Measurements of hadronic final states in e^{+}e^{-} e + e − collisions at centre-of-mass (CM) energies below the Z peak can notably extend the FCC-ee physics reach in terms of precision quantum chromodynamics (QCD) studies. Hadronic final states can be studied over a range of hadronic energies \sqrt{s_\mathrm{had}} ≈ 20-80\,\mathrm{GeV} s h a d ≈ 20 − 80 G e V by exploiting events with hard initial- and final-state QED radiation (ISR/FSR) during the high-luminosity Z-pole run, as well as in dedicated short (about one month long) e^{+}e^{-} e + e − runs at CM energies \sqrt{s} ≈ 40\,\mathrm{GeV} s ≈ 40 G e V and 60\,\mathrm{GeV} 60 G e V . Using realistic estimates and fast detector simulations, we show that data samples of about 10^{9} 10 9 hadronic events can be collected at the FCC-ee at each of the low-CM-energy points. Such datasets can be exploited in a variety of precision QCD measurements, including studies of light-, heavy-quark and gluon jet properties, hadronic event shapes, fragmentation functions, and nonperturbative dynamics. This will offer valuable insights into strong interaction physics, complementing data from nominal FCC-ee runs at higher center-of-mass energies, \sqrt{s} ≈ 91, 160, 240, s ≈ 91 , 160 , 240 , and 365\,\mathrm{GeV} 365 G e V .

This document summarizes the contributions to the 5th DPHEP workshop March 5-6, 2026, CERN, and reflects the advancements since 2024, as well as future milestones and tendencies. Impressive progress in HEP data preservation is observed. Legacy data revival was showcased through successful reanalysis of archived data using contemporary methods, demonstrating the long-term scientific value of preservation. Sustainability challenges were noted, emphasizing the need for long-term funding and institutional support to maintain data preservation infrastructure, particularly for legacy experiments transitioning to archival modes. Innovative transverse projects display constant progress towards common technologies for a robust and transferrable DP. In particular, there is a clear shift toward automation, with increasing use of AI and machine learning for data curation, metadata extraction, and workflow optimization. Open science momentum is growing, with wider adoption of FAIR principles and open data policies, and experiments committing to public releases.

We present a revived version of CERNLIB, the basis for software ecosystems of most of the pre-LHC HEP experiments. The efforts to consolidate CERNLIB are part of the activities of the Data Preservation for High Energy Physics collaboration to preserve data and software of the past HEP experiments. The presented version is based on CERNLIB version 2006 with numerous patches made for compatibility with modern compilers and operating systems. The code is available in the CERN GitLab repository with all the development history starting from the early 1990s. The updates also include a re-implementation of the build system in CMake to ensure CERNLIB compliance with the current best practices and to increase the chances of preserving the code in a compilable state for the decades to come. The revived CERNLIB project also includes updated documentation, which we believe is a cornerstone for any preserved software depending on it.

Data preservation significantly increases the scientific output of high-energy physics experiments during and after data acquisition. For new and ongoing experiments, the careful consideration of long-term data preservation in the experimental design contributes to improving computational efficiency and strengthening the scientific activity in HEP through Open Science methodologies. This contribution is based on 15 years of experience of the DPHEP collaboration in the field of data preservation and focuses on aspects relevant for the strategic programming of particle physics in Europe: the preparation of future programs using data sets preserved from previous similar experiments (e.g. HERA for EIC), and the use of LHC data long after the end of the data taking. The lessons learned from past collider experiments and recent developments open the way to a number of recommendations for the full exploitation of the investments made in large HEP experiments.

We introduce our novel Bayesian parton density determination code, artonensity.jl. The motivation for this new code, the framework, and its validation are described. As we show, artonensity.jl provides both a flexible environment for the determination of parton densities and a wealth of information concerning the knowledge update provided by the analyzed dataset. Published by the American Physical Society 2024

We introduce our novel Bayesian parton density determination code, PartonDensity.jl. The motivation for this new code, the framework and its validation are described. As we show, PartonDensity.jl provides both a flexible environment for the determination of parton densities and a wealth of information concerning the knowledge update provided by the analyzed data set.

The optimisation (tuning) of the free parameters of Monte Carlo event generators by comparing their predictions with data is important since the simulations are used to calculate experimental efficiency and acceptance corrections, or provide predictions for signatures of hypothetical new processes in experiments. We present a tuning procedure that is based on Bayesian reasoning and that allows for a proper statistical interpretation of the results. The parameter space is fully explored using Markov Chain Monte Carlo. We apply the tuning procedure to the Herwig7 event generator with both the cluster and the string hadronization models and a large set of measurements from hadronic Z-boson decays produced at LEP in $e^{+}e^{-}$ collisions. Furthermore, we introduce a coherent propagation of uncertainties from the realm of parameters to the realm of observables and we show the effects of including experimental correlations of the measurements. To allow comparison with the approaches of other groups, we repeat the tuning considering weights for individual measurements.

The installation and maintenance of scientific software for research in experimental, phenomenological, and theoretical High Energy Physics (HEP) requires a considerable amount of time and expertise. While many tools are available to make the task of installation and maintenance much easier, many of these tools require maintenance on their own, have little documentation and very few are used outside of HEP community. For the installation and maintenance of the software, we rely on the well tested, extensively documented, and reliable stack of software management tools with the RPM Package Manager (RPM) at its core. The precompiled HEP software packages can be deployed easily and without detailed Linux system knowledge and are kept up-to-date through the regular system update process. The precompiled packages were tested on multiple installations of openSUSE, RHEL clones, and Fedora. As the RPM infrastructure is adopted by many Linux distributions, the approach can be used on more systems. In this contribution, we discuss our approach to software deployment in detail, present the software repositories for multiple RPM-based Linux distributions to a wider public and call for a collaboration for all the interested parties.

We present a method of extracting the strong coupling at (NLO)-L-3 precision in QCD using a combination of O(alpha(3)(S)) perturbative calculations with estimations of the O(alpha(4)(S)) corrections from data. We apply the procedure to a set of event shape averages measured at the LEP, PETRA, PEP, and TRISTAN colliders. We account for non-perturbative effects using both modern Monte Carlo event generators as well as analytic models. Our results show that the precision of alpha(S) extraction cannot be significantly improved solely with higher-order perturbative QCD predictions, but requires also a significant refinement of the understanding and modeling of the hadronization process.

We study the use of deep learning techniques to reconstruct the kinematics of the neutral current deep inelastic scattering (DIS) process in electron-proton collisions. In particular, we use simulated data from the ZEUS experiment at the HERA accelerator facility, and train deep neural networks to reconstruct the kinematic variables $Q^2$ and $x$. Our approach is based on the information used in the classical construction methods, the measurements of the scattered lepton, and the hadronic final state in the detector, but is enhanced through correlations and patterns revealed with the simulated data sets. We show that, with the appropriate selection of a training set, the neural networks sufficiently surpass all classical reconstruction methods on most of the kinematic range considered. Rapid access to large samples of simulated data and the ability of neural networks to effectively extract information from large data sets, both suggest that deep learning techniques to reconstruct DIS kinematics can serve as a rigorous method to combine and outperform the classical reconstruction methods.

We present the HepMC3 library designed to perform manipulations with event records of High Energy Physics Monte Carlo Event Generators (MCEGs). The library is a natural successor of HepMC and HepMC2 libraries used in the present and in the past. HepMC3 supports all functionality of previous versions and significantly extends them. In comparison to the previous versions, the default event record has been simplified, while an option to add arbitrary information to the event record has been implemented. Particles and vertices are stored separately in an ordered graph structure, reflecting the evolution of a physics event and enabling usage of sophisticated algorithms for event record analysis. The I/O functionality of the library has been extended to support common input and output formats of HEP MCEGs, including formats used in Fortran HEP MCEGs, formats used in HepMC2 library and ROOT. The functionality of the library allows the user to implement a customised input or output format. The library is already supported by popular modern MCEGs (e.g. Sherpa and Pythia8) and can replace the older HepMC versions in many others.

In this proceedings we discuss a prescription to extract the QCD strong coupling constant at N^{3}LO N3LO precision in perturbative QCD using a combination of {{O}}(\alpha_{S}^{3}) O(αS3) calculations in pQCD and estimations of the {{O}}(\alpha_{S}^{4}) O(αS4) corrections from the data. The method is applied to a set of event shape averages measured in experiments at the LEP, PETRA, PEP and TRISTAN colliders. In our analysis we account for hadronization effects with models from modern Monte Carlo event generators and analytic hadronization models. We conclude that the precision of the \alpha_{S} αS extraction cannot be improved significantly only with pQCD predictions of higher orders, and further progress in these studies requires a significant advances in the studies and modeling of hadronization process.

This paper presents a search for direct top squark pair production in events with missing transverse momentum plus either a pair of jets consistent with Standard Model Higgs boson decay into b-quarks or a same-flavour opposite-sign dilepton pair with an invariant mass consistent with a Z boson. The analysis is performed using the proton–proton collision data at \n$$\\sqrt{s}=13$$\n\n TeV collected with the ATLAS detector during the LHC Run-2, corresponding to an integrated luminosity of 139 fb\n$$^{-1}$$\n\n. No excess is observed in the data above the Standard Model predictions. The results are interpreted in simplified models featuring direct production of pairs of either the lighter top squark (\n$$\\tilde{t}_1$$\n\n) or the heavier top squark (\n$$\\tilde{t}_2$$\n\n), excluding at 95% confidence level \n$$\\tilde{t}_1$$\n\n and \n$$\\tilde{t}_2$$\n\n masses up to about 1220 and 875 GeV, respectively.

Abstract We present state-of-the-art extractions of the strong coupling based on N3LO+NNLL accurate predictions for the two-jet rate in the Durham clustering algorithm at e + e − collisions, as well as a simultaneous fit of the two- and three-jet rates taking into account correlations between the two observables. The fits are performed on a large range of data sets collected at the LEP and PETRA colliders, with energies spanning from 35 GeV to 207 GeV. Owing to the high accuracy of the predictions used, the perturbative uncertainty is considerably smaller than that due to hadronization. Our best determination at the Z mass is α s (M Z) = 0.11881 ± 0.00063(exp.) ± 0.00101(hadr.) ± 0.00045(ren.) ± 0.00034(res.), which is in agreement with the latest world average and has a comparable total uncertainty.

A summary of the main points of discussion raised during the talks and their follow-up questions,as well as in the round table of the last day of the $α_s$ (2019) workshop, is presented. The discussions not only focused on particular issues affecting each one of the individual $α_s$ extractions, but also on the current PDG categorization of $α_s$ measurements and on the methods used to average them into a single $α_s$ ($m_Z$) value. Most of the listed points are open and sources of potential controversies, which we highlight here as one might expect that ongoing progress in the field will lead to their clarification and resolution.

We present a computation of energy-energy correlation in $e+e−$ annihilation at next-to-next-to-leading order accuracy in perturbative QCD matched with the next-to-next-to-leading logarithmic resummed calculation for the back-to-back limit. Using these predictions and state-of-the-art Monte Carlo tools to model hadronization corrections, we perform an extraction of the strong coupling from available data sets. We also show next-to-next-to-leading order results for soft-drop thrust, an observable specifically constructed to have reduced hadronization corrections. We study the impact of the soft drop on the convergence of the perturbative prediction and find that generally grooming improves perturbative stability. This improved stability, together with the reduced sensitivity to non-perturbative corrections makes soft-drop thrust a promising observable for precision measurements of the strong coupling at lepton colliders.

We present a comparison of the computation of energy-energy correlations and Durham algorithm jet rates in $e+e−$ collisions at next-to-next-to-leading logarithmic accuracy matched with the $O$ $(α3s)$ perturbative prediction to LEP, PEP, PETRA, SLC, and TRISTAN data. With these predictions we perform extractions of the strong coupling constant taking into account non-perturbative effects modelled with modern Monte Carlo event generators that simulate NLO QCD corrections.

We study jet production in e+e− annihilation to hadrons with data recorded by the OPAL experiment at LEP at centre-of-mass energies between 91 GeV and 209 GeV. The jet production rates were measured with Durham and for the first time with the anti-kt and SISCone jet clustering algorithms. We compare the data with predictions by modern Monte Carlo event generators.

We present a comparison of the computation of energy–energy correlation in \(e^{+}e^{-}\) collisions in the back-to-back region at next-to-next-to-leading logarithmic accuracy matched with the next-to-next-to-leading order perturbative prediction to LEP, PEP, PETRA, SLC and TRISTAN data. With these predictions we perform an extraction of the strong coupling constant taking into account non-perturbative effects modelled with Monte Carlo event generators. The final result at NNLO+NNLL precision is \(\alpha _{S}(M_{Z})= 0.11750\pm 0.00018 {\text{( } exp.)}\pm 0.00102{\text{( }hadr.)}\pm 0.00257{\text{( }ren.)}\pm 0.00078{\text{( }res.)}\).