1

Jet flavour tagging for the ATLAS Experiment

Jonathan Shlomion behalf of the ATLAS collaboration

August 23rd SUSY2021

2

Outline

• Overview of the ATLAS flavour tagging algorithms

• Specialised taggers and new applications• Soft b-hadron tagging• charm tagging• High pt calibration• Xbb tagging

https://twiki.cern.ch/twiki/bin/view/AtlasPublic/FlavourTaggingPublicResultsCollisionDataEnjoy browsing our public results here:

3

• Overview of the ATLAS flavour tagging algorithms

The input to the flavour tagging algorithm is the tracks associated to particle flow jets, or track jets.

The experimental signature of b/c-hadron decays is the secondary vertices inside the jet [Eur. Phys. J. C (2019) 79:970]

Detector

Primary Vertex

Secondary Vertices

Flavour tagging algorithm

4

• Overview of the ATLAS flavour tagging algorithms

Impact parameter:1. IPxD2. RNNIP3. DIPS

Jet axisPoint of closest distance to primary vertex

Impact parameter is a sign of secondary decays, without having to reconstruct secondary vertices.

5

• Overview of the ATLAS flavour tagging algorithms

Impact parameter:1. IPxD2. RNNIP3. DIPS

d0, z0

d0, z0

d0, z0

d0, z0

Tracks{…

The goal of all impact parameter algorithms is to take a set of track impact parameters, and output a probability for the jet to be a b/c/light jet

pb, pc, puImpact parameter algorithm

6

• Overview of the ATLAS flavour tagging algorithms

Impact parameter:1. IPxD2. RNNIP3. DIPS

d0, z0

d0, z0

d0, z0

d0, z0

Tracks{…

IPxDTreats the tracks individually, the final class probability is the product of all track probabilities

pb, pc, pu

pb, pc, pu

pb, pc, pu

pb, pc, pu

…

pb, pc, pu

7

• Overview of the ATLAS flavour tagging algorithms

Impact parameter:1. IPxD2. RNNIP3. DIPS

d0, z0

d0, z0

d0, z0

d0, z0

Tracks{…

pb, pc, pu

ATL-PHYS-PUB-2017-003

RNN - recurrent neural network.Arranges the tracks in a sequence based on impact parameter significance, then passes them through an RNN. Correlations are taken into account => better performance.

Inputs are the significance

100

101

102

103

104

LLght-IlDvRur jet rejectLRn

ATLAS 6LPulDtLRn 3relLPLnDry0s 13 7e9, t ̄t

R11,3D,36

0.6 0.7 0.8 0.9 1.0b-jet eIILcLency

1.0

1.2

RDtLR tR

R11,3

8

• Overview of the ATLAS flavour tagging algorithms

Impact parameter:1. IPxD2. RNNIP3. DIPS

d0, z0

d0, z0

d0, z0

d0, z0

Tracks{…

pb, pc, pu

ATL-PHYS-PUB-2020-014

DIPSUses a deep set neural network - now the order of the tracks does not matter - better performance than the RNN, and much faster to train.

9

• Overview of the ATLAS flavour tagging algorithms

Detector

Primary Vertex

Secondary Vertices

JetFitter:

Try to reconstruct multiple vertices based on the assumption that they all sit on a straight line from the primary vertex

Secondary Vertex Reconstruction:1. JetFitter2. SV1

10

• Overview of the ATLAS flavour tagging algorithms

Detector

Primary Vertex

Secondary Vertices

SV1:

Try to reconstruct a single secondary vertex containing all displaced tracks in the jet

Secondary Vertex Reconstruction:1. JetFitter2. SV1

11

• Overview of the ATLAS flavour tagging algorithms

DL1:Take the output of all algorithms and produce a class prediction

DL1

pb, pc, pulog ( pb

pc ⋅ fc + (1 − fc)pu ) > cut

“charm fraction”

12

• Soft b-hadron tagging

For jets in the range of , calorimeter jets are no longer a viable option - energy resolution is degraded.

the b-hadron energy is low, the secondary vertices are too close to the primary vertex

pT < 20 GeV

Either use jets built only from tracks,or reconstruct the b-hadron decays without considering jets.

ATLAS-CONF-2019-027

13

• Soft b-hadron tagging

Consider the vertex properties as discriminating variables between b-hadron vertices and fakes

Vertex mass, pt, impact parameter with respect to the primary vertex, angular spread of the tracks…

14

• Soft b-hadron tagging

For b-hadron below 15 GeV, the jet-less vertexing works best, while for track-jets are better.

pTpT > 15

Used in ATLAS -CONF-2020-003ATLAS SUSY-2018-12

T-LVT/TC-LVT Are different vertex reconstruction algorithms.

15

• Charm tagging

log ( pb

pc ⋅ fc + (1 − fc)pu ) > cut

log ( pc

pb ⋅ fb + (1 − fb)pu ) > cut

As the performance increases for the DL1 tagger, it becomes viable as a charm tagger by defining the discriminant slightly differently.

ATLAS-CONF-2021-021

pc ↔ pb

fb ↔ fc

16

• High pt calibration

Extend the data/MC comparison of b-tagging efficiency to jets in the range of 500 GeV < pT < 1000 GeV

ATL-PHYS-PUB-2021-004

17

• High pt calibration

Estimate the uncertainty on data/MC efficiency scale factors for jets in the range500 GeV < pT < 3000 GeV

ATL-PHYS-PUB-2021-003

Using simulation based modification to the tagging efficiency

- tracking efficiency/resolution- Jet energy resolution- Interaction of b hadrons with the inner

detector

18

• High pt calibration

Estimate the uncertainty on data/MC efficiency scale factors for jets in the range500 GeV < pT < 3000 GeV

ATL-PHYS-PUB-2021-003

Using simulation based modification to the tagging efficiency

- tracking efficiency/resolution- Jet energy resolution- Interaction of b hadrons with the inner

detector [http://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PLOTS/FTAG-2020-002/]

19

• Xbb tagging

Discussed in detail in the talk by Changqiao LiPerformance and calibration for the identification of boosted Higgs bosons decaying into beauty quark pairs in ATLAS

ATL-PHYS-PUB-2021-004

Key idea: Tagging boosted Higgs vs. Z jets

Large R jets

Sub-jets

20

• Summary

The ATLAS flavour tagging algorithms are continuously evolving.In addition to increasing the performance of the core algorithm we are branching out to other specialised domains, and those algorithms are also evolving and increasing in their performance.

Thanks for your attention!