Patrick Connor

Thermodynamics of the ideal Fermi gas trapped in an external generic power law potential U =ai | ni is investigated systematically from the grand thermodynamic potential in d-dimensional space.These properties are explored carefully in the degenerate limit (µ K B T ), where the thermodynamic properties are greatly dominated by the Pauli exclusion principle.Pressure and energy along with the isothermal compressibility are nonzero at T = 0K.The nonzero value of compressibility implies that zero point pressure is not a constant but depends on volume.

Collinear and transverse-momentum-dependent (TMD) parton densities are obtained from fits to precision measurements of deep-inelastic scattering (DIS) cross sections at HERA. The parton densities are evolved by Dokshitzer-Gribov-Lipatov-Altarelli-Parisi evolution with next-to-leading-order (NLO) splitting functions using the parton branching method, allowing one to determine simultaneously collinear and TMD densities for all flavors over a wide range in $x$, ${\ensuremath{\mu}}^{2}$ and ${k}_{t}$, relevant for predictions at the LHC. The DIS cross section is computed from the parton densities using perturbative NLO coefficient functions. Parton densities satisfying angular ordering conditions are presented. Two sets of parton densities are obtained, differing in the renormalization scale choice for the argument in the strong coupling ${\ensuremath{\alpha}}_{\mathrm{s}}$. This is taken to be either the evolution scale $\ensuremath{\mu}$ or the transverse momentum ${q}_{t}$. While both choices yield similarly good ${\ensuremath{\chi}}^{2}$ values for the fit to DIS measurements, the gluon density especially turns out to differ between the two sets. The TMD densities are used to predict the transverse momentum spectrum of $Z$ bosons at the LHC.

A common library, TMDlib2, for Transverse-Momentum-Dependent distributions (TMDs) and unintegrated parton distributions (uPDFs) is described, which allows for easy access of commonly used TMDs and uPDFs, providing a three-dimensional (3D) picture of the partonic structure of hadrons. The tool TMDplotter allows for web-based plotting of distributions implemented in TMDlib2, together with collinear pdfs as available in LHAPDF.

Transverse Momentum Dependent (TMD) parton distributions obtained from the Parton Branching (PB) method are combined with next-to-leading-order (NLO) calculations of Drell-Yan (DY) production. We apply the MCatNLO method for the hard process calculation and matching with the PB TMDs. We compute predictions for the transverse momentum, rapidity and $\phi^*$ spectra of Z-bosons. We find that the theoretical uncertainties of the predictions are dominated by the renormalization and factorization scale dependence, while the impact of TMD uncertainties is moderate. The theoretical predictions agree well, within uncertainties, with measurements at the Large Hadron Collider (LHC). In particular, we study the region of lowest transverse momenta at the LHC, and comment on its sensitivity to nonperturbative TMD contributions.

Share Icon Share Twitter Facebook Reddit LinkedIn Reprints and Permissions Cite Icon Cite Search Site Citation G. Herdan, M. L. Smith, W. H. Hardwick, P. Connor, E. J. Öpik; Small Particle Statistics. Physics Today 1 January 1962; 15 (1): 66. https://doi.org/10.1063/1.3057978 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentPhysics Today Search Advanced Search

The object of High Energy Physics is the study the constituents of matterMatter and their interactionsInteraction at quantic and relativistic scales, conditions only reachable with very high energies. At these scales, matter and interactions are both described in terms of particles.

Abstract It has been observed in the literature that measurements of low-mass Drell–Yan (DY) transverse momentum spectra at low center-of-mass energies $$\sqrt{s}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msqrt><mml:mi>s</mml:mi></mml:msqrt></mml:math> are not well described by perturbative QCD calculations in collinear factorization in the region where transverse momenta are comparable with the DY mass. We examine this issue from the standpoint of the Parton Branching (PB) method, combining next-to-leading-order (NLO) calculations of the hard process with the evolution of transverse momentum dependent (TMD) parton distributions. We compare our predictions with experimental measurements at low DY mass, and find very good agreement. In addition we use the low mass DY measurements at low $$\sqrt{s}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msqrt><mml:mi>s</mml:mi></mml:msqrt></mml:math> to determine the width $$q_s$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>q</mml:mi><mml:mi>s</mml:mi></mml:msub></mml:math> of the intrinsic Gauss distribution of the PB-TMDs at low evolution scales. We find values close to what has earlier been used in applications of PB-TMDs to high-energy processes at the Large Hadron Collider (LHC) and HERA. We find that at low DY mass and low $$\sqrt{s}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msqrt><mml:mi>s</mml:mi></mml:msqrt></mml:math> even in the region of $$p_\mathrm{T}/m_\mathrm{DY}\sim 1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msub><mml:mi>p</mml:mi><mml:mi>T</mml:mi></mml:msub><mml:mo>/</mml:mo><mml:msub><mml:mi>m</mml:mi><mml:mi>DY</mml:mi></mml:msub><mml:mo>∼</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:math> the contribution of multiple soft gluon emissions (included in the PB-TMDs) is essential to describe the measurements, while at larger masses ( $$m_\mathrm{DY}\sim m_{{\mathrm{Z}}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msub><mml:mi>m</mml:mi><mml:mi>DY</mml:mi></mml:msub><mml:mo>∼</mml:mo><mml:msub><mml:mi>m</mml:mi><mml:mi>Z</mml:mi></mml:msub></mml:mrow></mml:math> ) and LHC energies the contribution from soft gluons in the region of $$p_\mathrm{T}/m_\mathrm{DY}\sim 1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msub><mml:mi>p</mml:mi><mml:mi>T</mml:mi></mml:msub><mml:mo>/</mml:mo><mml:msub><mml:mi>m</mml:mi><mml:mi>DY</mml:mi></mml:msub><mml:mo>∼</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:math> is small.

Blockage by the human hand/body is an important impairment in realizing practical millimeter-wave wireless systems. Prior works on blockage modeling are either based on theoretical studies of double knife-edge diffraction or its modifications, high-frequency simulations of electromagnetic effects, or measurements with experimental millimeter-wave prototypes. While such studies are useful, they do not capture the form-factor constraints of user equipments (UEs), such as moderate array sizes, gains, and beamwidths. In this work, we study the impact of hand/body blockage with a UE at 28 GHz built on commercial millimeter-wave components. We report five controlled studies with different types of hand holdings/grips, antenna types, and directional/narrow beams. For both hard and loose hand grips, we report considerably lower blockage loss estimates than prior works. Critical in estimating the loss is the definition of a “region of interest” (RoI) around the UE where the impact of the hand/body is seen. Toward this goal, we define an RoI that includes the spatial area where significant energy is seen in either the no blockage or blockage modes. Our studies show that significant spatial area coverage improvement can be seen with a loose hand grip due to hand reflections.

Automated classification of astronomical sources is often challenging due to the scarcity of labelled training data. We present a data set with a total number of 2158 data items that contains radio galaxy images with their corresponding morphological labels taken from various catalogues [1,2]. The data set is curated by removing duplicates, ambiguous morphological labels and by different meta data formats. The image data was acquired by the VLA FIRST (Faint Images of the Radio Sky at Twenty-Centimeters) survey [3]. The morphological labels are collected and the catalogue specific classification definition is converted into a 4-class classification scheme: FRI, FRII, Compact and Bent sources. FRI and FRII correspond to the two classes of the widely used Faranoff-Riley classification [4]. We consider two more classes: compact sources and bent-tail galaxies. For duplicates with different morphological labels, the galaxy is regarded as ambiguously labeled and both coordinates are removed. For the remaining list of coordinates, the radio galaxy images are collected from the virtual observatory skyview (https://skyview.gsfc.nasa.gov/current/cgi/query.pl). The gray value images are provided in the size of 300 × 300 pixel and all pixels with a value below three times the local RMS of the noise are set to this threshold value. The data set is useful for the development of robust machine learning models that automate the classification of radio galaxy images.

Machine learning techniques that perform morphological classification of astronomical sources often suffer from a scarcity of labelled training data. Here, we focus on the case of supervised deep learning models for the morphological classification of radio galaxies, which is particularly topical for the forthcoming large radio surveys. We demonstrate the use of generative models, specifically Wasserstein GANs (wGANs), to generate data for different classes of radio galaxies. Further, we study the impact of augmenting the training data with images from our wGAN on three different classification architectures. We find that this technique makes it possible to improve models for the morphological classification of radio galaxies. A simple Fully Connected Neural Network (FCN) benefits most from including generated images into the training set, with a considerable improvement of its classification accuracy. In addition, we find it is more difficult to improve complex classifiers. The classification performance of a Convolutional Neural Network (CNN) can be improved slightly. However, this is not the case for a Vision Transformer (ViT).

At the CMS experiment, a growing reliance on the fast Monte Carlo application (FastSim) will accompany the high luminosity and detector granularity expected in Phase 2. The FastSim chain is roughly 10 times faster than the application based on the Geant4 detector simulation and full reconstruction referred to as FullSim. However, this advantage comes at the price of decreased accuracy in some of the final analysis observables. In this contribution, a machine learning-based technique to refine those observables is presented. We employ a regression neural network trained with a sophisticated combination of multiple loss functions to provide post-hoc corrections to samples produced by the FastSim chain. The results show considerably improved agreement with the FullSim output and an improvement in correlations among output observables and external parameters. This technique is a promising replacement for existing correction factors, providing higher accuracy and thus contributing to the wider usage of FastSim.

Two versions of a specific 2 nm rated filter containing filtration medium and all other components produced from high density polyethylene (HDPE), one subjected to standard cleaning, the other to specialized ultra-cleaning, were evaluated in terms of their cleanliness characteristics, and also defectivity of wafers processed with photoresist filtered through each. With respect to inherent cleanliness, the ultraclean version exhibited a 70% reduction in total metal extractables and 90% reduction in organics extractables compared to the standard clean version. In terms of particulate cleanliness, the ultraclean version achieved stability of effluent particles 30nm and larger in about half the time required by the standard clean version, also exhibiting effluent levels at stability almost 90% lower. In evaluating defectivity of blanket wafers processed with photoresist filtered through either version, initial defect density while using the ultraclean version was about half that observed when the standard clean version was in service, with defectivity also falling more rapidly during subsequent usage of the ultraclean version compared to the standard clean version. Similar behavior was observed for patterned wafers, where the enhanced defect reduction was primarily of bridging defects. The filter evaluation and actual process-oriented results demonstrate the extreme value in using filtration designed possessing the optimal intrinsic characteristics, but with further improvements possible through enhanced cleaning processes.

24th International Workshop on Deep-Inelastic Scattering and Related Subjects, DIS 2016, Hamburg, Germany, 11 Apr 2016 - 15 Apr 2016 ; Proceedings of Science (DIS2016), 039(2016).

The aim of this study was to evaluate change in tendon thickness, bioinduction of new tissue, and the rate of radiographic re-tear following the use of a bovine collagen implant as an adjunct to surgical repair in the treatment of supraspinatus tendon tears, as well as the functional improvements and safety associated with the use of this device.

We motivate and describe a method based on fits with polynomials to test the smoothness of differential distributions. As a demonstration, we apply the method to several measurements of inclusive jet double-differential cross section in the jet transverse momentum and rapidity at the Tevatron and LHC. This method opens new possibilities to test the quality of differential distributions used for the extraction of physics quantities such as the strong coupling.

At the CMS experiment, a growing reliance on the fast Monte Carlo application (FastSim) will accompany the high luminosity and detector granularity expected in Phase 2. The FastSim chain is roughly 10 times faster than the application based on the GEANT4 detector simulation and full reconstruction referred to as FullSim. However, this advantage comes at the price of decreased accuracy in some of the final analysis observables. In this contribution, a machine learning-based technique to refine those observables is presented. We employ a regression neural network trained with a sophisticated combination of multiple loss functions to provide post-hoc corrections to samples produced by the FastSim chain. The results show considerably improved agreement with the FullSim output and an improvement in correlations among output observables and external parameters. This technique is a promising replacement for existing correction factors, providing higher accuracy and thus contributing to the wider usage of FastSim.

Two versions of a specific 2nm rated filter containing filtration medium and all other components produced from high density polyethylene (HDPE), one subjected to standard cleaning, the other to specialized ultra-cleaning, were evaluated in terms of their cleanliness characteristics, and also defectivity of wafers processed with photoresist filtered through each. With respect to inherent cleanliness, the ultraclean version exhibited a 70% reduction in total metal extractables and 90% reduction in organics extractables compared to the standard clean version. In terms of particulate cleanliness, the ultraclean version achieved stability of effluent particles 30nm and larger in about half the time required by the standard clean version, also exhibiting effluent levels at stability almost 90% lower. In evaluating defectivity of blanket wafers processed with photoresist filtered through either version, initial defect density while using the ultraclean version was about half that observed when the standard clean version was in service, with defectivity also falling more rapidly during subsequent usage of the ultraclean version compared to the standard clean version. Similar behavior was observed for patterned wafers, where the enhanced defect reduction was primarily of bridging defects. The filter evaluation and actual process-oriented results demonstrate the extreme value in using filtration designed possessing the optimal intrinsic characteristics, but with further improvements possible through enhanced cleaning processes

Blockage by the human hand/body is an important impairment in realizing practical millimeter wave wireless systems. Prior works on blockage modeling are either based on theoretical studies of double knife edge diffraction or its modifications, high-frequency simulations of electromagnetic effects, or measurements with experimental millimeter wave prototypes. While such studies are useful, they do not capture the form-factor constraints of user equipments (UEs). In this work, we study the impact of hand/body blockage with a UE at $28$ GHz built on Qualcomm's millimeter wave modem, antenna modules and beamforming solutions. We report five exhaustive and controlled studies with different types of hand holdings/grips, antenna types, and with directional/narrow beams. For both hard as well as loose hand grips, we report considerably lower blockage loss estimates than prior works. Critical in estimating the loss is the definition of a "region of interest" (RoI) around the UE where the impact of the hand/body is seen. Towards this goal, we define a RoI that includes the spatial area where significant energy is seen in either the no blockage or blockage modes. Our studies show that significant spatial area coverage improvement can be seen with loose hand grip due to hand reflections.

We describe a hands-on introduction to deep learning in particle physics, performed during the 5th INFIERI school in Wuhan, China. We presented fundamental machine learning concepts to students from diverse backgrounds in physics and computing, and prepared them to apply these techniques to solve an example problem from particle physics (hadronic top quark tagging). We exploited the simplicity of tools like Jupyter notebooks, and the user-friendly approaches of data science libraries such as Keras with TensorFlow.

As the complexity of nanoparticle dispersions used in next-generation CMP slurries increases to meet stringent performance demands, it is necessary to develop polymeric filtration media that capture rouge particles in order to reduce overall surface defectivity without sacrificing planarization efficiency. Therefore, it is essential to probe the molecular level interactions at the nanoparticle/slurry chemistry/polymer filtration media interface and in turn correlate this to the key performance metrics such as substrate removal rate, post CMP defects, planarization efficiency. More specifically, simulated Cu CMP slurries with a wide range of film formation chemistries for performance control were evaluated with respect to the filterability through various polymeric filtration media. It was determined that a synergy exists between the structure of the additive (small organic molecule vs. surfactant-like additive) and the filter medias ability to regulate the dynamic adsorption processes of the slurry nanoparticles. Utilizing atomic force microscopy (AFM), electrochemical quartz crystal nanobalance (EQCN), modified ATR-IR spectroscopy, and in-situ rotating disk electrode corrosion measurements the static and dynamic nanoparticle binding mechanisms can be determined and correlated to the filterability studies. Furthermore, monitoring the pre/post filtration zeta potential and particle size distributions, bulk copper removal rate, and corrosion current as a function of filtration pressure, the role of filtration media slurry chemistry health can be surveyed on CMP performance based scale.

The latest versions of TMDlib and TMDplotter are presented. Parameterisations of TMDs in TMDlib are illustrated. New features of TMDplotter include plotting as a function of the momentum fraction, the transverse momentum, or the evolution scale, as well as integration of TMDs and comparison to sets from LHAPDF. Luminosity for collinear PDFs is also described. The tool is available at http://tmdplotter.desy.de.

Novel techniques are indispensable to process the flood of data from the new generation of radio telescopes. In particular, the classification of astronomical sources in images is challenging. Morphological classification of radio galaxies could be automated with deep learning models that require large sets of labelled training data. Here, we demonstrate the use of generative models, specifically Wasserstein GANs (wGAN), to generate artificial data for different classes of radio galaxies. Subsequently, we augment the training data with images from our wGAN. We find that a simple fully-connected neural network for classification can be improved significantly by including generated images into the training set.

V-shaped porphyrin dimers, with masked para-phenylene bridges, undergo efficient oxidative coupling to form meso-meso linked cyclic porphyrin oligomers. Reductive aromatization unmasks the para-phenylenes, increasing the strain. Oxidation then fuses the porphyrin dimers, providing a nanoring with curved walls. The strain in this macrocycle bends the para-phenylene and fused porphyrin dimer units (radii of curvature: 11.4 Å and 19.0 Å, respectively), but it does not significantly alter the electronic structure of the fused porphyrins.

It has been observed in the literature that measurements of low-mass Drell-Yan (DY) transverse momentum spectra at low center-of-mass energies $\sqrt{s}$ are not well described by perturbative QCD calculations in collinear factorization in the region where transverse momenta are comparable with the DY mass. We examine this issue from the standpoint of the Parton Branching (PB) method, combining next-to-leading-order (NLO) calculations of the hard process with the evolution of transverse momentum dependent (TMD) parton distributions. We compare our predictions with experimental measurements at low DY mass, and find very good agreement.In addition we use the low mass DY measurements at low $\sqrt{s}$ to determine the width $q_s$ of the intrinsic Gauss distribution of the PB-TMDs at low evolution scales. We find values close to what has earlier been used in applications of PB -TMDs to high-energy processes at the Large Hadron Collider (LHC) and HERA. We find that at low DY mass and low $\sqrt{s}$ even in the region of $p_t/m_{DY} \sim 1$ the contribution of multiple soft gluon emissions (included in the PB-TMDs) is essential to describe themeasurements, while at larger masses ($m_{DY} \sim m_{Z}$) and LHC energies the contribution from soft gluons in the region of $p_t/m_{DY}\sim 1$ is small.

We present measurements of the inclusive jet production at centre-of-mass energies of 8 and 13 TeV, and of multijets at 8 TeV. These measurements allow to constrain PDFs and the strong coupling constant. Two measurements of the azimuthal correlations at 8 and 13 TeV are also presented, testing higher order QCD calculations.

Several measurements of jet cross sections in proton-proton collisions at 5.02 and 13~TeV with the CMS experiment are presented. Jets are reconstructed using the anti-$k_T$ clustering algorithm with $R = 0.4$ and $R=0.7$. Double-differential measurements of inclusive jet production at 5.02 and 13~TeV are performed as a function of the jet transverse momentum and jet rapidity; furthermore, a triple-differential measurement as a function of leading jet transverse momentum, dijet azimuthal correlations, and jet multiplicity and a double-differential measurement as a function of jet multiplicity and single-jet transverse momentum are also provided for multijet production at 13~TeV. The measured jet cross sections are corrected for detector effects and compared with the predictions from perturbative QCD. Finally, a QCD interpretation of the inclusive jet measurement with $R=0.7$ at 13 TeV is also presented.

Measurements of jet production in proton-proton collisions at the LHC are crucial for precise tests of QCD, improving the understanding of the proton structure and are important tools for searches for physics beyond the standard model.We describe the most recent set of inclusive jet measurements performed using CMS data, mentioning methodological specificities with respect to other analyses.Finally, we compare them to various theoretical predictions.

Machine learning techniques that perform morphological classification of astronomical sources often suffer from a scarcity of labelled training data. Here, we focus on the case of supervised deep learning models for the morphological classification of radio galaxies, which is particularly topical for the forthcoming large radio surveys. We demonstrate the use of generative models, specifically Wasserstein GANs (wGANs), to generate data for different classes of radio galaxies. Further, we study the impact of augmenting the training data with images from our wGAN on three different classification architectures. We find that this technique makes it possible to improve models for the morphological classification of radio galaxies. A simple Fully Connected Neural Network (FCN) benefits most from including generated images into the training set, with a considerable improvement of its classification accuracy. In addition, we find it is more difficult to improve complex classifiers. The classification performance of a Convolutional Neural Network (CNN) can be improved slightly. However, this is not the case for a Vision Transformer (ViT).

We motivate and describe a method based on fits with polynomials to test the smoothness of differential distributions.As a demonstration, we apply the method to several measurements of inclusive jet double-differential cross section in the jet transverse momentum and rapidity at the Tevatron and LHC.This method opens new possibilities to test the quality of differential distributions used for the extraction of physics quantities such as the strong coupling.

We motivate and describe a method based on fits with Chebyshev polynomials to test the smoothness of differential distributions.We also provide a header-only tool in C++ called STEP to perform such tests.As a demonstration, we apply the method in the context of the measurement of inclusive jet double-differential cross section in the jet transverse momentum and rapidity at the Tevatron and LHC.This method opens new possibilities to test the quality of differential distributions used for the extraction of physics quantities such as the strong coupling.

We motivate and describe a method based on fits with Chebyshev polynomials to test the smoothness of differential distributions.We also provide a header-only tool in C++ called STEP to perform such tests.As a demonstration, we apply the method in the context of the measurement of inclusive jet double-differential cross section in the jet transverse momentum and rapidity at the Tevatron and LHC.This method opens new possibilities to test the quality of differential distributions used for the extraction of physics quantities such as the strong coupling.

As devices continue to shrink, the demands on chemical mechanical polishing (CMP) become more stringent, requiring slurries to be modulated in order to enhance surface quality. Achieving angstrom level uniformity and minimizing defects, results in the need to develop polymeric filtration media that also increases polishing efficiency. Probing the interactions that can occur at the particle/polymer/slurry interface is essential in order to understand the impact of altering the chemistry and the filtration media on the filterability, rate of diffusion, adhesion force, surface roughness, and activation energy (E A ). This work focuses on investigating the interactions between common abrasive particles, such as ceria and silica (in various chemistries), as well as different polymeric filtration media through the use of an electrochemical quartz crystal nanobalance (EQCN) and atomic force microscopy (AFM). The AFM tip is effectively functionalized in solution using epoxy glue to mimic a nanoparticle, which is used to measure the adhesion force between the particle and membrane via force distance curves. These nanoparticles can be reduced, oxidized, or functionalized and can be used to measure the adhesion force at different filtration time intervals, monitoring the change in the particle-polymer interaction. Preliminary results suggest that altering the complexing agent in the slurry can heavily influence the E A of the interaction between the slurry and filter membrane. Specifically, the activation energy for a copper CMP slurry and an amine-based filter was found to be negative, indicating a barrier-less reaction, due to non-covalent interactions present at the slurry-polymer interface. Changing the complexing agent from glycine to L-alanine and β-alanine revealed the E A to be positive and negative, respectively, which also changed with the structure of the membrane. Ultimately, both the backbone of the filter and the complexing agent alter the interactions on the surface of the polymer membrane and have shown to have a direct impact on CMP performance.

We present a determination of parton densities at NLO obtained with the Parton Branching method using precision measurements of deep inelastic scattering cross sections at HERA.The two sets of parton densities shown in this work are obtained with the same angular angular ordering condition for the evolution scale and they differ in the chosen scale for the α s evaluation, for which we consider two scenarios: the evolution scale, and the transverse momentum q T from the angular ordering prescription.The transverse momentum dependent densities obtained with the Parton Branching method are applied to two LHC processes: the Drell-Yan p T spectrum and the azimuthal correlation in high p T dijet events.For the Drell-Yan p T spectrum a significant effect from the α s scale choice is observed.

We present a determination of parton densities at NLO obtained with the Parton Branching method using precision measurements of deep inelastic scattering cross sections at HERA. The two sets of parton densities shown in this work are obtained with the same angular angular ordering condition for the evolution scale and they differ in the chosen scale for the strong coupling evaluation, for which we consider two scenarios: the evolution scale, and the transverse momentum qT from the angular ordering prescription. The transverse momentum dependent densities obtained with the Parton Branching method are applied to two LHC processes: the Drell-Yan pT spectrum and the azimuthal correlation in high pT dijet events. For the Drell-Yan pT spectrum a significant effect from the strong coupling scale choice is observed.

The 2018 International Workshop on the High Energy Circular Electron Positron Collider, CEPC 2018, Beijing, China, 12 Nov 2018 - 14 Nov 2018

In this chapter, the analysis is described at the level of the detector, i.e. without correcting the measurement from the artefacts of the detector. The selection is discussed, as well as several calibrations and their associated systematic uncertainties. At the end of this chapter, a global picture of the content of the sample at detector level is drawn.

The CERN is an international centre for experimental physics [1]. Originally founded by twelve European countries in 1952 to associate their research programmes, many other countries from all around the world have now joined CERN at various levels, and other topics of research have come up. CERN is nowadays one of the leading centres for research in nuclear and particle physics, for an internal budget of around one billion euros a year.

In this chapter, we summarise the analysis. In addition, we discuss additional results that can be obtained thanks to the techniques explained in this thesis. In this section, limitations and successes of these techniques are discussed and prospects for future measurements are given.

This book presents a measurement of the double differential cross section for inclusive b jet production in proton–proton collisions as well as fraction of b jets in the inclusive jet production as a function of the transverse momentum p T and the absolute rapidity |y|

In this chapter, some ideas of the theoretical aspects of modern HEP are presented, with a special emphasis on the topics underlying the measurement presented in Part II: first the Standard Model (SM), and in particular Quantum Chromodynamics (QCD); then additional phenomenological models used in the treatment of proton–proton collisions are introduced, in particular theEvolution evolution equations.

In HEP, Monte Carlo (MC) techniques are used in at least two different contexts: compute physics predictions, or simulate the interactions of particles with the detector. In this chapter, the basic techniques are presented. Then different physics generators, such as the ones used in Part II, are detailed.

In the first part of this chapter, the discovery and current knowledge of the properties of the b quark are reviewed. Then the current status and prospects for deepening this knowledge with the LHC experiments are detailed. Finally, tagging techniques and measurements of b quarks as a probe are discussed.

The chapter is organised as follows. First, the disagreement in the b-tagged fraction observed in the previous chapter is investigated; a correction to the simulation is applied to fix the disagreement. Then, the b-jet and n-jet cross sections are simultaneously extracted together with advanced techniques of unfolding; the treatment of the systematic uncertainties in the unfolding is also discussed.

The general strategy for the measurement of the double differential cross section of inclusive b jet production using pp collisions with $$\sqrt{s} = 13 \,\mathrm{TeV}$$ recorded with the CMS detector is presented here.

We compare the measurement to theory predictions. We first compare to LO predictions with pythia 8, MadGraphand herwig++; then we compare to NLO predictions with powhegincluding theoretical uncertainties.

Rule-based machine translation is more data efficient than the big data-based machine translation approaches, making it appropriate for languages with low bilingual corpus resources -- i.e., minority languages. However, the rule-based approach has declined in popularity relative to its big data cousins primarily because of the extensive training and labour required to define the language rules. To address this, we present a semantic representation that 1) treats all bits of meaning as individual concepts that 2) modify or further specify one another to build a network that relates entities in space and time. Also, the representation can 3) encapsulate propositions and thereby define concepts in terms of other concepts, supporting the abstraction of underlying linguistic and ontological details. These features afford an exact, yet intuitive semantic representation aimed at handling the great variety in language and reducing labour and training time. The proposed natural language generation, parsing, and translation strategies are also amenable to probabilistic modeling and thus to learning the necessary rules from example data.

The positions of the nearly twenty-thousand silicon sensors of the CMS central tracking system must be determined with a precision better than their intrinsic resolution in order to provide an optimal reconstruction of charged particle trajectories.The procedure, referred to as the alignment, includes also the adjustment of the orientations and the determination of the deviation from flatness of the sensor surfaces.Data-driven methods used to carefully align the detector and validate the alignment are presented using CMS Run 2 data, collected from 2016 to 2018.Systematic distortions such as weak modes are discussed, as well as the impact of the variation of the conditions during data taking over time, in particular effects related to radiation damage.Finally, we illustrate the impact on physics of the recent developments included in the Legacy Reprocessing, which was performed with the aim to greatly improve the physics potential for precision measurements, such as the reconstruction of the invariant mass spectrum of the dilepton systems.

It has been observed that measurements of low-mass Drell-Yan (DY) transverse momentum spectra at low center-of-mass energies $\sqrt{s}$ are not well described by perturbative QCD calculations in collinear factorization in the region where transverse momenta are comparable with the DY mass. This issue can be examined with the Parton Branching (PB) method combining next-to-leading-order (NLO) calculations of the hard process with the evolution of transverse momentum dependent (TMD) parton distribution. The predictions are compared with measurements of low mass DY production, and they are in very good agreement. Predictions have also been compared with the measurement of DY production at high energies at the LHC. We find that at low-mass DY and low $\sqrt{s}$ even in the region of $p_{T}/m_{DY}\sim 1$ the contribution of multiple soft gluon emissions (included in the PB-TMDs) is essential to describe the measurements, while at larger masses ($m_{DY}\sim m_{Z}$) and LHC energies the contribution from soft gluons in the region of $p_{T}/m_{DY}\sim 1$ is small.

Transverse Momentum Dependent (TMD) parton distribution functions obtained within the Parton Branching (PB) approach offer a wide spectrum of applications to describe processes in pp as well as in ep interactions.In this proceedings we show an example of application of the PB TMD method to describe Z-boson production in high energy pp interactions.The PB TMD approach together with dedicated parton showers provide a new and consistent way to describe not only inclusive but also multi-jet distributions at different center-of-mass energies.

We motivate and describe a method based on fits with Chebyshev polynomials to test the smoothness of differential distributions. We also provide a header-only tool in C++ called STEP to perform such tests. As a demonstration, we apply the method in the context of the measurement of inclusive jet double-differential cross section in the jet transverse momentum and rapidity at the Tevatron and LHC. This method opens new possibilities to test the quality of differential distributions used for the extraction of physics quantities such as the strong coupling.

We motivate and describe a method based on fits with Chebyshev polynomials to test the smoothness of differential distributions. We also provide a header-only tool in C++ called STEP to perform such tests. As a demonstration, we apply the method in the context of the measurement of inclusive jet double-differential cross section in the jet transverse momentum and rapidity at the Tevatron and LHC. This method opens new possibilities to test the quality of differential distributions used for the extraction of physics quantities such as the strong coupling.

With the proliferation of advanced metering infrastructure (AMI), more real-time data is available to electric utilities and consumers. Such high volumes of data facilitate innovative electricity rate structures beyond flat-rate and time-of-use (TOU) tariffs. One such innovation is real-time pricing (RTP), in which the wholesale market-clearing price is passed directly to the consumer on an hour-by-hour basis. While rare, RTP exists in parts of the United States and has been observed to reduce electric bills. Although these reductions are largely incidental, RTP may represent an opportunity for large-scale peak shaving, demand response, and economic efficiency when paired with intelligent control systems. Algorithms controlling flexible loads and energy storage have been deployed for demand response elsewhere in the literature, but few studies have investigated these algorithms in an RTP environment. If properly optimized, the dynamic between RTP and intelligent control has the potential to counteract the unwelcome spikes and dips of demand driven by growing penetration of distributed renewable generation and electric vehicles (EV). This paper presents a simple reinforcement learning (RL) application for optimal battery control subject to an RTP signal.

We motivate and describe a method based on fits with polynomials to test the smoothness of differential distributions. As a demonstration, we apply the method to several measurements of inclusive jet double-differential cross section in the jet transverse momentum and rapidity at the Tevatron and LHC. This method opens new possibilities to test the quality of differential distributions used for the extraction of physics quantities such as the strong coupling.