of all circuits, other than the magnets and their internal joints, can safely withstand a quench event at near-nominal current levels, allowing for magnet training to be carried out both systematically and without risk. This method has become known as the Copper Stabiliser Continuity Measurement (CSCM). Results were a success, with all circuits eventually being subject to a full current decay from 6.5 TeV equivalent current levels, with no measurable damage occurring. Research into UFO events led to the development of a numerical model capable of simulating typical UFO events, reproducing entire Run 1 measured event data sets and extrapolating to 6.5 TeV, predicting the likelihood of UFO-induced magnet quenches. Results provided interesting insights into the involved phenomena as well as confirming the possibility of UFO-induced magnet quenches. The model was also capable of predicting that such events, if left unaccounted for, are likely to be commonplace or not, resulting in significant long-term issues for 6.5+ TeV operation. Addressing the thesis hypothesis, the following written works detail the development and results of all CSCM qualification tests and subsequent magnet training as well as the development and simulation results of both 4 TeV and 6.5 TeV UFO event modelling. The thesis concludes, post-LS 1, with the LHC successfully sustaining 6.5 TeV proton beams, but with UFO events, as predicted, resulting in otherwise uninitiated magnet quenches and being at the forefront of system availability issues.

Identifying cancer-related microRNAs based on gene expression data

International audience

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.

Big Manistee River Tributaries as Potential Arctic Grayling Habitat

The Big Manistee River was one of the most well known Michigan rivers to historically support a population of Arctic grayling (Thymallus arctics). Overfishing, competition with introduced fish, and habitat loss due to logging are believed to have caused their decline and ultimate extirpation from the Big Manistee River around 1900 and from the State of Michigan by 1936. Grayling are a species of great cultural importance to Little River Band of Ottawa Indian tribal heritage and although past attempts to reintroduce Arctic grayling have been unsuccessful, a continued interest in their return led to the assessment of environmental conditions of tributaries within a 21 kilometer section of the Big Manistee River to determine if suitable habitat exists. Although data describing historical conditions in the Big Manistee River is limited, we reviewed the literature to determine abiotic conditions prior to Arctic grayling disappearance and the habitat conditions in rivers in western and northwestern North America where they currently exist. We assessed abiotic habitat metrics from 23 sites distributed across 8 tributaries within the Manistee River watershed. Data collected included basic water parameters, streambed substrate composition, channel profile and areal measurements of channel geomorphic unit, and stream velocity and discharge measurements. These environmental condition values were compared to literature values, habitat suitability thresholds, and current conditions of rivers with Arctic grayling populations to assess the feasibility of the abiotic habitat in Big Manistee River tributaries to support Arctic grayling. Although the historic grayling habitat in the region was disturbed during the era of major logging around the turn of the 20th century, our results indicate that some important abiotic conditions within Big Manistee River tributaries are within the range of conditions that support current and past populations of Arctic grayling. Seven tributaries contained between 20-30% pools by area, used by grayling for refuge. All but two tributaries were composed primarily of pebbles, with the remaining two dominated by fine substrates (sand, silt, clay). Basic water parameters and channel depth were within the ranges of those found for populations of Arctic grayling persisting in Montana, Alaska, and Canada for all tributaries. Based on the metrics analyzed in this study, suitable abiotic grayling habitat does exist in Big Manistee River tributaries.

Measurement of the inclusive and differential t/t cross sections at 13 TeV in the all-jets boosted regime with CMS

The t/t production cross section is measured with the CMS detector in the all-jets channel in $pp$ collisions at the centre-of-mass energy of 13 TeV. The analysis is based on the study of t/t events in the boosted topology, namely events in which decay products of the quark top have a high Lorentz boost and are thus reconstructed in the detector as a single, wide jet. The data sample used in this analysis corresponds to an integrated luminosity of 2.53 fb-1. The inclusive cross section is found to be sigma(t/t) = 727 +- 46 (stat.) +115-112 (sys.) +- 20~(lumi.) pb, a value which is consistent with the theoretical predictions. The differential, detector-level cross section is measured as a function of the transverse momentum of the leading jet and compared to the QCD theoretical predictions. Finally, the differential, parton-level cross section is reported, measured as a function of the transverse momentum of the leading parton, extrapolated to the full phase space and compared to the QCD predictions.

Geospatial Data Indexing Analysis and Visualization via Web Services with Autonomic Resource Management

With the exponential growth of the usage of web-based map services, the web GIS application has become more and more popular. Spatial data index, search, analysis, visualization and the resource management of such services are becoming increasingly important to deliver user-desired Quality of Service. First, spatial indexing is typically time-consuming and is not available to end-users. To address this, we introduce TerraFly sksOpen, an open-sourced an Online Indexing and Querying System for Big Geospatial Data. Integrated with the TerraFly Geospatial database [1-9], sksOpen is an efficient indexing and query engine for processing Top-k Spatial Boolean Queries. Further, we provide ergonomic visualization of query results on interactive maps to facilitate the user’s data analysis. Second, due to the highly complex and dynamic nature of GIS systems, it is quite challenging for the end users to quickly understand and analyze the spatial data, and to efficiently share their own data and analysis results with others. Built on the TerraFly Geo spatial database, TerraFly GeoCloud is an extra layer running upon the TerraFly map and can efficiently support many different visualization functions and spatial data analysis models. Furthermore, users can create unique URLs to visualize and share the analysis results. TerraFly GeoCloud also enables the MapQL technology to customize map visualization using SQL-like statements [10]. Third, map systems often serve dynamic web workloads and involve multiple CPU and I/O intensive tiers, which make it challenging to meet the response time targets of map requests while using the resources efficiently. Virtualization facilitates the deployment of web map services and improves their resource utilization through encapsulation and consolidation. Autonomic resource management allows resources to be automatically provisioned to a map service and its internal tiers on demand. v-TerraFly are techniques to predict the demand of map workloads online and optimize resource allocations, considering both response time and data freshness as the QoS target. The proposed v-TerraFly system is prototyped on TerraFly, a production web map service, and evaluated using real TerraFly workloads. The results show that v-TerraFly can accurately predict the workload demands: 18.91% more accurate; and efficiently allocate resources to meet the QoS target: improves the QoS by 26.19% and saves resource usages by 20.83% compared to traditional peak load-based resource allocation.

Examination of Inequalities in Hungary by Microsimulation in Consistency with Macro Data

Effective decision making uses various databases including both micro and macro level datasets. In many cases it is a big challenge to ensure the consistency of the two levels. Different types of problems can occur and several methods can be used to solve them. The paper concentrates on the input alignment of the households’ income for microsimulation, which means refers to improving the elements of a micro data survey (EU-SILC) by using macro data from administrative sources. We use a combined micro-macro model called ECONS-TAX for this improvement. We also produced model projections until 2015 which is important because the official EU-SILC micro database will only be available in Hungary in the summer of 2017. The paper presents our estimations about the dynamics of income elements and the changes in income inequalities. Results show that the aligned data provides a different level of income inequality, but does not affect the direction of change from year to year. However, when we analyzed policy change, the use of aligned data caused larger differences both in income levels and in their dynamics.

Reinforcement learning over encrypted data

Reinforcement learning is a particular paradigm of machine learning that, recently, has proved times and times again to be a very effective and powerful approach. On the other hand, cryptography usually takes the opposite direction. While machine learning aims at analyzing data, cryptography aims at maintaining its privacy by hiding such data. However, the two techniques can be jointly used to create privacy preserving models, able to make inferences on the data without leaking sensitive information. Despite the numerous amount of studies performed on machine learning and cryptography, reinforcement learning in particular has never been applied to such cases before. Being able to successfully make use of reinforcement learning in an encrypted scenario would allow us to create an agent that efficiently controls a system without providing it with full knowledge of the environment it is operating in, leading the way to many possible use cases. Therefore, we have decided to apply the reinforcement learning paradigm to encrypted data. In this project we have applied one of the most well-known reinforcement learning algorithms, called Deep Q-Learning, to simple simulated environments and studied how the encryption affects the training performance of the agent, in order to see if it is still able to learn how to behave even when the input data is no longer readable by humans. The results of this work highlight that the agent is still able to learn with no issues whatsoever in small state spaces with non-secure encryptions, like AES in ECB mode. For fixed environments, it is also able to reach a suboptimal solution even in the presence of secure modes, like AES in CBC mode, showing a significant improvement with respect to a random agent; however, its ability to generalize in stochastic environments or big state spaces suffers greatly.

Big Code Applications and Approaches

The availability of a huge amount of source code from code archives and open-source projects opens up the possibility to merge machine learning, programming languages, and software engineering research fields. This area is often referred to as Big Code where programming languages are treated instead of natural languages while different features and patterns of code can be exploited to perform many useful tasks and build supportive tools. Among all the possible applications which can be developed within the area of Big Code, the work presented in this research thesis mainly focuses on two particular tasks: the Programming Language Identification (PLI) and the Software Defect Prediction (SDP) for source codes. Programming language identification is commonly needed in program comprehension and it is usually performed directly by developers. However, when it comes at big scales, such as in widely used archives (GitHub, Software Heritage), automation of this task is desirable. To accomplish this aim, the problem is analyzed from different points of view (text and image-based learning approaches) and different models are created paying particular attention to their scalability. Software defect prediction is a fundamental step in software development for improving quality and assuring the reliability of software products. In the past, defects were searched by manual inspection or using automatic static and dynamic analyzers. Now, the automation of this task can be tackled using learning approaches that can speed up and improve related procedures. Here, two models have been built and analyzed to detect some of the commonest bugs and errors at different code granularity levels (file and method levels). Exploited data and models’ architectures are analyzed and described in detail. Quantitative and qualitative results are reported for both PLI and SDP tasks while differences and similarities concerning other related works are discussed.

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.

Search for same-sign top quark pairs with the ATLAS detector at the LHC

In this thesis, a search for same-sign top quark pairs produced according to the Standard Model Effective Field Theory (SMEFT) is presented. The analysis is carried out within the ATLAS Collaboration using collision data at a center-of-mass energy of $\sqrt{s} = 13$ TeV, collected by the ATLAS detector during the Run 2 of the Large Hadron Collider, corresponding to an integrated luminosity of $140$ fb$^{-1}$. Three SMEFT operators are considered in the analysis, namely $\mathcal{O}_{RR}$, $\mathcal{O}_{LR}^{(1)}$, and $\mathcal{O}_{LR}^{(8)}$. The signal associated to same-sign top pairs is searched in the dilepton channel, with the top quarks decaying via $t \longrightarrow W^+ b \longrightarrow \ell^+ \nu b$, leading to a final state signature composed of a pair of high-transverse momentum same-sign leptons and $b$-jets. Deep Neural Networks are employed in the analysis to enhance sensitivity to the different SMEFT operators and to perform signal-background discrimination. This is the first result of the ATLAS Collaboration concerning the search for same-sign top quark pairs production in proton-proton collision data at $\sqrt{s} = 13$ TeV, in the framework of the SMEFT.

The interplay of multiplicity and effective energy for (multi) strange hadron production in pp collisions at the LHC

The enhanced production of strange hadrons in heavy-ion collisions relative to that in minimum-bias pp collisions is historically considered one of the first signatures of the formation of a deconfined quark-gluon plasma. At the LHC, the ALICE experiment observed that the ratio of strange to non-strange hadron yields increases with the charged-particle multiplicity at midrapidity, starting from pp collisions and evolving smoothly across interaction systems and energies, ultimately reaching Pb-Pb collisions. The understanding of the origin of this effect in small systems remains an open question. This thesis presents a comprehensive study of the production of $K^{0}_{S}$, $\Lambda$ ($\bar{\Lambda}$) and $\Xi^{-}$ ($\bar{\Xi}^{+}$) strange hadrons in pp collisions at $\sqrt{s}$ = 13 TeV collected in LHC Run 2 with ALICE. A novel approach is exploited, introducing, for the first time, the concept of effective energy in the study of strangeness production in hadronic collisions at the LHC. In this work, the ALICE Zero Degree Calorimeters are used to measure the energy carried by forward emitted baryons in pp collisions, which reduces the effective energy available for particle production with respect to the nominal centre-of-mass energy. The results presented in this thesis provide new insights into the interplay, for strangeness production, between the initial stages of the collision and the produced final hadronic state. Finally, the first Run 3 results on the production of $\Omega^{\pm}$ ($\bar{\Omega}^{+}$) multi-strange baryons are presented, measured in pp collisions at $\sqrt{s}$ = 13.6 TeV and 900 GeV, the highest and lowest collision energies reached so far at the LHC. This thesis also presents the development and validation of the ALICE Time-Of-Flight (TOF) data quality monitoring system for LHC Run 3. This work was fundamental to assess the performance of the TOF detector during the commissioning phase, in the Long Shutdown 2, and during the data taking period.

Machine learning "as a service" for high energy physics (MLaaS4HEP): evolution of a framework for ML-based physics analyses

The scientific success of the LHC experiments at CERN highly depends on the availability of computing resources which efficiently store, process, and analyse the amount of data collected every year. This is ensured by the Worldwide LHC Computing Grid infrastructure that connect computing centres distributed all over the world with high performance network. LHC has an ambitious experimental program for the coming years, which includes large investments and improvements both for the hardware of the detectors and for the software and computing systems, in order to deal with the huge increase in the event rate expected from the High Luminosity LHC (HL-LHC) phase and consequently with the huge amount of data that will be produced. Since few years the role of Artificial Intelligence has become relevant in the High Energy Physics (HEP) world. Machine Learning (ML) and Deep Learning algorithms have been successfully used in many areas of HEP, like online and offline reconstruction programs, detector simulation, object reconstruction, identification, Monte Carlo generation, and surely they will be crucial in the HL-LHC phase. This thesis aims at contributing to a CMS R&D project, regarding a ML "as a Service" solution for HEP needs (MLaaS4HEP). It consists in a data-service able to perform an entire ML pipeline (in terms of reading data, processing data, training ML models, serving predictions) in a completely model-agnostic fashion, directly using ROOT files of arbitrary size from local or distributed data sources. This framework has been updated adding new features in the data preprocessing phase, allowing more flexibility to the user. Since the MLaaS4HEP framework is experiment agnostic, the ATLAS Higgs Boson ML challenge has been chosen as physics use case, with the aim to test MLaaS4HEP and the contribution done with this work.