Event generator tunes obtained from underlying event and multiparton scattering measurements (1512.00815v2)

Abstract: New sets of parameters ("tunes") for the underlying-event (UE) modeling of the PYTHIA8, PYTHIA6 and HERWIG++ Monte Carlo event generators are constructed using different parton distribution functions. Combined fits to CMS UE proton-proton (pp) data at sqrt(s) = 7 TeV and to UE proton-antiproton (p p-bar) data from the CDF experiment at lower sqrt(s), are used to study the UE models and constrain their parameters, providing thereby improved predictions for proton-proton collisions at 13 TeV. In addition, it is investigated whether the values of the parameters obtained from fits to UE observables are consistent with the values determined from fitting observables sensitive to double-parton scattering processes. Finally, comparisons of the UE tunes to "minimum bias" (MB) events, multijet, and Drell-Yan (q q-bar to Z / gamma* to lepton-antilepton + jets) observables at 7 and 8 TeV are presented, as well as predictions for MB and UE observables at 13 TeV.

• The paper introduces a novel tuning approach for UE modeling in MC generators through combined fits of CMS and CDF datasets.
• It identifies energy-dependent MPI parameters, employing power-law scaling to improve simulation accuracy for 7–13 TeV collisions.
• The research verifies tune consistency across UE and DPS, enhancing predictions for multijet and Drell–Yan processes.

Overview of Event Generator Tunes and Underlying Event Modeling

The paper "Event generator tunes obtained from underlying event and multiparton scattering measurements," presents an in-depth analysis of parameter tuning within Monte Carlo (MC) event generators, particularly focusing on the pythia8, pythia6, and herwig++ platforms. These generators are pivotal in simulating hadron-hadron collisions in high-energy physics and are essential tools for understanding the complex processes occurring in particle collisions at facilities such as the Large Hadron Collider (LHC).

Key Contributions and Methodology

The paper primarily addresses the construction of new parameter sets, or "tunes," for underlying-event (UE) modeling. The term "underlying event" encompasses aspects of particle collisions not directly associated with the hard scatter but includes beam-beam remnants, multiple-parton interactions (MPI), and associated initial- and final-state radiation.

Data and Parameters

• Datasets Used: The tuning process involved proton-proton (pp) collision data at $\sqrt{s} = 7\TeV$ from the Compact Muon Solenoid (CMS) experiment and proton-antiproton ($\Pp\PAp$) data from the CDF experiment at the Tevatron with lower energies. These data sets are valuable for evaluating and constraining the UE models to enhance predictive accuracy for collisions at energies up to 13\TeV.
• Parton Distribution Functions (PDFs): Multiple PDFs, including CTEQ6L1, HERAPDF, and NNPDF2.3LO, were employed to diversify the tuning and accommodate differences in partonic structure perceptions within nucleons.

Tuning Approach

• The tuning of parameters was carried out via combined fits to UE observables across these datasets, alongside comparisons to "minimum bias" (MB) events and processes like Drell-Yan, where consistency across different observables was evaluated.

Results and Observations

1. Energy Dependence: The paper highlights that parameters, such as the MPI cutoff p_{\rm T0}—a regulator for divergence in the QCD cross section—are influenced by the collision energy. The functional dependency is modeled as a power law, where exponents were tuned for each generator.
2. Parameter Consistency: A significant aspect of this paper was determining if parameter values optimized for UE also align with those from double-parton scattering (DPS) processes. Achieving concordance suggests robustness in the modeling across varying collision complexities.
3. Comparison with Observables: Predictions for multijet and Drell-Yan processes facilitated in verifying the tune's efficacy, where a detailed analysis indicated improved predictive performance, especially in the CMS 13\TeV dataset.
4. Numerical Robustness: Numerical stability and fidelity of these new tunes are underscored through "eigentunes" providing a systematic assessment of parameter uncertainties.

Implications

The implications of these optimized tunes are profound. Firstly, they enhance the accuracy of theoretical predictions concerning UE and DPS events, which are critical for precise background characterization in new physics searches at the LHC. Secondly, these findings contribute to a refined understanding of hadronic interaction modeling, which is essential for the broader field of high-energy particle physics.

Future Perspectives

Given the continual upgrades and increasing energy levels at the LHC, future developments could explore adaptive tuning procedures that respond dynamically to new data, potentially incorporating machine learning algorithms for real-time parameter optimization. Additionally, extending these methodologies to encompass next-generation detector data could bridge existing gaps in multiscale modeling of hadronic events.

In conclusion, the paper provides an essential advancement in the precision modeling of underlying events, demonstrating significant progress in the fine-tuning of parameters within Monte Carlo simulations. This work sets a foundation for future explorations into highly detailed and accurate particle physics simulations, facilitating deeper insights into the fundamental workings of our universe.