28 The ATLAS Collaboration / Physics Letters B 779 (2018) 24–45
Fig. 3. The m
JJ
mass distributions in the m
J
window of the X candidate jet from 133 GeV to 171 GeV after the likelihood fit for events in (a) the 1-tag signal region (SR1)
and (b) the 2-tag signal region (SR2). The highest mass bin includes any overflows. The background expectation is given by the filled histograms and the ratio of the observed
data to the background (Data/Bkg) is shown in the lower panel. The uncertainties shown are those after the fit described in the text. One particular example of a possible
signal model, namely with m(X) = 152 GeV and m(Y ) = 1.8TeV, is overlaid with an arbitrary overall normalization, illustrating the corresponding contributions that would
be expected in the SR1 and SR2 regions. Panel (c) shows the resultant 95% CL upper limit on the production cross-section, σ (pp → Y → XH →q
¯
q
b
¯
b). (For interpretation of
the references to colour in this figure, the reader is referred to the web version of this article.)
events in the CR0 sample with at least one (two) track-jet(s) asso-
ciated
with the Higgs jet to model the shape of the multijet back-
ground
in the SR1 (SR2) signal region. However, sources of possible
differences between the multijet components of CR0 versus SR1
and SR2 include changes in the underlying event populations due
to the absence or presence of b-quarks as well as kinematic differ-
ences
arising from the application of b-tagging, since the b-tagging
efficiency depends on the p
T
and η of the track-jet. The correc-
tions
required to take these differences into account are extracted
from the HSB events. In each of the HSB regions with different
number of b-tags (HSB0, HSB1, HSB2), a pair of two-dimensional
histograms is filled, one of p
T
versus η for the leading track-jet,
and the other for the subleading track-jet. Subsequently, leading
and subleading track jet reweighting maps are created by dividing
the HSB1 and HSB2 histograms by the corresponding HSB0 his-
togram.
A Gaussian kernel, similar to that applied in Ref. [59], is
used to smooth out statistical fluctuations by taking a weighted
sum of neighbouring bins. The weight is inversely proportional to
the width of the Gaussian kernel and depends on the statistical
uncertainty of each bin. The result is a reduction of statistical fluc-
tuations,
while adding negligible bias to the distributions.
To
describe the SR multijet background shapes, the CR0 events
are reweighted using the maps in bins of track-jet p
T
and η ex-
tracted
from the sideband. The reweighting is performed only for
the 2-tag samples, as the modelling of the shape of the multi-
jet
background in the 1-tag sideband regions without reweight-
ing
is observed to be adequate. Due to the correlations between
the kinematic variables of the two leading track-jets used for the
reweighting, multiple reweighting iterations are performed, until
the track-jet properties, p
T
and η, are matched within statistical
uncertainties.
The
background modelling is validated by using the same
method to predict the background in the LSB regions in the low
mass sidebands, and then comparing with the data and obtaining
good agreement. Agreement is also confirmed in the signal region
by integrating over all values of the mass of the X candidate jet,
thereby diluting any possible signal contamination to a negligible
level.