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[Paper Review] Sensitivity of potential future $pp$ colliders to quark compositeness

L. Apanasevich, Suneet Upadhyay|arXiv (Cornell University)|Jul 26, 2013
High-Energy Particle Collisions Research3 citations
TL;DR

This study evaluates the sensitivity of future proton-proton colliders to quark compositeness using dijet angular distributions, focusing on normalized $χ_{jj} = e^{|y_1 - y_2|}$ distributions to detect deviations from QCD predictions. It finds that a 100 TeV collider could probe compositeness scales above 125 TeV, significantly improving sensitivity over current and intermediate-energy facilities.

ABSTRACT

A study is presented of the sensitivity of potential future $pp$ colliders to quark compositeness. The analysis uses normalized dijet angular distributions compared to expectations from leading-order contact interaction models.

Motivation & Objective

  • To assess the sensitivity of future $pp$ colliders to quark compositeness through dijet angular distributions.
  • To investigate how increasing center-of-mass energy and luminosity enhance the detection of contact interactions signaling quark substructure.
  • To compare the performance of different collider facilities—LHC, 14 TeV, 33 TeV, and 100 TeV—under varying energy and luminosity conditions.
  • To validate the use of $\chi_{jj} = e^{|y_1 - y_2|}$ as a sensitive observable for distinguishing contact interactions from QCD backgrounds.
  • To determine the optimal $m_{jj}$ threshold that maximizes sensitivity by balancing statistical power and signal distortion.

Proposed method

  • Simulated events using MadGraph at leading order for QCD and contact interaction processes.
  • Applied Pythia for parton showering and hadronization, and Delphes for detector response simulation.
  • Defined $\chi_{jj} = e^{|y_1 - y_2|}$ as the primary observable, with rapidities $y_1, y_2$ of the two leading jets.
  • Used the CLs technique to set upper limits on $\Lambda$ by comparing likelihood ratios between QCD-only and QCD-plus-contact-interaction hypotheses.
  • Incorporated theoretical uncertainties from scale variations in $\chi_{jj}$ shape using estimates from Ref. Chatrchyan et al. (2012).
  • Restricted analysis to the highest $m_{jj}$ bin to optimize sensitivity, with $|y| < 2.5$ and $\chi_{jj} < 16$.

Experimental results

Research questions

  • RQ1How does increasing center-of-mass energy affect the sensitivity of $pp$ colliders to quark compositeness via dijet angular distributions?
  • RQ2What is the optimal $m_{jj}$ threshold that maximizes sensitivity to contact interactions while preserving statistical power?
  • RQ3How do different contact interaction types—$\eta_{LL}, \eta_{RR}, \eta_{V-A}$—affect the $\chi_{jj}$ distribution and detectability?
  • RQ4To what extent can the $\chi_{jj}$ observable distinguish contact interactions from QCD backgrounds across different collider energies?
  • RQ5What is the projected upper limit on the compositeness scale $\Lambda$ for future $pp$ colliders with $\sqrt{s} = 100$ TeV and $\mathcal{L} = 3000$ fb$^{-1}$?

Key findings

  • A future $pp$ collider with $\sqrt{s} = 100$ TeV and $\mathcal{L} = 3000$ fb$^{-1}$ is expected to probe quark compositeness scales above $\Lambda = 125$ TeV.
  • The $\chi_{jj}$ distribution is strongly peaked at low values for contact interactions, contrasting with the mild increase seen in QCD, enabling effective discrimination.
  • The sensitivity to $\Lambda$ increases significantly with center-of-mass energy, with the 100 TeV facility outperforming lower-energy options by a large margin.
  • The study reproduces the sensitivity limits of Ref. Chatrchyan et al. (2012) using an approximate method that fixes nuisance parameters and applies the CLs technique.
  • Theoretical uncertainties in $\chi_{jj}$ shape were estimated from scale variations and incorporated into the analysis to improve robustness.
  • Using only the highest $m_{jj}$ bin for analysis results in only a modest loss of sensitivity, validating the approach for optimizing signal-to-background discrimination.

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This review was created by AI and reviewed by human editors.