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Search for dark matter candidates in events with a jet and missing transverse momentum using the ATLAS detector Pierre-Hugues Beauchemin Tufts University Physical Sciences Symposia-2013, Waltham, MA, 09/05/2013

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Outline Monojet events Physics Motivation Main Standard Model backgrounds Data-driven background estimates Motivation Illustration of the techniques Application to monojet events Results and interpretation Comparison to data Constraints on dark matter Conclusions 2

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Monojet events 3

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Dark Matter Many observational evidences for a large amount of dark matter in the universe 4 One of the strongest motivation for new physics in HEP

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Signature at Colliders Most popular explanation for the nature of dark matter: Massive particles interacting very weakly with matter (WIMPs) Dark matter was more abundant in early universe Dark matter gets annihilated 5 Reverse is true: dark matter can be produced in colliders WIMPs escape detection but can be inferred from unbalance energy measurement in the transverse plane of the detector Need recoil activity, typically jets Dark matter can be signaled in jets+E T miss events at LHC

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New Physics in monojet events Many new physics scenario predicts high production rate for such final state: Generic dark matter produced via contact interaction Invisible Higgs Gauge-mediated SUSY breaking scenario: Gravitino+squark/gluino production o Assume Production of graviton Kaluza-Klein mode in large extra dimension scenarios Unparticle o Equivalent to LED+SUSY in the bulk 6

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Contribution from Standard Model Irreducible background Physics processes with same final state Z +jets 7 q q Reducible background Physics processes with different final states modified by detector effects Wl +jets QCD multijet Non-collision events Others o Dibosons (WW,WZ,ZZ) o Top (ttbar, single top)

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Data-driven background estimates 8

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9 To determine how many SM events should pass the selections defining the chosen final state, we must: predict the number of irreducible and reducible single jets events produced in LHC collisions : Estimate the probability that these SM events yield the monojet signal defined by our event selections: Theoretical calculation of various cross sections Number of collisions produced (Luminosity) Probability distribution of observables for each processes Detector effects on the distributions Standard Model Predictions (I)

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10 The sensitivity to new phenomena depends on: The sensitivity to new phenomena depends on: 2.The systematic uncertainty on the SM expectations 1.The relative amount of new physics and SM contribution Not under our control Is under our control 1. 2. THE KEY IS TO CONTROL SYSTEMATIC UNCERTAINTIES Standard Model Predictions (II)

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11 Use theory & simulations to estimate production rate and model detector effects on probability to select events Systematic uncertainty from approximation and inaccuracy in modeling of: Theoretical calculation Modeling of strong interaction effects at large distance Modeling of detector effects Number of collisions registered Monte Carlo-based estimates

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12 Reduce systematic uncertainty by replacing MC distribution with well understood data distribution similar to the process of interest to avoid bias Stat error only Simulation Data e-e- e+e+ Data-driven techniques 101 (I)

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13 To produce a data-driven predictions, we can: 1- Reverse one (few) signal selection(s) Avoid signal contamination X: Z X 2- Count the number of events in the Y Z (Z X) sample 3- Use ratios to compute mapping factors for the final prediction All events from a dataset Event cut 1 Event cut 2 Event cut N-1 Event cut N Set of selections defining signal (eg: monojet) Signal events Selections to reverse Y: All other selections X Y Data model of the signal If X is unbiased with respect to Y, then Y Z provides a good model for Y X Data-driven techniques 101 (II)

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Data-driven in monojet events 14 Jets observables present similar distributions e e Met Z ee + 1-jet Z + 1-jet E T miss can similarly be obtained after removing the two charged leptons with corrections Must now use ratio to normalize and correct for shape distortion

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Results and interpretation 15

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Various signal regions 16 We dont know the kinematic region in which new physics will get revealed Expectations vary with models Model-independence: dont select the kinematic region based on the indications of a particular model Or do a kinematic scan Lowest kinematic region determined by trigger requirement Statistics is a limitation for data-driven estimate in high kinematic regions ETET Jet 1 E T 0 120 220 350 500

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Background systematics uncertainties When taking all effects and all background into account: For the high stats low kinematic region 17 QCD prediction uncertainty is not the dominant background and is kept at a low level This estimate is a very conservative, essentially only reflecting the small statistics of the sample used to get the estimate. Recent studies suggest a factor of 3 to 5 smaller uncertainty on QCD effects

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The Results (I) 18 Results are consistent with SM regardless of the jet P T and E T miss selections Outstanding precision of

Conclusion Dark matter is an empirical fact established by astrophysics Most popular explanation: a new particle (WIMPs) Escape detections => jets + E T miss events Background predictions to monojet events typically suffer from large systematic uncertainties Use data-driven background estimate ATLAS performed the search and found no evidence for new physics in monojet events Constraints are set on generic effective dark matter scenario Complementary to direct and indirect dark matter searches 28

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Back-up slides 29

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LED: Model assumptions Limits assume extra dimensions are flat and compactified on n-dimensional torus SM fields attached to a 3-brane o Brane deformation ignored Continuous KK-spectrum is assumed, even for n=6 o Universal couplings of each modes o Assumed the spectrum stops at M D Fundamental to effective scale relationship: Prediction from minimal graviton emission model of GRW Valid: E 7 M D

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LED: Experimental signatures Direct graviton production in association with partons or photons Graviton interaction with detector suppressed by M Pl -2 Missing transverse energy Signature at the LHC Monojet o More jets due to QCD radiation Monophoton

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LED: Limits Typical efficiency for jets and Met selection: ~83% Similar for Zvv, and ADD and general dark matter model Set 95% C.L. limits on M D Truncation: quantify UV effects not modeled by L eff o = 0% (n=2), 6% (n=3), 20% (n=4), 45% (n=5), 60% (n=6) model does not make non-ambiguous predictions with SR4 o Limits of SR1 to SR3 are 35%, 15%, 5% worse, but less UV sensitive

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Electroweak background estimate (II) Data-driven prediction for Z +jets background is obtained from: Number of Z ll+jets events in each control regions E T miss bin (N i cand,CR ) Ratio of signal region to control region observable distribution The ratio mapping factor accounts for lepton acceptance and efficiency different cross section and branching ratios distortion of the measured observable due to the charged lepton in the CR Similar exercise can be done to estimate the reducible W+jets background Direct use of the R jets measurement The ratio is also corrected for the probability that the event survive the veto W+jets control region events are also used to estimate Z +jets 33

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QCD multijets background estimate 34 Sources of QCD contribution: 2-jets and 3-jets events for which one of the jets is lost (dominate) 3-jets events for which two jets are lost (smaller) obtained from MC To estimate the 2-jets contribution: 1- Select 2/3-jets events with E T miss vector toward one jet 2- Extrapolate this jet E T below energy threshold (loose a jet) 3- Background prediction = area under the fit in the extrapolated region MC correction 2-jets events Extrapolated region * Jet threshold lowered to 15 GeV to verify the extrapolation

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Results (IV) Preliminary studies with 10 fb -1 of 2012 8 TeV data yields very similar results than the 7 TeV 2011 results. 35