Data driven background estimation in HEP using Generative Adversarial Networks

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 $\gamma + \mathrm{jets}$ background of the $\mathrm{H}\to\gamma\gamma$ 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

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 → γγ 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 → γγ 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 → γγ analyses to provide the most optimal separation between background, SM production and AC production of the Higgs boson.Significant backgrounds to the H → γγ 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 → γγ 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.Après l'observation du boson de Higgs par les expériences ATLAS et CMS en 2012, les mesures de précision de ses propriétés sont aujourd'hui un des enjeux majeurs de la physique des hautes énergies et du Large Hadron Collider (LHC). En effet, il s'agit de tester la compatibilité de ce boson avec celui attendu par le modèle standard (MS) de la physique des particules. Dans son canal de désintégration en deux photons (H → γγ), le boson de Higgs est entièrement reconstruit, le pic de masse correspondant pouvant être mesuré avec une très bonne résolution expérimentale (autour de 1%). En conséquence, en dépit d'un taux d'embranchement très faible dans le MS (d’environ 0.2%), le canal H → γγ fut l'un des deux canaux ayant permis la découverte du boson de Higgs, le canal de désintégration en quatre leptons étant le second. Cette thèse pose des contraintes sur couplages anormaux (CA) du boson de Higgs avec des bosons de jauge. Un classificateur en multiples catégories basé sur des méthodes d'apprentissage profond (deep learning) est développé pour utiliser l'ensemble des informations disponibles dans l'analyse H → γγ et pour fournir la meilleure séparation possible entre le bruit de fond, les différents modes de production du boson de Higgs du MS et les productions CA du boson de Higgs.Un bruit de fond conséquent pour les analyses H → γγ vient des processus QCD produisant une paire diphoton. Même les événements avec seulement un, voire aucun photon, contribuent grandement à la contamination du signal si d’autres particules sont faussement identifiées comme des photons. De ce fait, une estimation précise du bruit de fond émergeant de ces photons mal identifiés est nécessaire pour atteindre une extraction optimale du signal. Cette thèse décrit une nouvelle méthode pour l’estimation précise du bruit de fond. Cette méthode s’appuie sur des modèles d'apprentissage profond avancés appelés réseaux antagonistes génératifs (ou GAN), pour générer des photons mal identifiés et améliorer la description du bruit de fond associé grâce à des régions de contrôle définis dans les données.D'autre part, le LHC subira dans les prochaines années une jouvence permettant d'augmenter sa luminosité (High Luminosity LHC, HL-LHC) d'un facteur 10 environ. En contrepartie, les conditions de prise de données seront beaucoup plus difficiles. En conséquence, le détecteur CMS sera également amélioré (jouvence Phase II) pour faire face à ces conditions. La possibilité d'associer à chaque objet reconstruit dans la collision un temps mesuré avec une grande précision constitue un enjeu majeur qui permettra d'améliorer la qualité des différentes mesures réalisées dans le canal H → γγ. Cette thèse fournit une contribution aux mesures de temps de haute résolution envisagées par CMS, en particulier sur la surveillance et la calibration ultra rapide du système de distribution d'horlog

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

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 → γγ 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 → γγ 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 → γγ analyses to provide the most optimal separation between background, SM production and AC production of the Higgs boson.Significant backgrounds to the H → γγ 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 → γγ 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.Après l'observation du boson de Higgs par les expériences ATLAS et CMS en 2012, les mesures de précision de ses propriétés sont aujourd'hui un des enjeux majeurs de la physique des hautes énergies et du Large Hadron Collider (LHC). En effet, il s'agit de tester la compatibilité de ce boson avec celui attendu par le modèle standard (MS) de la physique des particules. Dans son canal de désintégration en deux photons (H → γγ), le boson de Higgs est entièrement reconstruit, le pic de masse correspondant pouvant être mesuré avec une très bonne résolution expérimentale (autour de 1%). En conséquence, en dépit d'un taux d'embranchement très faible dans le MS (d’environ 0.2%), le canal H → γγ fut l'un des deux canaux ayant permis la découverte du boson de Higgs, le canal de désintégration en quatre leptons étant le second. Cette thèse pose des contraintes sur couplages anormaux (CA) du boson de Higgs avec des bosons de jauge. Un classificateur en multiples catégories basé sur des méthodes d'apprentissage profond (deep learning) est développé pour utiliser l'ensemble des informations disponibles dans l'analyse H → γγ et pour fournir la meilleure séparation possible entre le bruit de fond, les différents modes de production du boson de Higgs du MS et les productions CA du boson de Higgs.Un bruit de fond conséquent pour les analyses H → γγ vient des processus QCD produisant une paire diphoton. Même les événements avec seulement un, voire aucun photon, contribuent grandement à la contamination du signal si d’autres particules sont faussement identifiées comme des photons. De ce fait, une estimation précise du bruit de fond émergeant de ces photons mal identifiés est nécessaire pour atteindre une extraction optimale du signal. Cette thèse décrit une nouvelle méthode pour l’estimation précise du bruit de fond. Cette méthode s’appuie sur des modèles d'apprentissage profond avancés appelés réseaux antagonistes génératifs (ou GAN), pour générer des photons mal identifiés et améliorer la description du bruit de fond associé grâce à des régions de contrôle définis dans les données.D'autre part, le LHC subira dans les prochaines années une jouvence permettant d'augmenter sa luminosité (High Luminosity LHC, HL-LHC) d'un facteur 10 environ. En contrepartie, les conditions de prise de données seront beaucoup plus difficiles. En conséquence, le détecteur CMS sera également amélioré (jouvence Phase II) pour faire face à ces conditions. La possibilité d'associer à chaque objet reconstruit dans la collision un temps mesuré avec une grande précision constitue un enjeu majeur qui permettra d'améliorer la qualité des différentes mesures réalisées dans le canal H → γγ. Cette thèse fournit une contribution aux mesures de temps de haute résolution envisagées par CMS, en particulier sur la surveillance et la calibration ultra rapide du système de distribution d'horlog

Data driven background estimation in HEP using Generative Adversarial Networks

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 $\gamma + \mathrm{jets}$ background of the $\mathrm{H}\to\gamma\gamma$ 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

International audienceThe inclusive jet cross section is measured as a function of jet transverse momentum $p_\mathrm{T}$ and rapidity $y$. The measurement is performed using proton-proton collision data at $\sqrt{s}$ = 5.02 TeV, recorded by the CMS experiment at the LHC, corresponding to an integrated luminosity of 27.4 pb$^{-1}$. The jets are reconstructed with the anti-$k_\mathrm{T}$ algorithm using a distance parameter of $R$ = 0.4, within the rapidity interval $\lvert y\rvert$$\lt$ 2, and across the kinematic range 0.06 $\lt$$p_\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 $\alpha_\mathrm{S}$

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

The inclusive jet cross section is measured as a function of jet transverse momentum $p_{\mathrm{T}}$ and rapidity $y$. The measurement is performed using proton-proton collision data at $\sqrt{s} =$ 5.02 TeV, recorded by the CMS experiment at the LHC, corresponding to an integrated luminosity of 27.4$\,\text{pb}^{-1}$. The jets are reconstructed with the anti-$k_{\mathrm{T}}$ algorithm using a distance parameter of $R=$ 0.4, within the rapidity interval $|y| <$ 2, and across the kinematic range 0.06 $< p_{\mathrm{T}} <$ 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 $\alpha_\mathrm{S}$.The inclusive jet cross section is measured as a function of jet transverse momentum $p_\mathrm{T}$ and rapidity $y$. The measurement is performed using proton-proton collision data at $\sqrt{s}$ = 5.02 TeV, recorded by the CMS experiment at the LHC, corresponding to an integrated luminosity of 27.4 pb$^{-1}$. The jets are reconstructed with the anti-$k_\mathrm{T}$ algorithm using a distance parameter of $R$ = 0.4, within the rapidity interval $\lvert y\rvert$$\lt$ 2, and across the kinematic range 0.06 $\lt$$p_\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 $\alpha_\mathrm{S}$

Two-particle Bose-Einstein correlations and their Lévy parameters in PbPb collisions at sNN\sqrt{s_\mathrm{NN}} = 5.02 TeV

Two-particle Bose-Einstein momentum correlation functions are studied for charged-hadron pairs in lead-lead collisions at a center-of-mass energy per nucleon pair of $\sqrt{s_\mathrm{NN}}$ = 5.02 TeV. The data sample, containing 4.27$\times10^{9}$ minimum bias events corresponding to an integrated luminosity of 0.607 nb$^{-1}$, was collected by the CMS experiment in 2018. The experimental results are discussed in terms of a L\'evy-type source distribution. The parameters of this distribution are extracted as functions of particle pair average transverse mass and collision centrality. These parameters include the L\'evy index or shape parameter ($\alpha$), the L\'evy scale parameter ($R$), and the correlation strength parameter ($\lambda$). The source shape, characterized by $\alpha$, is found to be neither Cauchy nor Gaussian, implying the need for a full L\'evy analysis. Similarly to what was previously found for systems characterized by Gaussian source radii, a hydrodynamical scaling is observed for the L\'evy $R$ parameter. The $\lambda$ parameter is studied in terms of the core-halo model.Comment: Submitted to Physical Review C. All figures and tables can be found at http://cms-results.web.cern.ch/cms-results/public-results/publications/HIN-21-011 (CMS Public Pages

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

International audienceThe inclusive jet cross section is measured as a function of jet transverse momentum $p_\mathrm{T}$ and rapidity $y$. The measurement is performed using proton-proton collision data at $\sqrt{s}$ = 5.02 TeV, recorded by the CMS experiment at the LHC, corresponding to an integrated luminosity of 27.4 pb$^{-1}$. The jets are reconstructed with the anti-$k_\mathrm{T}$ algorithm using a distance parameter of $R$ = 0.4, within the rapidity interval $\lvert y\rvert$$\lt$ 2, and across the kinematic range 0.06 $\lt$$p_\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 $\alpha_\mathrm{S}$

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

International audienceThe inclusive jet cross section is measured as a function of jet transverse momentum $p_\mathrm{T}$ and rapidity $y$. The measurement is performed using proton-proton collision data at $\sqrt{s}$ = 5.02 TeV, recorded by the CMS experiment at the LHC, corresponding to an integrated luminosity of 27.4 pb$^{-1}$. The jets are reconstructed with the anti-$k_\mathrm{T}$ algorithm using a distance parameter of $R$ = 0.4, within the rapidity interval $\lvert y\rvert$$\lt$ 2, and across the kinematic range 0.06 $\lt$$p_\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 $\alpha_\mathrm{S}$

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

International audienceThe inclusive jet cross section is measured as a function of jet transverse momentum $p_\mathrm{T}$ and rapidity $y$. The measurement is performed using proton-proton collision data at $\sqrt{s}$ = 5.02 TeV, recorded by the CMS experiment at the LHC, corresponding to an integrated luminosity of 27.4 pb$^{-1}$. The jets are reconstructed with the anti-$k_\mathrm{T}$ algorithm using a distance parameter of $R$ = 0.4, within the rapidity interval $\lvert y\rvert$$\lt$ 2, and across the kinematic range 0.06 $\lt$$p_\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 $\alpha_\mathrm{S}$