Download ppt - Pierre-Hugues Beauchemin

Search for dark matter
candidates in events with a jet
and missing transverse
momentum using the ATLAS
detector
Pierre-Hugues Beauchemin
Tufts University
Physical Sciences Symposia-2013, Waltham, MA, 09/05/2013
Outline
 Monojet events
 Physics Motivation
 Main Standard Model backgrounds
 Data-driven background estimates
 Motivation
 Illustration of the techniques
 Application to monojet events
 Results and interpretation
 Comparison to data
 Constraints on dark matter
 Conclusions
2
Monojet events
3
Dark Matter
Many observational evidences for a large amount of dark
matter in the universe
One of the strongest motivation
for new physics in HEP
4
Signature at Colliders
 Most popular explanation for the nature of dark matter:
Massive particles interacting very weakly with matter (WIMPs)
 Dark matter was more abundant in early universe
 Dark matter gets annihilated
 Reverse is true: dark matter can
be produced in colliders
 WIMPs escape detection but can
be inferred from unbalance
energy measurement in the
transverse plane of the detector
 Need recoil activity, typically jets
 Dark matter can be signaled in jets+ETmiss events at LHC
5
New Physics in monojet events
 Many new physics scenario predicts high
production rate for such final state:
 Generic dark matter produced via contact
interaction
 Invisible Higgs
 Gauge-mediated SUSY breaking
scenario: Gravitino+squark/gluino
production
o
Assume
 Production of graviton Kaluza-Klein
mode in large extra dimension scenarios
 Unparticle
o
Equivalent to LED+SUSY in the bulk
6
Contribution from Standard Model
 Irreducible background
 Physics processes with same final state
① Znn+jets
 Reducible background
 Physics processes with different final
states modified by detector effects
② Wln+jets
③ QCD multijet
④ Non-collision events
⑤ Others…
o Dibosons (WW,WZ,ZZ)
o Top (ttbar, single top)
q
q
7
Data-driven background estimates
8
Standard Model Predictions (I)
To determine how many SM events should pass the selections
defining the chosen final state, we must:
predict the number of
irreducible and reducible
single jets events
produced in LHC
collisions :
Estimate the probability
that these SM events
yield the monojet signal
defined by our event
selections:
Theoretical calculation
of various cross sections
Probability distribution of
observables for each processes
Number of collisions
produced (Luminosity)
Detector effects on
the distributions 9
Standard Model Predictions (II)
1. The relative amount of new
physics and SM contribution
80
60
40
20
0
1.
60
40
SM+LED
20
SM
0
THE KEY IS TO CONTROL
JET P (GeV)
SM+LED
SYSTEMATIC
UNCERTAINTIES
100
SM
T
2.
JET PT (GeV)
2. The systematic uncertainty
on the SM expectations
 Is under our control
Number events/25 GeV
Number events/25 GeV
 Not under our control
Number events/25 GeV
The sensitivity to new phenomena depends on:
50
SM+LED
SM
0
JET PT (GeV)
Monte Carlo-based estimates
Use theory & simulations to estimate production rate and
model detector effects on probability to select events
 Systematic uncertainty from approximation and
inaccuracy in modeling of:

Theoretical calculation

Modeling of strong interaction effects at large
distance

Modeling of detector effects

Number of collisions registered
11
Data-driven techniques 101 (I)
Reduce systematic uncertainty by replacing MC distribution with well
understood data distribution similar to the process of interest to
avoid bias
e
n
+
e
n
Stat error only
Simulation
n
n
Data
12
Data-driven techniques 101 (II)
To produce a data-driven predictions, we can:
1- Reverse one (few) signal selection(s)
•
Avoid signal contamination
Set of selections defining
signal (eg: monojet)
All events from a dataset
Data model
of the signal
Signal
events
Z  ¬X
X
Y
Event cut 1
Event cut 2
…
Event cut N-1
Event cut N
Y: All other
selections
Selections
X: to reverse
2- Count the number
of events in
the respect
YZ (Z to
¬X)
If X is unbiased
with
Y,sample
3- Use ratios
to YZ
compute
mapping
factors
for the
then
provides
a good
model
forfinal
YXprediction
13
Data-driven in monojet events
Jets observables present similar distributions
Znn + 1-jet
e
Zee + 1-jet
e
ETmiss can similarly
be obtained after
removing the two
charged leptons
with corrections
Met
Must now use
ratio to
normalize and
correct for
shape
distortion 14
Results and interpretation
15
Various signal regions
 We don’t know the kinematic region in
which new physics will get revealed
 Expectations vary with models
 Model-independence: don’t select the
ET
500
350
kinematic region based on the indications 220
of a particular model
120
 Or do a kinematic scan
0 120 220 350 500
Jet 1 ET
 Lowest kinematic region determined by
trigger requirement
 Statistics is a limitation for data-driven
estimate in high kinematic regions
16
16
Background systematics uncertainties
 When systematic
taking all effectsuncertainties;
and all backgroundmono-jet
into account:
ckground
For the high stats “low” kinematic region
Systematic source
Uncertainty
Jet and E Tmiss energy scale and resolution
2-4 % on transfer factors
Lepton identification efficiencies
1-3 % on transfer factors
Non-electroweak backgrounds
Less than 1 % on total background
Parton shower and hadronisation modelling 3 % on total background
of simulation samples
 QCD prediction uncertainty is not the dominant background and is kept at a
Large low
MC level
statistical errors in signal regions 3 (350 GeV threshold) & 4 (500 GeV threshold):
 This estimate is a very conservative, essentially only reflecting the small
~ 5.5
% &the
15.8
%
statistics of the sample used
to get
estimate.
 Recent studies suggest a factor of 3 to 5 smaller uncertainty on QCD effects 17
Insignificant improvements of some of the limits compared to the 7 TeV mono-jet analysis
The Results (I)
JHEP 04 (2013) 075
From 5 fb-1 of 2011 ATLAS data
constrains on
Outstanding
precision
of withTight
Results are
consistent
SM
regardless

New physics models
<4% onof
SM
prediction
miss
the jet P and E
selections
T
T
18
Results (II)
Leading jet pT : 852 GeV
miss
E T : 863 GeV
Back U
19
The Results (III)
20
Dark Matter models considered (I)
 Monojet analysis can be used to constrain dark matter production
in a model-independent way
 Effective theory with contact interaction
 Assumes:
 WIMPs of mass between a few GeV to few TeV range
o
Detectable at the LHC
 Pair of WIMP produced from parton-parton interaction
o
Heavy mediator between SM and dark sector
 Dark Matter field represented as Dirac Fermion
o
Some interaction terms are disallowed
o
Cross section bigger than Majorana by x4
o
Can express t-channels as a sum of s-channels dirac terms
21
Dark Matter models considered (II)
 Examples of operators considered
 Chosen because they correspond to different ETmiss shapes and so
generate potentially different limits
 2 parameters: M* and Mc
 M* = M/sqrt(g1g2)
o
g1, g2 = couplings of the mediator of mass M between WIMP and SM
 Coupling flavor universal with 4 lighter quark flavors
22
Limits
 Thermal relic density observed by WMAP (green curve) is compatible with
DM having couplings and mass comparable to weak scale masses and force
 If M* above relic line, other annihilation processes are required for
consistency to WMAP
23
More limits
 Bounds on M∗ for a given mc can be converted to bounds on
WIMP-nucleon scattering cross sections
 Compare to direct DM experiments
 Compare to indirect DM searches
o
Use DM pair production from 4-flavors
quark-anti-quark annihilation and
translate it to a DM annihilation in
bb-pair
o
Compare to galactic high energy
Event select ion
Background est imat e
gamma
ray observations
by Fermi LAT
Int roduct ion
LED
WIM P
Gravit ino
Conclusio
• gsint
from
q-jet fragmentation
W IM P/ DM
erpret
at ion (AT LAS search, hep-ex/ 1210.4491)
⌦χ /
1
< σ⌫
>
⇠
mχ2
gχ4
8
⌦χ :
>
>
<
hσ⌫
i :
with
mχ :
>
>
:
gχ :
observed thermal relic density ⇠ 0.24
thermally-averaged annihilation cross section
DM part icle mass
24
coupling between DM and SM particles
Comparison to direct DM searches
Best spin independent
Limits for mc < 10 GeV
Best spin dependent
limits for mc < 1 TeV
25
Comparison to indirect DM searches
 LHC and Fermi LAT are complementary, especially below ~100
GeV
26
Conclusions
27
Conclusion
 Dark matter is an empirical fact established by astrophysics

Most popular explanation: a new particle (WIMPs)
 Escape detections => jets + ETmiss events
 Background predictions to monojet events typically suffer
from large systematic uncertainties
 Use data-driven background estimate
 ATLAS performed the search and found no evidence for new
physics in monojet events
 Constraints are set on generic effective dark matter scenario
 Complementary to direct and indirect dark matter searches
28
Back-up slides
29
LED: Model assumptions
 Limits assume extra dimensions are flat and
compactified on n-dimensional torus
 SM fields attached to a 3-brane
o
Brane deformation ignored
 Continuous KK-spectrum is assumed, even for n=6
o
Universal couplings of each modes
o
Assumed the spectrum stops at MD
 Fundamental to effective scale relationship:
 Prediction from minimal graviton emission model
of GRW
 Valid: E<<MD; Pert. exp. break: E > 7 MD
LED: Experimental signatures
 Direct graviton production in association
with partons or photons
 Graviton interaction with
detector suppressed by MPl-2
 Missing transverse energy
 Signature at the LHC
 Monojet
o
More jets due to QCD radiation
 Monophoton
LED: Limits
 Typical efficiency for jets and Met selection: ~83%
 Similar for Zvv, and ADD and general dark matter model
 Set 95% C.L. limits on MD
 Truncation: quantify UV effects not modeled by Leff
o
Ds/s = 0% (n=2), 6% (n=3), 20% (n=4), 45% (n=5), 60% (n=6)
 model does not make non-ambiguous predictions with SR4
o
Limits of SR1 to SR3 are 35%, 15%, 5% worse, but less UV sensitive
Electroweak background estimate (II)
 Data-driven prediction for Z→nn+jets background is obtained from:
 Number of Z→ll+jets events in each control region’s ETmiss bin (Nicand,CR)
 Ratio of signal region to control region observable distribution
 The ratio mapping factor accounts for lepton
 acceptance and efficiency
 different cross section and branching ratios
 distortion of the measured observable due to the charged lepton in the CR
 Similar exercise can be done to estimate the reducible W+jets background
 Direct use of the Rjets measurement
 The ratio is also corrected for the probability that the event survive the veto
 W+jets control region events are also used to estimate Znn+jets
33
QCD multijets background estimate
 Sources of QCD contribution:
 2-jets and 3-jets events for which one of the jets is lost (dominate)
 ≥3-jets events for which two jets are lost (smaller) → obtained
from MC
 To estimate the 2-jets contribution:
1- Select 2/3-jets events with
ETmiss vector toward one jet
2-jets events
2- Extrapolate this jet ET below
energy threshold (loose a jet)
3- Background prediction
=
area under the fit in the
extrapolated region
× MC correction
Extrapolated
region
* Jet threshold lowered to 15 GeV to verify the extrapolation 34
Results (IV)
 Preliminary studies with 10 fb-1 of 2012 8 TeV data yields very
results than
the 7mono-jet
TeV 2011 8results.
New similar
experimental
results:
TeV
ATLAS-CONF-2012-147
Signal region 3
35