"13.a. Disp. tau trigger eff. vs $d_{0}$" of "Strategy and performance of the CMS long-lived particle trigger program in proton-proton collisions at $\sqrt{s}$ = 13.6 TeV"

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Authors: CMS Collaboration
Cited by: 0

Abstract

The L1T+HLT efficiency of the displaced $\tau_\mathrm{h}$ trigger, for simulated $\mathrm{p}\mathrm{p} \to \tilde{\tau}\tilde{\tau},(\tilde{\tau} \to \tau\tilde{\chi}^{0}_{1})$ events, where the $\tilde{\tau}$ has $c\tau = 10$ cm and each $\tau$ decays hadronically. The efficiency is shown for the displaced di-$\tau_\mathrm{h}$ trigger path (blue filled triangles), the previously available $\mathrm{p_{T}^{miss}}$-based paths (orange open circles), the previously available prompt di-$\tau_\mathrm{h}$ paths (purple open squares), the combination of the $\mathrm{p_{T}^{miss}}$-based and prompt di-$\tau_\mathrm{h}$ paths (gray open triangles), and the combination of the $\mathrm{p_{T}^{miss}}$-based, prompt di-$\tau_\mathrm{h}$, and displaced di-$\tau_\mathrm{h}$ paths (red filled circles), using 2022 data-taking conditions. The efficiency is evaluated with respect to generator-level quantities. Efficiency of the highest $\mathrm{p_{T}}$ $\tau$ lepton in the event as a function of the $d_{0}$ (left). Efficiency as a function of $\mathrm{p_{T}^{miss}}$ (right). A selection on the visible component of the generator-level $\tau$ lepton $\mathrm{p_{T}} >$ 30 GeV and its pseudorapidity $|{\eta}| < 2.1$ is applied. The lower panels show the ratio (improvement in \%) of the trigger efficiency given by the combination of the displaced di-$\tau_\mathrm{h}$ trigger path with the $\mathrm{p_{T}^{miss}}$-based and prompt di-$\tau_\mathrm{h}$ paths to that of the combination of the previously available $\mathrm{p_{T}^{miss}}$-based and prompt di-$\tau_\mathrm{h}$ paths.

Fields of Study & Subjects

Particle Detector Development and Performance Particle physics theoretical and experimental studies High-Energy Particle Collisions Research

Publication details

Journal / source
HEPData
Publication date
27 January 2026
Language
EN
Document type
Dataset