EUROPEAN ORGANIZATION FOR NUCLEAR RESEARCH (CERN)

 CERN-EP-2017-211 LHCb-PAPER-2017-024 5 February 2018

First observation of forward $Z\rightarrow b\bar{b}$ production in $pp$
collisions at $\sqrt{s}=8$ TeV

The LHCb collaboration†††Authors are listed at the end of this letter.

The decay $Z\rightarrow b\bar{b}$ is reconstructed in $pp$ collision data,
corresponding to 2 fb-1 of integrated luminosity, collected by the LHCb
experiment at a centre-of-mass energy of $\sqrt{s}=8$ TeV. The product of the
$Z$ production cross-section and the $Z\rightarrow b\bar{b}$ branching
fraction is measured for candidates in the fiducial region defined by two
particle-level $b$-quark jets with pseudorapidities in the range
$2.2<\eta<4.2$, with transverse momenta $\mbox{$p_{\rm T}$}>20$ GeV and dijet
invariant mass in the range $45<m_{jj}<165$ GeV. From a signal yield of
$5462\pm 763$ $Z\rightarrow b\bar{b}$ events, where the uncertainty is
statistical, a production cross-section times branching fraction of $332\pm
46\pm 59$ pb is obtained, where the first uncertainty is statistical and the
second systematic. The measured significance of the signal yield is 6.0
standard deviations. This measurement represents the first observation of the
$Z\rightarrow b\bar{b}$ production in the forward region of $pp$ collisions.

Published in Phys. Lett. B776 (2017) 430-439

© CERN on behalf of the LHCb collaboration, licence CC-BY-4.0.

##  1 Introduction

Measurements of $Z$-boson production in $pp$ collisions constitute an
important test of the Standard Model (SM), since they allow the electroweak
sector to be precisely probed [1, 2, 3]. The LHCb experiment can be used to
measure the decay of the $Z$ boson into a $b\bar{b}$ quark pair in the forward
region that is inaccessible at other LHC experiments.

The decay $Z\rightarrow b\bar{b}$ provides a standard candle for searches in
final states with a $b\bar{b}$ quark pair. The inclusive search for the SM
Higgs decay to two $b$ quarks at the LHC is of great interest, since the
measurement of the Higgs boson coupling to $b$ quarks is an important test of
the SM [4]. Several extensions of the SM predict that new heavy particles that
decay to two energetic $b$ quarks could be accessible at LHC collision
energies [5, 6, 7]. A sizeable $Z\rightarrow b\bar{b}$ event sample will
enable the measurement of the $b\bar{b}$ forward-central asymmetry at the $Z$
pole, which could be enhanced by the contributions from new physics
processes[8]. The forward-central asymmetry in inclusive $b\bar{b}$ events has
previously been measured by the LHCb collaboration [9].

The measurements of this decay can also be used to demonstrate that no biases
are induced by the $b$-jet reconstruction procedure and that the
reconstruction efficiencies are evaluated correctly. In addition, the
$Z\rightarrow b\bar{b}$ decay is important to determine the so-called $b$-jet
energy scale. This is the factor that has to be applied to the reconstructed
$b$-jet energy in simulated events in order to reproduce the actual detector
response.

The reconstruction of the $Z\rightarrow b\bar{b}$ decay is challenging at
hadron colliders, due to the large QCD background. Many techniques to
reconstruct the $Z\rightarrow b\bar{b}$ decay channel have been developed by
the CDF [10], ATLAS [11] and CMS [12] collaborations. The CDF collaboration
reconstructed the $Z\rightarrow b\bar{b}$ decay in $p\bar{p}$ collisions at
1.96 TeV and determined the $b$-jet energy scale, obtaining a relative
uncertainty on the product of the cross-section and the branching fraction of
29$\%$. The analysis of the ATLAS collaboration reconstructed boosted
$Z\rightarrow b\bar{b}$ candidates in the central region of $pp$ collisions at
8 TeV, with pseudorapidity $|\eta|<2.5$, and determined the cross-section with
a relative uncertainty of 16$\%$. The CMS collaboration made the first
observation of the $Z\rightarrow b\bar{b}$ decay in a single-jet topology in
the same pseudorapidity region, with a significance of 5.1 standard
deviations.

This Letter describes a new method to study the $Z\rightarrow b\bar{b}$ decay,
performed on $pp$ collision data collected at a centre-of-mass energy of
$\sqrt{s}=8$ TeV, corresponding to an integrated luminosity of 2 fb-1. The low
trigger thresholds on the particle energies that are employed at LHCb and the
excellent $b$-jet identification performance make it possible to select
candidates within a large invariant mass range, including those with masses
below the $Z$-boson pole. Events are selected requiring two $b$-jet
candidates, referred to as a $b$ dijet, and an additional jet that balances
the transverse momentum of the $b\bar{b}$ system. The invariant mass
distribution of the $b$ dijet is used to determine the $Z\rightarrow b\bar{b}$
yield and the $b$-jet energy scale. The invariant mass distribution of the QCD
background is determined using a control region that is defined through
observables related to the $b$-dijet system and to the associated balancing
jet. Simulated data are used to evaluate the reconstruction efficiency and the
detector acceptance, enabling a measurement of the $Z$ production cross-
section multiplied by the $Z\rightarrow b\bar{b}$ branching fraction.

##  2 The LHCb detector, trigger and simulation

The LHCb detector [13, 14] is a single-arm forward spectrometer fully
instrumented in the pseudorapidity range $2<\eta<5$, which is designed for the
study of $b$ and $c$ hadrons. The detector includes a high-precision tracking
system consisting of a silicon-strip vertex detector surrounding the $pp$
interaction region, a silicon-strip detector located upstream of a dipole
magnet with a bending power of about $4{\mathrm{\,Tm}}$, and three stations of
silicon-strip detectors and straw drift tubes placed downstream of the magnet.
The tracking system provides a measurement of momentum, $p$, of charged
particles with a relative uncertainty that varies from 0.5% at low momentum to
1.0% at 200$\mathrm{\,Ge\kern-1.00006ptV}$.111In this Letter natural units
where $\hbar$ = $c$ = 1 are used. The minimum distance of a track to a primary
vertex, the impact parameter, is measured with a resolution of
$(15+29/\mbox{$p_{\rm T}$}){\,\upmu\rm m}$, where $p_{\rm T}$ is the component
of the momentum transverse to the beam, in $\mathrm{\,Ge\kern-1.00006ptV}$.
Different types of charged hadrons are distinguished using information from
two ring-imaging Cherenkov detectors. Photons, electrons and hadrons are
identified by a calorimeter system consisting of scintillating-pad (SPD) and
preshower detectors, an electromagnetic calorimeter and a hadronic
calorimeter. Muons are identified by a system composed of alternating layers
of iron and multiwire proportional chambers. The online event selection is
performed by a trigger system, which consists of a hardware stage, based on
information from the calorimeter and muon systems, followed by a software
stage, which applies a full event reconstruction.

Events are required to satisfy at least one of the following hardware trigger
requirements: contain a muon with $\mbox{$p_{\rm
T}$}>1.86\mathrm{\,Ge\kern-1.00006ptV}$, a hadron with transverse energy in
the calorimeters $E_{\mathrm{T}}>3.7\mathrm{\,Ge\kern-1.00006ptV}$, an
electron with $E_{\mathrm{T}}>3\mathrm{\,Ge\kern-1.00006ptV}$, a photon with
$E_{\mathrm{T}}>3\mathrm{\,Ge\kern-1.00006ptV}$ or a pair of muons with
$\mbox{$p_{\rm T}$}_{1}\cdot\mbox{$p_{\rm
T}$}_{2}>1.6\mathrm{\,Ge\kern-1.00006ptV}^{2}$. A global event cut (GEC) on
the number of hits in the SPD is applied in order to prevent high-multiplicity
events from dominating the processing time. At the software trigger stage
events are required to have a two-, three- or four-track secondary vertex (SV)
with significant displacement from any primary vertex. A multivariate
algorithm [15] is used for the identification of secondary vertices consistent
with the decay of a $b$ hadron, strongly suppressing the contamination from
charmed hadrons.

Simulated events generated with Pythia [16, *Sjostrand:2006za], with a
specific LHCb configuration [18], are used to model the properties of the
signal $Z\rightarrow b\bar{b}$ events and backgrounds such as $Z\rightarrow
c\bar{c}$, $W\rightarrow qq^{\prime}$ decays and $t\bar{t}$ events. Decays of
hadronic particles are described by EvtGen [19], where the final-state
radiation is generated using Photos [20]. The interaction of the generated
particles with the detector, and its response, are implemented using the
Geant4 toolkit [21, *Agostinelli:2002hh] as described in Ref. [23].

##  3 Candidate selection

Candidates are selected by requiring the presence of at least three jets,
which are reconstructed as detailed in Refs. [24, 25, 26, 27]. Jets are
reconstructed using a particle flow algorithm [27] and are clustered with the
$\text{anti-}k_{\mathrm{T}}$ algorithm [28] with a distance parameter $0.5$,
as implemented in the FastJet software package [29]. A jet energy correction
[27] determined from simulation is applied to recover the jet energy at
particle level and jet quality requirements are applied [27]. Jets are heavy-
flavour tagged, i.e. as containing a $b$ or $c$ hadron, if a SV is found with
a distance $\Delta R<0.5$ from the jet axis, where $\Delta R$ is the distance
in the ($\eta,\phi$) plane and $\phi$ is the azimuthal angle between the jet
axis and the vector that points from the $pp$ interaction point to the SV. The
details of the flavour-tagging algorithm are described in Ref. [30]. Two
heavy-flavour tagged jets are required to form a $Z\rightarrow b\bar{b}$
candidate. At least one of the two $b$-jet candidates must be tagged by a SV
selected by the software trigger requirements. The two heavy-flavour jets are
each required to have transverse momenta $\mbox{$p_{\rm
T}$}>20\mathrm{\,Ge\kern-1.00006ptV}$, pseudorapidities in the range
$2.2<\eta<4.2$, and a combined invariant mass ($m_{jj}$) in the range
$45<m_{jj}<165$ GeV. The fiducial region of the measurement within which the
cross-section is determined is defined by the kinematical requirements
described above applied to particle-level jets, which are jets reconstructed
in the simulation from stable particles (_i.e._ particles with lifetime in
excess of 10 ps, excluding neutrinos) using the default reconstruction
algorithm.

In order to increase the signal-to-background ratio, the absolute azimuthal
angle between the two $b$-jets is required to be greater than 2.5 radians. The
presence of a balancing jet is required to help discriminate $Z\rightarrow
b\bar{b}$ events from the QCD multijet background. The $Z$ \+ jet signal is
predominantly produced via quark-gluon scattering, while the QCD multijet
background is produced via gluon-gluon interactions [31]. The balancing jet is
defined as that which minimises the total $p_{\rm T}$ of the $Z$ boson and the
jet. This jet is required to have $\mbox{$p_{\rm
T}$}>10\mathrm{\,Ge\kern-1.00006ptV}$ and $2.2<\eta<4.2$. Given the SM cross-
sections [32] and the selection efficiencies, which are evaluated using
simulation, about 17$\times 10^{3}$ $Z\rightarrow b\bar{b}$ candidates, 600
$Z\rightarrow c\bar{c}$ candidates, 200 $W\rightarrow qq^{\prime}$ candidates
and 50 $t\bar{t}$ candidates are expected after the application of the
selection criteria. A sample of around $6\times 10^{5}$ candidates is selected
in data, dominated by the combinatorial background from the multijet QCD
events.

A multivariate classifier is trained to discriminate $Z\rightarrow b\bar{b}$
events from combinatorial QCD events. A uniform Gradient Boost Boosted
Decision Tree technique [33] is adopted, in order to ensure a selection
efficiency with a low dependence on the dijet invariant mass. The classifier
is trained using four kinematical variables of the three-jet system, chosen
for both their low correlation with the dijet invariant mass and for their
discriminating power. The variables are the absolute pseudorapidity difference
between the two heavy-flavour jets, the $p_{\rm T}$ of the balancing jet, the
angle between the balancing-jet momentum and the $Z$-boson candidate momentum
in the azimuthal plane with respect to the beam axis, and the polar angle
between the balancing-jet momentum and the $Z$-boson flight direction in the
$Z$-boson rest frame. The classifier is trained using 5$\%$ of the data sample
to represent the combinatorial QCD background. This training sample has a
negligible $Z\rightarrow b\bar{b}$, $Z\rightarrow c\bar{c}$, $W\rightarrow
qq^{\prime}$ and $t\bar{t}$ contamination and it is not used in the dijet
invariant mass fit described below. The signal process is modelled using
simulated $Z\rightarrow b\bar{b}$ events. The distributions of the input
observables related to the balancing-jet kinematics are validated by comparing
the high purity $Z(\rightarrow\mu^{+}\mu^{-})$ \+ jet data sample described in
Ref. [26] with the corresponding simulation sample.

The output of the classifier ($\mathrm{uGB}$) is shown in Fig. 1.

Figure 1:  Distribution of the multivariate classifier output for data and for
simulated $Z\rightarrow b\bar{b}$ decays, normalisted to unity. The signal
region is defined by $\mathrm{uGB}>x_{s}$ and the control region by events
with $\mathrm{uGB}<x_{c}$.

Candidates are selected in two different regions of uGB: the signal region
($\mathrm{uGB}>x_{s}$), which has enhanced $Z\rightarrow b\bar{b}$
contribution, and a control region ($\mathrm{uGB}<x_{c}$), which has a larger
contribution from QCD combinatorial events. The two regions are fitted
simultaneously to determine the $Z\rightarrow b\bar{b}$ yield, and the values
of $x_{s}$ and $x_{c}$ are chosen in order to achieve the best signal
significance.

##  4 Signal yield determination

A simultaneous fit to the $b$-dijet invariant mass distributions in the signal
and control regions is performed to determine the $Z\rightarrow b\bar{b}$
yield and the jet energy scale factor, $k_{\mathrm{JES}}$. A triple-Gaussian
model is used to describe the $Z\rightarrow b\bar{b}$ dijet invariant mass
distribution. The parameters of this model are obtained separately for the
candidates in the signal and control regions using simulation, and are fixed
in the fit to the data. The $k_{\mathrm{JES}}$ factor is also introduced in
the $Z\rightarrow b\bar{b}$ invariant mass distribution model in order to
account for differences between simulation and data in the jet four-momentum.
This is achieved by substituting $m_{jj}$ with $m_{jj}/k_{\mathrm{JES}}$ in
the model. The reconstructed invariant mass of dijets in $Z\rightarrow
b\bar{b}$ simulated events has a mean of 80 GeV, _i.e._ below the known
$Z$-boson mass [32], and a resolution of 16$\%$. The reduced mean is due to
parton radiation outside the jet cone, missing energy, and residual biases in
the reconstructed jet energy that are not recovered by the jet energy
correction.

The invariant mass distribution of the combinatorial background is
parametrized with a Pearson IV distribution, as is typical to describe the
multijet combinatorial background [10]. The four parameters of the Pearson IV
function are free to vary in the fit and they have approximately the same
values in the signal and control regions, since the uGB is trained to be as
uniform as possible with respect to the dijet invariant mass. To take into
account the residual correlation with the dijet invariant mass, the Pearson IV
distribution is multiplied in the signal (control) region by a linear transfer
function $t^{s(c)}(m_{jj})$, defined as

$t^{s(c)}(m_{jj})=a^{s(c)}+b^{s(c)}\cdot m_{jj},$

where the superscript $s$ ($c$) indicates the signal (control) region, and
$a^{s(c)}$ and $b^{s(c)}$ are parameters fixed in the invariant mass fit. The
parameters $a^{s(c)}$ and $b^{s(c)}$ are determined by fitting the transfer
function to the selection efficiency after the requirement that
$\mathrm{uGB}>x_{s}$ ($\mathrm{uGB}<x_{c}$) as a function of the dijet
invariant mass in the $45<m_{jj}<60$ GeV and $100<m_{jj}<165$ GeV intervals,
where the $Z\rightarrow b\bar{b}$ contribution is negligible. As a cross-
check, data events with $\mathrm{uGB}<x_{c}$ are fitted with only the QCD
background model, ignoring the small $Z\rightarrow b\bar{b}$ contribution, and
a good fit quality is obtained.

The invariant mass model used to fit the signal region is

$f^{s}(m_{jj})=N_{Q}^{s}Q(m_{jj})\cdot
t^{s}(m_{jj})+N_{Z}^{s}Z^{s}(m_{jj};k_{\mathrm{JES}}),$

where $N_{Q}^{s}$ and $N_{Z}^{s}$ are the number of QCD events and the number
of $Z$-boson events ($Z\rightarrow b\bar{b}$ plus $Z\rightarrow c\bar{c}$) in
the signal region respectively, and $Q(m_{jj})$, $t^{s}(m_{jj})$ and
$Z^{s}(m_{jj})$ are the Pearson IV distribution, the transfer function and the
$Z$-boson invariant mass distribution model in the signal region,
respectively. The $Z\rightarrow c\bar{c}$ invariant mass distribution is
assumed to be identical to that of $Z\rightarrow b\bar{b}$ events. This
assumption is verified using the simulation and the two components are
therefore fitted together. Backgrounds other than $Z\rightarrow c\bar{c}$ and
QCD multijet events are neglected in the fit. Since the $\mathrm{uGB}>x_{s}$
requirement is applied, the expected value of $N_{Z}^{s}$ is lower than the
17$\times 10^{3}$ $Z\rightarrow b\bar{b}$ events expected before the uGB
selection.

The invariant mass model that describes the control region is

$f^{c}(m_{jj})=N_{Q}^{c}Q(m_{jj})\cdot t^{c}(m_{jj})+R\cdot
N_{Z}^{s}Z^{c}(m_{jj};k_{\mathrm{JES}}),$

where $N_{Q}^{c}$ is the number of QCD events in the control region and
$Q(m_{jj})$, $t^{c}(m_{jj})$ and $Z^{c}(m_{jj})$ are the Pearson IV
distribution, the transfer function and the $Z$-boson invariant mass
distribution model in the control region. The parameter $R$ is the ratio of
the efficiency for $Z$-boson candidates selected with $\mathrm{uGB}<x_{c}$ and
$\mathrm{uGB}>x_{s}$ and is determined from simulation and fixed in the fit. A
simultaneous unbinned maximum likelihood fit is performed with the
$N_{Q}^{s}$, $N_{Q}^{c}$, $N_{Z}^{s}$, $k_{\mathrm{JES}}$ and the Pearson IV
parameters free to vary. Pseudoexperiments are used to verify that the fit is
stable and estimate any bias. The parameter $N_{Z}^{s}$ is determined with a
bias of about $2\%$ and the value returned by the fit is corrected accordingly
in the cross-section determination.

The fit result is shown in Fig. 2 and the background-subtracted data and
result of the fit are shown in Fig. 3. The $Z$-boson yield in the signal
region is 5462 $\pm$ 763 and the jet energy scale factor is measured to be
1.009 $\pm$ 0.015. Using Wilks’ theorem [34], the $Z\rightarrow b\bar{b}$
statistical significance is found to be 7.3 standard deviations.

Figure 2:  Simultaneous fit to the dijet invariant mass distribution of
$Z\rightarrow b\bar{b}$ candidates in the (left) signal and (right) control
regions. Figure 3:  Background-subtracted distribution compared with the
$Z\rightarrow b\bar{b}$ mass model in the (left) signal and (right) control
regions. The one standard deviation total uncertainty band in the background-
only hypothesis is also shown. This band includes statistical and systematic
uncertainties.

As an additional cross-check to validate the technique, a fit to the dijet
invariant mass distribution for candidates with $x_{c}<\mathrm{uGB}<x_{s}$ is
performed, with a model analogous to that used in the signal and control
regions. In this case, the parameters of the QCD background are fixed to the
values returned by the default fit, but the $Z\rightarrow b\bar{b}$ yield in
this region, $N_{Z}^{v}$, is left free. The goodness of this fit is acceptable
and the ratio $N_{Z}^{v}/N_{Z}^{s}$ is compatible with the expectation from
simulation.

##  5 Cross-section determination and systematic uncertainties

The product of the $Z$-boson production cross-section and the $Z\rightarrow
b\bar{b}$ branching fraction is determined using

$\sigma(pp\rightarrow Z)\mathcal{B}(Z\rightarrow
b\bar{b})=\frac{N_{Z}^{s}}{\mathcal{L}\cdot(1-f_{\mathrm{uGB}})\cdot\epsilon_{Z}^{s}\cdot(1+f_{Z\rightarrow
c\bar{c}})}$

where $\mathcal{L}$ is the integrated luminosity, $\epsilon_{Z}^{s}$ is the
efficiency of the selection requirements, including $\mathrm{uGB}>x_{s}$, for
events in the fiducial region, $f_{\mathrm{uGB}}$ is the fraction (5%) of data
events removed for the multivariate classifier training and 1+$f_{Z\rightarrow
c\bar{c}}$ is a factor applied to correct for the small $Z\rightarrow
c\bar{c}$ contamination. The selection efficiency is obtained from simulation,
but correction factors are applied to account for differences in the heavy-
flavour tagging efficiencies between data and simulation [30]. By using a
small sample with a looser trigger requirement and a technique similar to that
described in Ref. [26], the GEC efficiency is also corrected for differences
in data and simulation. The balancing-jet selection efficiency is corrected at
Next-to-Leading-Order (NLO) using simulated $Z\rightarrow b\bar{b}$ events
produced with aMC@NLO [35] plus Pythia for parton showers. The
$f_{Z\rightarrow c\bar{c}}$ fraction is obtained by multiplying the
$Z\rightarrow c\bar{c}$ and $Z\rightarrow b\bar{b}$ branching fraction ratio
[32] by the acceptance and the efficiency ratios, both determined using
simulation.

The sources of systematic uncertainty considered for the measurement are given
in Table 1.

Table 1:  Systematic uncertainties on the cross-section, $\sigma_{Z}=\sigma(pp\rightarrow Z)\mathcal{B}(Z\rightarrow b\bar{b})$, and jet energy scale in percent. The total uncertainty is the sum in quadrature of all the contributions. Systematic source |  $\sigma_{Z}$ [$\%$] |  $k_{\mathrm{JES}}$ [$\%$]
---|---|---
Heavy-flavour tagging efficiency | $16.6$ |  $\phantom{<}0.5$
Hardware trigger efficiency | $\phantom{1}1.9$ |  –
GEC efficiency | $\phantom{1}1.7$ |  –
Jet energy correction | $\phantom{1}2.7$ |  $\phantom{<}0.3$
Jet energy resolution | $\phantom{1}1.0$ |  $\phantom{<}0.2$
Jet identification efficiency | $\phantom{1}2.0$ | $<0.1$
Balancing-jet selection efficiency | $\phantom{1}1.8$ |  –
Signal model | $\phantom{1}2.0$ |  $\phantom{<}0.3$
QCD model | $\phantom{1}1.1$ | $<0.1$
Transfer functions | $\phantom{1}1.5$ |  $\phantom{<}0.8$
$R$ efficiencies ratio | $\phantom{1}0.3$ | $<0.1$
Fit bias | $\phantom{1}2.1$ |  –
Subdominant backgrounds ($t\bar{t}$, $W\rightarrow qq^{\prime}$) | $\phantom{1}1.9$ | $<0.1$
Final-state radiation | $\phantom{1}0.9$ |  –
$f_{Z\rightarrow c\bar{c}}$ fraction | $\phantom{1}0.1$ |  –
Luminosity | $\phantom{1}1.2$ |  –
Total | $17.7$ | $~{}\phantom{<}1.1$

Systematic effects that are associated with differences between data and
simulation can affect the signal invariant mass distribution model and the
selection efficiency. The impact of these differences is evaluated by
repeating the fit with a modified signal model and by recalculating the cross-
section varying $\epsilon_{Z}^{s}$. Other sources of systematic uncertainties
are related to the signal extraction procedure.

The method described in Ref. [30] is used to assess the systematic uncertainty
due to the heavy-flavour tagging efficiency which amounts to $5\%-10\%$ per
jet, depending on the $p_{\rm T}$ range. This uncertainty is dominated by the
size of the calibration samples used in the heavy-flavour tagging efficiency
measurement. Since one of the two $b$-jet candidates must be tagged by a SV
selected by the software trigger, the uncertainty on this trigger efficiency
is included in this contribution. The systematic uncertainty associated with
the hardware trigger efficiency is determined by measuring the efficiency with
a tag-and-probe technique, using the high purity
$Z(\rightarrow\mu^{+}\mu^{-})$ \+ jet data sample [26]. In order to avoid
trigger bias on the jet selection, the tag is the muon that triggered the
event and the probe is the associated jet. The hardware trigger efficiency
measured on probe jets is compared between data and simulation and the maximum
difference in intervals of the jet $p_{\rm T}$ is taken as an uncertainty. The
latter does not take into account the systematic uncertainty on the GEC
efficiency, which is determined separately by studying its dependence on the
$b$-dijet invariant mass and assigning the largest variation as the
uncertainty. The systematic uncertainty on the jet energy correction includes
biases due to jet flavour dependence, reconstruction of tracks which are not
associated to a real particle, the track momentum resolution and residual
differences between simulation and data, as described in Refs. [26, 27]. The
jet energy resolution is modelled in simulation with an uncertainty measured
in Refs. [25, 27]. The uncertainties related to the jet reconstruction and
identification are taken from Ref. [27]. The systematic uncertainty associated
with the balancing-jet selection efficiency is evaluated by measuring this
efficiency in the $Z(\rightarrow\mu^{+}\mu^{-})$ \+ jet data and simulation
samples and taking the difference as a systematic uncertainty.

The uncertainty on the model of the signal invariant mass distribution is
determined by repeating the fit with an alternative distribution, consisting
of the sum of two modified Gaussians. The uncertainty on the QCD model is
determined by considering an alternative parametrization, consisting of an
exponential decay model multiplied by a function that describes the effect of
the jet $p_{\rm T}$ requirements on the invariant mass distribution. It has
been verified, by generating pseudoexperiments with this alternative model and
by fitting them with the default model, that the choice of the QCD
distribution model introduces a small bias in the measurement. This bias is
taken as the systematic uncertainty. The systematic uncertainty associated
with the transfer functions is evaluated by repeating the fit using second-
order polynomial functions instead of linear functions. In these fits the
coefficients of the quadratic terms are varied in a range consistent with the
data in the invariant mass sidebands used in the determination of the transfer
functions. The maximum variation with respect to the default measurement is
taken as the uncertainty. The efficiency ratio $R$ is determined using both
$Z(\rightarrow\mu^{+}\mu^{-})$ \+ jet data and simulation, and the observed
difference is taken as a systematic uncertainty. The uncertainty associated
with a possible bias introduced by the fit procedure is determined using
pseudoexperiments.

The fit is repeated introducing contributions from the subdominant
backgrounds, $t\bar{t}$ and $W\rightarrow qq^{\prime}$, fixed to their SM
expectations [32] and modelled with the simulation. The difference in the
results is assigned as a systematic uncertainty. The final-state radiation
systematic uncertainty is determined as described in Ref. [16,
*Sjostrand:2006za]. The systematic uncertainty due to the $Z\rightarrow
c\bar{c}$ contribution is dominated by the knowledge of the $Z\rightarrow
c\bar{c}$ branching fraction [32] used in the evaluation of the
$f_{Z\rightarrow c\bar{c}}$ parameter. The systematic uncertainty on the
luminosity is determined as in Ref. [36].

The different sources of systematic uncertainties are considered to be
uncorrelated and the total, relative systematic uncertainty is $17.7\%$ for
the cross-section measurement, dominated by the heavy-flavour tagging
efficiency uncertainty ($16.6\%$). The total systematic uncertainty for the
jet energy scale measurement is $1.1\%$ and is dominated by the uncertainty on
the transfer functions ($0.8\%$). The significance of the signal yield,
including all statistical and systematic uncertainties, is 6.0 standard
deviations.

##  6 Results and conclusions

The product of the $Z$-boson production cross-section and the $Z\rightarrow
b\bar{b}$ branching fraction in $pp$ collisions at a centre-of-mass energy of
8 $\mathrm{TeV}$ is

$\sigma(pp\rightarrow Z)\mathcal{B}(Z\rightarrow b\bar{b})=332\pm 46\pm
59~{}\mathrm{pb},$

where the first uncertainty is statistical and the second is systematic. The
measurement is made in the fiducial region defined by two particle-level $b$
jets with $\mbox{$p_{\rm T}$}>20\mathrm{\,Ge\kern-1.00006ptV}$,
$2.2<\eta<4.2$, and $45<m_{jj}<165$ GeV.

The expected cross-section in the fiducial region of the experimental
measurement is calculated at NLO using aMC@NLO plus Pythia for the parton
showers and the NNPDF3.0 Parton Distribution Functions (PDFs) set [37]. The
theoretical prediction determined in this way is

$\sigma(pp\rightarrow Z)\mathcal{B}(Z\rightarrow
b\bar{b})=272^{+9}_{-12}(\mathrm{scale})\pm 5(\mathrm{PDFs})~{}\mathrm{pb},$

where the first uncertainty is related to the missing higher-order corrections
and to the value of the strong coupling constant, and the second uncertainty
is related to the PDFs. The uncertainty due to missing higher-order
corrections is evaluated by varying the renormalization and factorization
scales by a factor of two around the nominal choice, and taking the maximum
differences with respect to the nominal values. The uncertainty on the strong
coupling is included by varying it within its uncertainty and recalculating
the cross-section. The uncertainty on the PDFs is estimated by taking the
variance of the cross-section predictions, where each replica of the NNPDF3.0
set is used in turn. The prediction and the measurement are compatible within
one standard deviation. The additional data being collected by the LHCb
collaboration will allow a more stringent comparison with the theoretical
prediction in the future. Moreover, the systematic uncertainty on the heavy-
flavour tagging efficiency will be reduced by collecting more data [30].

The measured jet energy scale factor is

$k_{\mathrm{JES}}=1.009\pm 0.015\pm 0.011,$

where the first uncertainty is statistical and the second uncertainty is
systematic. The $k_{\mathrm{JES}}$ factor is compatible with unity, which
demonstrates that the LHCb simulation reproduces accurately the $b$-jet energy
in data for $b\bar{b}$-jet pairs with about 100 GeV of invariant mass. Since a
jet energy correction evaluated using simulation is already applied on $b$
jets, $k_{\mathrm{JES}}$ represents the residual correction obtained using the
data.

## Acknowledgements

We express our gratitude to our colleagues in the CERN accelerator departments
for the excellent performance of the LHC. We thank the technical and
administrative staff at the LHCb institutes. We acknowledge support from CERN
and from the national agencies: CAPES, CNPq, FAPERJ and FINEP (Brazil); MOST
and NSFC (China); CNRS/IN2P3 (France); BMBF, DFG and MPG (Germany); INFN
(Italy); NWO (The Netherlands); MNiSW and NCN (Poland); MEN/IFA (Romania);
MinES and FASO (Russia); MinECo (Spain); SNSF and SER (Switzerland); NASU
(Ukraine); STFC (United Kingdom); NSF (USA). We acknowledge the computing
resources that are provided by CERN, IN2P3 (France), KIT and DESY (Germany),
INFN (Italy), SURF (The Netherlands), PIC (Spain), GridPP (United Kingdom),
RRCKI and Yandex LLC (Russia), CSCS (Switzerland), IFIN-HH (Romania), CBPF
(Brazil), PL-GRID (Poland) and OSC (USA). We are indebted to the communities
behind the multiple open-source software packages on which we depend.
Individual groups or members have received support from AvH Foundation
(Germany), EPLANET, Marie Skłodowska-Curie Actions and ERC (European Union),
ANR, Labex P2IO, ENIGMASS and OCEVU, and Région Auvergne-Rhône-Alpes (France),
RFBR and Yandex LLC (Russia), GVA, XuntaGal and GENCAT (Spain), Herchel Smith
Fund, the Royal Society, the English-Speaking Union and the Leverhulme Trust
(United Kingdom).

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LHCb collaboration

R. Aaij40, B. Adeva39, M. Adinolfi48, Z. Ajaltouni5, S. Akar59, J. Albrecht10,
F. Alessio40, M. Alexander53, A. Alfonso Albero38, S. Ali43, G. Alkhazov31, P.
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Regueiro39, S. Vecchi17, M. van Veghel43, J.J. Velthuis48, M. Veltri18,r, G.
Veneziano57, A. Venkateswaran61, T.A. Verlage9, M. Vernet5, M. Vesterinen57,
J.V. Viana Barbosa40, B. Viaud7, D. Vieira63, M. Vieites Diaz39, H. Viemann67,
X. Vilasis-Cardona38,m, M. Vitti49, V. Volkov33, A. Vollhardt42, B. Voneki40,
A. Vorobyev31, V. Vorobyev36,w, C. Voß9, J.A. de Vries43, C. Vázquez Sierra39,
R. Waldi67, C. Wallace50, R. Wallace13, J. Walsh24, J. Wang61, D.R. Ward49,
H.M. Wark54, N.K. Watson47, D. Websdale55, A. Weiden42, M. Whitehead40, J.
Wicht50, G. Wilkinson57,40, M. Wilkinson61, M. Williams56, M.P. Williams47, M.
Williams58, T. Williams47, F.F. Wilson51, J. Wimberley60, M. Winn7, J.
Wishahi10, W. Wislicki29, M. Witek27, G. Wormser7, S.A. Wotton49, K.
Wraight53, K. Wyllie40, Y. Xie65, Z. Xu4, Z. Yang3, Z. Yang60, Y. Yao61, H.
Yin65, J. Yu65, X. Yuan61, O. Yushchenko37, K.A. Zarebski47, M.
Zavertyaev11,c, L. Zhang3, Y. Zhang7, A. Zhelezov12, Y. Zheng63, X. Zhu3, V.
Zhukov33, J.B. Zonneveld52, S. Zucchelli15.

1Centro Brasileiro de Pesquisas Físicas (CBPF), Rio de Janeiro, Brazil
2Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil
3Center for High Energy Physics, Tsinghua University, Beijing, China
4LAPP, Université Savoie Mont-Blanc, CNRS/IN2P3, Annecy-Le-Vieux, France
5Clermont Université, Université Blaise Pascal, CNRS/IN2P3, LPC, Clermont-
Ferrand, France
6Aix Marseille Univ, CNRS/IN2P3, CPPM, Marseille, France
7LAL, Université Paris-Sud, CNRS/IN2P3, Orsay, France
8LPNHE, Université Pierre et Marie Curie, Université Paris Diderot,
CNRS/IN2P3, Paris, France
9I. Physikalisches Institut, RWTH Aachen University, Aachen, Germany
10Fakultät Physik, Technische Universität Dortmund, Dortmund, Germany
11Max-Planck-Institut für Kernphysik (MPIK), Heidelberg, Germany
12Physikalisches Institut, Ruprecht-Karls-Universität Heidelberg, Heidelberg,
Germany
13School of Physics, University College Dublin, Dublin, Ireland
14Sezione INFN di Bari, Bari, Italy
15Sezione INFN di Bologna, Bologna, Italy
16Sezione INFN di Cagliari, Cagliari, Italy
17Universita e INFN, Ferrara, Ferrara, Italy
18Sezione INFN di Firenze, Firenze, Italy
19Laboratori Nazionali dell’INFN di Frascati, Frascati, Italy
20Sezione INFN di Genova, Genova, Italy
21Universita & INFN, Milano-Bicocca, Milano, Italy
22Sezione di Milano, Milano, Italy
23Sezione INFN di Padova, Padova, Italy
24Sezione INFN di Pisa, Pisa, Italy
25Sezione INFN di Roma Tor Vergata, Roma, Italy
26Sezione INFN di Roma La Sapienza, Roma, Italy
27Henryk Niewodniczanski Institute of Nuclear Physics Polish Academy of
Sciences, Kraków, Poland
28AGH - University of Science and Technology, Faculty of Physics and Applied
Computer Science, Kraków, Poland
29National Center for Nuclear Research (NCBJ), Warsaw, Poland
30Horia Hulubei National Institute of Physics and Nuclear Engineering,
Bucharest-Magurele, Romania
31Petersburg Nuclear Physics Institute (PNPI), Gatchina, Russia
32Institute of Theoretical and Experimental Physics (ITEP), Moscow, Russia
33Institute of Nuclear Physics, Moscow State University (SINP MSU), Moscow,
Russia
34Institute for Nuclear Research of the Russian Academy of Sciences (INR RAN),
Moscow, Russia
35Yandex School of Data Analysis, Moscow, Russia
36Budker Institute of Nuclear Physics (SB RAS), Novosibirsk, Russia
37Institute for High Energy Physics (IHEP), Protvino, Russia
38ICCUB, Universitat de Barcelona, Barcelona, Spain
39Universidad de Santiago de Compostela, Santiago de Compostela, Spain
40European Organization for Nuclear Research (CERN), Geneva, Switzerland
41Institute of Physics, Ecole Polytechnique Fédérale de Lausanne (EPFL),
Lausanne, Switzerland
42Physik-Institut, Universität Zürich, Zürich, Switzerland
43Nikhef National Institute for Subatomic Physics, Amsterdam, The Netherlands
44Nikhef National Institute for Subatomic Physics and VU University Amsterdam,
Amsterdam, The Netherlands
45NSC Kharkiv Institute of Physics and Technology (NSC KIPT), Kharkiv, Ukraine
46Institute for Nuclear Research of the National Academy of Sciences (KINR),
Kyiv, Ukraine
47University of Birmingham, Birmingham, United Kingdom
48H.H. Wills Physics Laboratory, University of Bristol, Bristol, United
Kingdom
49Cavendish Laboratory, University of Cambridge, Cambridge, United Kingdom
50Department of Physics, University of Warwick, Coventry, United Kingdom
51STFC Rutherford Appleton Laboratory, Didcot, United Kingdom
52School of Physics and Astronomy, University of Edinburgh, Edinburgh, United
Kingdom
53School of Physics and Astronomy, University of Glasgow, Glasgow, United
Kingdom
54Oliver Lodge Laboratory, University of Liverpool, Liverpool, United Kingdom
55Imperial College London, London, United Kingdom
56School of Physics and Astronomy, University of Manchester, Manchester,
United Kingdom
57Department of Physics, University of Oxford, Oxford, United Kingdom
58Massachusetts Institute of Technology, Cambridge, MA, United States
59University of Cincinnati, Cincinnati, OH, United States
60University of Maryland, College Park, MD, United States
61Syracuse University, Syracuse, NY, United States
62Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio), Rio de
Janeiro, Brazil, associated to 2
63University of Chinese Academy of Sciences, Beijing, China, associated to 3
64School of Physics and Technology, Wuhan University, Wuhan, China, associated
to 3
65Institute of Particle Physics, Central China Normal University, Wuhan,
Hubei, China, associated to 3
66Departamento de Fisica , Universidad Nacional de Colombia, Bogota, Colombia,
associated to 8
67Institut für Physik, Universität Rostock, Rostock, Germany, associated to 12
68National Research Centre Kurchatov Institute, Moscow, Russia, associated to
32
69National Research Tomsk Polytechnic University, Tomsk, Russia, associated to
32
70Instituto de Fisica Corpuscular, Centro Mixto Universidad de Valencia -
CSIC, Valencia, Spain, associated to 38
71Van Swinderen Institute, University of Groningen, Groningen, The
Netherlands, associated to 43

aUniversidade Federal do Triângulo Mineiro (UFTM), Uberaba-MG, Brazil
bLaboratoire Leprince-Ringuet, Palaiseau, France
cP.N. Lebedev Physical Institute, Russian Academy of Science (LPI RAS),
Moscow, Russia
dUniversità di Bari, Bari, Italy
eUniversità di Bologna, Bologna, Italy
fUniversità di Cagliari, Cagliari, Italy
gUniversità di Ferrara, Ferrara, Italy
hUniversità di Genova, Genova, Italy
iUniversità di Milano Bicocca, Milano, Italy
jUniversità di Roma Tor Vergata, Roma, Italy
kUniversità di Roma La Sapienza, Roma, Italy
lAGH - University of Science and Technology, Faculty of Computer Science,
Electronics and Telecommunications, Kraków, Poland
mLIFAELS, La Salle, Universitat Ramon Llull, Barcelona, Spain
nHanoi University of Science, Hanoi, Viet Nam
oUniversità di Padova, Padova, Italy
pUniversità di Pisa, Pisa, Italy
qUniversità degli Studi di Milano, Milano, Italy
rUniversità di Urbino, Urbino, Italy
sUniversità della Basilicata, Potenza, Italy
tScuola Normale Superiore, Pisa, Italy
uUniversità di Modena e Reggio Emilia, Modena, Italy
vIligan Institute of Technology (IIT), Iligan, Philippines
wNovosibirsk State University, Novosibirsk, Russia
†Deceased


# Uncovering the Birth of a Coronal Mass Ejection from Two-Viewpoint SECCHI
Observations

A. Vourlidas1P. Syntelis2,4K. Tsinganos3,4 1 Space Sciences Division, Naval
Research Laboratory, Washington DC, USA
2 Research Center for Astronomy and Applied Mathematics, Academy of Athens,
Athens, Greece
3 National Observatory of Athens, Athens, Greece
4 Section of Astrophysics, Astronomy and Mechanics, Department of Physics,
University of Athens, Athens, Greece

###### Abstract

We investigate the initiation and formation of Coronal Mass Ejections (CMEs)
via detailed two-viewpoint analysis of low corona observations of a relatively
fast CME acquired by the SECCHI instruments aboard the STEREO mission. The
event which occurred on January 2, 2008, was chosen because of several unique
characteristics. It shows upward motions for at least four hours before the
flare peak. Its speed and acceleration profiles exhibit a number of
inflections which seem to have a direct counterpart in the GOES light curves.
We detect and measure, in 3D, loops that collapse toward the erupting channel
while the CME is increasing in size and accelerates. We suggest that these
collapsing loops are our first evidence of magnetic evacuation behind the
forming CME flux rope. We report the detection of a hot structure which
becomes the core of the white light CME. We observe and measure unidirectional
flows along the erupting filament channel which may be associated with the
eruption process. Finally, we compare these observations to the predictions
from the standard flare-CME model and find a very satisfactory agreement. We
conclude that the standard flare-CME concept is a reliable representation of
the initial stages of CMEs and that multi-viewpoint, high cadence EUV
observations can be extremely useful in understanding the formation of CMEs.

###### keywords:

Coronal Mass Ejections, Low Coronal Signatures; Coronal Mass Ejections,
Initiation and Propagation; Magnetic Reconnection, Observational Signatures

##  1 Introduction

sec:Introduction

CMEs have been observed for more than 40 years now. They are one of the most
energetic phenomena in our solar system and the main driver of disturbances in
the terrestrial space environment. Despite observations of tens of thousands
of CMEs, the physical processes behind their formation and propagation have
not yet been understood completely [Klimchuk (2001), Forbes et al. (2006),
Chen (2011)].

To make progress, we need to select the model (or models) that best describe
the phenomenon. To accomplish this, it is necessary to test the theoretical
predictions of the various models against the observations as was discussed by
2001AGUGM.125..143K. Here, we concentrate on the ’standard’ flare-CME model,
also known as the CSHKP model [Švestka and Cliver (1992)]. This is not
actually a fully-fledged model derived from the solution of a set of
Magnetohydrodynamic (MHD) equations but it is rather a two-dimensional (2D)
cartoon representation of the erupting process. However it captures the key
ingredients of many MHD models (i.e., the three-part CME, the ejection of a
flux rope, post-CME flaring loops, etc) and demonstrates, in a straightforward
way, the possible connection between the erupting and flaring processes. For
our discussion, we use the detailed model representation in 2004ApJ…602..422L
(their Figure 1) but many more variations can be found in the literature.

Even as a cartoon, the CSHKP model makes several predictions that can be
tested against the observations. First, it predicts the eruption of a core
surrounded by a cavity (or bubble) that forms during the initiation process.
High temperatures are expected in both the cavity and the core as result of
magnetic reconnection [Chen (2011)]. Second, the reconnection behind the
erupting system creates a magnetic void which draws adjacent lines toward the
current sheet thereby creating an inflow of material from the surrounding flux
systems. Third, through the reconnection processes in the post-CME current
sheet, magnetic energy is transformed into thermal energy that powers the
flare and kinetic energy that powers the CME. Therefore, we expect a close
correspondence between the SXR light curve and the CME acceleration profile as
has been found in the past (e.g., 2004ApJ…604..420Z). A delay between the two
processes is also likely depending on the magnetic fields and reconnection
rates involved [Reeves (2006)]. Fourth, there are many candidates for the role
of the eruption trigger. Flux emergence, tether-cutting or even mass unloading
from the prominence channel, are all capable of driving the system out of its
equilibrium state to set off the eruption (see discussion and references in
2011LRSP….8….1C). Can the trigger be identified in the observations?

Many, if not all, of these predictions relate to the very first stages of the
CME; namely, its initiation and formation. However, the initiation and
formation stages of CMEs present some serious observational challenges. The
CME formation and initial evolution take place low in the corona which is
accessible only to imagers in the Extreme Ultraviolet (EUV) or (less often)
Soft X-Ray (SXR) wavelengths. These instruments observe in a relatively narrow
passband and hence are sensitive to only a narrow range of temperatures, at a
time. CME triggers, such as plasma instabilities occur within Alfvenic
temporal and spatial scales (of the order of tens of seconds or hundreds of km
for an active region). The subsequent energy release also occurs in similar
scales and the eruption is usually accompanied by other phenomena such as
flares, jets and lateral plasma motions that may have nothing to do with the
erupting structure but they complicate the interpretation of the EUV
observations.

Therefore, we need observations of the formation stages of a CME taken with
high cadence and spatial resolution but with minimal line-of-sight confusion.
The unique stereoscopic viewing and instrument complement provided by the Sun-
Earth Connection Coronal and Heliospheric Investigation (SECCHI;
2008SSRv..136…67H) on-board the Solar TErrestial RElations Observatory
(STEREO) [Kaiser et al. (2008)] fulfills these requirements nicely.

To demonstrate this, we undertake a CME initiation study for an event which
took place on January 2, 2008. The eruption in the low corona was observed
very well by both SECCHI Extreme Ultraviolet Imagers (EUVI). We are able to
examine in detail the various stages of the initiation of a CME and relate
them to the usual phenomena that accompany these eruptions, such as flares and
filament ejections. In addition, we capture the transition of loop arcades
into the forming flux rope and report the first three-dimensional observations
of loop ’implosion’. Taken together, these observations reveal many of the key
components of CME initiation and provide strong constraints for CME models.
The paper is organized as follows. In Section 2, we present the time history
of the CME and discuss in detail several key observations including the close
correspondence between the acceleration profile and the GOES SXR light curve,
the novel observation and 3D measurements of collapsing loops, the detection
of a hot CME core, and the observation of outflows along the filament channel.
We conclude in Section 3.

##  2 Stereoscopic Observations of the January 2, 2008 CME

The event under study erupted from active region NOAA 10980 located at S05E65
(Figure fig:context). The region has an alpha magnetic configuration with a
single leading negative polarity sunspot. The sunpot disappeared within a
couple of days leaving only an extended area of plage fields. The eruption
occurred along a filament channel (thick white line in Figure fig:context)
overlying a neutral line extending from the center of the region to its
periphery. The CME was accompanied by a GOES C1.2 flare starting at 06:51 UT,
peaking at 10:00 UT, and ending at 11:21 UT. It is therefore a long duration
Soft X-ray (SXR) event but the gradual rise of the light curve is also
indicative of a partially occulted event. Indeed, upward motions at the
location of the subsequent CME can be detected much earlier than the flare
peak as we shall see later. The event was observed by the SECCHI/EUVI imagers
on the STEREO-A and STEREO-B spacecraft which were located $21^{\circ}$ West
and $23^{\circ}$ East from the Sun-Earth line, respectively. Therefore, it was
a limb event for EUVI-A ($\sim 88^{\circ}$) and an eastern event for EUVI-B
($\sim 42^{\circ}$). The 3D kinematics of the CME in the SECCHI coronagraph
fields of view have been discussed in detail by [Zhao et al. (2010)]. Here, we
focus on the initiation of the CME in the low corona as witnessed in the EUVI
fields of view up to about 1.7 Rsun. We use mainly the 171Å images because of
their high cadence (150 sec) but we discuss the observations in the other
wavelengths as well. The images have been processed by the 2008ApJ…674.1201S
wavelet-based algorithm to enhance the visibility of the off-limb structures
by removing the instrumental stray light.

Figure 1.: Top panels: EUVI-A and -B 171Å full disk images on January 2, 2008
at 09:01 UT. The box marks the FOV around AR 10980 used in the subsequent
analysis. Bottom panels: MDI magnetograms of AR10980 on January 2 and 4
showing the magnetic field configuration for the event. The thick white line
marks the filament channel involved in the eruption. The arrow mark the
approximate location of collapsing loops discussed in sec:collapsing. The
magnetogram images are courtesy of SolarMonitor.org. fig:context

###  2.1 The time history of the CME formation in the low corona

Because of the unusual duration of the eruption, we have to find a reliable
marker for the start of the event. We use the time of the first unambiguous
detection of upward motion of EUV loops at the location of the subsequent CME.
This occurs at 06:13:30 UT (online movie and Figure fig:detail). We rely on
the EUVI-A images to describe the upward evolution since the CME is
propagating along the sky plane of STEREO-A and therefore the images are least
affected by projection effects.

The motion in EUVI-A originates in a high-lying loop system which appears to
encompass a cavity as evidenced by the lack of 171Å emission (Figure
fig:detail). Inside this cavity (in projection) we detect a single bright loop
(L1) that begins to collapse as the rest of the loop system expands slowly.
The loop is visible from 05:33:30 UT to 07:21:00 UT. The behavior of this
collapsing loop is almost immediately imitated by a larger loop arcade (L2).
Their collapse starts at around 08:18:30 UT. The CME front leaves the edge of
the EUVI-A field of view at 09:18:30 UT. The first evidence of a CME core, in
the traditional sense of a 3-part CME, becomes apparent at 09:15:30 UT while
the L2 system continues to collapse. At 10:03:30 UT, the loop arcade
disappears, the CME continues to accelerate and the usual post-eruptive arcade
forms. An EUV wave is launched by the expanding CME at around 10:33:30 UT.
Material continues to flow outward from the active region while the post-
eruptive arcade continues to grow until about 13:03:30 UT. We take this time
as the end of the eruption since it marks the end of the material outflow and
the growth of the flaring arcade.

The low-lying activity in the source region is not visible from EUVI-A but it
is clearly visible in EUVI-B. The images show that all the action takes place
along the filament channel running roughly east-west through the center of the
active region. The start of the event occurs at the easternmost edge of the
filament channel, closest to the leading sunspot of 10890. The post-eruption
loop system expands from that location toward the east. The collapsing loops
follow the same path as they collapse (see online movie). The time history is
summarized in Table tbl:history.

Table 1.: Time history of the CME eruption as marked by several key events.tbl:history Event | Time | Elapsed time
---|---|---
| (UT) | (min)
Upward motion (event starts) | 06:13:30 | 0
Single loop (L1) collapses | 06:36:00 | 22.5
SXR flare starts | 06:51:00 | 37.5
Single loop (L1) disappears | 07:21:00 | 67.5
Loop arcade (L2) collapses | 08:18:30 | 125.0
Core appears | 09:15:00 | 181.5
Flaring Arcade (FL1) appears | 09:21:00 | 207.5
SXR Flare peaks | 10:00:00 | 246.5
Loop Arcade (L2) disappears | 10:03:30 | 230.0
CME acceleration peaks | 10:23:00 | 249.5
EUV Wave appears | 10:33:30 | 260.0
End of SXR flare | 11:21:00 | 307.5
End of outflows (event ends) | 13:03:30 | 410.0

###  2.2 Height-Time Evolution of CME in the Low Corona

sec:height

Since the beginning of the day, the overlying loop system seems to be in a
steady state without noticeable motions other than the effect of the solar
rotation (the AR is rotating over the eastern limb as seen from EUVI-A).
Starting at around 6:13 UT, we can see upward motions within the loop system
and the whole system begins to expand after 6:31:30 UT. We choose to follow
the top of the loops for our height-time (ht) measurements. For the first two
hours, however, the motion is very slow and can be best appreciated by
examining the accompanying movie. Because of the slow rise, we use a running
cadence of 10 min (every four 171Å frames) for the ht measurements to make the
motion easier to see. Consequently, the height-time measurements were taken
with the full available cadence of 2.5 min.

EUVI-A is able to follow the loop top until 09:18 UT, when the CME exits the
telescope’s field of view (Figure fig:detail). We then turn to the COR1-A
images to obtain a complete set of ht measurements during the rise time of the
SXR flare. The measurements are presented in Figure fig:CME_height. The first
COR1-A ht point is plotted right next to the line labeled ’Core Appears’. We
did not attempt to triangulate the CME front positions in EUVI-B because the
front is visible only between 8:51 - 9:08 UT and is quite extended and
diffuse. The projection, however, does not affect our EUVI-A measurements
because it is clear that the CME lies very close to the EUVI-A plane of the
sky. To derive velocity and acceleration profiles from our sparse ht points,
it is always better to smooth the ht points first. We use the same smoothing
method as in 2010ApJ…724L.188P. Namely, we minimize the $\chi^{2}$ between the
data and a cubic spline plus a penalty function equal to the second derivative
of the spline multiplied by a weighting factor, $spar$, provided by the user.
In this case, $spar=0.6$ offers the best balance between noisy and overly
smooth acceleration profiles. The results are shown in Figure fig:CME_height
where the velocity is plotted in the top panel and the acceleration in the
bottom panel.

Figure 2.:  Snapshots of the eruption as seen in simultaneous images from
SECCHI/EUVI-A (right) and EUVI-B (left). The frames are taken from the online
movie and the labeled features are discussed in Sections sec:height -
sec:collapsing. The times correspond to the EUVI-A observation time.
fig:detail

Figure 3.:  The development of the eruption as seen through height-time and
velocity-time diagrams (top panel) and the Soft X-ray light curve and its
derivative (bottom panel). The heights correspond to the top of the CME
structure and the speed is derived using a smoothing procedure (Section 2.2).
Key events, such as collapsing loops, are also marked on the figure and are
discussed in Sections 2.3 and 2.4. fig:CME˙height

The last height measurement was taken in COR1-A at 10:50:18 UT but we show
results only until 10:30 UT. At that point the CME has reached a height of
$3R_{\odot}$ with velocity of $420$ km s-1. Both our speed and acceleration
results are consistent with the Zhao10 results which were based on ht
measurements after 10:00:00 UT and on a different technique.

In the bottom panel of Figure fig:CME_height, we compare the CME acceleration
to the 1-min GOES SXR light curve (1-8Å channel) and its time derivative which
is considered a proxy to energy release episodes. Both SXR curves are
normalized to their respective peaks.

First, we see that the CME acceleration profile follows closely the SXR rise
as seen before [Zhang et al. (2004), Temmer et al. (2008), Temmer et al.
(2010)], albeit with some time delay. This delay is consistent with the
gradual character of this CME. Generally speaking, impulsive CMEs tend to have
acceleration profiles leading the SXR flux profile [Patsourakos, Vourlidas,
and Stenborg (2010)] since it takes some time to heat the chromosphere and to
fill in the coronal loops with the hot plasma. In our case, the CME
acceleration peaks sharply after at about 10:23 UT when the flux rope core and
the post-CME flaring arcades appear. We return to this point in Section
sec:flux-rope.

Second, the impulsive phase of flare is a bit unusual because the rise of the
SXR flux is marked by two interim inflections (one at $\sim$8:25-8:50 and the
second at 9:20 UT) before the SXR peak at 10:00 UT. Remarkably, the CME
acceleration profile changes at almost the same times. We can discern
inflection points at approximately 8:30, 8:55, 9:10, 9:20, 9:55, and 10:25 UT
in the bottom panel of Figure fig:CME_height. These points bracket intensity
changes in the SXR light curve and coincide with peaks in the SXR derivative
(and hence energy release episodes). The correlations are positive
(acceleration) with the exception of the SXR derivative peak at 9:10 UT which
occurs during a decelerating phase of the CME. The time offsets between the
SXR and CME acceleration peaks are within 5 min of each other. There is even
indication for an earlier acceleration jump associated with a small step in
the SXR flux at around 7:00 UT. Since flaring and hence changes in the SXR
profile result from energy release in the low corona, it is tempting to
interpret the changes in the CME acceleration profile as a result of the same
energy release. For example, the CME speed increases from about 5 km s-1 to
almost 80 km s-1 during the first flaring episode, between 8:20 and 9:00 UT.
To investigate whether the correspondence between the SXR and CME acceleration
profiles is based on a causal relationship we look into the various phases of
the event in detail in the following.

###  2.3 Collapsing Loops

sec:collapsing

The observation of the two collapsing loop systems, L1 and L2, represents a
unique aspect of this event and drew our attention to it. The first system,
L1, appears to be a single loop which stands out because it is projected
against an area of reduced 171Å emission, possibly a cavity, as viewed from
EUVI-A. The loop appears to collapse starting at around 6:36 UT and disappears
at 7:21 UT. The loops do not appear to simply contract as has been seen in
other occasions (see 2011SSRv..158….5H and references therein) but it rather
seems to incline toward the cavity. At the same time, the cavity is slowly
rising and expanding. This behavior, especially the disappearance of the loop,
is seen for the first time and is suggestive of a magnetic relationship
between the loop and the cavity. But before we discuss this further, we have
to understand the 3D topology of the loop.

The loop is quite tall (0.15 R⊙ or $1.04\times 10^{5}$ km). However, it is
very hard to discern from the EUVI-B perspective because it is narrow (small
footpoint distance) and is oriented toward EUVI-B (Figure fig:detail, middle
panels). Nevertheless, its 3-dimensional (3D) orientation can be established
because it becomes visible in EUVI-B once it starts collapsing. We use
standard SECCHI software (the scc_measure routine) to derive its 3D parameters
as a function of time for the period 6:36-7:08 UT. Briefly, the algorithm
requires the user to select a point in the loop in one view. This selection
corresponds to a line (the epipolar line) in the other view. The successful
triangulation is achieved by identifying the location where the epipolar line
intersects the projection of the original point in the loop. In our case, the
obvious candidate is the bright loop-top in EUVI-A. Unfortunately it does not
have a clearly identifiable counterpart in EUVI-B because we view the loop-top
face-on. After careful examination of the movies, we decided to use a
relatively bright edge in EUVI-B as the starting point because it was easier
to find the intersection of the epipolar line with the loop in the EUVI-A
images. The intersection was located a few pixels below the bright loop apex
along the loop leg farthest from the EUVI-A observer. Here, we are primarily
interested in the temporal behavior of the loop height. The ht measurements
are shown in the Figure fig:collapsing-loops-ht. There is an obvious downward
trend despite some scatter in the measurements around 7 UT. The scatter arises
from inaccurate identification of the same part of the structure in the two
images. We repeated the measurements three times but we were not able to
improve the scatter in time. Although the scatter in the three measurements
(at the same time) was very small, we decided to adopt a conservative error
estimate equal to the standard deviation of all measurements in order to
account for the scatter in time. Given the scatter, we fit the ht data points
with a first order polynomial, assuming therefore, a constant speed. We
obtained a speed of $3$ km s-1.

Figure 4.: Height-time measurements of the two sets of collapsing loops
observed during this CME event. The heights are true radial distances obtained
via triangulation of the structures in the EUVI-A and -B 171Å images. The
solid lines represent linear fits to the ht points and result in speeds of 3
km s-1 and 2 km s-1 for the L1 and L2 systems, respectively.fig:collapsing-
loops-ht

Just an hour later, at 8:18 UT, a larger loop system (L2) begins to collapse
following an almost identical path to L1 (Figure fig:infalling-loops). The L2
system is located just a few pixels southeast of L1 and reaches almost the
same height, 0.15 R⊙. L2 is more discernible in the EUVI-B images but it could
easily be overlooked if it was not for the EUVI-A observations. This is a very
important point and explains the lack of such observations in the past. How
many times have we missed such inclining, collapsing loops in the past because
we had only one viewpoint available? Thanks to the two EUVI views, we can
derive the 3D orientation of L2 as we did for L1. The resulting ht points in
Figure fig:collapsing-loops-ht show a rather sharp drop in the first 15 mins
followed by a gradual contraction. We chose to fit again a first order
polynomial to describe the long-term evolution of the loop apex. In this case,
we derived a slight slower speed of $2$ km s-1. The L2 system collapses toward
the bottom of the erupting structure and the cavity is clearly rising while
the loop system is collapsing. The loops disappear similarly to L1, at a
height of 0.12 R⊙. We note that the CME clearly took off while the L2 system
was still collapsing and that the disappearance of the L2 loops coincides with
the flare peak. It is also worth noting (Figure fig:detail) that the first set
of bright flaring loops (in 171Å) appears at the location of the L2
footpoints.

Figure 5.: The collapsing loops toward the expanding CME cavity as seen from
EUVI-A (top right) and EUVI-B (top left). The arrows point to the direction of
the collapse. A flaring loop system with peculiar connectivity is also marked
(FL1). The bottom panels show snapshots at the time of the disappearance of
the L2 system and the appearance of bright flaring loops at their
footpoints.fig:infalling-loops

The coincidence of the collapsing loops to the rise and growth of the erupting
structure is very suggestive of a magnetic connection between the two and is
expected according to the standard CME models. Specifically, the models show
that as the flux rope rises and a current sheet forms behind it, the resulting
reconnection attracts nearby magnetic lines. The result is the creation of a
void which field lines further afield would rush to fill. The void, and
subsequent inflow, would occur across the erupting channel. Because most
models are essentially two-dimensional, the reconnection is symmetric and
proceeds from the center of the neutral line (or filament channel) outwards
and across the channel. In this situation, the inflows are depicted on either
side of the post-CME current sheet (e.g., 2004ApJ…602..422L). However, this
does not have to be, and most likely it is not the situation with the actual
observations. Erupting prominences (a usual proxy for the CME core) are often
seen rising asymmetrically and the majority of H$\alpha$ ribbons brighten
progressively both across and along the channel. If the eruption were to start
at one end of the filament channel then the ribbons would move from that end
of the channel to the opposite instead from starting at the middle and
propagate outwards along the channel as the symmetric picture would suggest
[Li and Zhang (2009)]. In that case, the void would form on end of the channel
and any likely inflows would occur there. Such an asymmetric eruption was
discussed by Patsourakos_Vourlidas_Kliem_2010. Therefore, we expect the
following: (i) inflows toward and behind the erupting structure, (ii) the
inflows would occur where the flux rope rises first, and (iii) the inflows and
flux rope growth would be correlated. The analysis of the collapsing loops
meets all three of these expectations and hence we claim that they constitute
the first direct evidence of the process of flux rope formation (or growth)
though the incorporation of neighboring flux systems into the erupting
structure.

###  2.4 The Detection of the Hot Flux Rope Core

sec:flux-rope

Figure 6.: Overlays of quasi-simultaneous EUVI-A observations at 284Å (green)
and 171Å (red) during the appearance of the CME core. The degree of color
dominance (green or red) at a given location can be used as a proxy for the
temperature of the material at that location. For example, the CME core
appears fully green at 9:26 UT which implies that most of the core material is
emitting at 284Å or about 1.8 MK, at that time.fig:fluxrope

The CME has a clear 3-part structure in the COR1 and COR2 observations [Zhao
et al. (2010)] and both the front and following cavity are easily discernible
in the 171Å observations. The counterpart for the core is not easy to identify
until 9:15 UT when a rather diffuse blob-like structure appears in the 195Å
images. No erupting prominence is detected in the 304Å observations. The core
is clearly visible in the 284Å image taken at 9:26:30 UT but it is very hard
to detect in the almost simultaneous 171Å image at 9:26 UT (Figure
fig:fluxrope). The dominant contribution in the 284Å bandpass comes from the
FeXV line which forms at around 1.8 MK. Therefore, the lack of 171Å emission
and the bright 284Å emission suggest that the majority of the core plasma
comes from hot temperatures. This is exactly what the models predict and
recent Solar Dynamics Observatory (SDO) observations show [Cheng et al.
(2011)]. Therefore, we conclude that the CME core in our event is hot and
comes at the tail end of the cavity within the erupting structure. Once the
core is identified in the 284Å and 194Å images, it is relatively
straightforward to follow in the 171Å as well although it remains quite faint
(see online movie).

###  2.5 Flows along the Filament Channel

Throughout the event, one can observe flows along the filament channel (FC).
They become more obvious along a bend of the FC at its eastern end. The
filament itself is observed as a collection of dark threads in the 171Å
channel due to the absorption from the cool material. It is anchored in the AR
on its western end and in the quiet sun at its eastern end. The flows seem to
evolve in two phases. In the first one, which lasts until 8:28 UT, the flows
are brighter. In the second phase, which lasts until 10:06 UT, the flowing
material acquires a blob-like character. Some of those blobs are depicted in
Figure fig:siphon. The symbols in this figure (cross, box, circle) indicate
the position the blobs we identified and measured at different time frames. In
Figure fig:blobs, the area of interest has been rotated to make the blob
movement more obvious. The position of each blob in this sequence of images is
connected with a line.

Figure 7.: Flows along the erupting filament channel. The symbols indicate a
particular blob tracked at different times in each of these EUVI-B 171Å
images. fig:siphon

Figure 8.: Demonstration of our tracking of the blobs in the EUVI-B images.
The area was rotated to make easier the display of lines connecting the blobs.
In the top panels, the upper line traces the blob marked with an X in Figure
fig:siphon, and the lower line traces the blob marked with a box. In the
bottom panels, the first two frames are repeats from the last two frames of
the top panel. The upper line is the continuation of the trace for the blob
marked with the box symbol, and the lower line traces the blob marked with the
circle. fig:blobs

After tracing the blobs, we measured their velocities. When the size of blobs
was small (e.g. at 09:23:00 UT), their position was assumed to be their
coordinates in the image. When the blobs became more extended (e.g. at
09:38:30 UT), we took the middle point as their average position, and their
length was taken as the error uncertainty.

Because the blobs were located very low in the corona, they were not visible
from EUVI-A. Because we know the angular distance of EUVI-A and the location
of the flows from EUVI-B, we can derive an upper limit for the height of the
channel of 0.015 R⊙ or $10.5\times 10^{4}$ km. Since they move parallel to the
surface and over a limited spatial extension, there was no need to correct for
spherical geometry. The effect is less than 4% for the full $30^{\circ}$
length of the filament which we did not use in our measurements. However, the
projection effect due to the proximity of the channel to the limb needs to be
taken into account. The flows are measured at about $65^{\circ}$ east
longitude so the correction factor is $\sim 1/cos(65^{\circ})\sim 2.36$. The
average deprojected velocities of the blobs are given in Table tab:siphon.
Each blob is named after the symbol we used to mark them in Figure fig:siphon.

The relation of the flows to the eruption is not immediately clear. First,
they appear to correspond to material flowing out of the AR into the quiet sun
because they propagate only in one direction, from the center of the AR toward
the quiet sun. Such behavior has been very common since the beginning of EUVI
observations and is always related to AR filaments that extend into the quiet
sun. Examples can be seen in the eruptions of 1, 16, and 19 May 2009, 5 and 9
April 2008, 14 and 18 August 2010. The event on 3 April 2010 has been analyzed
in the detail by 2011ApJ…727L..10S who connect such flows to off-loading of
cool plasma that may contributed to the subsequent CME eruption. Second, the
nature of the blobs changes at around 8:28 UT from thick elongated flows to
smaller blob-like features suggesting that the amount of the flowing material
has been reduced or the plasma has cooled down. It is interesting to note that
the CME underwent its first acceleration jump during that time. This apparent
correlation seems to support the 2011ApJ…727L..10S interpretation of the flows
as off-loading material and suggests that gravity may affect the early
acceleration profile of CMEs.

Table 2.: Average velocities for each of the three blobs. The names correspond
to the symbols used to mark the blobs in Figure fig:siphon.

Blob | Velocity $(km\,s^{-1})$ | Error
---|---|---
X | 125 | 5.3
Box | 116 | 4.9
Circle | 130 | 5.4

tab:siphon

There is an alternative explanation, however. The flows apparently trace
closed field lines along the filament. The movement of the blobs is directed
away from the site of the emerging fluxrope where energy input is taking place
leading to higher plasma pressures in its vicinity. Therefore, the observed
flows could be siphon flow imposed by a pressure difference between the two
footpoints of the filament [Cargill and Priest (1980), Cargill and Priest
(1982)].

##  3 Discussion and Conclusions

We investigate in detail the initiation and formation of a CME on January 2,
2008 using two-viewpoint EUV observations in the lower corona. The images are
obtained in the 171Å (150 sec cadence) and 284Å (20 min cadence) channels of
the EUVI instruments aboard the STEREO mission. The event evolves slowly for
several hours but it then quickly accelerates around the time of the
accompanying SXR flare. This allows us to study in detail both its evolution
toward the eruption, the subsequent formation of a CME, and its connection to
the flare energy release profile. Our main results can be summarized as
follows:

  * •

The acceleration profile of the CME is quite variable with peaks and valleys.
The acceleration changes are similar, in time of appearance and duration, with
corresponding changes in the GOES SXR light curve.

  * •

The CME acceleration peaks at 10:30 UT which is 30 mins after the peak of the
SXR flare.

  * •

The upward motions of the (eventually) erupting structure started at 6:13 UT,
about 1 hour before a small SXR flux increase and 2 hours before a significant
increase of SXR flux occurred (Figure fig:CME_height).

  * •

We detect, for the first time, two sets of collapsing loops. The two viewpoint
EUVI observations allow us to measure their 3D evolution. They shrink very
little (compared to past observations of shrinking loops) so most of their
collapse is due to their inclining toward the erupting channel, beneath the
rising cavity. They appear in all EUVI channels and they disappear in all of
them at a height of 0.12 R⊙. The post-CME arcades appear after the
disappearance of the collapsing loops and at the same location. The CME cavity
is clearly growing while the second loop system (L2) is collapsing. These
observations lead us to conclude that the two loop systems are likely drawn
behind the expanding magnetic cavity surrounding the CME core. This appears to
be the first detection of this process predicted by CME initiation models.

  * •

We detect the core of the CME mostly in the hot EUVI channel at 284Å (1.8 MK)
and the 195Å channel. This observation provides further support that the CME
cavity contains hot plasma as recent AIA observations have shown [Cheng et al.
(2011)].

  * •

We detect significant and long duration ($\sim 3$ hours) plasma flows along
the filament channel before its eruption. Their nature changes abruptly at
around 8:30 UT coincident with a sudden change in the rising speed of the
cavity. This coincidence suggests that mass unloading is perhaps playing a
role in the early CME kinematics.

  * •

The direction of the flows, from the western to the eastern part of the active
region, is also in agreement with the temporal evolution of the flaring
ribbons and post-eruptive flaring arcades, and the direction of the collapsing
loops. Clearly, the eruption starts at the center of the active region and
propagates to the east along the filament channel and toward the quiet sun
footpoints of that channel.

  * •

Despite the large number of novel observations and detailed measurements we
cannot tell with certainty whether the erupted flux rope was pre-existing or
was formed during the eruption. However, we are fairly certain that additional
flux was introduced in the erupting flux rope during its ascent. This is the
second event we reach this conclusion [Patsourakos, Vourlidas, and Kliem
(2010)] and is the expected outcome of several models [Lin, Raymond, and van
Ballegooijen (2004), Forbes et al. (2006), Chen (2011)]. It is, therefore,
important to take this effect into account in the estimation of magnetic flux
entrained in CMEs.

All these observations confirm corresponding expectations of the standard
flare-CME models and suggest that such models are likely reliable
representations of the eruption process in the corona. Our analysis
demonstrates the power of two-viewpoint observations of the low corona and the
importance of extended fields of view for EUV instruments so that the
acceleration profile of the CME and the relationships among the various
erupting structures can be measured consistently.

#### Acknowledgements

We thank the referee for the very useful comments and G. Stenborg for
providing the wavelet-enhanced EUVI images and S. Patsourakos for fruitful
discussions. The work of AV is supported by NASA contract S-136361-Y to the
Naval Research Laboratory. The SECCHI data are produced by an international
consortium of the NRL, LMSAL and NASA GSFC (USA), RAL and Univ. Bham (UK), MPS
(Germany), CSL (Belgium), IOTA and IAS (France).

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