AtlasPublic Web 
Internal TWiki
|
Search for anomalous production of events with same-sign dileptons and bjets in 14.3/fb of pp collisions at √s = 8 TeV with the ATLAS detector ATLAS-CONF-2013-051 13 May 2013
|
||
| Content | Preview | |
|---|---|---|
| Main document (CDS record) - internal | pdf from CDS | |
| Figures | - | |
| Abstract | ||
| A search is presented for exotic processes that result in final states containing jets including at least one $b$ jet, sizable missing transverse momentum, and a pair of leptons with the same electric charge. There are several models that predict an enhanced rate of production of such events beyond the expectations of the Standard Model (SM); the ones considered in this note are pair production of chiral $b^\prime$ quarks, pair production of vector-like quarks, enhanced four top quark production and production of two positively-charged top quarks. Using a sample of 14.3 fb$^{-1}$ of $pp$ collisions at $\sqrt{s} =8$ TeV recorded by the ATLAS detector at the Large Hadron Collider, with selection criteria optimised for each signal, no significant excess of events over the background expectation is observed. This observation is interpreted as constraining the signal hypotheses, and it is found at 95% confidence level that: the mass of the $b^\prime$, assuming 100% branching fraction to $Wt$, must be $> 0.72$ TeV; the mass of a vector-like $B$ ($T$) quark, assuming branching ratios to $W$, $Z$, and $H$ decay modes consistent with the $B$ or $T$ being a singlet, must be $>$ 0.59 (0.54) TeV; the four top production cross section must be $< 85$ fb in the SM and $< 59$ fb for production via a contact interaction; the mass of an sgluon must be $> 0.80$ TeV; in the context of models with two universal extra dimensions the inverse size of the extra dimensions must be $>0.90$ TeV; and the cross section for production of two positively-charged top quarks must be $< 210$ fb. | ||
| Figures | ||
|
Figure 01a: Distributions of discriminant variables in the eμ channel, after the standard object selection and with the requirement that ETmiss>40 GeV. The cross section for each signal model is scaled up so that the signal integrals are all equal to the background integral. The assumed strength of the contact interaction is C/Λ2 = -4π TeV-2. The background histograms are stacked to show the total expected background, while each signal histogram is independent of the others. The uncertainties on the background shown here consist of the production cross section uncertainty for the backgrounds modelled with Monte Carlo simulation, and a 30% uncertainty for instrumental backgrounds (see Section 5). png (48kB) eps (19kB) pdf (7kB) |
 |
|
|
Figure 01b: Distributions of discriminant variables in the eμ channel, after the standard object selection and with the requirement that ETmiss>40 GeV. The cross section for each signal model is scaled up so that the signal integrals are all equal to the background integral. The assumed strength of the contact interaction is C/Λ2 = -4π TeV-2. The background histograms are stacked to show the total expected background, while each signal histogram is independent of the others. The uncertainties on the background shown here consist of the production cross section uncertainty for the backgrounds modelled with Monte Carlo simulation, and a 30% uncertainty for instrumental backgrounds (see Section 5). png (44kB) eps (16kB) pdf (6kB) |
 |
|
|
Figure 01c: Distributions of discriminant variables in the eμ channel, after the standard object selection and with the requirement that ETmiss>40 GeV. The cross section for each signal model is scaled up so that the signal integrals are all equal to the background integral. The assumed strength of the contact interaction is C/Λ2 = -4π TeV-2. The background histograms are stacked to show the total expected background, while each signal histogram is independent of the others. The uncertainties on the background shown here consist of the production cross section uncertainty for the backgrounds modelled with Monte Carlo simulation, and a 30% uncertainty for instrumental backgrounds (see Section 5). png (53kB) eps (21kB) pdf (7kB) |
 |
|
|
Figure 01d: Distributions of discriminant variables in the eμ channel, after the standard object selection and with the requirement that ETmiss>40 GeV. The cross section for each signal model is scaled up so that the signal integrals are all equal to the background integral. The assumed strength of the contact interaction is C/Λ2 = -4π TeV-2. The background histograms are stacked to show the total expected background, while each signal histogram is independent of the others. The uncertainties on the background shown here consist of the production cross section uncertainty for the backgrounds modelled with Monte Carlo simulation, and a 30% uncertainty for instrumental backgrounds (see Section 5). png (52kB) eps (21kB) pdf (7kB) |
 |
|
|
Figure 02a: (a) Charge misidentification rate measured in data using the likelihood method on Z events, as a function of |η| (black points). Applying the pT-dependent correction factor determined with simulated tt̄ events results in the final charge misidentification rates for higher-pT electrons (red squares and blue triangles). (b) Correction factor applied to the charge mismeasurement rates to account for the overlap between the estimation of backgrounds from charge mismeasurement and fake electrons. png (43kB) eps (12kB) pdf (6kB) |
 |
|
|
Figure 02b: (a) Charge misidentification rate measured in data using the likelihood method on Z events, as a function of |η| (black points). Applying the pT-dependent correction factor determined with simulated tt̄ events results in the final charge misidentification rates for higher-pT electrons (red squares and blue triangles). (b) Correction factor applied to the charge mismeasurement rates to account for the overlap between the estimation of backgrounds from charge mismeasurement and fake electrons. png (48kB) eps (12kB) pdf (6kB) |
 |
|
|
Figure 03a: Distribution of kinematic variables for the data (points) and for the estimated background (histograms), after applying the b′/VLQ signal selection. The shaded areas correspond to the total uncertainties on the background, where statistical uncertainties are dominant. For the Monte Carlo simulated samples, systematic uncertainties include only the production cross section uncertainties. png (56kB) eps (22kB) pdf (8kB) |
 |
|
|
Figure 03b: Distribution of kinematic variables for the data (points) and for the estimated background (histograms), after applying the b′/VLQ signal selection. The shaded areas correspond to the total uncertainties on the background, where statistical uncertainties are dominant. For the Monte Carlo simulated samples, systematic uncertainties include only the production cross section uncertainties. png (57kB) eps (22kB) pdf (8kB) |
 |
|
|
Figure 03c: Distribution of kinematic variables for the data (points) and for the estimated background (histograms), after applying the b′/VLQ signal selection. The shaded areas correspond to the total uncertainties on the background, where statistical uncertainties are dominant. For the Monte Carlo simulated samples, systematic uncertainties include only the production cross section uncertainties. png (57kB) eps (23kB) pdf (8kB) |
 |
|
|
Figure 03d: Distribution of kinematic variables for the data (points) and for the estimated background (histograms), after applying the b′/VLQ signal selection. The shaded areas correspond to the total uncertainties on the background, where statistical uncertainties are dominant. For the Monte Carlo simulated samples, systematic uncertainties include only the production cross section uncertainties. png (60kB) eps (24kB) pdf (8kB) |
 |
|
|
Figure 03e: Distribution of kinematic variables for the data (points) and for the estimated background (histograms), after applying the b′/VLQ signal selection. The shaded areas correspond to the total uncertainties on the background, where statistical uncertainties are dominant. For the Monte Carlo simulated samples, systematic uncertainties include only the production cross section uncertainties. png (55kB) eps (21kB) pdf (8kB) |
 |
|
|
Figure 03f: Distribution of kinematic variables for the data (points) and for the estimated background (histograms), after applying the b′/VLQ signal selection. The shaded areas correspond to the total uncertainties on the background, where statistical uncertainties are dominant. For the Monte Carlo simulated samples, systematic uncertainties include only the production cross section uncertainties. png (53kB) eps (20kB) pdf (8kB) |
 |
|
|
Figure 04: Expected and observed upper limits on the pair production cross section of b′ → Wt, as a function of the b′ mass. png (70kB) eps (13kB) pdf (7kB) |
 |
|
|
Figure 05: Expected and observed upper limit contours on the pair production cross section of b′ → Wq, as a function of the b′ mass and the branching ratio to Wt, calculated from the b′ → Wq sample. The MC simulated signal samples used in this plot are statistically independent of those used to produce the 1D limits (Fig. 5), leading to a slight difference in the mass limit for BR(b′ → Wt) = 100%. png (64kB) eps (9kB) pdf (5kB) |
 |
|
|
Figure 06a: Observed limits on the mass of (a) vector-like B and (b) vector-like T quarks. These limits assume pair production, with branching ratios given by model where the B and T quarks exist as singlets [25]. png (97kB) eps (14kB) pdf (7kB) |
 |
|
|
Figure 06b: Observed limits on the mass of (a) vector-like B and (b) vector-like T quarks. These limits assume pair production, with branching ratios given by model where the B and T quarks exist as singlets [25]. png (92kB) eps (14kB) pdf (7kB) |
 |
|
|
Figure 07: Observed and expected vector-like B quark branching fractions excluded at 95% C.L. for several B quark mass hypotheses. For reference, the branching fractions expected in models [25] where the B quark is in a SU(2) singlet (doublet) are indicated by a circle (star). png (138kB) eps (39kB) pdf (10kB) |
 |
|
|
Figure 08: Observed and expected vector-like T quark branching fractions excluded at 95% C.L. for several T quark mass hypotheses. For reference, the branching fractions expected in models [25] where the T quark is in a SU(2) singlet (doublet) are indicated by a circle (star). png (130kB) eps (40kB) pdf (10kB) |
 |
|
|
Figure 09a: Observed limits on positively-charged top quark pair production in the plane of the coupling constant C as a function of the new physics energy scale Λ (as defined in Eq. 9.1 of [41]) for (a) left-left, (b) right-right, and (c) left-right chiralities. The hashed region is excluded at 95% C.L.. png (72kB) eps (47kB) pdf (63kB) |
 |
|
|
Figure 09b: Observed limits on positively-charged top quark pair production in the plane of the coupling constant C as a function of the new physics energy scale Λ (as defined in Eq. 9.1 of [41]) for (a) left-left, (b) right-right, and (c) left-right chiralities. The hashed region is excluded at 95% C.L.. png (73kB) eps (47kB) pdf (63kB) |
 |
|
|
Figure 09c: Observed limits on positively-charged top quark pair production in the plane of the coupling constant C as a function of the new physics energy scale Λ (as defined in Eq. 9.1 of [41]) for (a) left-left, (b) right-right, and (c) left-right chiralities. The hashed region is excluded at 95% C.L.. png (69kB) eps (35kB) pdf (42kB) |
 |
|
|
Figure 10a: Observed limits on the four top quarks events production interpreted in the context of (a) sgluon pair production, (b) the 2UED/RPP model, and (c) the four-fermion contact interaction of Eq. 1 (in (c) the hashed region is excluded at 95% C.L.). png (81kB) eps (19kB) pdf (9kB) |
 |
|
|
Figure 10b: Observed limits on the four top quarks events production interpreted in the context of (a) sgluon pair production, (b) the 2UED/RPP model, and (c) the four-fermion contact interaction of Eq. 1 (in (c) the hashed region is excluded at 95% C.L.). png (57kB) eps (12kB) pdf (6kB) |
 |
|
|
Figure 10c: Observed limits on the four top quarks events production interpreted in the context of (a) sgluon pair production, (b) the 2UED/RPP model, and (c) the four-fermion contact interaction of Eq. 1 (in (c) the hashed region is excluded at 95% C.L.). png (65kB) eps (18kB) pdf (12kB) |
 |
|
|
Figure 11a: Distributions of discriminant variables in the ee channel, after the standard object selection and with the requirement that ETmiss>40 GeV. The cross section for each signal model is scaled up so that the signal integrals are all equal to the background integral. The assumed strength of the contact interaction is C/Λ2 = -4π TeV-2. The background histograms are stacked to show the total expected background, while each signal histogram is independent of the others. The uncertainties on the background shown here consist of the production cross section uncertainty for the backgrounds modelled with Monte Carlo simulation, and a 30% uncertainty for instrumental backgrounds (see Section 5). png (48kB) eps (19kB) pdf (7kB) |
 |
|
|
Figure 11b: Distributions of discriminant variables in the ee channel, after the standard object selection and with the requirement that ETmiss>40 GeV. The cross section for each signal model is scaled up so that the signal integrals are all equal to the background integral. The assumed strength of the contact interaction is C/Λ2 = -4π TeV-2. The background histograms are stacked to show the total expected background, while each signal histogram is independent of the others. The uncertainties on the background shown here consist of the production cross section uncertainty for the backgrounds modelled with Monte Carlo simulation, and a 30% uncertainty for instrumental backgrounds (see Section 5). png (44kB) eps (16kB) pdf (6kB) |
 |
|
|
Figure 11c: Distributions of discriminant variables in the ee channel, after the standard object selection and with the requirement that ETmiss>40 GeV. The cross section for each signal model is scaled up so that the signal integrals are all equal to the background integral. The assumed strength of the contact interaction is C/Λ2 = -4π TeV-2. The background histograms are stacked to show the total expected background, while each signal histogram is independent of the others. The uncertainties on the background shown here consist of the production cross section uncertainty for the backgrounds modelled with Monte Carlo simulation, and a 30% uncertainty for instrumental backgrounds (see Section 5). png (54kB) eps (20kB) pdf (7kB) |
 |
|
|
Figure 11d: Distributions of discriminant variables in the ee channel, after the standard object selection and with the requirement that ETmiss>40 GeV. The cross section for each signal model is scaled up so that the signal integrals are all equal to the background integral. The assumed strength of the contact interaction is C/Λ2 = -4π TeV-2. The background histograms are stacked to show the total expected background, while each signal histogram is independent of the others. The uncertainties on the background shown here consist of the production cross section uncertainty for the backgrounds modelled with Monte Carlo simulation, and a 30% uncertainty for instrumental backgrounds (see Section 5). png (53kB) eps (20kB) pdf (7kB) |
 |
|
|
Figure 12a: Distributions of discriminant variables in the μμ channel, after the standard object selection and with the requirement that ETmiss>40 GeV. The cross section for each signal model is scaled up so that the signal integrals are all equal to the total background integral. The assumed strength of the contact interaction is C/Λ2 = -4π TeV-2. The background histograms are stacked to show the total expected background, while each signal histogram is independent of the others. The uncertainties on the background shown here consist of the production cross section uncertainty for the backgrounds modelled with Monte Carlo simulation, and a 30% uncertainty for instrumental backgrounds (see Section 5). png (44kB) eps (18kB) pdf (7kB) |
 |
|
|
Figure 12b: Distributions of discriminant variables in the μμ channel, after the standard object selection and with the requirement that ETmiss>40 GeV. The cross section for each signal model is scaled up so that the signal integrals are all equal to the total background integral. The assumed strength of the contact interaction is C/Λ2 = -4π TeV-2. The background histograms are stacked to show the total expected background, while each signal histogram is independent of the others. The uncertainties on the background shown here consist of the production cross section uncertainty for the backgrounds modelled with Monte Carlo simulation, and a 30% uncertainty for instrumental backgrounds (see Section 5). png (42kB) eps (15kB) pdf (6kB) |
 |
|
|
Figure 12c: Distributions of discriminant variables in the μμ channel, after the standard object selection and with the requirement that ETmiss>40 GeV. The cross section for each signal model is scaled up so that the signal integrals are all equal to the total background integral. The assumed strength of the contact interaction is C/Λ2 = -4π TeV-2. The background histograms are stacked to show the total expected background, while each signal histogram is independent of the others. The uncertainties on the background shown here consist of the production cross section uncertainty for the backgrounds modelled with Monte Carlo simulation, and a 30% uncertainty for instrumental backgrounds (see Section 5). png (50kB) eps (19kB) pdf (7kB) |
 |
|
|
Figure 12d: Distributions of discriminant variables in the μμ channel, after the standard object selection and with the requirement that ETmiss>40 GeV. The cross section for each signal model is scaled up so that the signal integrals are all equal to the total background integral. The assumed strength of the contact interaction is C/Λ2 = -4π TeV-2. The background histograms are stacked to show the total expected background, while each signal histogram is independent of the others. The uncertainties on the background shown here consist of the production cross section uncertainty for the backgrounds modelled with Monte Carlo simulation, and a 30% uncertainty for instrumental backgrounds (see Section 5). png (51kB) eps (19kB) pdf (7kB) |
 |
|
|
Figure 13a: Distributions of some kinematic variables in the ee channel, after the ETmiss control region selection, for both the background estimation and the data. The bottom plots show the significance of the measured number of events in the data over the estimated number of events from the backgrounds. png (51kB) eps (24kB) pdf (8kB) |
 |
|
|
Figure 13b: Distributions of some kinematic variables in the ee channel, after the ETmiss control region selection, for both the background estimation and the data. The bottom plots show the significance of the measured number of events in the data over the estimated number of events from the backgrounds. png (51kB) eps (24kB) pdf (8kB) |
 |
|
|
Figure 13c: Distributions of some kinematic variables in the ee channel, after the ETmiss control region selection, for both the background estimation and the data. The bottom plots show the significance of the measured number of events in the data over the estimated number of events from the backgrounds. png (55kB) eps (28kB) pdf (9kB) |
 |
|
|
Figure 13d: Distributions of some kinematic variables in the ee channel, after the ETmiss control region selection, for both the background estimation and the data. The bottom plots show the significance of the measured number of events in the data over the estimated number of events from the backgrounds. png (57kB) eps (29kB) pdf (9kB) |
 |
|
|
Figure 13e: Distributions of some kinematic variables in the ee channel, after the ETmiss control region selection, for both the background estimation and the data. The bottom plots show the significance of the measured number of events in the data over the estimated number of events from the backgrounds. png (54kB) eps (23kB) pdf (8kB) |
 |
|
|
Figure 13f: Distributions of some kinematic variables in the ee channel, after the ETmiss control region selection, for both the background estimation and the data. The bottom plots show the significance of the measured number of events in the data over the estimated number of events from the backgrounds. png (61kB) eps (26kB) pdf (9kB) |
 |
|
|
Figure 13g: Distributions of some kinematic variables in the ee channel, after the ETmiss control region selection, for both the background estimation and the data. The bottom plots show the significance of the measured number of events in the data over the estimated number of events from the backgrounds. png (55kB) eps (23kB) pdf (8kB) |
 |
|
|
Figure 13h: Distributions of some kinematic variables in the ee channel, after the ETmiss control region selection, for both the background estimation and the data. The bottom plots show the significance of the measured number of events in the data over the estimated number of events from the backgrounds. png (61kB) eps (25kB) pdf (9kB) |
 |
|
|
Figure 14a: Distributions of some kinematic variables in the eμ channel, after the ETmiss control region selection, for both the background estimation and the data. The bottom plots show the significance of the measured number of events in the data over the estimated number of events from the backgrounds. png (54kB) eps (26kB) pdf (9kB) |
 |
|
|
Figure 14b: Distributions of some kinematic variables in the eμ channel, after the ETmiss control region selection, for both the background estimation and the data. The bottom plots show the significance of the measured number of events in the data over the estimated number of events from the backgrounds. png (50kB) eps (24kB) pdf (8kB) |
 |
|
|
Figure 14c: Distributions of some kinematic variables in the eμ channel, after the ETmiss control region selection, for both the background estimation and the data. The bottom plots show the significance of the measured number of events in the data over the estimated number of events from the backgrounds. png (56kB) eps (28kB) pdf (9kB) |
 |
|
|
Figure 14d: Distributions of some kinematic variables in the eμ channel, after the ETmiss control region selection, for both the background estimation and the data. The bottom plots show the significance of the measured number of events in the data over the estimated number of events from the backgrounds. png (56kB) eps (28kB) pdf (9kB) |
 |
|
|
Figure 14e: Distributions of some kinematic variables in the eμ channel, after the ETmiss control region selection, for both the background estimation and the data. The bottom plots show the significance of the measured number of events in the data over the estimated number of events from the backgrounds. png (60kB) eps (26kB) pdf (9kB) |
 |
|
|
Figure 14f: Distributions of some kinematic variables in the eμ channel, after the ETmiss control region selection, for both the background estimation and the data. The bottom plots show the significance of the measured number of events in the data over the estimated number of events from the backgrounds. png (60kB) eps (25kB) pdf (9kB) |
 |
|
|
Figure 14g: Distributions of some kinematic variables in the eμ channel, after the ETmiss control region selection, for both the background estimation and the data. The bottom plots show the significance of the measured number of events in the data over the estimated number of events from the backgrounds. png (55kB) eps (23kB) pdf (8kB) |
 |
|
|
Figure 14h: Distributions of some kinematic variables in the eμ channel, after the ETmiss control region selection, for both the background estimation and the data. The bottom plots show the significance of the measured number of events in the data over the estimated number of events from the backgrounds. png (60kB) eps (25kB) pdf (9kB) |
 |
|
|
Figure 15a: Distributions of some kinematic variables in the μμ channel, after the ETmiss control region selection, for both the background estimation and the data. The bottom plots show the significance of the measured number of events in the data over the estimated number of events from the backgrounds. png (54kB) eps (25kB) pdf (9kB) |
 |
|
|
Figure 15b: Distributions of some kinematic variables in the μμ channel, after the ETmiss control region selection, for both the background estimation and the data. The bottom plots show the significance of the measured number of events in the data over the estimated number of events from the backgrounds. png (50kB) eps (23kB) pdf (8kB) |
 |
|
|
Figure 15c: Distributions of some kinematic variables in the μμ channel, after the ETmiss control region selection, for both the background estimation and the data. The bottom plots show the significance of the measured number of events in the data over the estimated number of events from the backgrounds. png (55kB) eps (27kB) pdf (9kB) |
 |
|
|
Figure 15d: Distributions of some kinematic variables in the μμ channel, after the ETmiss control region selection, for both the background estimation and the data. The bottom plots show the significance of the measured number of events in the data over the estimated number of events from the backgrounds. png (55kB) eps (27kB) pdf (9kB) |
 |
|
|
Figure 15e: Distributions of some kinematic variables in the μμ channel, after the ETmiss control region selection, for both the background estimation and the data. The bottom plots show the significance of the measured number of events in the data over the estimated number of events from the backgrounds. png (56kB) eps (23kB) pdf (9kB) |
 |
|
|
Figure 15f: Distributions of some kinematic variables in the μμ channel, after the ETmiss control region selection, for both the background estimation and the data. The bottom plots show the significance of the measured number of events in the data over the estimated number of events from the backgrounds. png (61kB) eps (24kB) pdf (9kB) |
 |
|
|
Figure 15g: Distributions of some kinematic variables in the μμ channel, after the ETmiss control region selection, for both the background estimation and the data. The bottom plots show the significance of the measured number of events in the data over the estimated number of events from the backgrounds. png (58kB) eps (24kB) pdf (8kB) |
 |
|
|
Figure 15h: Distributions of some kinematic variables in the μμ channel, after the ETmiss control region selection, for both the background estimation and the data. The bottom plots show the significance of the measured number of events in the data over the estimated number of events from the backgrounds. png (63kB) eps (25kB) pdf (9kB) |
 |
|
|
Figure 16a: Distribution of kinematic variables for the data (points) and for the estimated background (histograms), after applying the b′/VLQ signal selection. The shaded areas correspond to the total uncertainties on the background, where statistical uncertainties are dominant. For the Monte Carlo simulated samples, systematic uncertainties include only the production cross section uncertainties. png (52kB) eps (19kB) pdf (7kB) |
 |
|
|
Figure 16b: Distribution of kinematic variables for the data (points) and for the estimated background (histograms), after applying the b′/VLQ signal selection. The shaded areas correspond to the total uncertainties on the background, where statistical uncertainties are dominant. For the Monte Carlo simulated samples, systematic uncertainties include only the production cross section uncertainties. png (53kB) eps (19kB) pdf (7kB) |
 |
|
|
Figure 16c: Distribution of kinematic variables for the data (points) and for the estimated background (histograms), after applying the b′/VLQ signal selection. The shaded areas correspond to the total uncertainties on the background, where statistical uncertainties are dominant. For the Monte Carlo simulated samples, systematic uncertainties include only the production cross section uncertainties. png (57kB) eps (21kB) pdf (8kB) |
 |
|
|
Figure 16d: Distribution of kinematic variables for the data (points) and for the estimated background (histograms), after applying the b′/VLQ signal selection. The shaded areas correspond to the total uncertainties on the background, where statistical uncertainties are dominant. For the Monte Carlo simulated samples, systematic uncertainties include only the production cross section uncertainties. png (52kB) eps (19kB) pdf (7kB) |
 |
|
|
Figure 16e: Distribution of kinematic variables for the data (points) and for the estimated background (histograms), after applying the b′/VLQ signal selection. The shaded areas correspond to the total uncertainties on the background, where statistical uncertainties are dominant. For the Monte Carlo simulated samples, systematic uncertainties include only the production cross section uncertainties. png (51kB) eps (19kB) pdf (7kB) |
 |
|
|
Figure 16f: Distribution of kinematic variables for the data (points) and for the estimated background (histograms), after applying the b′/VLQ signal selection. The shaded areas correspond to the total uncertainties on the background, where statistical uncertainties are dominant. For the Monte Carlo simulated samples, systematic uncertainties include only the production cross section uncertainties. png (53kB) eps (19kB) pdf (7kB) |
 |
|
|
Figure 17a: Distribution of kinematic variables for the data (points) and for the estimated background (histograms), after applying the b′/VLQ signal selection. The shaded areas correspond to the total uncertainties on the background, where statistical uncertainties are dominant. For the Monte Carlo simulated samples, systematic uncertainties include only the production cross section uncertainties. png (57kB) eps (20kB) pdf (7kB) |
 |
|
|
Figure 17b: Distribution of kinematic variables for the data (points) and for the estimated background (histograms), after applying the b′/VLQ signal selection. The shaded areas correspond to the total uncertainties on the background, where statistical uncertainties are dominant. For the Monte Carlo simulated samples, systematic uncertainties include only the production cross section uncertainties. png (60kB) eps (18kB) pdf (7kB) |
 |
|
|
Figure 17c: Distribution of kinematic variables for the data (points) and for the estimated background (histograms), after applying the b′/VLQ signal selection. The shaded areas correspond to the total uncertainties on the background, where statistical uncertainties are dominant. For the Monte Carlo simulated samples, systematic uncertainties include only the production cross section uncertainties. png (55kB) eps (21kB) pdf (8kB) |
 |
|
|
Figure 17d: Distribution of kinematic variables for the data (points) and for the estimated background (histograms), after applying the b′/VLQ signal selection. The shaded areas correspond to the total uncertainties on the background, where statistical uncertainties are dominant. For the Monte Carlo simulated samples, systematic uncertainties include only the production cross section uncertainties. png (57kB) eps (18kB) pdf (7kB) |
 |
|
|
Figure 17e: Distribution of kinematic variables for the data (points) and for the estimated background (histograms), after applying the b′/VLQ signal selection. The shaded areas correspond to the total uncertainties on the background, where statistical uncertainties are dominant. For the Monte Carlo simulated samples, systematic uncertainties include only the production cross section uncertainties. png (57kB) eps (20kB) pdf (7kB) |
 |
|
|
Figure 17f: Distribution of kinematic variables for the data (points) and for the estimated background (histograms), after applying the b′/VLQ signal selection. The shaded areas correspond to the total uncertainties on the background, where statistical uncertainties are dominant. For the Monte Carlo simulated samples, systematic uncertainties include only the production cross section uncertainties. png (57kB) eps (18kB) pdf (7kB) |
 |
|
|
Figure 18a: The charge misidentification rates shown in Fig. 2(a) have been computed using the likelihood method after correction. They are defined as ε(|η|,pT)=ε(|η|)×α(|η|,pT) where ε(|η|) corresponds to the misidentification rates measured in data on Z events using the likelihood without correction, and α(|η|,pT) is the pT dependent correction factor extracted from a tt̄ sample. The reason for which the rates ε(|η|) need to be corrected is that they are applied to events mostly coming from tt̄: the misidentification rates also depend on the electron pT, but the pT spectrum of the electrons coming from a Z decay is not the same as the one in the tt̄. The left plot (a) shows the distribution of the ratio ε(|η|,pT)/ε(|η|) for Z MC events computed by truth-matching as a function of |η| and for different pT bins. It shows that the average rate ε(|η|) is largely dominated by the first bin in pT. The right plot (b) shows the pT dependent correction factor α(|η|,pT)=ε(|η|,pT)/ε(|η|,pT<100 GeV) extracted from tbar t events computed by truth-matching, as a function of |η| and for different pT bins. By definition, the correction factor is 1 in the first pT bin. png (44kB) eps (12kB) pdf (5kB) |
 |
|
|
Figure 18b: The charge misidentification rates shown in Fig. 2(a) have been computed using the likelihood method after correction. They are defined as ε(|η|,pT)=ε(|η|)×α(|η|,pT) where ε(|η|) corresponds to the misidentification rates measured in data on Z events using the likelihood without correction, and α(|η|,pT) is the pT dependent correction factor extracted from a tbar t sample. The reason for which the rates ε(|η|) need to be corrected is that they are applied to events mostly coming from tbar t: the misidentification rates also depend on the electron pT, but the pT spectrum of the electrons coming from a Z decay is not the same as the one in the t bar t. The left plot (a) shows the distribution of the ratio ε(|η|,pT)/ε(|η|) for Z MC events computed by truth-matching as a function of |η| and for different pT bins. It shows that the average rate ε(|η|) is largely dominated by the first bin in pT. The right plot (b) shows the pT dependent correction factor α(|η|,pT)=ε(|η|,pT)/ε(|η|,pT<100 GeV) extracted from tbar t events computed by truth-matching, as a function of |η| and for different pT bins. By definition, the correction factor is 1 in the first pT bin. png (40kB) eps (11kB) pdf (5kB) |
 |
|
|
Figure 19a: One way to validate the misidentification rate measurement method is via the closure test, where the number of measured same-sign events is compared with respect to the estimated number of same-sign events. This last one is computed by reweighing the measured number of opposite-sign events. This comparison was done using simulated Z→ e+e- samples and using data. In simulated Z samples (a), the number of same-sign Z events is 32995±790, while the estimation is 32493+6394-6306. In data (b), the number of same-sign Z events is 22067 for an estimate of 21824+5678-5706. In both cases, the uncertainties combine the statistical and systematic uncertainties. A last validation of the method can be done by applying the misidentification rates measured using simulated Z events after pT correction to simulated opposite-sign tbar t events and compare to the number of simulated same-sign tbar t events. The distribution of the leading electron pT can be seen in (c) and shows very good agreement within uncertainties: above 100 GeV, the total number of same-sign events is 121±19, for an estimate of 146±36 after the pT correction. The distribution of the leading electron pT before the pT dependent correction is also shown in (c). png (55kB) eps (18kB) pdf (13kB) |
 |
|
|
Figure 19b: One way to validate the misidentification rate measurement method is via the closure test, where the number of measured same-sign events is compared with respect to the estimated number of same-sign events. This last one is computed by reweighing the measured number of opposite-sign events. This comparison was done using simulated Z→ e+e- samples and using data. In simulated Z samples (a), the number of same-sign Z events is 32995±790, while the estimation is 32493+6394-6306. In data (b), the number of same-sign Z events is 22067 for an estimate of 21824+5678-5706. In both cases, the uncertainties combine the statistical and systematic uncertainties. A last validation of the method can be done by applying the misidentification rates measured using simulated Z events after pT correction to simulated opposite-sign tbar t events and compare to the number of simulated same-sign tbar t events. The distribution of the leading electron pT can be seen in (c) and shows very good agreement within uncertainties: above 100 GeV, the total number of same-sign events is 121±19, for an estimate of 146±36 after the pT correction. The distribution of the leading electron pT before the pT dependent correction is also shown in (c). png (59kB) eps (17kB) pdf (13kB) |
 |
|
|
Figure 19c: One way to validate the misidentification rate measurement method is via the closure test, where the number of measured same-sign events is compared with respect to the estimated number of same-sign events. This last one is computed by reweighing the measured number of opposite-sign events. This comparison was done using simulated Z→ e+e- samples and using data. In simulated Z samples (a), the number of same-sign Z events is 32995±790, while the estimation is 32493+6394-6306. In data (b), the number of same-sign Z events is 22067 for an estimate of 21824+5678-5706. In both cases, the uncertainties combine the statistical and systematic uncertainties. A last validation of the method can be done by applying the misidentification rates measured using simulated Z events after pT correction to simulated opposite-sign tbar t events and compare to the number of simulated same-sign tbar t events. The distribution of the leading electron pT can be seen in (c) and shows very good agreement within uncertainties: above 100 GeV, the total number of same-sign events is 121±19, for an estimate of 146±36 after the pT correction. The distribution of the leading electron pT before the pT dependent correction is also shown in (c). png (50kB) eps (12kB) pdf (7kB) |
 |
|
|
Figure 20: The likelihood method used to compute the misidentification rates as a function of |η| can be validated using simulated Z→ e+e- samples. The validation is done by comparing the rates extracted with the likelihood method with respect to the rates computed using the truth-matching method. As shown in the bottom plot, the likelihood measured rates are compatible with the true rates within uncertainties. png (47kB) eps (14kB) pdf (7kB) |
 |
|
|
Figure 21: Expected 95% C.L. upper limit on the four tops contact interaction production cross section as a function of the cut on HT, and parametrized with the requirement on the number of jets (different colors), and on the number of b-jets (different markers). The final event selection is chosen so that it can provide the minimum value on the cross-section. Following the information provided by this plot, the best choice for the final selection is HT ≥ 650 GeV, Njets ≥ 2 and Nb-jets ≥ 2. png (119kB) eps (19kB) pdf (8kB) |
 |
|
|
Figure 22: Expected limit on the b′ pair-production cross section as a function of the bprime mass assuming statistical uncertainties only. png (69kB) eps (55kB) pdf (7kB) |
 |
|
|
Figure 23a: Expected limits on the production cross section of a vector like quark pair, as a function of the quark mass, with statistical uncertainties only for bottom like quarks (left) and top like quarks (right). png (84kB) eps (12kB) pdf (7kB) |
 |
|
|
Figure 23b: Expected limits on the production cross section of a vector like quark pair, as a function of the quark mass, with statistical uncertainties only for bottom like quarks (left) and top like quarks (right). png (81kB) eps (12kB) pdf (7kB) |
 |
|
|
Figure 24: Expected limits on the production cross section of four top quarks events from the 2UED/RPP model, with only statistical uncertainties considered. png (52kB) eps (12kB) pdf (6kB) |
 |
|
|
Figure 25: Expected limit on the sgluon pair-production cross section as a function of the sgluon mass assuming statistical uncertainties only. png (75kB) eps (18kB) pdf (8kB) |
 |
|
