Radek Žlebčík

We study parton-branching solutions of QCD evolution equations and present a method to construct both collinear and transverse momentum dependent (TMD) parton densities from this approach. We work with next-to-leading-order (NLO) accuracy in the strong coupling. Using the unitarity picture in terms of resolvable and non-resolvable branchings, we analyze the role of the soft-gluon resolution scale in the evolution equations. For longitudinal momentum distributions, we find agreement of our numerical calculations with existing evolution programs at the level of better than 1% over a range of five orders of magnitude both in evolution scale and in longitudinal momentum fraction. We make predictions for the evolution of transverse momentum distributions. We perform fits to the high-precision deep inelastic scattering (DIS) structure function measurements, and we present a set of NLO TMD distributions based on the parton branching approach.

QCD evolution equations can be recast in terms of parton branching processes. We present a new numerical solution of the equations. We show that this parton-branching solution can be applied to analyze infrared contributions to evolution, order-by-order in the strong coupling $\alpha_s$, as a function of the soft-gluon resolution scale parameter. We examine the cases of transverse-momentum ordering and angular ordering. We illustrate that this approach can be used to treat distributions which depend both on longitudinal and on transverse momenta.

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.

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.

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.

We present results of a toy model study of performance of the Time-of-Flight detectors integrated into forward proton detectors. The goal of the ToF device is to suppress effects of additional soft processes (so called pile-up) accompanying the hard-scale central diffractive event, characterized by two tagged leading protons, one on each side from the interaction point. The method of mitigation of the pile-up effects exemplified in this study is based on measuring a difference between arrival times of these leading protons at the forward proton detectors and hence estimate the z-coordinate of the production vertex. We evaluate effects of the the pile-up background by studying in detail its components, and estimate the performance of the ToF method as a function of the time and spatial resolution of the ToF device and of the number of pile-up interactions per bunch crossing. We also propose a new observable with a potential to efficiently separate central diffractive signal from the harsh pile-up environment.

The possibility to measure jet-gap-jet final states in double-Pomeron-exchange events at the LHC is presented. In the context of the ATLAS experiment with additional forward physics detectors, cross sections for different experimental settings and gap definitions are estimated. This is done in the framework of the forward physics Monte Carlo interfaced with a perturbative QCD model that successfully reproduces standard jet-gap-jet cross sections at the Tevatron. The extrapolation to LHC energies follows from the Balitsky-Fadin-Kuraev-Lipatov dynamics, implemented in the model at next-to-leading logarithmic accuracy.

Hard processes in diffractive deep-inelastic scattering can be described by a factorisation into parton-level subprocesses and diffractive parton distributions. In this framework, cross sections for inclusive dijet production in diffractive deep-inelastic electron-proton scattering (DIS) are computed to next-to-next-to-leading order (NNLO) QCD accuracy and compared to a comprehensive selection of data. Predictions for the total cross sections, 39 single-differential and four double-differential distributions for six measurements at HERA by the H1 and ZEUS collaborations are calculated. In the studied kinematical range, the NNLO corrections are found to be sizeable and positive. The NNLO predictions typically exceed the data, while the kinematical shape of the data is described better at NNLO than at next-to-leading order (NLO). A significant reduction of the scale uncertainty is achieved in comparison to NLO predictions. Our results use the currently available NLO diffractive parton distributions, and the discrepancy in normalisation highlights the need for a consistent determination of these distributions at NNLO accuracy.

The development of Low-Gain Avalanche Diodes (LGADs) has made possible to manufacture silicon detectors with output signals that are about a factor of 10 larger than those of traditional sensors.This increased output brings many benefits such as the possibility of developing thin detectors with large enough signals, a good immunity towards low charge collection efficiency and it is key for excellent timing capabilities.In this paper, we report on the development of silicon sensors based on the LGAD design optimized to achieve excellent timing performance, the so-called Ultra-Fast Silicon Detectors (UFSDs).In particular, we demonstrate the possibility of obtaining ultra-fast silicon detectors with time resolution of less than 30 picosecond.

We present the novel implementation of a non-differentiable metric approximation and a corresponding loss-scheduling aimed at the search for new particles of unknown mass in high energy physics experiments. We call the loss-scheduling, based on the minimisation of a figure-of-merit related function typical of particle physics, a Punzi-loss function, and the neural network that utilises this loss function a Punzi-net. We show that the Punzi-net outperforms standard multivariate analysis techniques and generalises well to mass hypotheses for which it was not trained. This is achieved by training a single classifier that provides a coherent and optimal classification of all signal hypotheses over the whole search space. Our result constitutes a complementary approach to fully differentiable analyses in particle physics. We implemented this work using PyTorch and provide users full access to a public repository containing all the codes and a training example.

We present a scheme for the generation of central exclusive final states in the program. The implementation allows for the investigation of higher-order corrections to such exclusive processes as approximated by the initial-state parton shower in . To achieve this, the spin and colour decomposition of the initial-state shower has been worked out, in order to determine the probability that a partonic state generated from an inclusive sub-process followed by a series of initial-state parton splittings can be considered as an approximation of an exclusive colour- and spin-singlet process. We use our implementation to investigate the effects of parton showers on some examples of central exclusive processes, and we find sizeable effects on di-jet production, while the effects on e.g. central exclusive Higgs production are minor.

Recent experimental data on dijet cross sections in diffractive photoproduction at HERA collider are analysed with an emphasis on QCD factorisation breaking effects. The possible sources of the different conclusions of H1 and ZEUS collaborations are studied.

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.

Solar supergranulation remains a mystery in spite of decades of intensive studies. Most of the papers about supergranulation deal with its surface properties. Local helioseismology provides an opportunity to look below the surface and see the vertical structure of this convective structure. We present a concept of a (3+1)-D segmentation algorithm capable of recognising individual supergranules in a sequence of helioseismic 3-D flow maps. As an example, we applied this method to the state-of-the-art data and derived descriptive statistical properties of segmented supergranules -- typical size of 20--30 Mm, characteristic lifetime of 18.7 hours, and estimated depth of 15--20 Mm. We present preliminary results obtained on the topic of the three-dimensional structure and evolution of supergranulation. The method has a great potential in analysing the better data expected from the helioseismic inversions, which are being developed.

We present a solution of the DGLAP evolution equations, written in terms of Sudakov form factors to describe the branching and no-branching probabilities, using a parton branching Monte Carlo method.We demonstrate numerically that this method reproduces the semi-analytical solutions.We show how this method can be used to determine Transverse Momentum Dependent (TMD) parton distribution functions, in addition to the usual integrated parton distributions functions.We discuss numerical effects of the boundary of soft gluon resolution scale parameter on the resulting parton distribution functions.We show that a very good fit of the integrated TMDs to high precision HERA data can be obtained over a large range in x and Q 2 .

We present results from a Parton branching solution of the DGLAP equation at LO, NLO and NNLO which is is capable to extract both the collinear part and the transverse momentum de- pendent part of the parton densities. We demonstrate that within our method the collinear part of parton densities exactly corresponds to the semi-analytical solution of the DGLAP equation up to NNLO. Moreover, we study the transverse momentum of parton densities with respect to the parton flavour, ordering condition used during the evolution and the intrinsic momentum.

A new fit of diffractive parton distribution functions (DPDFs) to the HERA inclusive and di-jet data in diffractive deep-inelastic scattering (DDIS) at next-to-next-to-leading order accuracy (NNLO) is presented. The inclusion of the most comprehensive dijet cross section data, together with their NNLO predictions, provides enhanced constraints to the gluon component of the DPDF, which is of particular importance for diffractive PDFs. Compared to previous HERA fits, the presented fit includes the high-precision HERA II data of the H1 collaboration, which corresponds to a 40-fold increase in luminosity for inclusive data and six-fold increase for di-jet data. In addition to the inclusive DDIS data sample at the nominal centre-of-mass energy $\sqrt{s} = 319$ GeV, H1 data at $252$ GeV and $225$ GeV are included. The extracted DPDFs are compared to previous DPDF fits and are used to predict cross sections for a large number of available measurements and differential observables.

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.

The alignment of the Belle II tracking system, composed of a pixel and strip vertex detectors and central drift chamber, is described by approximately sixty thousand parameters; from local alignment of sensors and wires to relative global alignment of the sub-detectors. In the next data reprocessing, scheduled since Spring 2021, we aim to determine all parameters in a simultaneous fit by Millepede II, where recent developments allow to achieve a direct solution of the full problem in about one hour and make it practically feasible for regular detector alignment. The tracking detectors and the alignment technique are described and the alignment strategy is discussed in the context of studies on simulations and experience obtained from recorded data. Preliminary results and further refinements based on studies of real Belle II data are presented.

Recent experimental data on dijet cross sections in diffractive photoproduction at HERA collider are analysed with an emphasis on QCD factorisation breaking effects. The possible sources of the different conclusions of H1 and ZEUS collaborations are studied.

Cross sections for inclusive dijet production in diffractive deep-inelastic scattering are calculated for the first time in next-to-next-to-leading order (NNLO) accuracy. These cross sections are compared to several HERA measurements published by the H1 and ZEUS collaborations. We computed the total cross sections, 49 single-differential and five double-differential distributions for six HERA measurements. The NNLO corrections are found to be large and positive. The normalization of the resulting predictions typically exceeds the data, while the kinematical shape of the data is described better at NNLO than at next-to-leading order (NLO). Our results use the currently available NLO diffractive parton distributions, and the discrepancy in normalization highlights the need for a consistent determination of these distributions at NNLO accuracy.

Cross sections for inclusive dijet production in diffractive deep-inelastic scattering are calculated for the first time in next-to-next-to-leading order (NNLO) accuracy.These cross sections are compared to several HERA measurements published by the H1 and ZEUS collaborations.We computed the total cross sections, 49 single-differential and five double-differential distributions for six HERA measurements.The NNLO corrections are found to be large and positive.The normalization of the resulting predictions typically exceeds the data, while the kinematical shape of the data is described better at NNLO than at next-to-leading order (NLO).Our results use the currently available NLO diffractive parton distributions, and the discrepancy in normalization highlights the need for a consistent determination of these distributions at NNLO accuracy.

Cross sections for inclusive dijet production in diffractive deep-inelastic scattering are calculated for the first time in next-to-next-to-leading order (NNLO) accuracy. These cross sections are compared to several HERA measurements published by the H1 and ZEUS collaborations. We computed the total cross sections, 49 single-differential and five double-differential distributions for six HERA measurements. The NNLO corrections are found to be large and positive. The normalization of the resulting predictions typically exceeds the data, while the kinematical shape of the data is described better at NNLO than at next-to-leading order (NLO). Our results use the currently available NLO diffractive parton distributions, and the discrepancy in normalization highlights the need for a consistent determination of these distributions at NNLO accuracy.

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.

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.

The strong coupling constant $\alpha_S$ is determined from inclusive jet and dijet cross sections in neutral-current deep-inelastic ep scattering (DIS) measured at HERA by the H1 collaboration using next-to-next-to-leading order (NNLO) QCD predictions. The dependence of the NNLO predictions and of the resulting value of $\alpha_S(M_Z)$ at the $Z$-boson mass $M_Z$ are studied as a function of the choice of the renormalisation and factorisation scales. Using inclusive jet and dijet data together, the strong coupling constant is determined to be $\alpha_S (M_Z) =0.1157(20)_\text{exp}(29)_\text{th}$. Complementary, $\alpha_S$ is determined together with parton distribution functions of the proton (PDFs) from jet and inclusive DIS data measured by the H1 experiment. The value $\alpha_S(M_Z) = 0.1142(28)_\text{tot}$ obtained is consistent with the determination from jet data alone. The impact of the jet data on the PDFs is studied. The running of the strong coupling is tested at different values of the renormalisation scale and the results are found to be in agreement with expectations.

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.

We present measurements of differential jet cross sections over a wide range in transverse momenta from inclusive jets to multi-jet final states. Studies on the impact that these measurements have on the determination of the strong coupling $\alpha_S$ as well as on parton density functions are reported. Several LO and NLO MC generator matched with the parton shower were tested against the CMS data.

The cross sections fo the dijet production in diffractive deep-inelastic scattering are calculated for the first time in next-to-next-to-leading accuracy. The next-to-next-to-leading order corrections are found to be positive and sizable. The cross sections are compared to a considerable number of HERA measurements performed by H1 and ZEUS collaborations. We observe an overall good agreement of the next-to-leading order calculation with data, whereas the next-to-next-to-leading calculations tend to overestimate the data, probably due to poorly constrained gluon component of the diffractive parton distribution functions.

The LHC, Tevatron and HERA experimental results are reviewed with a focus on the low-$x$ kinematic domain where the BFKL dynamics, saturation effects and the gluon's transverse momentum play a role. In particular, the low-$x$ DIS region, the central exclusive production in the $\mathrm{p}\mathrm{p}$ and $\mathrm{p}\mathrm{A}$ collisions and the forward jets produced in $\mathrm{p}\mathrm{p}$ and $\mathrm{p}\mathrm{A}$ are discussed.

A new fit of diffractive parton distribution functions (DPDFs) to the HERA inclusive and jet data in diffractive deep-inelastic scattering (DDIS) at next-to-next-to-leading order accuracy (NNLO) is presented. The inclusion of the most comprehensive dijet cross section data, together with their NNLO predictions, provide enhanced constraints to the gluon component of the DPDF, which is of particular importance for diffractive PDFs. Compared to previous HERA fits, the presented fit includes the high-precision HERA II data of the H1 collaboration, which corresponds to a 40-fold increase in luminosity for inclusive data (six-fold increase for jet data). In addition to the inclusive sample at nominal centre-of-mass energy $\sqrt{s} = 319\,\text{GeV}$, inclusive H1 data at $252\,\text{GeV}$ and $225\,\text{GeV}$ are included. The extracted DPDFs are compared to previous DPDF fits and are used to predict cross sections for a large number of available measurements and differential observables.

The first measurement of lepton-jet momentum imbalance and azimuthal correlation in lepton-proton scattering at high momentum transfer is presented. These data, taken with the H1 detector at HERA, are corrected for detector effects using an unbinned machine learning algorithm OmniFold, which considers eight observables simultaneously in this first application. The unfolded cross sections are compared to calculations performed within the context of collinear or transverse-momentum-dependent (TMD) factorization in Quantum Chromodynamics (QCD) as well as Monte Carlo event generators. The measurement probes a wide range of QCD phenomena, including TMD parton distribution functions and their evolution with energy in so far unexplored kinematic regions.

We present the novel implementation of a non-differentiable metric approximation and a corresponding loss-scheduling aimed at the search for new particles of unknown mass in high energy physics experiments. We call the loss-scheduling, based on the minimisation of a figure-of-merit related function typical of particle physics, a Punzi-loss function, and the neural network that utilises this loss function a Punzi-net. We show that the Punzi-net outperforms standard multivariate analysis techniques and generalises well to mass hypotheses for which it was not trained. Our result constitutes a step towards fully differentiable analyses in particle physics. This work is implemented using PyTorch, and we provide users full access to a public repository containing all the codes.

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.

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.