Search for High-Mass Resonances in e + e - Jia Liu Madelyne Greene, Lana Muniz, Jane Nachtman Goal for the summer Searching for new particle Z’ --- a massive gauge boson in Proton-antiproton collision at CDF

Summary of analysis  Signature-based search for resonance in e+ e- mass spectrum  Hypothetical new particle (Z prime) decaying to e+ e-  Reconstruct its mass -- look in high mass region  We are starting with existing code, analysis method from previous analysis  His analysis – 1.3/fb; ours – 2/fb  Requires understanding and running his code, validating new data  We are now focusing on CC( two electron in the Central detector), but we have starting on CP( one Central, one plug electron)  Main pieces of analysis  Selecting electrons  Understanding composition of e + e - sample  Scan mass spectrum, look for bump (quantify probability)  Limits on Z’ production

Signature-based search for resonance in e+ e- mass spectrum that gives evidence for a new particle Reconstruct its mass-- We expect it to be high-mass (hundreds of GeV/c2) due to previous searches

Run period for the 4 data set 0i, p9,p10 and p11 0iP9 (pb-1) p10 (pb-1) P11 (pb-1) Luminosity / / /- 14 Run range Total Dataset (including 0d and 0h data) = 2 fb-1 Sam’s analysis through p8(0i) used 1.3 fb-1  In today’s talk we use 4 datasets: 0i for comparison, p9, p10,p11( in progress) is the new data which we validate

Checking the new data  Previous analysis covered up to p8 (0i data)  We want to extend the analysis through p11, using the same code, same MC and scale factors  Validate the new data  --check the electron ID distributions  --Check mean and sigma for Z  --Check number of Z

Event Selection  Events are required to have one electron in the central region and another in either the central or plug regions  Two channels, CC and CP  Use both CC and CP  Pros and Cons of CP electrons  Find more Z’ particle  Adds angular acceptance  Limited tracking information  Contribute more fakes  Central electrons must pass the identification cuts shown next

CEM Selection Cuts VariableTight CC (CEMCC) Region= CEM FiducialFid = 1 or 2 ETET ≥ 25 GeV Track Z 0 ≤ 60 cm Track P T (E T <100GeV)≥ 15 GeV/c Track P T (E T ≥100GeV)≥ 25 GeV/c Had/em≤ x E Isolation E T ≤ x E T GeV L shr Track≤ 0.2 E / P(E T <100GeV)≤ x E T GeV E / P(E T ≥100GeV)Track P T ≥ 25 GeV/c CES ∆Z≤ 5.0 cm CES ∆X≤ 3.0 cm PEM is on the way…. These are the standard cuts used for electron ID with some modifications made by previous search to account for very high ET events.

Electron ID  Had/em The ratio of the total hadronic to total electro-magnetic energy of all the towers composing the cluster  Isolation  the sum of the hadronic and electromagnetic transverse energies in a cone of 0.4 radius centered on the cluster with the electron and leakage transverse energies subtracted off  Isolation Et is corrected for multiple interactions by subtracting 0.35 GeV or 0.27 GeV per additional vertex for data and Monte Carlo respectively.  Lshr Track Lateral Shower Sharing Variable. A measure of how well the energy deposits in the adjacent towers matches that expected for an electromagnetic shower.  E/P The transverse energy of the electron divided by the track p T  CES ∆Z The difference between the z position of the highest pT beam-constrained track extrapolated to the CES plane and the z position of the electromagnetic shower as measured by the CES.  CES ∆X The difference between the x position of the highest pT beam-constrained track extrapolated to the CES plane and the x position of the electromagnetic shower as measured by the CES.

Validation Plots

Efficiencies of electron ID variable for each dataset 0i dataP9 dataP10 data Had/em.992± ±.001 Isolation.974± ± ±.002 Lshr track.99± ± ±.001 E/P.9996± ± ±.0002 CES ∆Z.9977± ± ±.0007 CES ∆x.993± ± ±.001

Total Efficiencies Run period 0ip9p10p11MC Total Efficiency / / / ## +/- 0.00# / The efficiencies for each run period agree within statistical error. Therefore, we can continue to use the Scale Factors calculated and the Monte Carlo used for the 0i calculations.  We checked the each ID variable for each run period

Check Z peak position and width  Subdivide data into smaller run periods  Fit z peak, extract mean and width  The reason for checking mean and sigma 1) Z peak mean: verifies electron energy calibration 2) Z peak Sigma: verifies momentum reconstruction

Example Z peaks of cc from 0i period from the fit

Example Z Peaks of cc from P9 data

Example of Z peaks of cc from P10 data

Z mass mean value of cc for 0i, p9 and p10 data 0i data P9 data P10 data

Z mass sigma value of cc for 0i, p9 and 0i data 0i data P9 data P10 data

Example of Z peaks of cp from 0i data

Example of Z peaks of cp from P9 data

Example of Z peaks of cp from P10 data

Z mass mean value of cp for 0i, p9 and p10 data 0i data P9 data P10 data

Z mass sigma value of cp for 0i, p9 and p10 data 0i data P9 data P10 data

Checking the Number of Z  Count number of Z’s reconstructed in each subdivided run period Calculate the N/L for each run period that used to check the mean and sigma  This checks 1)the detector ( including trigger) operate, 2)electron reconstruction

Number of Z 0i data P9 dataP10 data

Finding New Physics in the dielectron mass spectrum  We expect a narrow resonance, but how do we tell a real peak from a statistical fluctuation?  Look at poisson probability for the expected number of events to fluctuate to the number observed or higher Z Possible Z’

Example from Monte Carlo background, with no signal:  Look at expected vs observed  Example to show method: MC with no signal  Calculate probability to observe N_observed or more Input distribution expectedobserved Probability to observe N_observed or more events

Goal: less model-dependent search  Scan mass range, calculate probability assuming no signal, take into account number of bins searched  Produce plot such as was done for previous analysis 

Search for Z’  Using Sam’s simple program to calculate probability of Z’ in the data  requires input :  Data, MC signal, background distributions (nominal and errors)  Will extend to full Z’ mass spectrum Data Background Signal M Z’ = 300

Summary  We are updating Sam Harper’s analysis to 2 fb- 1 using his code, method  We are validate our implementation using old data  We add p9, p10, p11( in progress)  We will scan the Di-electron mass spectrum  We are understanding the output probability and limit code  Maybe we will see something new? Or, set limits on Z’

ThAnk YoU

N-1 Efficiencies  We calculated the efficiency of each individual cut (N-1 Efficiencies)  E i N-1 = 2 x N TT N TT + N i N-1  where N TT is the number of events with both legs passing all tight cuts and N i N-1 is the number of events with one leg passing all tight cuts and the other leg passing all tight cuts except the i th cut.