Any discovery of new physics relies on detailed understanding of the Standard Model background. At the LHC, we expect to extract the backgrounds from the data itself, with minimum reliance on Monte Carlo simulations. We describe new developments in ATLAS on such data-driven techniques, and prospects for their application on first data

Legger, F

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ATL-PHYS-PROC-2009-08018August2009New Developments in Data-driven BackgroundDeterminations for SUSY Searches in ATLASF. Legger on behalf of the ATLAS collaborationMax-Planck-Instutut für Physik, München, GermanyAbstract. Any discovery of new physics relies on detailed understanding of the Standard Modelbackground. At the LHC, we expect to extract the backgrounds from the data itself, with minimumreliance on Monte Carlo simulations. We describe new developments in ATLAS on such data-driventechniques, and prospects for their application on first data.Keywords: LHC, Supersymmetry, ATLAS, data-driven estimation, inclusive searchesPACS: 11.30.Pb, 14.80.Ly, 29.85.FjINTRODUCTIONHigh-energy jets, missing transverse energy (EmissT ) and possibly leptons are the typicalsignature of R-parity conserved SUSY events at the LHC. The observation of an excessof events with these signatures with respect to the Standard Model prediction maymanifest the presence of SUSY. Due to the poor knowledge of Standard Model cross-sections, parton distribution functions, underlying event and parton showering at theLHC energy scale, as well as insufficient knowledge of the detector itself, a reliableprediction of the Standard Model backgrounds should be derived mainly from theexperimental data with reduced reliance on Monte Carlo simulations. In ATLAS thesearches for a SUSY signal in events with a multijet and EmissT signature are classifiedaccording to the number of reconstructed leptons (electrons or muons). In this notewe present an overview of data-driven methods recently developed by the ATLAScollaboration to estimate the Standard Model background in SUSY searches in theone-lepton mode. The detailed description of the cuts applied in the one-lepton modecan be found in [1]. The most important Standard Model processes contributing to thebackground in this mode are t ¯t and W plus jets production.mT-METHOD: REVERSING THE mT CUTIn Standard Model processes the main source of the EmissT and isolated leptons is thedecay of the W boson (produced directly in the pp collisions or in the top quark decays).Consequently the invariant mass constructed from the transverse component of theisolated lepton and EmissT (called transverse mass, mT) should not exceed the mass ofthe W boson in an Standard Model event. In SUSY events, the EmissT is produced mainlyby the pair of non-interacting lightest supersymmetric particles and the restriction onmT does not hold. Following this, a signal region is defined by mT> 100 GeV, while the [GeV]TMissing E0 100 200 300 400 500 600 700 800 900 1000 / 50GeV−1Events / 1fb−110110210 truth BGtruth BG+SUSYtruth SUSYest. BG (old MT)ATLAS [GeV]TMissing E0 100 200 300 400 500 600 700 800 900 1000 / 50GeV−1Events / 1fb−110110210 truth BGtruth BG+SUSYtruth SUSYest. BG (new MT)ATLASFIGURE 1. Missing transverse energy distribution for events passing one-lepton SUSY search cuts.Dashed histogram show the Standard Model background and circles show the result of data-drivenestimation with mT-method in the presence of SUSY with the mass scale of 600 GeV (SU3). Left (right)figure shows the estimate before (after) correcting for the SUSY contamination in the control sample.events with mT< 100 GeV constitute the control sample. The number of Standard Modelevents in the signal region and at high values of EmissT is derived from the control sample,applying the efficiency of the mT cut. This efficiency is estimated in the low-EmissT region(normalization region), where the contribution of SUSY events is expected to be low.The method is based on two assumptions. First, it requires low contribution of SUSYevents both in the control sample and in the normalization region. Second, the method re-quires that EmissT and mT are uncorrelated. The first assumption might not hold especiallyfor low-mass SUSY. The second assumption is not true mainly because the fractions ofsemi-leptonic and di-leptonic top events depends on mT. However the correlation be-tween EmissT and mT is not large, the linear correlation factor is about 4%. Due to theSUSY contamination in the control sample and in the normalization region, the estimatedelivered by this method is above the true Standard Model background. The correlationbetween EmissT and mT leads to background under-estimation for high-mass SUSY andto over-estimation for low-mass SUSY.One can correct for the signal contamination in the control sample in the followingway. First, the background in the signal region is estimated, and the remaining eventsare taken as the signal estimate. Then the signal estimate is translated back to the controlregion (mT< 100 GeV) and subtracted from the background. Figure 1 compares thetrue Standard Model background to the estimated one in the presence of mSUGRASUSY signal with mass scale of 600 GeV. The left (right) figure shows the comparisonbefore (after) correcting the estimate for the SUSY contamination in the control sample.The correction procedure may not work for low-mass SUSY, as in this case the controlsample contamination would be too large. Another disadvantage of the method is theneed of an explicit assumption on the signal shape to translate it from the signal regionto the control region.COMBINED FITThis method [1] estimates separately the contribution of the various Standard Modelprocesses. Each Standard Model process, semi-leptonic t ¯t, di-leptonic t ¯t and W (+ jets) [GeV]TmissE100 150 200 250 300 350 400 450 500-1Events / 1fb020406080100120tt -> bb lv qq ATLAS-1Events / 1fb [GeV]Tm0 50 100 150 200 250 300 350 400-1Events / 1fb020406080100120 tt -> bb lv qq-1Events / 1fb [GeV]topm0 200 400 600 800 1000 1200 1400-1Events / 1fb050100150200 tt -> bb lv qq-1Events / 1fb [GeV]TmissE100 150 200 250 300 350 400 450 500-1Events / 1fb05101520tt -> bb lv lv-1Events / 1fb [GeV]Tm0 50 100 150 200 250 300 350 400-1Events / 1fb-202468101214 tt -> bb lv lv-1Events / 1fb [GeV]topm0 200 400 600 800 1000 1200 1400-1Events / 1fb05101520 tt -> bb lv lv-1Events / 1fb [GeV]TmissE100 150 200 250 300 350 400 450 500-1Events / 1fb051015202530354045W+jets-1Events / 1fb [GeV]Tm0 50 100 150 200 250 300 350 400-1Events / 1fb05101520253035 W+jets-1Events / 1fb [GeV]topm0 200 400 600 800 1000 1200 1400-1Events / 1fb051015202530 W+jets-1Events / 1fb [GeV]TmissE100 150 200 250 300 350 400 450 500-1Events / 1fb05101520253035SU3-1Events / 1fb [GeV]Tm0 50 100 150 200 250 300 350 400-1Events / 1fb051015202530SU3-1Events / 1fb [GeV]topm0 200 400 600 800 1000 1200 1400-1Events / 1fb051015202530354045SU3-1Events / 1fbFIGURE 2. Distributions of EmissT , mT and mtop in semi-leptonic and di-leptonic t ¯t events, W (+ jets)events and SUSY events for mSUGRA point with SUSY scale of 600 GeV. Superimposed lines are theparametrizations used in combined fit method.is parametrized in terms of the distributions of three variables, EmissT , mT and mtop1.The parametrization is based on the Monte Carlo prediction for the distribution of thevariables, as well as on the analysis of dedicated control samples. The contribution ofSUSY signal is described with a “generic shape”, derived from the shapes of the ATLASbenchmark SUSY points [2]. The simultaneous fit of the distributions of EmissT , mT andmtop is performed for the sum of background components and the “generic shape” of theSUSY contribution. The fit parameters include the normalizations of each backgroundand SUSY contributions as well as parameters of the backgrounds parametrization andthe SUSY “generic shape”. Figure 2 shows the distributions of the variables EmissT , mTand mtop compared to the chosen parametrization for the background components andthe ATLAS mSUGRA benchmark point with a mass scale of 600 GeV. The advantage ofthis method with respect to the mT-method is the explicit accounting of the correlationbetween the variables.TILES METHODIn the Tiles Method [3] the meff–mT plane2 is divided in quadrants, called tiles. Eachtile has contributions from SUSY signal and from the background. The fractions of theStandard Model events entering each tile is estimated from the Monte Carlo. The yields1 mtop is defined as an invariant mass of three jets with largest vector-summed transverse momentum2 Effective mass meff is defined as a scalar sum of transverse momenta of the four leading jets, transversemomenta of all identified leptons and EmissTof SUSY and background events is estimated by solving the system of equationsnA = fA nSM+SUSYAnB = fB nSM+SUSYBnC = fC nSM+SUSYC (1)nD = fD nSM+SUSYDSUSYA SUSYD = SUSYB SUSYCHere A, B, C and D denote four tiles, nX is the measured number of events in the tile X ,fX is the fraction of Standard Model events in the tile X , nSM is the unknown number ofStandard Model events in all tiles and SUSYX is the unknown number of SUSY eventsin the tile X . The last equation comes from the assumption of no correlation betweenmeff and mT for signal events. The correlation of the variables for the Standard Modelcontribution is fully taken into account by the values of fX . The method can be general-ized to a NN tiles configuration. In such a configuration the system of equations (1) isover-constrained and a solution can be found by minimizing the extended log-likelihoodestimator constructed from the measured number of events in the tile and the expectednumber of events calculated as the sum of Standard Model and SUSY contributions.The statistical error of the method depends on the tiles configuration and can beoptimized for a given luminosity. With a small number of tiles the system only hasa few degrees of freedom and the statistical fluctuations of the number of measuredevents in the tiles have a large influence on the estimated yields of signal and StandardModel events. In the configuration with many tiles, statistics in each tile is low, thus thestatistical error of the estimate is high. The optimal number of tiles for a luminosity of1 fb 1 is found to be 8 8. A configuration with many tiles has the advantage of thepossibility to add new parameters to the fit, for example, a linear correlation factor, orseparation of the various background contributions.CONCLUSIONSeveral data-driven methods for the estimation of Standard Model background to theone-lepton SUSY searches are presented. The conventional mT-method has been studiedand several developments have been proposed. The over-estimate due to the presenceof SUSY events in the control sample can be corrected by an iterative procedure. Thecombined fit technique and the Tiles Method allow the explicit accounting of variablecorrelation and presence of SUSY.REFERENCES1. G. Aad, et al., “Data-Driven Determinations of W, Z and Top Backgrounds to Supersymmetry,” inExpected Performance of the ATLAS Experiment - Detector, Trigger and Physics, arXiv:0901.0512,CERN-OPEN-2008-020, 2009, pp. 1525–1561.2. G. Aad, et al., “Supersymmetry Searches,” in Expected Performance of the ATLAS Experiment -Detector, Trigger and Physics, arXiv:0901.0512, CERN-OPEN-2008-020, 2009, pp. 1514–1524.3. G. Aad, et al., Background Estimation for Inclusive SUSY Searches - The Tiles Method (2009), aTLASnote ATL-PHYS-PUB-2009-077.

New Developments in Data-driven Background Determinations for SUSY Searches in ATLAS

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