Search for supersymmetry in pp collisions at sqrt(s) = 13 TeV in the single-lepton final state using the sum of masses of large-radius jets

TL;DR

This paper reports a search for supersymmetric particles in proton-proton collisions at 13 TeV using the CMS detector, finding no excess over standard model predictions and setting exclusion limits on gluino masses up to 1.9 TeV.

Contribution

It introduces a new analysis method using the sum of large-radius jet masses to search for supersymmetry in single-lepton final states.

Findings

No significant excess observed over standard model backgrounds.

Gluino masses up to 1.9 TeV are excluded at 95% confidence level.

The analysis constrains supersymmetric models involving gluino decays to top squarks.

Abstract

Results are reported from a search for supersymmetric particles in proton-proton collisions in the final state with a single lepton; multiple jets, including at least one b-tagged jet; and large missing transverse momentum. The search uses a sample of proton-proton collision data at sqrt(s) = 13 TeV recorded by the CMS experiment at the LHC, corresponding to an integrated luminosity of 35.9 inverse femtobarns. The observed event yields in the signal regions are consistent with those expected from standard model backgrounds. The results are interpreted in the context of simplified models of supersymmetry involving gluino pair production, with gluino decay into either on- or off-mass-shell top squarks. Assuming that the top squarks decay into a top quark plus a stable, weakly interacting neutralino, scenarios with gluino masses up to about 1.9 TeV are excluded at 95% confidence level for neutralino masses up to about 1 TeV.

Equations

$$\kappa =\frac{{\mu }_{\text{R4}}^{\text{MC bkg}}/{\mu }_{\text{R2}}^{\text{MC bkg}}}{{\mu }_{\text{R3}}^{\text{MC bkg}}/{\mu }_{\text{R1}}^{\text{MC bkg}}}.$$

Full text

A central goal of the physics program of the CMS experiment at the CERN LHC [1] is the search for new particles and phenomena beyond the standard model (SM), in particular, for supersymmetry (SUSY) [2, 3, 4, 5, 6, 7, 8, 9]. During 2016, CMS recorded a data sample of proton-proton collisions at a center-of-mass energy of 13\TeV, corresponding to an integrated luminosity of 35.9\fbinv, significantly extending the sensitivity to the production of new heavy particles. The search described here focuses on a generically important experimental signature that is also strongly motivated by SUSY phenomenology. This signature includes a single lepton (an electron or a muon); several jets, arising from the hadronization of energetic quarks and gluons; at least one \PQb-tagged jet, indicative of processes involving third generation quarks; and, finally, $\ptvecmiss$, the missing momentum in the direction transverse to the beam. A large value of $\ptmiss\equiv\abs{\ptvecmiss}$ can arise from the production of high momentum, weakly interacting particles that escape detection. Searches for SUSY in the single-lepton final state have been performed by both ATLAS and CMS at $\sqrt{s}=7$ and 8\TeV [10, 11, 12, 13] and at $\sqrt{s}=13$\TeV [14, 15, 16, 17]. The present analysis, which introduces extended binning and other improvements, is based largely on methodologies described in detail in Ref. [16], which include the use of large-radius jets and related kinematic variables.

In models based on SUSY, new particles are introduced such that all fermionic (bosonic) degrees of freedom in the SM are paired with corresponding bosonic (fermionic) degrees of freedom in the extended theory. The discovery of a Higgs boson with low mass [18, 19, 20, 21, 22, 23] provides a key motivation for SUSY. Stabilizing the Higgs boson mass at a low value, without invoking extreme fine tuning of parameters, is a major theoretical challenge, referred to as the gauge hierarchy problem [24, 25, 26, 27, 28, 29]. This stabilization can be achieved in so-called natural SUSY models [30, 31, 32, 33, 34], in which several of the SUSY partners are constrained to be light [33]: the top squarks, $\PSQt_{\mathrm{L}}$ and $\PSQt_{\mathrm{R}}$, which have the same electroweak couplings as the left- (L) and right- (R) handed top quarks, respectively; the bottom squark with L-handed couplings ($\PSQb_{\mathrm{L}}$); the gluino ($\PSg$); and the higgsinos ($\sHig$). This search targets gluino pair production, which has a relatively large cross section for a given mass, with gluino decay $\PSg\to\ttbar\PSGczDo$. This process can arise from $\PSg\to\PSQt_{1}\cPaqt$, where the lighter top squark mass eigenstate $\PSQt_{1}$ is produced either on or off mass shell. The symbol $\PSGczDo$ denotes the lightest neutralino, an electrically neutral mass eigenstate that is in general a mixture of the higgsinos and electroweak gauginos. In $R$-parity conserving SUSY models [35, 36] in which the $\PSGczDo$ is the lightest supersymmetric particle (LSP), the $\PSGczDo$ is stable and can, in principle, account for some or all of the astrophysical dark matter [37, 38, 39]. The scenario with off-mass-shell top squarks is denoted as T1tttt [40] in simplified model scenarios [41, 42, 43]. In natural SUSY models, the top squark is typically lighter than the gluino, so we also search for scenarios with on-shell top squarks, denoted as T5tttt.

Simulated event samples for SM background processes are used to determine correction factors, typically near unity, that are used in conjunction with observed event yields in control regions to determine the SM background contribution in the signal regions. The production of $\ttbar\text{+jets}$, $\PW\text{+jets}$, $\Z\text{+jets}$, and quantum chromodynamics (QCD) multijet events is simulated with the Monte Carlo (MC) generator \MGvATNLO 2.2.2 [44], with parton distribution functions taken from NNPDF 3.0 [45]. Details on the simulated SM background samples, including other processes with smaller contributions (single top quark, $\ttbar+$bosons, diboson, and $\ttbar\ttbar$ production) are given in Ref. [16]. The detector simulation is performed with \GEANTfour [46]. Simulated event samples for SUSY signal models, used to determine the selection efficiency for signal events, are generated with \MGvATNLO 2.2.2 with up to two additional partons at leading order accuracy and are normalized to cross sections based on Ref. [47]. Because of the large number of mass hypotheses examined in this analysis, the detector simulation in this case is performed with the CMS fast simulation package [48].

Two T1tttt benchmark models are used to illustrate typical signal behavior. The T1tttt(1800,100) model, which we refer to as a noncompressed-spectrum model (NC), has $m(\PSg)=1800$\GeV, $m(\PSGczDo)=100$\GeV, and a cross section of 2.8\unitfb, and corresponds to a scenario with a large gluino-neutralino mass splitting. The T1tttt(1400,1000) model, with $m(\PSg)=1400$\GeV, $m(\PSGczDo)=1000$\GeV, and a cross section of 25\unitfb, corresponds to a scenario with a small gluino-neutralino mass splitting and is referred to as a compressed-spectrum model (C).

The data were recorded with the CMS detector [49], which is constructed around a superconducting solenoid of 6\unitm diameter, providing a magnetic field of 3.8\unitT. Within the solenoid volume are the charged particle tracking systems, composed of silicon-pixel and silicon-strip detectors, and the calorimeter systems, consisting of a lead tungstate crystal electromagnetic calorimeter (ECAL) and a brass and scintillator hadron calorimeter. Muons are identified and measured by gas-ionization detectors embedded in the magnetic flux-return yoke outside the solenoid. Events were selected using several triggers [50] that require either large $\ptmiss$ or a single lepton (an electron or a muon), with and without significant hadronic activity. The trigger efficiency is measured in data for our analysis requirements to be nearly 100%.

Event reconstruction proceeds from particles identified by the particle-flow (PF) algorithm [51], which uses information from the tracker, calorimeters, and muon systems to identify PF candidates as electrons, muons, charged or neutral hadrons, or photons. Electrons are reconstructed by associating a charged-particle track with ECAL superclusters [52]. The resulting candidate electrons are required to have transverse momentum $\pt>20\GeV$ and pseudorapidity $\abs{\eta}<2.5$, and to satisfy identification criteria designed to reject light-parton jets and photon conversions. Muons are reconstructed by associating tracks in the muon system with those found in the silicon tracker [53]. Muon candidates are required to satisfy $\pt>20\GeV$ and $\abs{\eta}<2.4$. To select leptons from $\PW$ boson decays, leptons are required to be isolated from other PF candidates. Isolation is quantified using an optimized version [16] of the mini-isolation variable originally suggested in Ref. [54], in which the transverse energy of the particles within a cone around the lepton momentum vector is computed using a cone size that decreases as $1/\pt^{\ell}$, where $\pt^{\ell}$ is the transverse momentum of the lepton.

To suppress dilepton backgrounds, we veto events that contain a broader category of candidates for the second lepton, referred to as veto tracks. These include two categories of charged-particle tracks: isolated leptons satisfying looser identification criteria than lepton candidates, including a relaxed momentum requirement, $\pt>10$\GeV, and isolated charged-hadron PF candidates, which must satisfy $\pt>15$\GeVand $\abs{\eta}<2.5$. In either case, the charge of the veto track must be opposite to that of the lepton candidate in the event. To maintain a high selection efficiency for signal events, lepton veto tracks must satisfy a requirement on the quantity [55, 56] $M_{\mathrm{T}2}(\vec{p}^{\kern 1.0pt\ell},\vec{p}_{\mathrm{T}}^{\kern 2.0ptv},\ptvecmiss)<80$\GeVand hadronic veto tracks must satisfy $M_{\mathrm{T}2}(\vec{p}^{\kern 1.0pt\ell},\vec{p}_{\mathrm{T}}^{\kern 2.0ptv},\ptvecmiss)<60$\GeV, where $v$ refers to the veto track.

Charged and neutral PF candidates are clustered into jets using the anti-$\kt$ algorithm [57] with radius parameter $R=0.4$, as implemented in the \FASTJETpackage [58]. Jets are required to satisfy $\pt>30$\GeVand $\abs{\eta}\leq 2.4$. Additional details and references are given in Ref. [16] on the $\pt$- and $\eta$-dependent jet energy calibration [59], the jet identification requirements, and the subtraction of the energy contribution to the jet $\pt$ from multiple proton-proton interactions from the same or neighboring beam crossings (pileup) [60]. A subset of the jets are tagged as originating from \PQbquarks using the combined secondary vertex algorithm [61, 62].

We further cluster the jets with $R=0.4$ (small-$R$ jets), including those associated with isolated leptons, into $R=1.4$ (large-$R$) jets using the anti-$\kt$ algorithm. The masses, $m(J_{i})$, of the large-$R$ jets reflect the \ptspectrum and multiplicity of the clustered objects, as well as their angular spread. The variable $M_{J}$ is defined as the sum of all large-$R$ jet masses: $M_{J}\!\!=\!\!\sum_{J_{i}={\text{large-}R\text{ jets}}}m(J_{i})$. For \ttbarevents with a small contribution from initial-state radiation (ISR), the $M_{J}$ distribution has an approximate cutoff at $2m_{\PQt}$. In contrast, the $M_{J}$ distribution for signal events extends to larger values because of the presence of multiple top quarks in the decay chain. The presence of a significant amount of ISR generates a high-$M_{J}$ tail in the \ttbarbackground, producing the main source of background in the analysis.

The missing transverse momentum, $\ptvecmiss$, is defined as the negative vector sum of the transverse momenta of all PF candidates. To separate backgrounds characterized by the presence of a single $\PW$ boson decaying leptonically, but without any other source of $\ptmiss$, we use the transverse mass $m_{\mathrm{T}}=\sqrt{\smash[b]{2\pt^{\ell}\ptmiss[1-\cos(\Delta\phi_{\ell,\ptvecmiss})]}}$, where $\Delta\phi_{\ell,\ptvecmiss}$ is the difference between the azimuthal angles of $\pt^{\ell}$ and $\ptvecmiss$. The quantity \HTis defined as the scalar sum of the transverse momenta of all the small-$R$ jets passing the selection, while $S_{\mathrm{T}}=\HT+\pt^{\ell}$.

We select events with exactly one isolated charged lepton (an electron or a muon), no veto tracks, $S_{\mathrm{T}}>500$\GeV, $\ptmiss>200$\GeV, and at least six small-$R$ jets, at least one of which is \PQbtagged. After this set of requirements, referred to as the baseline selection, about 80% of the SM background arises from \ttbarproduction. The contributions from events with a single top quark or a \PW boson in association with jets are each about 6–8%; much of the remainder arises from events with a $\ttbar$ pair produced in association with a vector boson. After applying the baseline selection, the background from QCD multijet events is negligible.

The analysis is performed using four regions in the $M_{J}$-$m_{\mathrm{T}}$ plane: three control regions (CR) and one signal region (SR):

• •

R1 (CR): $m_{\mathrm{T}}\leq 140$ \GeV,  $250\leq M_{J}\leq 400$ \GeV,

• •

R2 (CR): $m_{\mathrm{T}}\leq 140$ \GeV,  $M_{J}>400$ \GeV,

• •

R3 (CR): $m_{\mathrm{T}}>140$ \GeV,  $250\leq M_{J}\leq 400$ \GeV,

• •

R4 (SR): $m_{\mathrm{T}}>140$ \GeV,  $M_{J}>400$ \GeV.

All four regions are divided in bins of \ptmiss, forming three largely independent $M_{J}$-$m_{\mathrm{T}}$ planes:

• •

three \ptmissbins: $200<\ptmiss\leq 350\GeV$, $350<\ptmiss\leq 500\GeV$, $\ptmiss>500\GeV$

Regions R2 and R4, which have high $M_{J}$, are further divided into bins according to the number of small-$R$ jets ($N_{\text{jets}}$) and the number of \PQb-tagged jets ($N_{\PQb}$) as follows:

• •

two $N_{\text{jets}}$ bins: $6\leq N_{\text{jets}}\leq 8$, $N_{\text{jets}}\geq 9$,

• •

three $N_{\PQb}$ bins: $N_{\PQb}=1$, $N_{\PQb}=2$, $N_{\PQb}\geq 3$,

giving a total of 18 bins each. Backgrounds with a single \PW boson decaying leptonically are strongly suppressed by the requirement $m_{\mathrm{T}}>140$\GeV, so the background in R3 and R4 is dominated by dilepton \ttbarevents. Approximately half of the dilepton background events in R4 contain a missed electron or muon, and the other half contain a hadronically decaying $\tau$ lepton. Given that the main background processes have two or fewer \PQbquarks, the total SM contribution to the $N_{\PQb}\geq 3$ bins is very small and is driven by the \PQbtag misidentification rate. Signal events in the T1tttt and T5tttt models populate primarily the bins with $N_{\PQb}\geq 2$.

The method for predicting the background yields takes advantage of the near absence of correlation between the $M_{J}$ and $m_{\mathrm{T}}$ variables in R1–R4, which is a consequence of the high jet multiplicity, \ptmiss, and $S_{\mathrm{T}}$ requirements applied in the baseline selection [16]. To satisfy these requirements, background events must typically contain additional jets from ISR. Even though the background at low $m_{\mathrm{T}}$ arises largely from single-lepton $\ttbar$ events, while the background at high $m_{\mathrm{T}}$ is dominated by dilepton $\ttbar$ events, the shapes of the $M_{J}$ distributions at low and high $m_{\mathrm{T}}$ become very similar in the presence of multiple ISR jets. We therefore measure this shape at low $m_{\mathrm{T}}$ (R1, R2) and extrapolate it to high $m_{\mathrm{T}}$ to obtain the background prediction in R4. The fitted mean background yields in R1–R4 are thus related by the constraint ${\mu_{\text{R4}}^{\text{bkg}}=\kappa\,\mu_{\text{R3}}^{\text{bkg}}\,\mu_{\text{R2}}^{\text{bkg}}/\mu_{\text{R1}}^{\text{bkg}}}$. Here, $\kappa$ is a near-unity correction factor obtained from MC simulation of the total background that accounts for a residual $m_{\mathrm{T}}$-$M_{J}$ correlation:

[TABLE]

This constraint is imposed by relating the expected yields in R1–R4 to three parameters: an overall background normalization $\lambda$ and two ratios $R(m_{\mathrm{T}})$ and $R(M_{J})$, where the expected background yields are given by $\mu_{\text{R1}}^{\text{bkg}}=\lambda$, $\mu_{\text{R2}}^{\text{bkg}}=\lambda\,R(M_{J}),$ $\mu_{\text{R3}}^{\text{bkg}}=\lambda\,R(m_{\mathrm{T}})$, and $\mu_{\text{R4}}^{\text{bkg}}=\kappa\,\lambda\,R(M_{J})\,R(m_{\mathrm{T}})$. These quantities are defined such that there is one value of $R(M_{J})$ and $\kappa$ for each bin of \ptmiss, $N_{\text{jets}}$, and $N_{\PQb}$. Because regions R1 and R3 are integrated in $N_{\text{jets}}$ and $N_{\PQb}$, the fit parameters $\lambda$ and $R(m_{\mathrm{T}})$ are defined such that there is only one value of these quantities for each bin in \ptmiss.

We perform two types of maximum likelihood fits, which are described in detail in Ref. [16]. The predictive fit uses the observed yields in R1–R3, assuming no signal contribution, to propagate the uncertainties to $\lambda$, $R(M_{J})$, and $R(m_{\mathrm{T}})$. The global fit uses the observed yields in all four regions R1–R4 and allows a signal contribution with a single normalization parameter. The global fit accounts for signal contamination in R1–R3, which is typically less than 10%, and is used to compute signal limits and significances. The results from the predictive fit simplify theoretical reinterpretation in terms of other models by only requiring comparison of observed and predicted yields in R4 rather than all four regions. In both cases, the likelihood function is written as a product of Poisson distributions for the relevant contributions in bins of $\ptmiss$, $N_{\text{jets}}$, and $N_{\PQb}$ within R2 and R4, taking into account the correlated yields between the unbinned regions R1 and R3.

Systematic uncertainties in the background prediction are incorporated in the uncertainty in the double ratio correction factor $\kappa$. Discrepancies between the value of $\kappa$ predicted by simulation and the true value of $\kappa$ in the data can in principle arise from mismodeling of the background composition or its properties, including detector effects.

To assess the potential impact of such effects on $\kappa$, two control samples in data are used: a 5-jet control sample and a dilepton control sample. The 5-jet control sample is completely dominated by background processes and has an SM composition very similar to that of the analysis regions. In particular, this sample probes the rate at which \ptmissis mismeasured in single-lepton events, which could increase the tail of the $m_{\mathrm{T}}$ distribution. Such events account for about 7% of the background in the signal region at high \ptmiss. This small event category can have a $\kappa$ value that departs significantly from unity, and it is important to validate the modeling of such effects. Using the analogous R1–R4 regions in the $N_{\text{jets}}=5$ control sample, $\kappa$ values are measured in data and are found to be consistent with those obtained from simulation. Because of this consistency, the statistical uncertainty obtained from the comparison in the $N_{\text{jets}}=5$ control sample is assigned as an uncertainty in $\kappa$ for each \ptmissbin. These uncertainties are taken to be fully correlated over the $N_{\text{jets}}$ and $N_{\PQb}$ bins.

The dilepton control sample is used to test the degree of similarity between the $M_{J}$ shapes of single-lepton and dilepton $\ttbar$ events in the presence of ISR. This sample includes not only events with two identified isolated leptons, but also events with one lepton and an oppositely charged veto track. The usual R3 and R4 regions are replaced by dilepton events, and the quantity $\kappa$ is measured in bins of $N_{\text{jets}}$. As in the 5-jets control sample, the values of $\kappa$ measured in data are found to be consistent with those observed in simulation, and uncertainties are assigned in a similar way. The uncertainties are treated as independent across $N_{\text{jets}}$ bins but fully correlated across $N_{\PQb}$ and $\ptmiss$ bins. The uncertainties from the dilepton and 5-jet control samples are treated as uncorrelated. Studies of a broad range of potential mismodeling effects in simulation show that all such effects would be evident in these control samples.

Systematic uncertainties in the expected signal yields account for uncertainties in the trigger, lepton identification, jet identification, and b tagging efficiencies in simulated data; uncertainties in the distributions of \ptmiss, number of pileup vertices, and ISR jet multiplicity; and uncertainties in the jet energy corrections, QCD scales, and integrated luminosity [63]. The combined effect of all signal-related uncertainties is typically about 25%.

Table Search for supersymmetry in $\Pp\Pp$ collisions at $\sqrt{s}=13\TeV$ in the single-lepton final state using the sum of masses of large-radius jets lists the observed event yields in region R4 in data, together with the mean background yields from the predictive fit and the expected signal yields from two benchmark model points.