In the Light Neutralino and Gravitino GMSB models, all but the neutralino and gravitino can be inaccessible at Tevatron energies. In this case, sparticles are produced through the production and decay of a Higgs boson. These models suggest exclusive γ γ + MET if the neutralino has a short lifetime or delayed γ + MET if the lifetime is long, on the order of 5 ns.

a) Prompt photons have an arrivaltime consistent with traveling from the collision point to the calorimeter at the speed of light. However, multiple collisions can occur in each event, and sometimes the right vertex is not reconstructed. The selection of the wrong vertex can significantly affect the timing distribution for promptly produced photons.

b) Photons coming from the decay of heavy, long-lived neutralinos come from a point displaced in space and time from the collision point so photons tend to arrive late relative to expectations from prompt photons.

b) A cartoon showing the expected timing distribution divided into components. Standard model backgrounds contribute right and wrong-vertex Gaussians and cosmics contribute a flact background. Signal from a long-lived neutralino would look approximately like a falling exponential smeared by the detector resolution for positive values of tcorr.

a) Hadronic energy cut for cosmic ray removal.

b) CES energy cut for cosmic ray removal.

a) Right-vertex distribution for e + MET in data.

b) Wrong-vertex distribution for e + MET in data.

c) The tcorr distribution for e + MET events where the electron track is ignored in the vertexing and the highest SumPt vertex is selected as is done for γ+MET events. The red and blue showthe best-fit result from the double-Gaussian fit even though we do not know whether it was a right vertex or a wrong vertex on an event-by-event basis. Note that the timing distribution is well-described by the two-Gaussian model.

a) Both the Et and the tcorr are mismeasured by choosing a wrong vertex. There is a high degree of correlation between the mismeasurements because picking a wrong vertex causes the apparent path length (TOF^WV) to increase relative to the true path length (TOF^RV), causing the measured corrected time and the measured Et to both decrease. If picking a wrong vertex causes the apparent path length to decrease, the measured t_corr and the measured Et both increase.

b) This shows the Et distribution as measured around the true collision point for MC W -> eν -> γ+MET Monte Carlo with Et^WV greater than 25 GeV (white) and Et^WV greater than 45 GeV (green) as measured around the selected vertex. The events to the left of the line at 45 GeV are those promoting over threshold. The difference between the white and the green above 45 GeV are those demoting below threshold. Both effects conspire to cause a net positive shift in the wrong-vertex distribution.

A comparison of the average path length for W -> eν events where the electrons are reconstructed as electrons, and where they are reconstructed as photons. In (a) we show the full path length and in (b) we show the difference of the time of flight from the center of the detector which is a fixed point in space and effectively averages over all WV positions. In both cases, the path length is bigger for photons than fake electrons.

Some plots showing the new e -> γ_fake rejection techniques used in this analysis.

a) Δφ vs. Δη (between the reconstructed photon and the closest track, for both variables) for the MC W -> eν -> γ_fake + MET sample. The oval indicates the DeltaRpull > 5 cut.

b) The closest track-photon distributions in ΔRpull for the control sample and the sample of fake-photon events. Making a cut on ΔRpull provides a better MC Zγ -> ννγ -> γ+MET efficiency and e -> γfake rejection power than cutting on ΔR . Note that both samples are set to the same normalization.

c) The efficiency and rejection power of our cut as a function of ΔRcut pull. Note that a cut at ΔRpull > 5 (red dashed line) results in approximately 95% efficiency of MC Zγ -> ννγ -> γ + MET and 73% rejection of e -> γfake candidates.

Double Gaussian fits to the MC background and data e + MET samples.

a) MC W -> eν -> γ_fake + MET

b) MC γ + jet

c) MC Z γ

d) data e + MET

e) RMS vs. Mean of the wrong vertex distribution

(a) Wrong-Vertex sideband region and signal region.

(b) The ratio of N(signal region) to N(wrong-vertex sideband region) vs. the fitted wrong-vertex mean. We find that all samples agree well with the prediction from the double Gaussian approximation for a wide range of wrong-vertex means. (MC and two electron datasets)

For a number of MC datasets as well as two electron datasets from data, we isolate wrong-vertex (using generator quantities or the electron track) and no-vertex events.

(a) If the characteristics of the true collisions are similar for wrong and no-vertex events, on average, the no-vertex and wrong-vertex times dier only by a small geometrical factor in their times-of-light.

b) The no-vertex mean is as predictive of the N(signal region)/N(wrong-vertex sideband) ratio as the wrong-vertex mean.

c)The fitted no-vertex RMS vs. the fitted no-vertex mean. We find that the no-vertex RMS is consistent with the assumption of 1.6 ns for all samples, regardless of the fitted no-vertex mean. (MC & 2 electron datasets)

d) The no-vertex mean is an excellent predictor of the N(signal region)/N(wrong-vertex sideband) ratio as the wrong as the wrong-vertex mean. Note that the line is not a fit, but rather the integrated ratio from the Gaussian assumption using the mean from the x-axis.

a) This shows the estimation of the wrong-vertex mean using the no-vertex distribution.

b) Exclusive γ + MET events across the full tcorr range, signal region blinded.

The likelihood methods predict the number of events in the signal region for our pseudoexperiments as well as their uncertainty.

a) This figure shows the pull distribution for pseudoexperiemnts generated using perfect Gaussians for right, wrong, and no-vertex distributions and uniform distributions for the cosmics contribution in the good and no-vertex samples. The pull distribution has a mean very close to zero, which indicates a lack of bias. It has an RMS very close to 1, which indicates that the fit uncertainty is well estimated.

b) Uncertainty on background estimation.

c) For generated wrong-vertex means from 0.0 to 0.8 ns, the fit remains unbiased and the uncertainties remain well estimated.

a) The final tcorr distribution for our data sample along with the best fit values of the backgrounds from timing distributions for right vertex, wrong vertex, and cosmics as a function of tcorr.

b) Results with expected backgrounds subtracted.