The Search for Neutral Higgs Bosons at DØ

Andy Haas

Columbia University
DØ Experiment

Princeton University

March 23, 2005

The Final Piece of the Standard Model

The Higgs boson is needed for the Standard Model to be consistent

Only one parameter of the Higgs is undetermined: its mass...

Precision measurements predict:
114 GeV < mh < 211 GeV

We're looking hard for the SM Higgs at the Tevatron...

More to the Story?

Hierarchy problem:

Why is mh << mpl ?

Hierarchy stability problem:

QFT predicts radiative corrections

How does mh stay << mpl ?

Can the gauge couplings unify?

Needed for Grand Unified Theories (GUT)

Ruled out in the SM !

Supersymmetry to the Rescue?

Boson and fermion loop integral contributions differ by a factor of (-1)

A fermion for every boson:
scalar field masses are stabilized

Nobody works at Boselab... Supersymmetry is slightly broken

Supersymmetry demands an even number of Higgs doublets

A light higgs is predicted
(mh<mZ at tree-level)

With 2 Higgs doublets, couplings unify at 1016 GeV

This is the MSSM

Cosmic Connections

Supersymmetry has a natural dark matter candidate:
the neutralino

tan controls the annihilation rate in the early universe

The Higgs fields could have
CP-asymmetry

Extra matter / anti-matter asymmetry of the Universe

Dark energy is a scalar field, like the Higgs

Supersymmetry solves the dark energy problem “halfway”...

The inflaton is also a scalar field

Higgseses in Supersymmetry

Higgs fields come in pairs (Hu and Hd)

5 Higgs bosons : h, H, A, H+, H-

tan = Hu / Hd

At tan=~50, coupling of A to b = A to t

Cross-sections for bbh grow like tan2!

h is predicted to be light, <~135 GeV

LEP limits on Higgs mass -- 92 GeV
(not the usual 114 GeV like in the SM...)

LEP limits are much looser (~50 GeV) if
CP-violation is allowed in the Higgs sector... this talk assumes CP-conserving Higgses !

At high tan (>~20),
the A is degenerate with the h,H:
cs(A) =~ cs(h/H)
width(A) =~ width(h/H)
m(A) =~ m(h/H)
BR(A→bb)=~BR(h/H→bb) =~ 90%

H

h

A

total

Higgs Mass (GeV)

tanB = 30

tan

A

Highly

degenerate
region

Higgs Widths

Higgs bosons get wider, proportional to tan2

Widths are modeled with BW shape, weighted by cross-section

Not a large effect on sensitivity, for reasonable tan values, after considering kinematic criteria

No
cuts

Kine.
cuts

Kine.
cuts and
3 b-tags

tan=60

tan=30

tan=10

Detector resolution

Mass-splitting of the Higgses

Mass splitting, weighted by cross-section

What about when all 3 Higgs are nearly degenerate?

What matters is the shape and height of the invariant mass peak

When cross-sections are significant, the mass-splitting is small compared to the detector resolution

Signal at NLO in QCD

Cross-section (fb)

There are two ways to calculate the signal production at a ppbar collider:

ppbar->bbh (4-flavor scheme)

ppbar->bh (5-flavor scheme) : use b-PDF

Both methods now agree at NLO

PDF errors are also in agreement

D0 has performed a measurement to test the predictions of the b-quark PDF calculations

Z+b / Z+j

Z+b is a probe of b-quark PDF 
(Z+b) / (Z+j) = 0.023  0.005
= 0.018 (CTEQ6 prediction)

(DØ – hep-ex/0410078)

bbh

bh

Monte Carlo Higgs Spectra

Our Monte Carlo uses Pythia, which is semi-leading-order

The events have been re-weighted, to mimic the NLO kinematics as closely as possible

Comparisons of the bh and bbh processes are in good agreement

bh

bbh

bh (dashed)
bbh (solid)

Comparison of bh
LO to NLO with Pythia

Fermilab Tevatron Accelerator – Run II

1 km radius p-pbar super-syncrotron

World's most powerful: 1.96 TeV

36 bunches of p and pbar

396 ns crossing period

25 cm long interaction region

One store every day or so...

DØ

Tevatron Performance

Run II began 4 years ago

Instantaneous luminosity has steadily increased

Last year was excellent, and this year is also right on track

Almost 1 fb-1 delivered so far

Should collect 4-8 fb-1 in the next few years

Current Detector Data / Efficiency

Data range for
this analysis

The DØ Detector for Run II

Tracking Systems

Calorimeters

Showers are induced by sheets of depleted Ur

Cells measure the ionization in liquid Ar gaps

Cells are arranged in a projective geometry

Highly granular (.1x.1)(-)

Jets are almost completely contained: >20 hadronic interaction lengths

2.3mm

Muon System

Used in this analysis for:

b-tagging studies...

jet energy corrections

A big toroidal Iron magnet:
~2000 Ton and 1.9 Tesla

Only muons with |p|>3 GeV penetrate the magnet

Very low background thanks to heavy shielding

3 drift-tube layers and scintillating panels determine the position and timing of muons

Coverage out to |eta|<2.0

A-layers and scintillators create the best muon trigger ever built

Trigger System

Level 1

Calorimeter EM and TOT ET (.2x.2)(-)

Muon stubs

Central high-pT axial tracks

Level 2

Clustered calorimeter EM and TOT ET

Missing transverse calorimeter ET

Matched muon segments

Displaced high-pT axial tracks

Level 3 trigger algorithms:

Jets (no split/merge)

Central stereo tracks

Muons matched to central stereo tracks

Primary vertex

Jet lifetime b-tags

Level 1
2.5 MHz

Level 2

1.6 kHz

Level 3

1 kHz

Tape

50 Hz

Data / Triggering

Custom multi-jet triggers

Level 1:
Three towers with TOT ET > 5 GeV

Level 2:
Three clusters with ET > 8 GeV
and HT >50 GeV

Each triggerlist had new Level 3 criteria, as we improved them to remain within bandwidth limits while retaining signal efficiency
Three jets with ET > 15 GeV with two above 25 GeV in |eta|< 3.0
Jets use 0.5 cone algorithm and
PV Z position to correct jet ET and eta
Require a good L3 PV with |z|<35cm

260pb-1 of good data

87.5 Million events

Level 1 : CJT(3,5)

Simulation

Trigger efficiencies measured

using data:
> mu-based unbiased trigger for L1/L2
> special run for L3 study

68-80% efficient for mA of 90-150 GeV

Track Reconstruction

Reconstruct detector energy into clusters

Pattern recognition combines clusters into tracks

Efficiency for pT>1GeV is about 95% in the central region, 85% for the forward

Path resolution is ~20 microns

An axial view of a 3-jet event in data,

with reconstructed clusters and tracks

IP res. (m)

Primary Vertex Reconstruction

Common locations of tracks along the beam-line are clustered into “primary vertices”

There can be more than one interaction in a crossing

Very unlikely to have more than one interesting event in a single crossing

About 0.4 interactions per crossing on average for this data set - depends on instantaneous luminosity

The interesting vertex is selected based on the momenta of the vertices' tracks

This “hard-scatter” primary vertex is used to calculate jet ET

Jet Reconstruction

The ET in (0.1x0.1)(-) towers is summed, and seeds are found

Energy is clustered in cones of ∆R=sqrt(∆2+∆2)=0.5 around the seeds

The midpoints (in -) between stable cones are also used as seeds

The unique cones with
ET>8 GeV are merged or split, depending on whether they share more or less than 50% of a jet's ET

Jets' ET are calibrated, using the jet energy scale (JES)

accounts for out-of-cone showering and the underlying event, on average

EM

Hadronic

MET

b-Jet Identification

B

IP

Identify jets containing bottom hadrons

main weapon again the huge QCD background

b-hadrons travel ~3 mm before decaying

Their mass leads to an opening angle between tracks --- large IP significances

Additional large IP significance tracks may arise from daughter decays (charms, tau)

High-IP-significance tracks are used to find the secondary vertex(es)

Jets with a secondary vertex in ∆R<0.5 are “tagged” as b-jets

Backgrounds consist of:

mis-tags: poorly reconstructed tracks which randomly happen to form a vertex

long-lived strange / charm decays
or photon to e+e- conversions

“gluon splitting” into nearly collinear charm or bottom pairs

3 mm

b-Tagging Efficiency

The b-tagging efficiency is measured in data and compared to simulations

Very difficult because no pure b-jet data sample is available

Use the pTrel spectrum of muons in jets to extract the b, c, light – jet fractions

Then apply tagging and measure the flavor fractions again

Muon PTrel spectra, before/after b-tag

b

c

light

b

c

light

b-tag efficiency

b-tag efficiency

jet |eta|

jet pT (GeV)

Secondary Vertices in Jets

Analysis Method

Signal:

3 or more b-tagged jets

Invariant mass of leading jets is
peaked at mA

Backgrounds:

Determined from data!

Shape estimated from the
double b-tagged data sample
(taking into account the kinematic bias from requiring a 3rd b-tag)

Normalized outside the “signal region”

Also modeled in MC as a cross-check

“fakes”: all light-quark/gluon jets (measured from data)

“heavy flavor”: (ALPGEN)
bbj(j), ccj(j), bbcc, cccc, bbbb

“other”: tt, Z(→bb)+jets (Pythia)

Events with >=3
b-tagged jets

“Signal window”

Triple b-tagged Background

Full multi-jet

data sample

Calculate TRF

(ET & 3 regions of )

Apply TRF to
un-b-tagged jets

Triple b-tagged
background shape

Double b-tagged
data sample

Tag-rate-function (TRF)

Background is completely determined from data !

Triple b-tagged
background

Fit outside the
signal region to data

|eta|<1.1

Jet pT (GeV)

Cross-check Using Double b-tagged Data

Full multi-jet

data sample

Calculate TRF

(ET & 3 regions of )

Apply TRF to
un-b-tagged jets

Double b-tagged
background

Full multi-jet

data sample

Double b-tagged

data sample

Compare

Contribution from “heavy-flavor” MC

Good agreement in shape.

Normalization of “heavy-flavor” MC agrees to within 10%.

TRF is iteratively corrected for “heavy-flavor”
in the un-b-tagged data sample

MC Cross-checks

Double b-tag

Triple b-tag

Di-jet mass of leading b-tagged jets (GeV)

Monte Carlo accurately predicts
many other distributions in the
double b-tagged data as well.

g->bb !

Monte Carlo predicts a background
shape and normalization in good
agreement with the triple b-tagged data.

Systematic Uncertainties

Signal efficiency uncertainties
(total = 21%):
b-tagging efficiency (15%)
Trigger efficiency (9%)
Jet energy scale (8%)
Integrated luminosity (6.5%)
NLO kinematics (5%)
Jet reco/ID efficiency (4%)
Jet energy resolution (1%)

Background shape / normalization uncertainties (total = 3%):
TRF shape (2%)
Statistics outside signal window (1%)
Width of signal window (1%)

Events with >=3
b-tagged jets

“Signal window”

MSSM Tree-level Limits

Limits set using CLS method (TLimit)

For each mA, set a cross-section limit

Interpret in MSSM at tree-level, assuming tan2 cross-section enhancement

Cross-section uncertainties are calculated by varying the factorization / renormalization scales, and the PDF sets (CTEQ6)

Uncertainty on expected limit comes from varying the background by ±1 sigma

We got a little lucky

The current result excludes much more parameter space than our result from last year

PDF+scale
uncertainty

MSSM Beyond Tree-Level

Supersymmetric loop corrections are calculated using “CPSuperH”

Production doesn't scale like tan2... can be faster or slower depending on the supersymmetric model and its parameters

We set limits on tan vs. mA in two of the “benchmark scenarios”:

no mixing and maximal mixing in the stop-quark sector

A Quadruple b-tagged Event

Future of the Analysis

With no analysis improvements, expect to exclude down to tan=40 with 4fb-1

But we also look forward to a much improved analysis with next year's data set (1fb-1)

Inclusion of more triggers

Better calorimeter and jet energy calibration / resolution

Improved b-tagging

Use quadruple b-tagged events

Neural network?

Combine with bbH() ?

Also keeping an eye on our competitors...

Lum. (pb-1)

Expected tree-level tan limits

D0 bbh(→bb), 260pb-1

CDF bbh(→)

Improving the b-tagging

This analysis is very sensitive to the b-tagging efficiency and the background tagging rate

One obvious way to improve the
b-tagging S/B is to use more information about the secondary vertex(es) in each jet

Number of secondary vertices

Angles between secondary vertices?

Number of tracks on vertex

The 2

The “mass”

Decay length significances

... combine into neural network?

Initial studies show large gains in performance are possible

2d/3d signed
decay length

significance
in QCD MC

2d/3d signed
decay length

significance
in Z→bb MC

Conclusions

A search has been completed for neutral Higgs bosons in the MSSM
using 260 pb-1 of DØ Run II data

Good agreement between heavy-flavor multijet simulations and data

Methods were developed for estimating the triple b-tagged backgrounds

Solid theoretical understanding of the production process in the MSSM

This is a very challenging analysis!

Triggering: optimization and calculation of efficiency

Jets: efficiency, energy scale, energy resolution

Tracking and primary vertexing

Secondary vertexing and b-jet identification

Monte Carlo generators and simulations

We hope to make a discovery, or place impressive limits on tan vs. mA in the future using larger data sets and more advanced analysis techniques

Andy Haas

2004-07-07T15:59:13