291 research outputs found
Heavy-flavour tagging and measurement of the tt̅H production cross-section in the H → bb̅ decay channel at the ATLAS experiment
No full textWith the final missing piece of the Standard Model of particle physics (SM), the Higgs boson, discovered in 2012 by the ATLAS and CMS experiments at the Large Hadron Collider (LHC) at CERN, both experiments have shifted their focus towards a precise determination of the properties of the new boson. The large datasets collected by both experiments during the second run of the LHC, Run 2, enable such precision measurements of the Higgs boson properties. One of the properties under study is the Yukawa coupling of the top quark, the heaviest of all particles in the SM, to the Higgs boson. The top-Higgs Yukawa coupling, the largest in the SM, is varied in many postulated theoretical extensions of the SM and is sensitive to the effects and the possible presence of physics Beyond the SM (BSM). The coupling itself can be measured in an indirect way, e.g using H → γγ measurements in which the coupling is involved via virtual loops, and in a direct way, which can be measured in the rare process where a Higgs boson is produced in association with a pair of top quarks, t ̄tH. Exploiting the dominant decay mode of the Higgs boson into a pair of b-quarks, t ̄tH(H →b ̄b), this thesis investigates the direct measurement of the top-Higgs Yukawa coupling in a challenging yet promising final state with at least four b-quark initiated jets.The t ̄tH(H → b ̄b) legacy analysis presented in this thesis is part of the re-analysis of the complete Run 2 dataset of 140 fb−1 at √s = 13 TeV recorded by the ATLAS experiment between 2015 and 2018. The focus lies on enhancing and integrating the latest physics object reconstruction algorithms, as well as employing an improved modelling of the main background process, t ̄t + b ̄b. Alongside state-of-the-art classification and reconstruction neural networks, a new estimate for the fake-lepton contribution in the single-lepton channel is derived. Further, the derivation of data-driven correction factors for the mis-modelled HT, observed in various t ̄t + jets processes, are presented. Finally, studies of the performance of profile likelihood fits as employed in the full analysis are presented with blinded data, yielding an expected significance of the t ̄tH process at 5.5 σ.Effective separation of the signal from the expected background requires dedicated and high-performing b-jet identification algorithms, the so-called b-taggers. In this thesis, the development and integration of a novel track-based b-tagger, DIPS, is presented. In addition, the DIPS tagger was added as a component to the ATLAS high-level b-tagger DL1r, replacing the previous track-based b-tagger, RNNIP. The resulting version, DL1d, is the new recommended b-tagger in ATLAS, thanks to its significantly improved performance compared to its predecessor. Furthermore, an extension of the DIPS b-tagger is introduced in this thesis, which is referred to as DIPS Tau which incorporates a new jet-class: τ-jets.Mit der Entdeckung des letzten fehlenden Teils des Standardmodells der Teilchenphysik (SM), dem Higgs-Boson, das 2012 von den Experimenten ATLAS und CMS am Large Hadron Collider (LHC) am CERN entdeckt wurde, haben beide Experimente ihren Schwerpunkt auf die genaue Bestimmung der Eigenschaften des neuen Bosons gelegt. Die von beiden Experimenten während des zweiten Laufs des LHCs, Run 2, gesammelten großen Datensätze ermöglichen Präzisionsmessungen der Eigenschaften des Higgs-Bosons. Eine der untersuchten Eigenschaften ist die Yukawa-Kopplung des Top-Quarks, des schwersten Teilchens im SM, an das Higgs-Boson. Die Top-Higgs-Yukawa-Kopplung, die größte im SM, wird in vielen postulierten theoretischen Erweiterungen des SM variiert und ist sensitiv auf Effekte und das mögliche Vorhandensein von Physik jenseits des SM (BSM). Die Kopplung selbst kann auf indirekte Weise gemessen werden, z.B. mit H → γγ Messungen an denen die Kopplung über virtuelle Schleifen beteiligt ist, und auf direktem Weg, wo sie messbar ist im seltenen Produktionsprozess eines Higgs-Bosons in Verbindung mit einem Top-Quark Paar, t ̄tH. Unter Verwendung des dominanten Zerfalls des Higgs-Bosons in ein Paar von b-Quarks, t ̄tH(H → b ̄b), untersucht diese Arbeit die direkte Messung der Top-Higgs Yukawa Kopplung in einem herausfordernden, aber vielversprechenden Endzustand mit mindestens vier durch b-Quarks initiierte Jets. Die in dieser Arbeit vorgestellte t ̄tH(H → b ̄b)-Legacy-Analyse ist Teil der erneuten Analyse des kompletten Run 2-Datensatzes mit 140 fb−1 bei √s = 13 TeV, welcher vom ATLAS-Experiment zwischen den Jahren 2015 bis 2018 aufgezeichnet wurde. Der Fokus der Analyse liegt auf Verbesserung und Integration neuster Algorithmen zur Rekonstruktion von physikalischen Objekten und Anwendung einer verbesserten Modellierung vom Hauptuntergruprozess t ̄t + b ̄b. Neben State-of-the-Art neuronale Netzwerke zur Klassifizierung und Rekonstruktion von Ereignissen wurde ebenfalls eine neue Schätzung für den Fake-Lepton-Beitrag im Single-Lepton-Kanal entwickelt. Desweiteren wird die Entwicklung datengetriebenen Korrekturfaktoren für das, in einigen t ̄t + jets Prozessen fehlerhaft modellierte, HT präsentiert. Abschließend werden die Ergebnisse von Profile Likelihood Fits, wie sie in der vollständigen Analyse verwendet werden, mit blinden Daten vorgestellt, welche eine erwartete Signifikanz des t ̄tH-Prozesses von 5, 5 σ liefern.Eine effektive Separation des Signals vom erwarteten Untergrund erfordert spezielle und leistungsstarke Algorithmen zur Identifizierung von b-Jets, die sogenannten b-Tagger. In dieser Arbeit wird die Entwicklung und Integration eines neuartigen spurbasierten b-Taggers, DIPS, vorgestellt. Darüber hinaus wurde der DIPS-Tagger als Komponente in den ATLAS-High-Level b-Tagger DL1r integriert, wo er den bisherigen spurbasierten b-Tagger RNNIP ersetzt. Die daraus resultierende Version, DL1d, ist der neue empfohlene b-Tagger in ATLAS, dank seine deutlich verbesserte Leistung im Vergleich zu seinem Vorgänger. Zudem wird eine Erweiterung des DIPS-Taggers vorgestellt, genannt DIPS Tau, welche eine zusätzliche Jet-Klasse einführt: τ-jet
The ATLAS Run 2 Legacy Analysis
No full textThis talk gives a general introduction to the ATLAS Run 2 Legacy Analysis for the LHC Higgs Working Group (LHCHWG) meeting in December 202
(Heavy) Flavour Tagging in ATLAS and CMS
No full textIn this talk, I will present the current developments in heavy flavour tagging for ATLAS and CMS. I will present the different techniques used by both experiments as well as the different algorithms used. Furhermore I will show the performance of both ATLAS and CMS state-of-the-art taggers and how they are traine
Flavour Tagging with Graph Neural Networks with the ATLAS Detector
No full textThe identification of jets containing -hadrons is key to many physics analyses at the LHC, including measurements involving Higgs bosons or top quarks, and searches for physics beyond the Standard Model. In this contribution, the most recent enhancements in the capability of ATLAS to separate -jets from jets stemming from lighter quarks will be presented. The improved performance originates from the usage of state-of-the-art machine learning algorithms based on graph networks. A factor of more than 2 to reject light- and -quark-initiated jet is observed compared to the current performance
Heavy-Flavour Tagging and Measurement of the Production Cross-Section in the Decay Channel at the ATLAS Experiment
No full textWith the final missing piece of the Standard Model of particle physics (SM), the Higgs boson, discovered in 2012 by the ATLAS and CMS experiments at the Large Hadron Collider (LHC) at CERN, both experiments have shifted their focus towards a precise determination of the properties of the new boson. The large datasets collected by both experiments during the second run of the LHC, Run~2, enable such precision measurements of the Higgs boson properties. One of the properties under study is the Yukawa coupling of the top quark, the heaviest of all particles in the SM, to the Higgs boson. The top-Higgs Yukawa coupling, the largest in the SM, is varied in many postulated theoretical extensions of the SM and is sensitive to the effects and the possible presence of physics Beyond the SM (BSM). The coupling itself can be measured in an indirect way, e.g using measurements in which the coupling is involved via virtual loops, and in a direct way, which can be measured in the rare process where a Higgs boson is produced in association with a pair of top quarks, . Exploiting the dominant decay mode of the Higgs boson into a pair of -quarks, , this thesis investigates the direct measurement of the top-Higgs Yukawa coupling in a challenging yet promising final state with at least four -quark initiated jets. The legacy analysis presented in this thesis is part of the re-analysis of the complete Run~2 dataset of at recorded by the ATLAS experiment between 2015 and 2018. The focus lies on enhancing and integrating the latest physics object reconstruction algorithms, as well as employing an improved modelling of the main background process, . Alongside state-of-the-art classification and reconstruction neural networks, a new estimate for the fake-lepton contribution in the single-lepton channel is derived. Further, the derivation of data-driven correction factors for the mis-modelled , observed in various processes, are presented. Finally, studies of the performance of profile likelihood fits as employed in the full analysis are presented with blinded data, yielding an expected significance of the process at . Effective separation of the signal from the expected background requires dedicated and high-performing -jet identification algorithms, the so-called -taggers. In this thesis, the development and integration of a novel track-based -tagger, DIPS, is presented. In addition, the DIPS tagger was added as a component to the ATLAS high-level -tagger DL1r, replacing the previous track-based -tagger, RNNIP. The resulting version, DL1d, is the new recommended -tagger in ATLAS, thanks to its significantly improved performance compared to its predecessor. Furthermore, an extension of the DIPS -tagger is introduced in this thesis, which is referred to as DIPS Tau which incorporates a new jet-class: -jets
Large few-layer hexagonal boron nitride flakes for nonlinear optics
No full textHexagonal boron nitride (hBN) is a layered dielectric material with a wide range of applications in optics and photonics. In this work, we demonstrate a fabrication method for few-layer hBN flakes with areas up to 5000µm2. We show that hBN in this form can be integrated with photonic microstructures: as an example, we use a circular Bragg grating (CBG). The layer quality of the exfoliated hBN flake on and off a CBG is confirmed by Raman spectroscopy and second-harmonic generation (SHG) microscopy. We show that the SHG signal is uniform across the hBN sample outside the CBG and is amplified in the center of the CBG.No Full Tex
umami-hep/puma: v0.1.9
No full textAdding boosted categories for Xbb to utils !138
Running pylint also for tests #133
Fix handling of nan values in histograms #125
Adding support for under- and overflow bins in puma.HistogramPlot #124
(Documentation) Adding copy-button to code cells in documentation #13
umami-hep/puma: v0.1.4
No full textRenamed the puma.FractionScan and puma.FractionScanPlot classes to more general puma.Line2DPlot and pumal.Line2D #84
Splitting force argument of set_log() method into force_x and force_y #83
Adding puma.PiePlot class. Pie chart plots with puma.HistogramPlot are no longer possible #70
Change default labels of singlebjets and singlecjets #82
Support linestyles for variable vs. efficiency plots #7
umami-hep/puma: v0.2.8
No full textWhat's Changed
Add optimal fc plot by @dkobylianskii in https://github.com/umami-hep/puma/pull/188
Update fraction scan plot by @samvanstroud in https://github.com/umami-hep/puma/pull/193
Update uncertainty bands by @dkobylianskii in https://github.com/umami-hep/puma/pull/194
HL Fraction scan bugfix by @dkobylianskii in https://github.com/umami-hep/puma/pull/201
numpy 1.21 u2s can't convert dtype by @biemster in https://github.com/umami-hep/puma/pull/198
Support for older versions of matplotlib.Figure by @biemster in https://github.com/umami-hep/puma/pull/199
add support to set a per ROC reference for the ratios by @biemster in https://github.com/umami-hep/puma/pull/200
Add integrated efficiency plot by @dkobylianskii in https://github.com/umami-hep/puma/pull/195
Bump to version 0.2.8 by @samvanstroud in https://github.com/umami-hep/puma/pull/203
New Contributors
@biemster made their first contribution in https://github.com/umami-hep/puma/pull/198
Full Changelog: https://github.com/umami-hep/puma/compare/v0.2.7...v0.2.
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