Experimentalists view on Shower Deconstruction

Nicolas Gutierrez

2 Using jet substructure - Oregon

Jet substructure techniques in ATLAS

Substructure techniques are usually divided in two categories, simple and complicated. Having more than two equations in the description of a particular technique do take away popularity points.

3 Using jet substructure - Oregon

Jet substructure techniques in ATLAS

A second layer divides these techniques in those on which a particular group is and is not involved. No involvement usually means a technique is madness. Shower Deconstruction as a start up method could be very well placed in the complicated- madness group.

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Lets  take  one  boosted  top:    

 Fairly  boosted  pT  ~  850  GeV    Fairly  central    η  ~  -­‐0.1  

   With  a  relaAvely  large  microjet  mulAplicity  :      6  microjets      pT  from  20  up  to  400  GeV  

η

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Using jet substructure - Oregon

Shower Deconstruction

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η

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χlog -10 -8 -6 -4 -2 0 2 4 6 8 100

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Shower  DeconstrucAon  χ  observable  would  be  ~  550    

Around  here  

Using jet substructure - Oregon

Shower Deconstruction

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χlog -10 -8 -6 -4 -2 0 2 4 6 8 100

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Shower  DeconstrucAon  χ  observable  would  be  ~  550    

Around  here  

There  are  more  than  1800  possible  shower  histories.    About  20  histories  have  dominaAng  weights.    Some  of  which  are  permutaAons  of  the  same  physics  case.  Reducing  the  number  to  4.  

Using jet substructure - Oregon

Shower Deconstruction

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mW  ~  77.3  GeV  

Using jet substructure - Oregon

Shower Deconstruction

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η

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2.22.4 mW  ~  83.1  GeV  

Using jet substructure - Oregon

Shower Deconstruction

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η

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-0.5

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1.21.4

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mW  ~  77.3  GeV  

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Shower Deconstruction

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η

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-0.5

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0.60.81

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2.22.4

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igh

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0.60.81

1.21.4

1.61.82

2.22.4 mW  ~  83.1  GeV  

Using jet substructure - Oregon

Shower Deconstruction

η

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Shower Deconstruction

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Which  shower  is  the  right  one  ?  

We  don’t  know  !  

Let’s  take  the  QM  approach  use  them  all  through    Shower  DeconstrucAon  

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Using jet substructure - Oregon

On the menu

Although we wont be showing any ATLAS performance plots, we hope to present a diagnosis of the technique. Something we should probably do before jumping into an actual analysis. Plots on the following slides are to be compared only to other slides on this talk. We wont be comparing Shower Deconstruction to other techniques.

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large-R-jet mass [GeV]100 150 200 250 300 350 4000

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MCsmeared MC

microjet energy [GeV]10 210 310

JER

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0.5A simple exercise      We  will  take  a  MC  sample  and  apply  a  

parameterizaAon  of  the  microjet’s  Energy  resoluAon.          Then  see  how  does  this  affect  the  performance.            Also  we  will  see  how  does  a  flat  energy  uncertainty  of  4%  affect  the  performance.    This  by  no  means  could  replace  a  full  simulaAon  of  the  detector  effects  !      

~10%  @  100  GeV  

~20%  @  20  GeV  

40-­‐50%  @  5  GeV  

Top-­‐jet  mass  distribuAon  before/a`er  smearing  

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Samples and selections

Event cuts: Reject events with W -> l + v Require jet with mass > 120 GeV

Object selection: Clustering with AntiKt with radii parameter 1.0 Required to have |η| < 0.3 and pT ≥ 350 GeV

Binning: pT [600-800]

MC Pythia Zprime {mass 1750 GeV} Pythia8 dijets

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Effect of microjet energy smearing on χ

χ-10 -8 -6 -4 -2 0 2 4 6 8 100

0.002

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smeared MC

χ-10 -8 -6 -4 -2 0 2 4 6 8 100

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smeared MC

Dijets   Tops  

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Effect of microjet energy smearing on performance

Signal Efficiency0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

Fake

rate

-310

-210

-110 MCsmeared MC

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Effect of microjet’s minimum transverse momentum

Signal Efficiency0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

Fake

rate

-310

-210

-110 MC minimum pT 20MC minimum pT 10MC minimum pT 5

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Effect of microjet’s minimum transverse momentum

Signal Efficiency0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

Fake

rate

-310

-210

-110 smeared MC minimum pT 20smeared MC minimum pT 10smeared MC minimum pT 5

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Effect of microjet energy uncertainty

χlog -10 -8 -6 -4 -2 0 2 4 6 8 100

0.002

0.004

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smeared MC

smeared MC JESup

smeared MC JESdown

χlog -10 -8 -6 -4 -2 0 2 4 6 8 100

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0.07 smeared MC

smeared MC JESup

smeared MC JESdown

Dijets   Tops  

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Signal Efficiency0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

Fake

rate

-310

-210

-110 smeared MCsmeared MC JESupsmeared MC JESdown

Using jet substructure - Oregon

Effect of microjet energy uncertainty

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Signal Efficiency0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

Fake

rate

-310

-210

-110

= 1.75 TeVZ'

jet pT [600,800] M

smeared MC microjet radii 0.2smeared MC microjet radii 0.15

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Effect of microjet radii

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Signal Efficiency0 0.1 0.2 0.3 0.4 0.5 0.6

Fake

rate

-310

-210

-110

= 3 TeVZ'

jet pT [1000,2000] M

smeared MC microjet radii 0.2smeared MC microjet radii 0.15

Using jet substructure - Oregon

Effect of microjet radii

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Wrap up

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Using jet substructure - Oregon

Energy resolution might very well be a dominating factor on the actual performance of Shower Deconstruction. The further down we go in transverse momentum the more we face this downfalls. 20 GeV seems to be a good benchmark to cut on the microjet’s momentum. A ~ 4% in energy uncertainty does not seem to be a deal breaker. At the Terascale resolving the W->qq system indicates the need of a finer description of the microjets. Could it be easier to adapt the taggers rather than push the limits of ATLAS calorimetry ? Can we reconstruct with enough accuracy the microjets with the ATLAS detector ? How would pileup, in particular additional pileup microjets affect the performance ?

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The team

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Prof.  Tony  Doyle   Dr.  James  Ferrando   Dr.  Deepak  Kar  

Growing  ..