We update the well-known BLNY fit to the low transverse momentum Drell-Yan lepton pair productions in hadronic collisions, by considering the constraints from the semi-inclusive hadron production in deep inelastic scattering (SIDIS) from HERMES and COMPASS experiments. We follow the Collins-Soper-Sterman (CSS) formalism with the b * -prescription. A universal non-perturbative form factor associated with the transverse momentum dependent quark distributions is found in the analysis with a new functional form different from that of BLNY. This releases the tension between the BLNY fit to the Drell-Yan data with the SIDIS data from HERMES/COMPASS in the CSS resummation formalism.

We present a novel integrator based on normalizing flows which can be used to improve the unweighting efficiency of Monte-Carlo event generators for collider physics simulations. In contrast to machine learning approaches based on surrogate models, our method generates the correct result even if the underlying neural networks are not optimally trained. We exemplify the new strategy using the example of Drell-Yan type processes at the LHC, both at leading and partially at nextto-leading order QCD. I. INTRODUCTIONNumerical simulation programs are a cornerstone of collider physics. They are used for the planning of future experiments, analysis of current measurements and, finally, reinterpretation based on an improved theoretical understanding of nature. They employ Monte Carlo methods to link theory and experiment by generating virtual collider events, which can then be analyzed like actual events observed in detectors [1,2].With more and more data available from the Large Hadron Collider (LHC) and the high-luminosity upgrade, the task of simulating collisions at high precision becomes a matter of concern for the high-energy physics community. The projected amount of computational resources falls far short of the needs for precision event generation [3]. Past studies of the scaling behavior of multi-jet simulations have shown that the compute needs are largely determined by the gradually decreasing unweighting efficiency [4,5]. Except for dedicated integrators, which require a detailed understanding of the physics problem at hand, adaptive Monte-Carlo methods seem the only choice to address this problem [6][7][8][9][10][11][12][13].With the rise of machine learning, this topic has seen a resurgence of interest recently. The possibility of using these techniques for integration in high-energy physics was first discussed in Ref. [14]. Boosted Decision Trees and Generative Adversarial Networks (GANs) were investigated as possible general purpose integrators. This new technique improved the integration of non-separable high dimensional functions, for which traditional algorithms failed. The first true physics application was presented in Ref. [15]. The authors used Dense Neural Networks (DNN) in order to perform a variable transformation and demonstrate that they obtain significantly larger efficiencies for three body decay integrals than standard approaches [16]. The major drawback of this method is its computational cost. Since the network acts as a variable transformation, its gradient must be computed for each inference point in order to determine the Jacobian. This becomes computationally heavy for high multiplicity processes.A completely orthogonal approach utilizes machine learning techniques directly for amplitude evaluation [17] or event generation [18][19][20][21][22][23][24]. Training data for these approaches are obtained from traditional event generation techniques, and hence the problem of efficient event generation still remains. In addition, one needs to ensure that the neural networks are trained well ...

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Parton shower algorithms are key components of theoretical predictions for high-energy collider physics. Work towards more accurate parton shower algorithms is thus pursued along many different avenues. The systematic treatment of subleading color corrections in parton shower algorithms is however technically challenging and remains elusive. In this article, we present an efficient and numerically stable algorithm to sample color configurations at fixed NC = 3, using the correct color factor including subleading corrections with a parton shower. The algorithm is implemented as stand-alone program that can be interfaced to the Pythia event generator. Preliminary comparisons to to LEP data are presented.