identification of jets with heavy flavors at dØ · 2013-07-01 · identification of jets with...

Identification of Jets with Heavy Flavors at DØ

Joseph Zennamo SUNY at Buffalo

Erice School on Subnuclear Physics

June 25th, 2013

Motivation

J. Zennamo, SUNY at Buffalo

•  The ability to identify a heavy flavor jets is of paramount importance to any collider experiment’s physics program

•  The ability to model the responses of these identification tools is also of great importance

•  The backgrounds associated with a light jet faking a heavy flavor jet can quickly swamp many searches

•  I will present a novel approach to modeling these backgrounds

2

Higg

s Bos

on

3

DØ

CDF

Booster Anti- proton source

Main Injector

Linear accelerator Fixed target area

The Tevatron


The DØ Detector


•  General purpose hadron collider experiment •  Central tracking system is of particular importance for heavy flavor jet identification

•  Allows for precise reconstruction of the primary interaction vertex and secondary vertices

•  Enables an accurate determination of the impact parameter

4

Pseudorapidity = η = − ln[tan(θ

2)]

Jet Structure

6/25/13 J.Zennamo, SUNY at Buffalo 5

•  Jets are cascades of particles coming from initial state quarks or gluons •  These appear in our detector as tracks and energy deposits •  This track information can be utilized to help us determine which particle

initiated the shower

Heavy Flavor Jet Identification


•  B and C hadrons have a relatively long lifetime (~1 ps) and large mass •  This allows the hadron to travel a measureable distance away before

decaying (~100-500 µm)

•  These properties are utilized to create a discriminant which allows us to enrich the sample in heavy flavor jets

6

Heavy Flavor Jet Identification


•  B and C hadrons have a relatively long lifetime (~1 ps) and large mass •  This allows the hadron to travel a measureable distance away before

decaying (~100-500 µm)

•  These properties are utilized to create a discriminants which allows us to enrich the sample in heavy flavor jets

The MVAbl Algorithm

6/25/13 J. Zennamo, SUNY at Buffalo 8

•  We can combine a great number of variables to help better distinguish the various flavors of jets

•  Six random forests are trained with a variety of variables •  One based on impact parameter information, and five others are

based on secondary vertex information •  These are then combined with a single neural network

MVAbl

Modeling Heavy Flavor Response, System8 Method


•  The efficiency of selecting a heavy flavor jet must be corrected for data to MC differences

•  First select a dijet data sample which is enriched with heavy flavor jets •  A system of eight equations can be built to give the efficiency for

selecting a b-jet in data with the corresponding systematic uncertainties •  Results are parameterized as a function of jet pT and pseudorapidity

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Corrected Heavy Flavor Selection Efficiencies

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•  The correction factors which are derived from the heavy flavor enriched sample give us data driven “tag rate function”

Misidentification Rates


•  While the misidentification rate for the tagging procedure is on the order of 1%, there is a large number of light jets making them a significant portion of our samples

•  Previous methods relied heavily on MC inputs, this was known as the NT method

•  DØ has a novel approach to extract the fake rates directly from data •  SystemN method

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Jet Secondary Vertex

Primary Vertex

New SystemN method


•  Using the heavy flavor efficiencies measured previously we can build a system of N equations •  Where N-1 is the number of

selected operating points •  The number of b, c, and light jets

in the event must also be measured

•  To determine this a separate procedure is used to extract the flavor composition directly from the data

1 2 3 4 5

1

… 5

1 1 1 1 1 1

5 5 5 5 5 5

Sample Composition Fits


•  First a sample is created with the requirement that a ‘good’ secondary vertex can be formed

•  From here we take the secondary vertex mass distribution from data and fit it with MC templates for b, c, and light jets [GeV]SVM

0 1 2 3 4 5 6

Even

ts /

0.38

500

1000

1500

DataFitted Totalb jetsc jetslight jets

< 45 GeVT

DØ, 35 GeV < p| < 1.51.1 < |

Secondary Vertex Mass [GeV]

•  These fits are parameterized in terms of jet pT and pseudorapidity •  Template shapes are corrected to data before the fitting but any difference

are taken as a systematic uncertainty

Resulting Fake Rates and Scale factors


•  Once we have the sample composition the last remaining unknown is the light jet tagging efficiency

•  This is then extracted directly from data and can be compared to the MC predicted values

•  A correction factor is created by taking the ratio of the rate in data to MC, with corresponding uncertainties

•  This is used to model the light jet responses

Comparison to old method


•  If we compare the results of our data driven fake rates to those found with the previous method we can see a large deviation at high jet pT

•  This discrepancy grows if we required a stricter cut on our algorithm

•  This discrepancy points to the fact that the previous method for determining the fake rates highly underestimated their contributions at high pT

Conclusions


•  Robust heavy flavor tagging algorithms are essential to any collider experiment’s physics program

•  Additionally it is important to accurately model the responses to these algorithms in a data driven fashion

•  I have presented a novel method for determining the light jet’s response to any tagging algorithm

•  Further, this new method yields light jet efficiencies which are significantly different to those estimated with the previous methods

•  All of these advances will be published shortly in a NIM article

BACKUPS


System8


Intermediate Random Forests


identification of jets with heavy flavors at dØ · 2013-07-01 · identification of jets with...

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