Dark LHC 2018 Search for dark matter in the channel of Mono-H(γγ) at the ATLAS experiment and truth-level reweighting

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1 Dark LHC 2018 Search for dark matter in the channel of Mono-H(γγ) at the ALAS experiment and truth-level reweighting Kristian Bjørke on behalf of the ALAS Collaboration kristian.bjoerke@cern.ch April 5, 2018 Kristian Bjørke DM@LHC 2018 DM in Mono-H(γγ) and truth-level reweighting April 5, 2018 p. 1

2 Mono-H(γγ) DM paper and DM models q Z B model Z Z h χ q χ 3 PRD 96, (2017) ALAS data: Integrated luminosity: 36.1 fb 1 q Z A 0 h χ g H h χ Analysis of three DM models q χ g χ Z -2HDM model Heavy-scalar model Kristian Bjørke DM@LHC 2018 DM in Mono-H(γγ) and truth-level reweighting April 5, 2018 p. 2

3 DM models: Benchmarks and grids Parameters from LHC Dark Matter Forum recommendations 1 : Z B model g χ = 1.0, g q = 1/3, g hz Z /m Z = 1.0 sinθ = 0.3 Benchmark signal point: m Z = 200 GeV m χ = 1 GeV Grid: m χ [1,00] GeV m Z [1,2000] GeV Z -2HDM model m H 0 = m H ± = 300 GeV m χ = 0 GeV tanβ = 1.0, g Z = 0.8 Benchmark signal point: m Z = 00 GeV m A 0 = 200 GeV m χ = 0 GeV Grid: m Z [400,1400] GeV m A 0 [200,450] GeV Heavy-scalar model 2m h < m H < 2m top BR(H hχχ) = 0% m χ = 60 GeV Benchmark signal point: m χ = 60 GeV m H = 275 GeV Grid: m H [260, 350] GeV 1 D. Abercrombie et al., DM models for LHC run-2: Report of the ALAS/CMS DM forum, arxiv: Kristian Bjørke DM@LHC 2018 DM in Mono-H(γγ) and truth-level reweighting April 5, 2018 p. 3

4 Analysis: Event selection and categories Missing E with Higgs ( γγ) Event selection: p > 25 GeV (E /m γγ> 0.35) (E /m > 0.25) γ γγ γ E miss 5 GeV < m γγ < 160 GeV η = 1.37 η = 1.52 η = 2.37 S E miss S E miss S E miss S E miss Mono-Higgs > 7 GeV, p γγ > 90 GeV, lepton veto High-E miss > 5.5 GeV, PV highest = PV γγ Intermediate-E miss > 4 GeV, p hard > 40 GeV, PV highest = PV γγ Different-Vertex > 4 GeV, p hard > 40 GeV, PV highest PV γγ Rest p γγ > 15 GeV Kristian Bjørke DM@LHC 2018 DM in Mono-H(γγ) and truth-level reweighting April 5, 2018 p. 4

5 Analysis: Selection efficiencies Efficiencies for event reconstruction of selection variables at good level (> 85 %) Improvements in pileup robustness for the Higgs to diphoton in ALAS for Run-2 2. Plot shows diphoton isolation efficiency as a function of the number of reconstructed primary vertices. Diphoton isolation efficiency H γ γ (ggh), m = 125 GeV H s = 8 ev s = 13 ev ALAS Simulation Number of reconstructed primary vertices 2 ALAS Collaboration, Masurements of Higgs boson properties in..., arxiv: Kristian Bjørke DM@LHC 2018 DM in Mono-H(γγ) and truth-level reweighting April 5, 2018 p. 5

6 Analysis: Signal and backgrounds Signal and SM Higgs backgrounds: Estimated by theoretical prediction and simulated distributions. Nonresonant backgrounds: (γγ, γ + jet, Wγ, Zγ, Wγγ, Zγγ) Analytic functions fitted to invariant mass distribution. GeV Events / ALAS -1 s = 13 ev, 36.1 fb Z'-2HDM, Dirac DM m χ = 0 GeV, m Z' = 1 ev, m 0 = 200 GeV, m 0,± A H = 300 GeV Z' B, Dirac DM m χ = 1 GeV, m Z'B = 200 GeV Heavy scalar, Scalar DM m χ = 60 GeV, m H = 275 GeV Data SM Higgs boson Syst. Stat. Unc. γ γ Vγ γ+jets Vγγ An overview of the systematic uncertainties are included in the backup slides! E miss significance: S E miss = E miss / E E : Scalar sum of transverse momenta of photons, electrons, muons and jets used for E miss, as well as other tracks associated with PV. Data / MC S miss [ E GeV] Kristian Bjørke DM@LHC 2018 DM in Mono-H(γγ) and truth-level reweighting April 5, 2018 p. 6

7 Analysis: Mono-Higgs category Event yields within 120 < m γγ < 130 GeV: Events / GeV Data-Bkg Background 12.3 ± 1.9 (stat+syst) Data ALAS -1 s = 13 ev, 36.1 fb Non-resonant bkg Non-resonant bkg + h Data h (m h = GeV) Z' B, sinθ = 0.3, g = 1/3, Dirac DM q m χ = 1 GeV, m Z'B = 200 GeV Pre-fit Z' B otal [GeV] m γγ Z B model benchmark: Expected: 20.0 ± 4.5 Z -2HDM model benchmark: Expected: 28.0 ± 5.3 Heavy-scalar model benchmark: Expected:.9 ± 1.4 Visible cross section from BSM: σ BSM vis < 0.19 fb (95% C.L.) [σ BSM vis (A ε σ B) BSM ] Kristian Bjørke DM@LHC 2018 DM in Mono-H(γγ) and truth-level reweighting April 5, 2018 p. 7

8 Analysis: High-E miss category Event yields within 120 < m γγ < 130 GeV: Events / GeV Data-Bkg Background 67.5 ± 5 (stat+syst) Data ALAS -1 s = 13 ev, 36.1 fb miss High-E Non-resonant bkg Non-resonant bkg + h Data h (m h = GeV) Heavy scalar, Scalar DM m χ = 60 GeV, m H = 275 GeV Pre-fit Heavy scalar otal [GeV] m γγ Heavy-scalar model benchmark: Expected: 23.8 ± 3.2 Visible cross section from BSM: σ BSM vis < 0.67 fb (95% C.L.) [σ BSM vis (A ε σ B) BSM ] Kristian Bjørke DM@LHC 2018 DM in Mono-H(γγ) and truth-level reweighting April 5, 2018 p. 8

9 Exclusion limits for DM models Heavy-scalar model: B(H γγχχ) [fb] 95% CL limit on σ(pp H) Observed Expected Expected ± 1σ Expected ± 2σ B(H γγχχ) σ th pp H h( γ γ ) + χ χ, Heavy scalar model = 60 GeV, B(H hχχ) = 0% m χ ALAS -1 s = 13 ev, 36.1 fb Benchmark point [GeV] m H Kristian Bjørke DM@LHC 2018 DM in Mono-H(γγ) and truth-level reweighting April 5, 2018 p. 9

10 Exclusion limits for DM models B(h γγ) [fb] 95% CL limit on σ(pp hχχ) 3 Observed Expected Expected ± 1σ Expected ± 2σ 2 σ th B 1 1 Z B model: pp h( γ γ ) + χ χ, Z' model B sinθ = 0.3, g = 1/3, g = 1, m χ = 1 GeV q χ ALAS s = 13 ev, 36.1 fb Benchmark point m Z'B [GeV] -1 B(h γγ) [fb] 95% CL limit on σ(pp hχχ) Z -2HDM model: Observed Expected Expected ± 1σ Expected ± 2σ σ th BR ALAS s = 13 ev, 36.1 fb pp h( γ γ ) + χ χ, Z'-2HDM model tanβ = 1.0, g Z' m χ = 0 GeV, m = 1 ev, m 0,± Z' H Benchmark point [GeV] m A 0 = 0.8 = 300 GeV -1 Kristian Bjørke DM@LHC 2018 DM in Mono-H(γγ) and truth-level reweighting April 5, 2018 p.

11 Exclusion limits for DM models Z B model: Z -2HDM model: 1D scan Benchmark point 1D scan Benchmark point Kristian Bjørke DM@LHC 2018 DM in Mono-H(γγ) and truth-level reweighting April 5, 2018 p. 11

12 ruth-level reweighting 3 Event-by-event reweighting: Model point 1 Generally quick Generator-level Hard-process events ime-consuming Computationally expensive Detector-level Detector environment Model point 2 Generator-level Hard-process events Detector-level Detector environment 2 Gainer, J.S. et al. J. High Energ. Phys. (2014) 2014: 78. arxiv: Kristian Bjørke DM@LHC 2018 DM in Mono-H(γγ) and truth-level reweighting April 5, 2018 p. 12

13 ruth-level reweighting 3 Event-by-event reweighting: Model point 1 Generally quick Generator-level Hard-process events ω i (MP1) ime-consuming Computationally expensive Detector-level Detector environment ω i(mp2) ω i (MP1) Model point 2 Generator-level Hard-process events ω i (MP2) Detector-level Detector environment 2 Gainer, J.S. et al. J. High Energ. Phys. (2014) 2014: 78. arxiv: Kristian Bjørke DM@LHC 2018 DM in Mono-H(γγ) and truth-level reweighting April 5, 2018 p. 12

14 ruth-level reweighting 3 Event-by-event reweighting: Model point 1 Generally quick Generator-level Hard-process events ω i (MP1) ime-consuming Computationally expensive Detector-level Detector environment ω i(mp2) ω i (MP1) ω i(mp2) ω i (MP1) Model point 2 Generator-level Hard-process events ω i (MP2) Detector-level Detector environment 2 Gainer, J.S. et al. J. High Energ. Phys. (2014) 2014: 78. arxiv: Kristian Bjørke DM@LHC 2018 DM in Mono-H(γγ) and truth-level reweighting April 5, 2018 p. 12

15 ruth-level reweighting for Mono-Higgs(γγ) Method: hree model points (1, 2, 3) to reweight. Generator-level Events Detector-level Events p γγ S miss E Kristian Bjørke DM@LHC 2018 DM in Mono-H(γγ) and truth-level reweighting April 5, 2018 p. 13

16 ruth-level reweighting for Mono-Higgs(γγ) Method: hree model points (1, 2, 3) to reweight. Select MP2 as base sample, because of representative kinematics. Generator-level Events Detector-level Events p γγ S miss E Kristian Bjørke DM@LHC 2018 DM in Mono-H(γγ) and truth-level reweighting April 5, 2018 p. 13

17 ruth-level reweighting for Mono-Higgs(γγ) Method: hree model points (1, 2, 3) to reweight. Select MP2 as base sample, because of representative kinematics. Reweight from MP2 using 1D weights based on p γγ to get MP1 and MP3. Generator-level Events 1D Detector-level Events p γγ S miss E weight(p γγ ) = NormBinCountMP1/3 (p γγ ) NormBinCount MP2 (p γγ ) Kristian Bjørke DM@LHC 2018 DM in Mono-H(γγ) and truth-level reweighting April 5, 2018 p. 13

18 ruth-level reweighting for Mono-Higgs(γγ) Method: hree model points (1, 2, 3) to reweight. Select MP2 as base sample, because of representative kinematics. Reweight from MP2 using 1D weights based on p γγ to get MP1 and MP3. Not satisfactory results of reweighting, introduce new truth variable p χχ. Generator-level Events p γγ 1D Detector-level Events Events S miss E p χχ weight(p γγ ) = NormBinCountMP1/3 (p γγ ) NormBinCount MP2 (p γγ ) Kristian Bjørke DM@LHC 2018 DM in Mono-H(γγ) and truth-level reweighting April 5, 2018 p. 13

19 ruth-level reweighting for Mono-Higgs(γγ) Method: hree model points (1, 2, 3) to reweight. Select MP2 as base sample, because of representative kinematics. Reweight from MP2 using 1D weights based on p γγ to get MP1 and MP3. Not satisfactory results of reweighting, introduce new truth variable p χχ. Improve reweighting by using 2D weights based on p γγ and pχχ. Generator-level Events Events p γγ 2D Detector-level Events S miss E p χχ weight(p γγ,pχχ ) = NormBinCountMP1/3 (p γγ,pχχ ) NormBinCount MP2 (p γγ,pχχ ) Kristian Bjørke DM@LHC 2018 DM in Mono-H(γγ) and truth-level reweighting April 5, 2018 p. 13

20 ruth-level reweighting for Mono-Higgs(γγ) Method: hree model points (1, 2, 3) to reweight. Select MP2 as base sample, because of representative kinematics. Reweight from MP2 using 1D weights based on p γγ to get MP1 and MP3. Not satisfactory results of reweighting, introduce new truth variable p χχ. Improve reweighting by using 2D weights based on p γγ and pχχ. Generator-level Events Events p γγ 2D Detector-level Events S miss E Concerns: Curse of dimensionality with many dimensional weights. Possible solution: BD reweighting 4. p χχ weight(p γγ,pχχ ) = NormBinCountMP1/3 (p γγ,pχχ ) NormBinCount MP2 (p γγ,pχχ ) Alex Rogozhnikov 2016 J. Phys.: Conf. Ser , arxiv: Kristian Bjørke DM@LHC 2018 DM in Mono-H(γγ) and truth-level reweighting April 5, 2018 p. 13

21 ] Summary ALAS Mono-H(γγ) DM analysis paper: Analysis of three DM models: Z B model Z -2HDM model Heavy-scalar model Observed limits set for model benchmarks and for relevant parameter grids. Comparison of inferred limits on SI DM-nucleon cross section for Z B model. ruth-level reweighting: 2 DM-nucleon cross section [cm 47 Method to study different models/large parameter spaces efficiently Considerably reduce number of needed full detector level simulations. pp h( γ γ ) + χ χ, Z', Dirac DM B sinθ = 0.3, g = 1/3, g = 1 q χ Spin-independent 1 supercdms CRESS-II 2016 XENON1 2 ALAS -1 s = 13 ev, 36.1 fb PandaX-II LUX % CL DM mass m χ [GeV] 3 Kristian Bjørke DM@LHC 2018 DM in Mono-H(γγ) and truth-level reweighting April 5, 2018 p. 14

22 Backup slides Exclusion contours for Z -2HDM model from Mono-H(b b) search in ALAS 5. Same set parameters as used for Mono-H(γγ) search. More stringent limits than for Mono-H(γγ) search presented here. [GeV] m A ALAS -1 s = 13 ev, 36.1 fb miss h(bb) + E, all limits at 95% CL Z -2HDM tanβ = 1, g = 0.8, m χ = 0 GeV Z m H = m ± H = 300 GeV Kin. limit : m A = m Z - m h Observed limit Expected limit ±1σ -1 s = 13 ev, 3.2 fb [GeV] m Z 5 ALAS Collaboration, Phys. Rev. Lett. 119, (2017) Kristian Bjørke DM@LHC 2018 DM in Mono-H(γγ) and truth-level reweighting April 5, 2018

23 Backup slides Results: Intermediate-E miss category Event yields within 120 < m γγ < 130 GeV: Events / GeV Data-Bkg Background 430 ± (stat+syst) Data ALAS -1 s = 13 ev, 36.1 fb miss Intermediate-E Non-resonant bkg Non-resonant bkg + h Data h (m h = GeV) Heavy scalar, Scalar DM m χ = 60 GeV, m H = 275 GeV Pre-fit Heavy scalar otal [GeV] m γγ Heavy-scalar model benchmark: Expected: 43 ± 5 Visible cross section from BSM: σ BSM vis < 1.6 fb (95% C.L.) [σ BSM vis (A ε σ B) BSM ] Kristian Bjørke DM@LHC 2018 DM in Mono-H(γγ) and truth-level reweighting April 5, 2018

24 Backup slides Results: Different-vertex category Event yields within 120 < m γγ < 130 GeV: Events / GeV Data-Bkg Background 1535 ± 21 (stat+syst) Data ALAS 800 Data -1 s = 13 ev, 36.1 fb 0 50 Different-Vertex Non-resonant bkg Non-resonant bkg + h h (m h = GeV) Heavy scalar, Scalar DM m χ = 60 GeV, m H = 275 GeV Pre-fit Heavy scalar otal [GeV] m γγ Heavy-scalar model benchmark: Expected: 33 ± 5 Visible cross section from BSM: σ BSM vis < 1.5 fb (95% C.L.) [σ BSM vis (A ε σ B) BSM ] Kristian Bjørke DM@LHC 2018 DM in Mono-H(γγ) and truth-level reweighting April 5, 2018

25 Backup slides Results: Rest category Event yields within 120 < m γγ < 130 GeV: Events / GeV Data-Bkg Background ± 170 (stat+syst) Data ALAS s = 13 ev, 36.1 fb Rest Non-resonant bkg Non-resonant bkg + h Data h (m h = GeV) Heavy scalar, Scalar DM m χ = 60 GeV, m H = 275 GeV Pre-fit Heavy scalar otal [GeV] m γγ Heavy-scalar model benchmark: Expected: 24.9 ± 0.4 Visible cross section from BSM: σ BSM vis < 11 fb (95% C.L.) [σ BSM vis (A ε σ B) BSM ] Kristian Bjørke DM@LHC 2018 DM in Mono-H(γγ) and truth-level reweighting April 5, 2018

26 Backup slides Analysis results: Mass window: 120 < m γγ < 130 GeV, Selection acceptance times efficiency: A ε Category Mono-Higgs High-E miss Intermediate-E miss Different-Vertex Rest Data Backgrounds SM Higgs boson 2.43 ± ± ± ± 1360 ± 1 Non-resonant 9.9 ± ± ± 1490 ± ± 1 otal background 12.3 ± ± ± 1535 ± ± 170 Z B model, m Z B = 200 GeV, m χ = 1 GeV Expected yields 20.0 ± 4.5 A ɛ [%] 17.4 ± 0.2 Z -2HDM model, m Z = 00 GeV, m A 0 = 200 GeV, m H 0,± = 300 GeV, and m χ = 0 GeV Expected yields 28.0 ± 5.3 A ɛ [%] 70.7 ± 0.2 Heavy-scalar model, m H = 275 GeV, m χ = 60 GeV Expected yields.9 ± ± ± 5 33 ± ± 20 A ɛ [%] 1.22 ± ± ± ± ± 0.4 Kristian Bjørke DM@LHC 2018 DM in Mono-H(γγ) and truth-level reweighting April 5, 2018

27 Backup slides Observed and expected upper limits: Limits at 95% C.L. on the visible cross section for BSM physics processes producing Mono-H(γγ): Category σvis BSM [fb] A ɛ [%] Observed Expected Z -2HDM Z B Heavy scalar Mono-Higgs High-E miss Intermediate-E miss Different-Vertex Rest Kristian Bjørke DM@LHC 2018 DM in Mono-H(γγ) and truth-level reweighting April 5, 2018

28 Backup slides Event category variables: E miss significance: S E miss = E miss / E E : Scalar sum of transverse momenta of photons, electrons, muons and jets used in E miss calculations as well as other tracks associated with PV. Diphoton transverse momentum: p γγ p hard : Magnitude of the vectorial sum of the transverse momenta of photons and jets in the event. Leton veto: No leptons in event. PV γγ Primary vertex of the selected photon pair. PV highest Primary vertex with highest p γγ (sum of squared transverse momenta of tracks associated with reconstructed vertex) Kristian Bjørke DM@LHC 2018 DM in Mono-H(γγ) and truth-level reweighting April 5, 2018

29 Backup slides Distributions for p γγ and phard : Mass window: 120 < m γγ < 130 GeV Events / GeV Data / MC ALAS -1 s = 13 ev, 36.1 fb Z'-2HDM, Dirac DM m χ = 0 GeV, m Z' = 1 ev, m 0 = 200 GeV, m 0,± A H = 300 GeV Z' B, Dirac DM m χ = 1 GeV, m Z'B = 200 GeV Heavy scalar, Scalar DM m χ = 60 GeV, m H = 275 GeV Data SM Higgs boson Syst. Stat. Unc γγ p [GeV] γ γ Vγ γ+jets Vγγ Events / GeV Data / MC ALAS -1 s = 13 ev, 36.1 fb Z'-2HDM, Dirac DM m χ = 0 GeV, m Z' = 1 ev, m 0 = 200 GeV, m 0,± A H = 300 GeV Z' B, Dirac DM m χ = 1 GeV, m Z'B = 200 GeV Heavy scalar, Scalar DM m χ = 60 GeV, m H = 275 GeV Data SM Higgs boson Syst. Stat. Unc [GeV] γ γ Vγ hard p γ+jets Vγγ Kristian Bjørke DM@LHC 2018 DM in Mono-H(γγ) and truth-level reweighting April 5, 2018

30 Backup slides Signal and backgrounds Signal and SM Higgs backgrounds: heoretical cross sections Normalization: Multiplied with acceptance times efficiency from detector simulation. Shape: Double-sided crystal ball fitted to simulated diphoton mass distribution. Nonresonant backgrounds: (γγ, γ + jet, Wγ, Zγ, Wγγ, Zγγ) Normalization and Shape: Fitting diphoton invariant mass distributions to analytic functions (exponentials of different-order polynomials, Bernstein polynomials, etc.). Select background parametrization with smallest modeling uncertainty Nbkg nonres and minimum number of free parameters. GeV Events / Data / MC ALAS -1 s = 13 ev, 36.1 fb Z'-2HDM, Dirac DM m χ = 0 GeV, m Z' = 1 ev, m 0 = 200 GeV, m 0,± A H = 300 GeV Z' B, Dirac DM m χ = 1 GeV, m Z'B = 200 GeV Heavy scalar, Scalar DM m χ = 60 GeV, m H = 275 GeV Data SM Higgs boson Syst. Stat. Unc S miss [ GeV] E γ γ Vγ γ+jets Vγγ An overview of the systematic uncertainties are included in the backup slides! Kristian Bjørke DM@LHC 2018 DM in Mono-H(γγ) and truth-level reweighting April 5, 2018

31 Backup slides Signal and background parametrization: he table lists the analytic functions used to model the nonresonant m γγ distributions for each category. Category Function N non res bkg N non res bkg /N non res. bkg [%] N non res bkg /N signal [%] Mono-Higgs exp(a x) High-E miss (1 x 1/3 ) b x a Intermediate-E miss exp(a x + b x 2 ) Different-Vertex exp(a x + b x 2 ) Rest j=0 Cj 3 xj (1 x) 3 j b j,3 61 < Nonresonant background modeling uncertainty N nonres bkg : Signal event yield extracted from a signal-plus-background maximum-likelihood fit to a background-only diphoton invariant mass distribution with small statistical fluctuations. Kristian Bjørke DM@LHC 2018 DM in Mono-H(γγ) and truth-level reweighting April 5, 2018

32 Backup slides Systematic uncertainties: Kristian Bjørke 2018 DM in Mono-H(γγ) and truth-level reweighting April 5, 2018

33 Backup slides Simulated samples: MC signal samples: Z B, Z -2HDM, Heavy-scalar models Generated using MadGraph_aMC@NLO at LO in quantum chromodynamics (QCD) using NNPDF3.0LO parton distribution function (PDF) set. Parton showering and hadronization are handles by Pythia event generator with the A14 set of tuned parameters (tune), using the NNPDF2.3LO PDF set. MC background samples: Resonant SM Higgs-boson Wh, Zh: Pythia with the A14 tune and the NNPDF2.3LO PDF set. ggf, VBF: Powheg-Box 2 interfaced with Pythia with A14 tune and the NNPDF2.3LO PDF set. t th: MadGraph_aMC@NLO interfaced to Pythia with the NNPDF3.0LO PDF set. b bh: MadGraph_aMC@NLO interfaced to Pythia with A14 tune and NNPDF2.3LO PDF set. Nonresonant backgrounds γγ, γ+jet, Wγ, Zγ, Wγγ, Zγγ: Sherpa with C PDF set. Kristian Bjørke DM@LHC 2018 DM in Mono-H(γγ) and truth-level reweighting April 5, 2018

34 Backup slides ruth-level reweighting Solution 6 : Many BSM models, many model parameters, many observables many, many, many simulations! 1. Generator level samples {G i } for a model (A, α). 2. Pass {G i } through detector sim. and cuts to get {D i }. 3. Cross-section and detector level predictions from {D i }. 4. Apply weight ω(b,β,g i )/ω(a,α,g i ) for model (B,β) 6 Gainer, J.S. et al. J. High Energ. Phys. (2014) 2014: 78. arxiv: Kristian Bjørke DM@LHC 2018 DM in Mono-H(γγ) and truth-level reweighting April 5, 2018

35 Backup slides ruth-level reweighting for the LHC Event-by-event reweighting 7 : Keep track of hard-process/parton-level events that are subjected to detector simulation. Calculate weights for each of the events. Calculate corresponding weights for another model/parameter-point for the same events. Apply weight ratios to corresponding detector level events to get detector level estimation for new model/parameter-point. Mono-H(γγ) approach: Select one or more observables relevant for analysis. Get distributions from generator-level events. Get corresponding distributions for another model/parameter-point at generator-level. Calculate bin-weights as the ratio between the bin contents from the generator-level distributions. Reweight detector-level by applying the bin-weights event-by-event to the detector-level samples. Reweighting is validated in a Closure est using selected points simulated at detector-level. 7 Mattelaer, O. Eur. Phys. J. C (2016) 76: 674. arxiv: Kristian Bjørke DM@LHC 2018 DM in Mono-H(γγ) and truth-level reweighting April 5, 2018