1,720,971 research outputs found
Recherche de nouvelle physique avec un boson de Higgs dans le canal bbγγ avec le détecteur ATLAS
No full textThe Standard Model (SM) is the current theory that describes the elementary particles and their interactions. The Higgs boson discovery in 2012 at the Large Hadron Collider (LHC) at CERN marked a remarkable success of its predictive power even if the SM has shortcomings.Since then, the Higgs boson and the Higgs mechanism have been thoroughly studied in the hope to find a hint for new physics.These researches are made possible by high quality experimental infrastructures like the LHC and the ATLAS detector. Its current Inner Detector will be replaced by the brand new Inner Tracker to maintain a high level of tracking and object reconstruction performance in the harsher environment of High-Luminosity LHC (HL-LHC).This thesis presents the adaptation of a neural network based flavour tagging algorithm in the HL-LHC configuration. The track selection used in the training and the resampling method have notably been optimised.A research for new scalar particles in the Higgs sector and in the channel is also presented.The analysis is performed using 140~fb of ATLAS Run~2 data at ~TeV.Parameterised Neural Networks (PNNs) are used to probe a large range of masses and .Results show a local (global) excess of 3.55 (2.0) with respect to the background only hypothesis. 95\% CL upper limits between 0.09 and 39~fb are set on the signal production cross section in the final state.Le Modèle Standard est la théorie actuelle décrivant les particules élémentaires et leurs interactions. Sa validité a été renforcée par la découverte du boson de Higgs au grand collisionneur de hadrons du CERN, le LHC, en 2012 bien que l'on sache qu'il est incomplet.Les propriétés du boson et du mécanisme de Higgs sont étudiées en détail au LHC dans l'espoir d'observer des signes de nouvelle physique.Ces recherches sont permises grâce à des moyens expérimentaux comme le LHC et le détecteur ATLAS.Le trajectographe interne d'ATLAS sera entièrement remplacé par un nouveau détecteur appelé ITk dans l'objectif de maintenir de bonnes performances de reconstruction des traces avec la nouvelle configuration plus exigeante du LHC à haute luminosité (HL-LHC).Cette thèse présente l'adaptation d'un algorithme d'étiquetage des jets issus de quarks basé sur un réseau de neurones à apprentissage profond dans la configuration d'ITk.La sélection de traces utilisées par le réseau de neurones et la méthode de réechantillonage ont notamment été optimisées.Elle présente également une recherche de nouvelle physique via des particules scalaires et dans le canal de désintégration réalisée avec 140~fb de données collectés par ATLAS à ~TeV.L'analyse utilise des réseaux de neurones paramétriques pour sonder une vaste région de masses et . Les résultats révèlent un léger excès local de 3.55 (2.0 global) par rapport à l'hypothèse bruit de fond uniquement. Des limites supérieures sont posées sur la section efficace de production du signal dans cet état final et s'étendent entre 0.09 et 39~fb
Search for new physics with one Higgs boson in the channel with the ATLAS detector
No full textThe Standard Model (SM) is the current theory that describes the elementary particles and their interactions. The Higgs boson discovery in 2012 at the Large Hadron Collider (LHC) at CERN marked a remarkable success of its predictive power even if the SM has shortcomings. Since then, the Higgs boson and the Higgs mechanism have been thoroughly studied in the hope to find a hint for new physics. These researches are made possible by high quality experimental infrastructures like the LHC and the ATLAS detector. Its current Inner Detector will be replaced by the brand new Inner Tracker to maintain a high level of tracking and object reconstruction performance in the harsher environment of High-Luminosity LHC (HL-LHC). This thesis presents the adaptation of a neural network based flavour tagging algorithm in the HL-LHC configuration. The track selection used in the training and the resampling method have notably been optimised. A research for new scalar particles in the Higgs sector and in the channel is also presented. The analysis is performed using 140~fb of ATLAS Run~2 data at ~TeV. Parameterised Neural Networks (PNNs) are used to probe a large range of masses and . Results show a local (global) excess of 3.55 (2.0) with respect to the background only hypothesis. 95\% CL upper limits between 0.09 and 39~fb are set on the signal production cross section in the final state
Search for a resonance decaying into a scalar particle and a Higgs boson in the final state with two bottom quarks and two photons in proton–proton collisions at TeV with the ATLAS detector
No full textThis analysis submitted to JHEP \footnote{ATLAS Collaboration. Search for a resonance decaying into a scalar particle and a Higgs boson in the final state with two bottom quarks and two photons in proton-proton collisions at a center of mass energy of 13~TeV with the ATLAS detector, \href{https://arxiv.org/abs/2404.12915}{arXiv:2404.12915} (2024)} looks for a heavy scalar resonance decaying into a Higgs boson and a new lighter scalar in the decay channel. The search is performed with 140 fb of LHC Run 2 data recorded by the ATLAS detector. The investigated mass range spans 170 1000~GeV and 15 500~GeV. Parameterised neural networks (PNN) are used to discriminate signal from background and to achieve continuous sensitivity in the probed domain of the (, ) mass plane. A log-likelihood fit is performed on the PNN score distribution to look for an excess with respect to expected background compatible with signal. No significant excess above the expected background is found and 95\% CL upper limits are set on the cross section times branching ratio, ranging from 39~fb to 0.09~fb. The largest deviation from the background-only expectation occurs for (, ) = (575, 200)~GeV with a local (global) significance of 3.5 (2.0) standard deviations
Recherche de nouvelle physique avec un boson de Higgs dans le canal bbγγ avec le détecteur ATLAS
No full textThe Standard Model (SM) is the current theory that describes the elementary particles and their interactions. The Higgs boson discovery in 2012 at the Large Hadron Collider (LHC) at CERN marked a remarkable success of its predictive power even if the SM has shortcomings.Since then, the Higgs boson and the Higgs mechanism have been thoroughly studied in the hope to find a hint for new physics.These researches are made possible by high quality experimental infrastructures like the LHC and the ATLAS detector. Its current Inner Detector will be replaced by the brand new Inner Tracker to maintain a high level of tracking and object reconstruction performance in the harsher environment of High-Luminosity LHC (HL-LHC).This thesis presents the adaptation of a neural network based flavour tagging algorithm in the HL-LHC configuration. The track selection used in the training and the resampling method have notably been optimised.A research for new scalar particles in the Higgs sector and in the channel is also presented.The analysis is performed using 140~fb of ATLAS Run~2 data at ~TeV.Parameterised Neural Networks (PNNs) are used to probe a large range of masses and .Results show a local (global) excess of 3.55 (2.0) with respect to the background only hypothesis. 95\% CL upper limits between 0.09 and 39~fb are set on the signal production cross section in the final state.Le Modèle Standard est la théorie actuelle décrivant les particules élémentaires et leurs interactions. Sa validité a été renforcée par la découverte du boson de Higgs au grand collisionneur de hadrons du CERN, le LHC, en 2012 bien que l'on sache qu'il est incomplet.Les propriétés du boson et du mécanisme de Higgs sont étudiées en détail au LHC dans l'espoir d'observer des signes de nouvelle physique.Ces recherches sont permises grâce à des moyens expérimentaux comme le LHC et le détecteur ATLAS.Le trajectographe interne d'ATLAS sera entièrement remplacé par un nouveau détecteur appelé ITk dans l'objectif de maintenir de bonnes performances de reconstruction des traces avec la nouvelle configuration plus exigeante du LHC à haute luminosité (HL-LHC).Cette thèse présente l'adaptation d'un algorithme d'étiquetage des jets issus de quarks basé sur un réseau de neurones à apprentissage profond dans la configuration d'ITk.La sélection de traces utilisées par le réseau de neurones et la méthode de réechantillonage ont notamment été optimisées.Elle présente également une recherche de nouvelle physique via des particules scalaires et dans le canal de désintégration réalisée avec 140~fb de données collectés par ATLAS à ~TeV.L'analyse utilise des réseaux de neurones paramétriques pour sonder une vaste région de masses et . Les résultats révèlent un léger excès local de 3.55 (2.0 global) par rapport à l'hypothèse bruit de fond uniquement. Des limites supérieures sont posées sur la section efficace de production du signal dans cet état final et s'étendent entre 0.09 et 39~fb
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Recherche de nouvelle physique avec un boson de Higgs dans le canal bbγγ avec le détecteur ATLAS
No full textThe Standard Model (SM) is the current theory that describes the elementary particles and their interactions. The Higgs boson discovery in 2012 at the Large Hadron Collider (LHC) at CERN marked a remarkable success of its predictive power even if the SM has shortcomings.Since then, the Higgs boson and the Higgs mechanism have been thoroughly studied in the hope to find a hint for new physics.These researches are made possible by high quality experimental infrastructures like the LHC and the ATLAS detector. Its current Inner Detector will be replaced by the brand new Inner Tracker to maintain a high level of tracking and object reconstruction performance in the harsher environment of High-Luminosity LHC (HL-LHC).This thesis presents the adaptation of a neural network based flavour tagging algorithm in the HL-LHC configuration. The track selection used in the training and the resampling method have notably been optimised.A research for new scalar particles in the Higgs sector and in the channel is also presented.The analysis is performed using 140~fb of ATLAS Run~2 data at ~TeV.Parameterised Neural Networks (PNNs) are used to probe a large range of masses and .Results show a local (global) excess of 3.55 (2.0) with respect to the background only hypothesis. 95\% CL upper limits between 0.09 and 39~fb are set on the signal production cross section in the final state.Le Modèle Standard est la théorie actuelle décrivant les particules élémentaires et leurs interactions. Sa validité a été renforcée par la découverte du boson de Higgs au grand collisionneur de hadrons du CERN, le LHC, en 2012 bien que l'on sache qu'il est incomplet.Les propriétés du boson et du mécanisme de Higgs sont étudiées en détail au LHC dans l'espoir d'observer des signes de nouvelle physique.Ces recherches sont permises grâce à des moyens expérimentaux comme le LHC et le détecteur ATLAS.Le trajectographe interne d'ATLAS sera entièrement remplacé par un nouveau détecteur appelé ITk dans l'objectif de maintenir de bonnes performances de reconstruction des traces avec la nouvelle configuration plus exigeante du LHC à haute luminosité (HL-LHC).Cette thèse présente l'adaptation d'un algorithme d'étiquetage des jets issus de quarks basé sur un réseau de neurones à apprentissage profond dans la configuration d'ITk.La sélection de traces utilisées par le réseau de neurones et la méthode de réechantillonage ont notamment été optimisées.Elle présente également une recherche de nouvelle physique via des particules scalaires et dans le canal de désintégration réalisée avec 140~fb de données collectés par ATLAS à ~TeV.L'analyse utilise des réseaux de neurones paramétriques pour sonder une vaste région de masses et . Les résultats révèlent un léger excès local de 3.55 (2.0 global) par rapport à l'hypothèse bruit de fond uniquement. Des limites supérieures sont posées sur la section efficace de production du signal dans cet état final et s'étendent entre 0.09 et 39~fb
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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