[Paper Review] Monte Carlo generators for top quark physics at the LHC

B. S. Acharya, F. Cavallari+3 more|Research Portal (King's College London)|Apr 25, 2008

Particle physics theoretical and experimental studies3 citations

TL;DR

This paper reviews Monte Carlo generators for top quark physics at the LHC, comparing HERWIG, PYTHIA, MC@NLO, POWHEG, and matrix-element generators like ALPGEN and SHERPA. It demonstrates that while parton shower generators agree with NLO calculations at low transverse momentum, only NLO-accurate generators like MC@NLO and POWHEG correctly describe large-$p_T$ $t\bar{t}$ production, with significant differences in $B$-hadron spectra between HERWIG and PYTHIA impacting top mass measurements.

ABSTRACT

We review the main features of Monte Carlo generators for top quark phenomenology and present some results for t-tbar and single-top signals and backgrounds at the LHC.

Motivation & Objective

• To evaluate the performance and reliability of Monte Carlo generators for top quark production and decay at the LHC.
• To assess the impact of different parton shower models, matrix-element corrections, and hadronization schemes on top quark observables.
• To quantify theoretical uncertainties in top quark mass measurements arising from underlying event, parton density, scale variations, and $b$-quark fragmentation.
• To compare multi-jet final state predictions across multiple generators (ALPGEN, SHERPA, MadGraph, etc.) for $Z+n$ jets and $W+$ jets backgrounds.
• To guide experimental analyses by identifying systematic effects in $m_t$ reconstruction from ISR, FSR, UE, and jet clustering algorithms.

Proposed method

• Utilizes leading-order (LO) and next-to-leading-order (NLO) matrix element calculations matched to parton showers in MC@NLO and POWHEG frameworks.
• Employs HERWIG and PYTHIA for parton showering with matrix-element corrections via the MLM prescription and CKKW–L matching for multi-jet final states.
• Applies the $k_T$-algorithm and cone jet algorithms to cluster jets, comparing results across generators and jet reconstruction methods.
• Fits hadronization models (cluster and string models) to $e^+e^-$ data from LEP and SLD to assess $B$-hadron spectrum predictions.
• Uses the Les Houches Accord to interface matrix-element generators (e.g., ALPGEN, SHERPA, MadEvent) with shower and hadronization programs.
• Evaluates theoretical uncertainties via scale variations, parton density functions, and underlying event model tuning in PYTHIA.

Experimental results

Research questions

• RQ1 How do different Monte Carlo generators (HERWIG, PYTHIA, MC@NLO, POWHEG) compare in predicting the transverse momentum distribution of $t\bar{t}$ pairs at the LHC?
• RQ2 To what extent do $b$-quark fragmentation models in HERWIG and PYTHIA affect the $B$-hadron spectrum in top quark decays and the resulting $m_t$ measurement uncertainty?
• RQ3 How do initial- and final-state radiation, underlying event, and jet clustering algorithms influence the reconstructed top quark mass in the lepton+jets channel?
• RQ4 How well do matrix-element generators (ALPGEN, SHERPA, MadEvent, etc.) agree on $Z+n$ jet cross sections at the LHC, and what does this imply for background modeling?
• RQ5 What is the impact of NLO corrections and matrix-element matching on the reliability of $t\bar{t}$ cross section predictions at large $p_T$?

Key findings

• At large $p_T^{(t\bar{t})}$, only NLO-accurate generators like MC@NLO and POWHEG correctly describe the $t\bar{t}$ transverse momentum spectrum, while HERWIG underestimates the rate.
• The NLO calculation peaks sharply at $p_T^{(t\bar{t})}=0$, whereas MC@NLO and HERWIG show broader distributions due to parton showering effects.
• The $B$-hadron spectrum in top decay differs significantly between HERWIG and PYTHIA after fitting to $e^+e^-$ data, with PYTHIA providing a good fit and HERWIG only marginally consistent.
• Theoretical uncertainty in $m_t$ from underlying event modeling in PYTHIA is estimated at $\Delta m_t \simeq \pm 0.5$ GeV, with scale and PDF uncertainties contributing further.
• Using the $k_T$ jet algorithm, $m_t$ reconstruction is sensitive to FSR, ISR, and UE, while with the cone algorithm, hadronization and FSR dominate the systematic shift.
• Matrix-element generators (ALPGEN, SHERPA, MadEvent, etc.) agree well on $Z+n$ jet cross sections for $n \leq 6$, indicating robustness in modeling $W/Z+$ jets backgrounds.