Cross-section systematics / inputs for VALOR DUNE ND analysis - - PowerPoint PPT Presentation

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Cross-section systematics / inputs for VALOR DUNE ND analysis - - PowerPoint PPT Presentation

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Cross-section systematics / inputs for VALOR DUNE ND analysis Costas Andreopoulos, Steve Dennis, Lorena Escudero, March 2016 Cross-section systematics in VALOR General approach for the 2nd pass-through implementation: 1. Cover all modelling

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SLIDE 1

Cross-section systematics / inputs for VALOR DUNE ND analysis

Costas Andreopoulos, Steve Dennis, Lorena Escudero,

March 2016

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SLIDE 2

Cross-section systematics in VALOR

Lorena Escudero, March 2016 2

1. Cover all modelling aspects

  • For the 1st pass-through, only 6 simple normalizations cross-section systematics were

used

  • For the 2nd pass-through ~40 systematics are included to cover neutrino-nucleon/

neutrino-nucleus cross-section, hadronization and intranuclear rescattering (next slides)

2. Use predominantly linear parameters

  • Some of them are not linear thought (e.g. pion mean free path systematic), and prior

response functions are necessary (next slides)

3. Maintain relative independence from DUNE-specific tools (e.g weighting schemes)

  • Medium-term goal is to reuse this analysis for SBN by allowing multiple detectors in the

fit, swapping the NuMI with the Booster flux covariance matrix and plugging-in

  • scillation probabilities. Aiming for all components of this analysis to be easily re-usable

in SBN.

General approach for the 2nd pass-through implementation:

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SLIDE 3

Cross-section systematics in VALOR

Lorena Escudero, March 2016 3

General approach for the 2nd pass-through implementation:

4. Use predominantly model-independent parameters

  • Model-independent parameters (e.g. `normalisation of CCQE events in a Q2 (or
  • ther kinematic variable range’ instead of MA, MV, Eb, kF,…)
  • 1. Easier to explain and defend
  • 2. Simplify treatment of different models choices within a single GENIE

release

  • 3. Simplify migration from one GENIE release/tune to another (where default

models/parameters will change)

  • VALOR in sync with GENIE development, will use v3.0.0 tune and error analysis

as soon as it becomes available

  • A covariance matrix with the input prefit errors will be computed (next slides)
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SLIDE 4

Lorena Escudero, March 2016 4

  • 6 correlated CCQE parameters, 3 kinematic bins each for Nu/NuBar.
  • 12 correlated CC1pi parameters 3 kinematic bins each for Nu/NuBar and

charged/neutral pions.

  • 1 MEC normalisation systematic
  • 1 Other CC resonance (eg 1-gamma) systematic
  • 6 CC DIS (>1 pion) systematics, 3 kinematic bins each for Nu/NuBar
  • 1 NuE/NuMu normalisation systematic
  • 1 Nu/NuBar normalisation systematic
  • 1 NC normalisation
  • 1 Coherent normalisation
  • 1 Pion and Nucleon mean free path systematic
  • 1 Pion charge exchange fraction systematic
  • 1 Pion absorption & multi-nucleon knockout systematic
  • 1 Pion inelastic fraction systematic
  • 1 Hadronization systematic for events containing Etas etc

Studies to define binning ongoing

Total: 37 parameters

  • 1. Cover all modelling aspects

Not final but a comprehensive list for the 2nd pass-through, even more complexity can be added later

Cross-section systematics in VALOR

Even for the 2nd pass-through, still possible to tweak this list, should be finalised in the upcoming weeks

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SLIDE 5

Lorena Escudero, March 2016 5

  • 1. Cover all modelling aspects
  • There are many modelling aspects to be constrained by the ND.
  • The VALOR DUNE/ND analysis is already substantially complex, currently constraining ~100

flux + ~40 interaction modelling systematics.

  • The chosen number of interaction modelling parameters (~40) is a trade-of between the

desire to start covering all modelling aspects and technical limitations (fit should be fast enough to allow numerous early studies / ~140-parameter fit using the standard 18 VALOR DUNE/ND samples takes several minutes)

  • The goal during the second pass-through is to validate and understand the fit performance
  • Informed by the fit performance studies, the effect of individual cross-section systematics
  • n CP sensitivity, and by parallel studies comparing new GENIE comprehensive model

configurations with external data, new cross-section systematics parameters will be added (or, possibly, existing ones will be removed) early in the summer.

  • At the same time we will manage the complexities arising from constructing the marginal

likelihood (integrating out detector systematics via toy-MC generation) at each minimisation step.

  • Goal is to have a nearly-final version of the VALOR DUNE/ND fit by the Sept. collaboration

meeting

Why ~40 interaction modelling systematics?

Cross-section systematics in VALOR

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SLIDE 6

Lorena Escudero, March 2016 6

Prefit cross-section errors in VALOR

  • Considering 40 interaction modelling systematics, we plan to feed into the VALOR

DUNE/ND analysis a 40 x 40 interaction modelling error matrix

  • We develop a procedure to construct this 40 x 40 error matrix using the to large extent

(but not exclusively) the standard GENIE reweighing tools

  • A covariance matrix is created by tweaking GENIE systematic parameters of the

different interaction models. For each tweak, a weight of the number of the events in each model-independent parameters is calculated and the ensemble of those weights allows us to construct the covariance matrix (more details in next slides).

  • The procedure itself is largely model-agnostic, so can be easily adapted to the new

GENIE tune (v3.0.0).

  • This error matrix encapsulates a systematic error analysis on the default model.
  • Will be supported by detailed data/MC comparisons performed internally by GENIE.
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SLIDE 7

Lorena Escudero, March 2016 7

Prefit cross-section errors in VALOR

  • The error matrix can be extended to incorporate the result of other

independent studies, so it will serve as interface with other (non VALOR/ GENIE) groups interested in interaction modelling

  • If you think there is an important source of systematic uncertainty

which is neglected in our studies, please provide a covariance matrix in the appropriate format and we will add it to the one we use.

  • This systematic error analysis on the default model used in the VALOR

DUNE/ND fit is not critically important.

  • The only requirement is that error assignments are amply conservative.

We will validate using GENIE data/MC comparisons.

  • We will be presenting studies and constructed covariance matrix in following

meetings

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SLIDE 8

Lorena Escudero, March 2016 8

Prefit cross-section errors in VALOR

Considering 40 interaction modelling systematics, we plan to feed into the VALOR DUNE/ND analysis a 40 x 40 matrix of interaction modelling pre-fit errors

  • 6 correlated CCQE parameters, 3

kinematic bins each for Nu/NuBar.

  • 12 correlated CC1pi parameters 3

kinematic bins each for Nu/NuBar and charged/neutral pions.

  • etc

Tweak internal model- dependent parameters in GENIE Translate into effect in the correlated model-independent parameters used in the VALOR/ND fit

Using simple GENIE reweight tools

  • MA
  • MV
  • EB
  • kF
  • etc

Construct covariance matrix by tweaking multiple internal parameters at the same time (using a normal distribution with their 1σ error inside GENIE), reweighting the number of events to obtain the effect in terms of the model-independent parameters

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SLIDE 9

Lorena Escudero, March 2016 9

Quick test!

Prefit cross-section errors in VALOR

  • With simple MC events (25k ν+ 25k

anti-ν) with 0-120 GeV

  • Just hundred tweaks of only MA CC

QE and MA CC RES

  • In a preliminary binning in y_reco

(smeared with a 10% resolution from true values)

FRACTIONAL COVARIANCE CORRELATION

0: ν CCQE yreco (bin1) 1: ν CCQE yreco (bin2) 2: ν CCQE yreco (bin3) 3: anti ν CCQE yreco (bin1) 4: anti ν CCQE yreco (bin2) 5: anti ν CCQE yreco (bin3) 6: ν CC1piC yreco (bin1) 7: ν CC1piC yreco (bin2) 8: ν CC1piC yreco (bin3)

yreco binning different for each category and flavour

9: anti ν CC1piC yreco (bin1) 10: anti ν CC1piC yreco (bin2) 11: anti ν CC1piC yreco (bin3) 12: ν CC1pi0 yreco (bin1) 13: ν CC1pi0 yreco (bin2) 14: ν CC1pi0 yreco (bin3) 15: anti ν CC1pi0 yreco (bin1) 16: anti ν CC1pi0 yreco (bin2) 17: anti ν CC1pi0 yreco (bin3)

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SLIDE 10

Input response functions

Lorena Escudero, March 2016 10

We will be using predominantly linear parameters (normalizations), but some parameters are not linear (e.g. pion mean free path systematic) For those non linear parameters we will construct a response function:

  • Precomputed functions storing averaged weights as a

function of the tweak of the parameter

  • They allow to account for variations in the interaction

models without re-generating the MC

  • One response function is to be calculated for each bin of

the 3D(2D) templates used to build our prediction, i.e. for each neutrino flavour, interaction mode, true and reconstructed variables bin

Dummy example They are computed as well using the reweighting functionality of GENIE Methods to use response functions are already in place in the VALOR DUNE ND fit, only necessary to compute them now

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SLIDE 11

Summary

Lorena Escudero, March 2016 11

We will be using a ~40x40 covariance matrix with the pre-fit interaction modelling errors, which will be the input to our VALOR ND fit

  • Currently performing studies to define binning and construct it
  • It will be validated with GENIE data/MC comparisons
  • It will be redone using a more realistic approach, with more statistics, more throws of the systematic

parameters, and reweighting multiple GENIE parameters at the same time

This matrix will serve as interface with other groups interested in interaction modelling, as it can be easily extended to incorporate the effect of other systematic uncertainties if necessary For non linear systematics, response functions are necessary

  • Machinery is ready to use response functions inside VALOR DUNE ND fit
  • Constructing them
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SLIDE 12

BACKUP

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SLIDE 13

Samples

Lorena Escudero, March 2016 13

We are currently fitting 9 samples for each FHC and RHC modes: Binning for MC templates (optimization ongoing): The true reaction modes considered in each sample are (for the species νμ, νe, anti-νμ anti-νe)

Further subdivided in the future

  • 3D templates for CC modes (Ereco, yreco, Etrue) with 19 Etrue bins (from 0-120

GeV, matching flux norm error), variable Ereco, yreco binning

  • 2D templates for NC modes (Evis, Etrue)
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SLIDE 14

Preliminary binning selection

Lorena Escudero, March 2016 14

Quick binning selection to have approximately the same number of events per bin using kinematic variable y_reco (for now just smearing y_true with a resolution of 10%) Study done over total 100k events

# events y_reco y_reco # events CC 1piC CC 1pi0 # events y_reco CCQE

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SLIDE 15

Covariance - MAQE

Lorena Escudero, March 2016 15

10k events for nu and 10k for nubar 100 tweaks of MACCQE

ν CCQE antiν CCQE ν CC1pi anti ν CC1pi ν CC1pi0 anti ν CC1pi0 FRACTIONAL COVARIANCE CORRELATION

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SLIDE 16

Covariance - MARES

Lorena Escudero, March 2016 16

10k events for nu and 10k for nubar 100 tweaks of MACROS

ν CCQE antiν CCQE ν CC1pi anti ν CC1pi ν CC1pi0 anti ν CC1pi0 FRACTIONAL COVARIANCE CORRELATION ν CCQE antiν CCQE ν CC1pi anti ν CC1pi ν CC1pi0 anti ν CC1pi0