341 research outputs found

    Data driven background estimation in HEP using Generative Adversarial Networks

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    Data-driven methods are widely used to overcome shortcomings of Monte Carlo simulations (lack of statistics, mismodeling of processes, etc.) in experimental high energy physics. A precise description of background processes is crucial to reach the optimal sensitivity for a measurement. However, the selection of the control region used to describe the background process in a region of interest biases the distribution of some physics observables, rendering the use of such observables impossible in a physics analysis. Rather than discarding these events and/or observables, we propose a novel method to generate physics objects compatible with the region of interest and properly describing the correlations with the rest of the event properties. We use a generative adversarial network (GAN) for this task, as GANs are among the best generator models for various applications. We illustrate the method by generating a new misidentified photon for the ő≥+jets\gamma + \mathrm{jets} background of the H‚Üíő≥ő≥\mathrm{H}\to\gamma\gamma analysis at the CERN LHC, and demonstrate that this GAN generator is able to produce a coherent object correlated with the different properties of the rest of the event

    La mesure des propriétés du boson de Higgs et l'étalonnage temporel du détecteur CMS à l'aide de méthodes d'apprentissage automatique

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    After the observation of a Higgs boson which is compatible with the predictions of the standard model (SM) of particle physics at the ATLAS and CMS detectors in 2012, the precise measurement of its properties is now one of the primary goals of high energy physics. The Higgs boson decaying into two photons (H ‚Üí ő≥ő≥ decay channel) provides a fully reconstructed final state and its invariant mass peak can be measured with a very good mass resolution (around 1%). Consequently, despite the small branching ratio predicted by the SM (approximately 0.2%), H ‚Üí ő≥ő≥ was one of the two most essential channels involved in the discovery of the Higgs boson together with its decay to four leptons. This PhD thesis establishes constraints on the Higgs boson anomalous couplings (AC) to gauge bosons. A multiclassifier based on a deep learning model is designed to use all possible ingredients of the H ‚Üí ő≥ő≥ analyses to provide the most optimal separation between background, SM production and AC production of the Higgs boson.Significant backgrounds to the H ‚Üí ő≥ő≥ analysis originate from QCD-induced production of diphoton, or diphoton-like, pairs. Processes producing only one or no photon contribute significantly to the contamination of signal if other particles are misidentified as photons. As such, a precise estimation of the background emerging from misidentified photons is necessary to reach an optimal signal extraction. This thesis describes a novel method relying on advanced machine learning models named generative adversarial networks or GAN to generate misidentified photons and improve the description of such backgrounds from data control regions.Furthermore, the LHC will undergo a High Luminosity (HL) upgrade, delivering around ten times more integrated luminosity with the downside of imposing harsher conditions on the CMS detector. An accompanying upgrade of the CMS detector (Phase II upgrade) is foreseen to not only cope with these harsher conditions but also significantly improve the performance of the detector. One of the most critical aspects of this upgrade is the ability to tag events with very high timing resolution, which will also improve the study of the H ‚Üí ő≥ő≥ decay channel. This thesis provides a contribution to the timing upgrade of the CMS detector, particularly to the fast monitoring and calibration of the high-precision clock distribution.Apr√®s l'observation du boson de Higgs par les exp√©riences ATLAS et CMS en 2012, les mesures de pr√©cision de ses propri√©t√©s sont aujourd'hui un des enjeux majeurs de la physique des hautes √©nergies et du Large Hadron Collider (LHC). En effet, il s'agit de tester la compatibilit√© de ce boson avec celui attendu par le mod√®le standard (MS) de la physique des particules. Dans son canal de d√©sint√©gration en deux photons (H ‚Üí ő≥ő≥), le boson de Higgs est enti√®rement reconstruit, le pic de masse correspondant pouvant √™tre mesur√© avec une tr√®s bonne r√©solution exp√©rimentale (autour de 1%). En cons√©quence, en d√©pit d'un taux d'embranchement tr√®s faible dans le MS (d‚Äôenviron 0.2%), le canal H ‚Üí ő≥ő≥ fut l'un des deux canaux ayant permis la d√©couverte du boson de Higgs, le canal de d√©sint√©gration en quatre leptons √©tant le second. Cette th√®se pose des contraintes sur couplages anormaux (CA) du boson de Higgs avec des bosons de jauge. Un classificateur en multiples cat√©gories bas√© sur des m√©thodes d'apprentissage profond (deep learning) est d√©velopp√© pour utiliser l'ensemble des informations disponibles dans l'analyse H ‚Üí ő≥ő≥ et pour fournir la meilleure s√©paration possible entre le bruit de fond, les diff√©rents modes de production du boson de Higgs du MS et les productions CA du boson de Higgs.Un bruit de fond cons√©quent pour les analyses H ‚Üí ő≥ő≥ vient des processus QCD produisant une paire diphoton. M√™me les √©v√©nements avec seulement un, voire aucun photon, contribuent grandement √† la contamination du signal si d‚Äôautres particules sont faussement identifi√©es comme des photons. De ce fait, une estimation pr√©cise du bruit de fond √©mergeant de ces photons mal identifi√©s est n√©cessaire pour atteindre une extraction optimale du signal. Cette th√®se d√©crit une nouvelle m√©thode pour l‚Äôestimation pr√©cise du bruit de fond. Cette m√©thode s‚Äôappuie sur des mod√®les d'apprentissage profond avanc√©s appel√©s r√©seaux antagonistes g√©n√©ratifs (ou GAN), pour g√©n√©rer des photons mal identifi√©s et am√©liorer la description du bruit de fond associ√© gr√Ęce √† des r√©gions de contr√īle d√©finis dans les donn√©es.D'autre part, le LHC subira dans les prochaines ann√©es une jouvence permettant d'augmenter sa luminosit√© (High Luminosity LHC, HL-LHC) d'un facteur 10 environ. En contrepartie, les conditions de prise de donn√©es seront beaucoup plus difficiles. En cons√©quence, le d√©tecteur CMS sera √©galement am√©lior√© (jouvence Phase II) pour faire face √† ces conditions. La possibilit√© d'associer √† chaque objet reconstruit dans la collision un temps mesur√© avec une grande pr√©cision constitue un enjeu majeur qui permettra d'am√©liorer la qualit√© des diff√©rentes mesures r√©alis√©es dans le canal H ‚Üí ő≥ő≥. Cette th√®se fournit une contribution aux mesures de temps de haute r√©solution envisag√©es par CMS, en particulier sur la surveillance et la calibration ultra rapide du syst√®me de distribution d'horlog

    Data driven background estimation in HEP using Generative Adversarial Networks

    No full text
    Data-driven methods are widely used to overcome shortcomings of Monte Carlo simulations (lack of statistics, mismodeling of processes, etc.) in experimental high energy physics. A precise description of background processes is crucial to reach the optimal sensitivity for a measurement. However, the selection of the control region used to describe the background process in a region of interest biases the distribution of some physics observables, rendering the use of such observables impossible in a physics analysis. Rather than discarding these events and/or observables, we propose a novel method to generate physics objects compatible with the region of interest and properly describing the correlations with the rest of the event properties. We use a generative adversarial network (GAN) for this task, as GANs are among the best generator models for various applications. We illustrate the method by generating a new misidentified photon for the ő≥+jets\gamma + \mathrm{jets} background of the H‚Üíő≥ő≥\mathrm{H}\to\gamma\gamma analysis at the CERN LHC, and demonstrate that this GAN generator is able to produce a coherent object correlated with the different properties of the rest of the event

    Measurement of the double-differential inclusive jet cross section in proton-proton collisions at s\sqrt{s} = 5.02 TeV

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    International audienceThe inclusive jet cross section is measured as a function of jet transverse momentum pTp_\mathrm{T} and rapidity yy. The measurement is performed using proton-proton collision data at s\sqrt{s} = 5.02 TeV, recorded by the CMS experiment at the LHC, corresponding to an integrated luminosity of 27.4 pb‚ąí1^{-1}. The jets are reconstructed with the anti-kTk_\mathrm{T} algorithm using a distance parameter of RR = 0.4, within the rapidity interval ‚ą£y‚ą£\lvert y\rvert<\lt 2, and across the kinematic range 0.06 <\ltpTp_\mathrm{T}<\lt 1 TeV. The jet cross section is unfolded from detector to particle level using the determined jet response and resolution. The results are compared to predictions of perturbative quantum chromodynamics, calculated at both next-to-leading order and next-to-next-to-leading order. The predictions are corrected for nonperturbative effects, and presented for a variety of parton distribution functions and choices of the renormalization/factorization scales and the strong coupling őĪS\alpha_\mathrm{S}

    Measurement of inclusive and differential cross sections for single top quark production in association with a W boson in proton-proton collisions at s \sqrt{s} = 13 TeV