CMS-PAS-EXO-22-015

CMS-PAS-EXO-22-015
Search for new physics with emerging jets in proton-proton collisions at $\sqrt{s} =$ 13 TeV
CMS Collaboration
20 February 2024

Abstract: A search for emerging jets produced in proton-proton collisions at a center-of-mass energy of 13 TeV is performed using data collected by the CMS experiment corresponding to an integrated luminosity of 138 fb $^{-1}$ . This search examines a hypothetical dark quantum chromodynamics (QCD) sector that couples to the standard model (SM) through a scalar mediator. The scalar mediator decays into an SM quark and a dark sector quark. As the dark sector quark showers and hadronizes, it produces long-lived dark mesons that subsequently decay into SM particles, resulting in a jet, known as an emerging jet, with multiple displaced vertices. This search looks for pair production of the scalar mediator at the LHC, which yields events with two SM jets and two emerging jets at leading order. The results are interpreted using two dark sector models with different flavor structures, and exclude mediator masses up to 1950 (1800) GeV for an unflavored (flavor-aligned) dark QCD model.
Links: CDS record (PDF) ; CADI line (restricted) ; These preliminary results are superseded in this paper, Submitted to JHEP. The superseded preliminary plots can be found here.

Figures & Tables	Summary	References	CMS Publications

Figures
png pdf	Figure 1: Feynman diagrams for pair production of dark mediator particles via gluon-gluon fusion (left) and quark-antiquark annihilation (right), with mediators decaying to an SM quark and a dark quark.
png pdf	Figure 1-a: Feynman diagrams for pair production of dark mediator particles via gluon-gluon fusion (left) and quark-antiquark annihilation (right), with mediators decaying to an SM quark and a dark quark.
png pdf	Figure 1-b: Feynman diagrams for pair production of dark mediator particles via gluon-gluon fusion (left) and quark-antiquark annihilation (right), with mediators decaying to an SM quark and a dark quark.
png pdf	Figure 2: Distributions of the jet variables $< d_{xy} >$ (left) and $\alpha_\text{3D}$ with $D_{N}^\text{max}=$ 4 (right) used for the model-agnostic EJ tagging that targets the unflavored dark sector models are shown for data (points), SM multijet events from simulation (gray line), and signal jets in simulation (colored lines). The distributions are normalized to unit area.
png pdf	Figure 2-a: Distributions of the jet variables $< d_{xy} >$ (left) and $\alpha_\text{3D}$ with $D_{N}^\text{max}=$ 4 (right) used for the model-agnostic EJ tagging that targets the unflavored dark sector models are shown for data (points), SM multijet events from simulation (gray line), and signal jets in simulation (colored lines). The distributions are normalized to unit area.
png pdf	Figure 2-b: Distributions of the jet variables $< d_{xy} >$ (left) and $\alpha_\text{3D}$ with $D_{N}^\text{max}=$ 4 (right) used for the model-agnostic EJ tagging that targets the unflavored dark sector models are shown for data (points), SM multijet events from simulation (gray line), and signal jets in simulation (colored lines). The distributions are normalized to unit area.
png pdf	Figure 3: Distributions of the jet variables used for the model-agnostic EJ tagging targeting flavor-aligned dark sector models for jets obtained in data (points), SM multijet events from simulation (gray line), and signal jets in simulation (colored lines). The distribution of the number of tracks with $d_{xy} >$ 10 $^{-2.2}$ cm (jet girth) is shown on the left (right). The distributions are normalized to unit area.
png pdf	Figure 3-a: Distributions of the jet variables used for the model-agnostic EJ tagging targeting flavor-aligned dark sector models for jets obtained in data (points), SM multijet events from simulation (gray line), and signal jets in simulation (colored lines). The distribution of the number of tracks with $d_{xy} >$ 10 $^{-2.2}$ cm (jet girth) is shown on the left (right). The distributions are normalized to unit area.
png pdf	Figure 3-b: Distributions of the jet variables used for the model-agnostic EJ tagging targeting flavor-aligned dark sector models for jets obtained in data (points), SM multijet events from simulation (gray line), and signal jets in simulation (colored lines). The distribution of the number of tracks with $d_{xy} >$ 10 $^{-2.2}$ cm (jet girth) is shown on the left (right). The distributions are normalized to unit area.
png pdf	Figure 4: Distributions of the GNN output score for jets obtained from SM multijet simulation events (dark gray line), signal jets from the unflavored model (colored lines, left), and signal jets from the flavor-aligned models (colored lines, right). Results of the uGNN (aGNN) are shown on the left (right). The performance of the GNN varies only slightly over a wide range of the c $\tau$ of the dark mesons. The distributions are normalized to unit area.
png pdf	Figure 4-a: Distributions of the GNN output score for jets obtained from SM multijet simulation events (dark gray line), signal jets from the unflavored model (colored lines, left), and signal jets from the flavor-aligned models (colored lines, right). Results of the uGNN (aGNN) are shown on the left (right). The performance of the GNN varies only slightly over a wide range of the c $\tau$ of the dark mesons. The distributions are normalized to unit area.
png pdf	Figure 4-b: Distributions of the GNN output score for jets obtained from SM multijet simulation events (dark gray line), signal jets from the unflavored model (colored lines, left), and signal jets from the flavor-aligned models (colored lines, right). Results of the uGNN (aGNN) are shown on the left (right). The performance of the GNN varies only slightly over a wide range of the c $\tau$ of the dark mesons. The distributions are normalized to unit area.
png pdf	Figure 5: Template fit of the DEEPJET discriminator used to determine the b jet fraction of the non-EJ tagged jets in data events that pass the 1-EJ selection of the ``u-set validation'' criteria.
png pdf	Figure 6: The EJ tagger misidentification probability for b quark jets (red, orange) and light jets (blue) as a function of jet $p_{\mathrm{T}}$ for the model-agnostic tagger ``u-tag 1'' (left) and the ML-based tagger ``uGNN tag 1'' (right)---as defined in Tables 3 and 5---evaluated using data (red, dark blue) and generator-level flavor information from simulated samples (orange, light blue) in events containing a high- $p_{\mathrm{T}}$ photon. The lower panel shows the difference between the mistag rate calculated in MC to the mistag rate in data divided by the uncertainty measured in data.
png pdf	Figure 6-a: The EJ tagger misidentification probability for b quark jets (red, orange) and light jets (blue) as a function of jet $p_{\mathrm{T}}$ for the model-agnostic tagger ``u-tag 1'' (left) and the ML-based tagger ``uGNN tag 1'' (right)---as defined in Tables 3 and 5---evaluated using data (red, dark blue) and generator-level flavor information from simulated samples (orange, light blue) in events containing a high- $p_{\mathrm{T}}$ photon. The lower panel shows the difference between the mistag rate calculated in MC to the mistag rate in data divided by the uncertainty measured in data.
png pdf	Figure 6-b: The EJ tagger misidentification probability for b quark jets (red, orange) and light jets (blue) as a function of jet $p_{\mathrm{T}}$ for the model-agnostic tagger ``u-tag 1'' (left) and the ML-based tagger ``uGNN tag 1'' (right)---as defined in Tables 3 and 5---evaluated using data (red, dark blue) and generator-level flavor information from simulated samples (orange, light blue) in events containing a high- $p_{\mathrm{T}}$ photon. The lower panel shows the difference between the mistag rate calculated in MC to the mistag rate in data divided by the uncertainty measured in data.
png pdf	Figure 7: The 95% CL upper limits on the production cross section for various signal models in the unflavored scenario (upper plots) and the flavor-aligned scenario (lower plots) with $m_{\pi_\text{dark}}=$ 10 GeV using the model-agnostic (GNN) EJ tagging method on the left (right). The red curve is the expected exclusion limit, with the band representing its 68% CL variation. The black curve is the observed limit. The dark blue dotted curves in the upper plots are the expected and observed limits previously obtained by CMS [20].
png pdf	Figure 7-a: The 95% CL upper limits on the production cross section for various signal models in the unflavored scenario (upper plots) and the flavor-aligned scenario (lower plots) with $m_{\pi_\text{dark}}=$ 10 GeV using the model-agnostic (GNN) EJ tagging method on the left (right). The red curve is the expected exclusion limit, with the band representing its 68% CL variation. The black curve is the observed limit. The dark blue dotted curves in the upper plots are the expected and observed limits previously obtained by CMS [20].
png pdf	Figure 7-b: The 95% CL upper limits on the production cross section for various signal models in the unflavored scenario (upper plots) and the flavor-aligned scenario (lower plots) with $m_{\pi_\text{dark}}=$ 10 GeV using the model-agnostic (GNN) EJ tagging method on the left (right). The red curve is the expected exclusion limit, with the band representing its 68% CL variation. The black curve is the observed limit. The dark blue dotted curves in the upper plots are the expected and observed limits previously obtained by CMS [20].
png pdf	Figure 7-c: The 95% CL upper limits on the production cross section for various signal models in the unflavored scenario (upper plots) and the flavor-aligned scenario (lower plots) with $m_{\pi_\text{dark}}=$ 10 GeV using the model-agnostic (GNN) EJ tagging method on the left (right). The red curve is the expected exclusion limit, with the band representing its 68% CL variation. The black curve is the observed limit. The dark blue dotted curves in the upper plots are the expected and observed limits previously obtained by CMS [20].
png pdf	Figure 7-d: The 95% CL upper limits on the production cross section for various signal models in the unflavored scenario (upper plots) and the flavor-aligned scenario (lower plots) with $m_{\pi_\text{dark}}=$ 10 GeV using the model-agnostic (GNN) EJ tagging method on the left (right). The red curve is the expected exclusion limit, with the band representing its 68% CL variation. The black curve is the observed limit. The dark blue dotted curves in the upper plots are the expected and observed limits previously obtained by CMS [20].
png pdf	Figure 8: The 95% CL upper limits on the production cross section for various signal models in the unflavored scenario (upper plots) and the flavor-aligned scenario (lower plots) with $m_{\pi_\text{dark}}=$ 20 GeV using the model-agnostic (GNN) emerging jet tagging method on the left (right). The red curve is the expected exclusion limit, with the band representing its 68% CL variation. The black curve is the observed limit.
png pdf	Figure 8-a: The 95% CL upper limits on the production cross section for various signal models in the unflavored scenario (upper plots) and the flavor-aligned scenario (lower plots) with $m_{\pi_\text{dark}}=$ 20 GeV using the model-agnostic (GNN) emerging jet tagging method on the left (right). The red curve is the expected exclusion limit, with the band representing its 68% CL variation. The black curve is the observed limit.
png pdf	Figure 8-b: The 95% CL upper limits on the production cross section for various signal models in the unflavored scenario (upper plots) and the flavor-aligned scenario (lower plots) with $m_{\pi_\text{dark}}=$ 20 GeV using the model-agnostic (GNN) emerging jet tagging method on the left (right). The red curve is the expected exclusion limit, with the band representing its 68% CL variation. The black curve is the observed limit.
png pdf	Figure 8-c: The 95% CL upper limits on the production cross section for various signal models in the unflavored scenario (upper plots) and the flavor-aligned scenario (lower plots) with $m_{\pi_\text{dark}}=$ 20 GeV using the model-agnostic (GNN) emerging jet tagging method on the left (right). The red curve is the expected exclusion limit, with the band representing its 68% CL variation. The black curve is the observed limit.
png pdf	Figure 8-d: The 95% CL upper limits on the production cross section for various signal models in the unflavored scenario (upper plots) and the flavor-aligned scenario (lower plots) with $m_{\pi_\text{dark}}=$ 20 GeV using the model-agnostic (GNN) emerging jet tagging method on the left (right). The red curve is the expected exclusion limit, with the band representing its 68% CL variation. The black curve is the observed limit.
png pdf	Figure 9: The 95% CL upper limits on the production cross section for various signal models in the flavor-aligned scenario with $m_{\pi_\text{dark}}=$ 6 GeV using the model-agnostic (GNN) EJ tagging method on the left (right). The red curve is the expected exclusion limit, with the band representing its 68% CL variation.
png pdf	Figure 9-a: The 95% CL upper limits on the production cross section for various signal models in the flavor-aligned scenario with $m_{\pi_\text{dark}}=$ 6 GeV using the model-agnostic (GNN) EJ tagging method on the left (right). The red curve is the expected exclusion limit, with the band representing its 68% CL variation.
png pdf	Figure 9-b: The 95% CL upper limits on the production cross section for various signal models in the flavor-aligned scenario with $m_{\pi_\text{dark}}=$ 6 GeV using the model-agnostic (GNN) EJ tagging method on the left (right). The red curve is the expected exclusion limit, with the band representing its 68% CL variation.

Tables
png pdf	Table 1: Model parameters for the unflavored model.
png pdf	Table 2: Parameters used for the flavor-aligned model. In order to probe a range of lifetimes, the value of $\kappa_0$ is tuned to give the desired $c\tau_{\pi_\text{dark}}^{\text{max}}$ (the last five columns of the table). In addition to these tuned values of $\kappa_0$ , samples were made with $\kappa_0=$ 1, which have varying $c\tau_{\pi_\text{dark}}^{\text{max}}$ values depending on the other model parameters and therefore are listed in the column marked as $\text{---}$ . In a few cases, the model with $\kappa_0 =$ 1 corresponds to one of the tuned values, so the value in the third column is omitted ( $\text{---}$ ).
png pdf	Table 3: Emerging jet selection criteria for the model-agnostic analysis designed for the unflavored scenario. The validation tag is described in Section 5.4. The symbols in parentheses indicate a minimum $( > )$ or maximum $( < )$ requirement.
png pdf	Table 4: Emerging jet selection criteria for the model-agnostic analysis designed for the flavor-aligned scenario. The validation tag is described in Section 5.4. The symbols in parentheses indicate a minimum $( > )$ or maximum $( < )$ requirement.
png pdf	Table 5: The GNN score range used to identify a jet as an EJ. The uGNN (aGNN) tag indicates that the tagger uses the output score of the GNN trained on the unflavored (flavor-aligned) simulated signal samples. The validation tags are described in Section 5.4.
png pdf	Table 6: Event selection criteria used for the analysis. The validation selection criteria are described in Section 5.4.
png pdf	Table 7: The observed yield of events in data satisfying the validation selection criteria with at least two jets passing the corresponding validation tag, and the estimation based on the misidentification rate calculated using validation events with exactly one jet passing the validation tagger scaled by the factor given in Eq. (4). The statistical and systematic uncertainties are reported for the estimated yields.
png pdf	Table 8: Mean and standard deviation (std.) of the relative uncertainty calculated across the surveyed signal samples, by source, in percent.
png pdf	Table 9: The estimated number of events from the background prediction based on control samples in data and the observed event yields. Statistical and systematic uncertainties in the estimated background are provided.

Summary

A search for emerging jet signatures arising from a strongly interacting dark sector produced in hadron collisions has been presented, using data corresponding to a total integrated luminosity of 138 fb

$^{-1}$ at

$\sqrt{s}=$ 13 TeV. The signal model contains a family of dark quarks that couples to the standard model (SM) quarks via a scalar mediator

$\text{X}_\text{dark}$ . Dark pions (

$\pi_\text{dark}$ ) with a significant lifetime (

$c\tau_{\pi_\text{dark}}$ ) are produced by the hadronization of the dark quarks and then decay to SM particles at vertices displaced from the original interaction point. As the scalar mediator is assumed to be produced in pairs, with each decaying to a SM quark and a dark quark, the signature of this process is two SM jets plus two jets of particles with the constituents emerging from displaced vertices. Both unflavored and flavor-aligned couplings between the SM quarks and the dark quarks are examined in the search. The selection criteria were optimized for each class of couplings and as a function of the mediator particle mass, the dark pion mass, and the dark pion lifetime, using both a traditional cut-based selection and a graph neural network. The observed 95% confidence level exclusion limits agree with the expected limits. For the unflavored model, dark mediator masses up to

$m_{\text{X}_\text{dark}} <$ 1950 GeV are excluded for

$c\tau_{\pi_\text{dark}}\sim$ 100 mm and

$m_{\pi_\text{dark}}\sim$ 10 GeV, while the flavor-aligned model result excludes

$m_{\text{X}_\text{dark}} <$ 1850 GeV at

$c\tau_{\pi_\text{dark}}\sim$ 500 mm for

$m_{\pi_\text{dark}}\sim$ 10 GeV. This result greatly surpasses the previous search for emerging jets in the unflavored scenario, increasing the experimental limit of the dark mediator particle by

${\sim}$ 500 GeV to set the most stringent limits to date, and provides the first direct exclusion of the flavor-aligned scenario.

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