Recent anti-kT jet measurements using the archived ALEPH e+e- data at 91.2 GeV

ORAL

Abstract

We present a study of jet substructure using archived ALEPH e+e- data at √s = 91.2 GeV. Jets are reconstructed with the anti-kT algorithm over R ∈ [0.1, 1.2], providing a clean test of QCD without hadronic initial states. The fixed collision energy enables direct confrontation with perturbative predictions and establishes precision baselines. Beyond inclusive jet spectra and traditional substructure based on jet grooming, we examine further particle distributions inside jets, offering insight into parton shower dynamics. We introduce a new method based on Fourier decomposition of intra-jet particle azimuthal distribution, inspired by effects from flow in QGP in heavy-ion collisions. We study its dependence on jet energy, and jet axis choice. Detector effects are corrected with iterative unfolding, and systematic uncertainties are assessed. This serves as a set of novel substructure observables sensitive to jet evolution in vacuum. Looking forward, jet drift may also be explored in heavy-ion collisions to study possible flow effects. Collectively, these measurements broaden the understanding of jet substructure and inform future electron-ion collider studies.

Publication: arXiv:2108.04877

Presenters

  • Kuan L Lu

    Vanderbilt University

Authors

  • Kuan L Lu

    Vanderbilt University

  • Yi (Luna) Chen

    Vanderbilt University

  • Anthony Badea

    University of Chicago

  • Austin Baty

    University of Illinois at Chicago

  • Benjamin Nachman

    Lawrence Berkeley National Laboratory

  • Christopher McGinn

    Massachusetts Institute of Technology

  • Hannah Bossi

    Massachusetts Institute of Technology

  • Marcello Maggi

    INFN

  • Michael Peters

    Massachusetts Institute of Technology

  • Shuangyi Zhou

    Vanderbilt University

  • Tzu-An Sheng

    Massachusetts Institute of Technology

  • Gian Michelle Innocenti

    Massachusetts Institute of Technology

  • Yen-Jie Lee

    Massachusetts Institute of Technology

  • Jingyu Zhang

    Vanderbilt University

  • Yu-Chen Chen

    Massachusetts Institute of Technology

  • Kuan L Lu

    Vanderbilt University