11institutetext: CERN, Geneva, Switzerland

# Beam Transfer and Machine Protection

V. Kain

###### Abstract

Beam transfer, such as injection into or extraction from an accelerator, is
one of the most critical moments in terms of machine protection in a high-
intensity machine. Special equipment is used and machine protection aspects
have to be taken into account in the design of the beam transfer concepts. A
brief introduction of the principles of beam transfer and the equipment
involved will be given in this lecture. The main concepts of machine
protection for injection and extraction will be presented, with examples from
the CERN SPS and LHC.

Keywords
Beam Transfer; machine protection; kicker systems; interlocking system;
single-turn failures.

##  1 Introduction

Injection and extraction from an accelerator are critical operations on short
time-scales with a high risk of significant beam loss in the case of equipment
malfunction or badly adjusted parameters. Figure 1, which is taken from a
presentation at an LHC workshop on beam-induced quenches, highlights how
prominent losses induced by beam transfer actions are in the day-to-day
operation of accelerators. The example is from the Relativistic Heavy Ion
Collider accelerator at Brookhaven National Laboratory. While in the first
part of this lecture some of the typical injection, extraction, and transfer
line configurations, along with associated equipment, are discussed, the
second part is dedicated to a presentation of the machine protection systems
developed for the LHC injection and beam dump system, as examples for machine
protection systems for beam transfer.

Figure 1: Most frequent causes of beam loss at the Relativistic Heavy Ion
Collider accelerator at Brookhaven National Laboratory. Issues during beam
transfer, injection and beam abort, are prominent. Presentation by M. Bei at
the Workshop on Beam-induced Quenches at CERN [1].

##  2 Injection

Various different injection techniques exist in the different machines around
the world. The chosen technique depends on the requirements in terms of loss
acceptance, brightness, and particle type used. This section, as well as
Sections 3 and 4, is based on the lectures by B. Goddard, W. Bartmann, M.
Barnes and M. Meddahi at the CERN Accelerator School [2].

####  2.0.1 Single-turn injection for hadrons

This type of injection is typically used for transfer between machines in an
accelerator chain in association with boxcar stacking, where a larger ring is
filled sequentially by a smaller one. Angle and position errors at the
injection point lead to injection oscillations, while optical errors lead to
betatron mismatch. Both can cause emittance blow-up. As most of the machine
protection concepts will be introduced in connection with this type of
injection, it will be described in detail in Section 2.1.

####  2.0.2 Multiturn injection for hadrons

In the case of multiturn injection for hadrons, phase-space painting is used
to increase the total circulating intensity. The variant of H--injection
allows injection into the same phase-space area after stripping the H- ions to
protons. In this way the intensity is increased while keeping the emittance
small.

####  2.0.3 Lepton injection

In the case of high-energy leptons, as were used for the LEP accelerator,
injection precision and matching is of less concern for emittance blow-up,
owing to damping with synchrotron radiation. This fact can be used to inject
beams off-momentum or at an angle and increase the intensity in the same
phase-space volume after damping. These techniques are called betatron and
synchrotron accumulation, respectively.

###  2.1 Single-turn injection and injection equipment

Figure 2 shows the principle of single-turn injection. A transfer line brings
the beam close to the orbit of the circular machine. The last two magnetic
elements are special injection magnets. The first of these is called a septum
magnet. It is already so close to the circulating beam that the iron yoke and
coils of a conventional magnet would no longer fit between the trajectory of
the injected beam and the orbit of the circulating beam. Instead, a septum
magnet has an aperture with a magnetic field for the injected beam and another
aperture without a magnetic field through which the circulating beam passes.
Between the two regions, there is a thin septum. The septum deflects the beam
onto the closed orbit at the centre of a kicker magnet. The kicker magnet
compensates for the remaining angle. The kicker magnet, however, has only one
aperture, through which both the injected and circulating beams pass. Kicker
magnets are pulsed magnets with very fast rise and fall times, such that they
provide the required magnetic field at the moment of the passage of the
injected beam and no field during the passage of the circulating beam. The
septum and kicker magnets are typically installed on either side of a
defocusing quadrupole in the plane of the kicker magnet to minimize the kick
strength. The actions of the two magnet systems in normalized phase-space are
shown in Fig. 3.

Figure 2: Single-turn injection Figure 3: The single-turn injection process in
normalized phase-space. The beam indicated by the blue circle undergoes a
large deflection as it passes through the septum magnet (plot 1), then travels
through a $\pi/2$ phase advance to meet the kicker magnet (plot 2). The kicker
deflection aligns the beam on the central orbit (plot 3).

####  2.1.1 Septum magnet

A general introduction to septa can be found in Ref. [3]. Two main types of
septa exist – electrostatic septa and magnetic septa. Electrostatic septa have
a very thin (typically ${\leq}100\Uum$) separation between the zero-field and
high-field regions. They are often used for extraction systems, to minimize
losses at extraction and the required kick strength or strength of the
following septa.

Magnetic septa are pulsed or DC dipole magnets with a 2–20 separation between
the zero-field and high-field regions. Figure 4 shows a direct-drive pulsed
septum magnet. This type of magnet is often used under vacuum to minimize the
distance between the circulating and deflected beams. To minimize the self-
inductance and for mechanical reasons, the coil generally consists of a single
turn, resulting in large required currents of typically 5–25kA. Single-turn
injection uses magnetic septa.

Figure 4: Direct-drive pulsed septum

####  2.1.2 Kicker magnet

A full lecture on kicker magnets can be found in Ref. [4]. Kicker magnets
typically produce rectangular field pulses with fast rise and fall times. The
field strength is lower than can be achieved with septum magnets. For
comparison, the LHC injection septum provides roughly 12 of bending angle in
five magnets, whereas the injection kicker magnets provide 0.8 in four
magnets. The injection energy in the LHC is 450. To achieve fast rise and fall
times, a voltage pulse is pre-charged in a so-called pulse forming line, a
coaxial cable, or a pulse forming network, all of which are lumped elements.
This voltage pulse is launched towards the kicker when the main switch is
closed. The main sub-systems of a typical kicker system are shown in Fig. 5.
Pulse forming networks and lines accumulate electric energy over a
comparatively long time from a power supply (a few milliseconds in the case of
a fast resonant charging power supply) and then release it in the form of a
square pulse of relatively short duration (a few microseconds). Figure 6 shows
the LHC injection kicker magnet.

Figure 5: The main sub-systems of a kicker system: The pulse forming network
(PFN) or pulse forming line (PFL) is charged to a voltage $V_{p}$ by the
resonant charging power supply (RCPS). When the Main Switch closes a pulse of
magnitude $V_{p}/2$ is launched towards the magnet through the transmission
line (coaxial cable). The impedances in the system should be matched, to avoid
reflections. The length of the pulse in the magnet can be controlled between 0
and 2 $\tau=\sqrt{L\cdot C}$ by adjusting the timing of the dump switch
relative to the main switch.  Figure 6: The LHC injection kicker MKI

A typical kicker cross-section is shown in Fig. 7. The characteristics of the
field in the kicker gap are:

$\displaystyle B$ $\displaystyle=\frac{\mu_{0}I}{g},$ (1) $\displaystyle L$
$\displaystyle=\frac{\mu_{0}wl}{g},$ (2)
$\displaystyle\frac{\mathrm{d}I}{\mathrm{d}t}$ $\displaystyle=\frac{V}{L},$
(3)

where $B$ is the magnetic field, $I$ the current, $g$ the gap height, $l$ the
length, $w$ the gap width, $L$ the inductance, and $V$ the kicker voltage. A
typical kicker $\mathrm{d}I/\mathrm{d}t$ would be 3kA in 1. In general,
kickers have a low inductance. Nevertheless, for short rise times, very high
voltages of the order of several tens of kilovolts are required. Kicker
switches, as indicated in Fig. 5, therefore need to hold high voltages and to
switch high currents within short rise times. Thyratron switches (gas tubes)
or solid-state switches are frequently used for this purpose. As can be
anticipated, kicker switches are associated with a number of possible failure
scenarios. There are two main categories.

  * -

Erratic turn-on: The switches turn on spontaneously and the kickers fire
asynchronously, during the circulating beam passage instead of the injected
beam.

  * -

Missing: The switch does not fire.

So-called magnet flash-overs are another kicker failure category, and are
independent of the switches. A flash-over is a discharge between kicker
electrodes while the kicker has high field. Depending on which cell, and hence
where in the kicker, the flash-over occurs, it results in a reduced or even
increased kick strength, owing to reflection.

Figure 7: Simplified cross-section of kicker magnet: $B$, magnetic field; $g$,
gap height; $I$, current; $w$, gap width

As the bending angle exerted by the kicker magnets is generally large, kicker
failures belong to the group of very fast beam loss mechanisms. If the kicker
magnets are being used to inject a beam, the resulting injection oscillations
would be too large to establish a circulating beam. The beam would be lost on
the first turn. In case of an asynchronous kick on the circulating beam, the
resulting oscillations around the ring would be so large that the beam would
be lost within a single turn. Not all kicker failures can be prevented by
design but the risk for some can be minimized. For example, fast resonant
charging power supplies are used to charge the pulse forming networks quickly.
The charging then only takes place a few milliseconds before the kick is
required. The probability of an error is reduced in this way. Many kicker
failures require passive protection, see Section 5.2.

###  2.2 Mismatch at injection

Errors during injection can lead to beam loss due to mismatch.

####  2.2.1 Steering errors

Steering errors, a difference in position and angle ($x$ and $x^{\prime}$)
between the circulating orbit and the injected trajectory at the injection
point, lead to injection oscillations. The injected beam will oscillate around
the closed orbit. With the presence of non-linearities in the machine, this
oscillation will decohere after a number of turns. As a result, the beam
emittance and hence the beam sizes around the ring will increase. If the
initial oscillation amplitude is large enough, it can lead to beam loss as
early as the first turn. The mechanism of generating injection oscillations is
illustrated in Fig. 8. Figure 9 shows the LHC injection oscillation display in
the LHC control room. Instead of showing the trajectory turn after turn at one
beam position monitor, the injection oscillation display shows the trajectory
along many beam position monitors, covering many oscillation phases.

Figure 8: Normalized phase-space at the injection point in the horizontal
plane over three turns. The blue beam is injected with an angle error, leading
to an oscillation of the particle distribution around the central orbit turn
after turn. Figure 9: Injection oscillation display from the LHC control room,
showing the trajectory with respect to the reference in the transfer line and
the first turn minus the closed orbit in the first 3 of the LHC.

####  2.2.2 Optical mismatch

The Twiss parameters at the injection point define the shape and orientation
of the particles’ phase-space ellipse. One speaks of optical mismatch if the
shape and orientation of the ellipse of the injected beam does not match the
ellipse of the circulating beam at the injection point. An example of such a
situation is shown in Fig. 10. The black mismatched ellipse will change its
orientation turn after turn, with its extremities following the contour of the
dashed ellipse. This causes beam size beating at the injection point or any
other point of observation. Non-linear effects, magnetic field multipoles,
however, will introduce amplitude dependent differences in particle motion
and, over many turns, a phase-space oscillation is transformed into an
emittance increase. This is illustrated in Fig. 11. So-called filamentation
eventually fills a larger ellipse. The larger ellipse has the same shape and
orientation as the matched one.

Figure 10: Phase-space plot showing an optical mismatch at the injection point
Figure 11: Owing to non-linearities, optical mismatch will lead to emittance
blow-up over many turns

##  3 Transfer lines

Transfer lines transport the beam between accelerators and onto targets,
dumps, and instruments. The beam dynamics in a transfer line are not only
determined by the strength of the elements in the transfer line. Instead, the
Twiss parameters at any point in the line, $\alpha$(s), $\beta$(s), are also a
function of the initial functions, $\alpha_{1}$ and $\beta_{1}$. This is
illustrated in Fig. 12. The strengths of the line are kept the same but the
initial conditions are modified. As a result, the beta functions change. The
same is true for the dispersion function.

Figure 12: The horizontal beta function in a transfer line as a function of
the longitudinal position $s$: The strengths of the magnetic elements in the
line are kept the same, but the initial conditions are modified to produce the
red and black optics.

Another difference with respect to circular machines is the effect of an error
at a particular location in the line. Whereas in a circular machine the error
will introduce a perturbation around the entire ring, in a transfer line the
error will affect only the part of the line downstream of the error location,
see Fig. 13.

Figure 13: The effect of an error in a transfer line is only seen downstream
of the error location. For the red beta function, a quadrupole error was
introduced at location $s\approx 2300\Um$.

If transfer lines are used to link two machines, the Twiss parameters of the
extraction point have to be propagated through the line and matched to the
Twiss parameters at the injection point of the next machine by adjusting the
quadrupole strengths in the line accordingly, see Fig. 14. From the previous
discussion, we know that the Twiss parameters, and hence the orientation of
the phase-space ellipse of the injected beam, have to be the same as the Twiss
parameters, and orientation, of the phase-space ellipse of the circulating
beam at the injection point, to avoid optical mismatch. The Twiss parameters
can be propagated if the transfer matrix $M_{1\to 2}$ is known:

Figure 14: If transfer lines are used to link two machines, the Twiss
parameters of the extraction point have to be propagated through the line and
matched to the Twiss parameters at the injection point of the next machine.

$\pmatrix{x}\\\ x^{\prime}_{s_{2}}=M_{1\to 2}\pmatrix{x}\\\
x^{\prime}_{s_{1}}=\pmatrix{C}&S\\\ C^{\prime}S^{\prime}\cdot\pmatrix{x}\\\
x^{\prime}_{s_{1}}\ ,$ (4) $\pmatrix{\beta}_{2}\\\ \alpha_{2}\\\
\gamma_{2}=\pmatrix{C}^{2}&-2CSS^{2}\\\
-CC^{\prime}CS^{\prime}+SC^{\prime}-SS^{\prime}\\\ C^{\prime
2}-2C^{\prime}S^{\prime}S^{\prime 2}\cdot\pmatrix{\beta}_{1}\\\ \alpha_{1}\\\
\gamma_{1}\ .$ (5)

Typically, eight variables need to be matched ($\alpha$, $\beta$, the
dispersion $D$ and its derivative $D^{\prime}$ in both planes). Frequently,
constraints such as phase advance requirements for transfer line collimators
or insertions for special equipment such as stripping foils have to be
respected. Independently powered quadrupole magnets with adjustable strength
are used for this purpose. If the transfer lines are long, the problem can be
simplified by designing the lines in separate sections. The main part of the
line then consists of a regular central section with a regular lattice, a FODO
or doublet with the quadrupoles at a regular spacing and powered in series. At
the end and the beginning of the line, initial and final matching sections
with independently powered quadrupoles are installed. An example of this type
of layout is the LHC transfer line shown in Fig. 15.

Figure 15: Layout of the SPS to LHC transfer line with a long central section
with a regular lattice and initial and final matching sections.

To counteract trajectory deviations due to magnet misalignments and field and
powering errors, transfer lines are usually also equipped with beam position
monitors and independently powered small dipole corrector magnets. Horizontal
correctors and beam position monitors are located at large $\beta_{x}$ and
vertical correctors and beam position monitors are located at large
$\beta_{y}$.

##  4 Extraction

As in the case of injection, there are various different techniques to extract
the beam from a circular machine. A few methods will be briefly summarized.

###  4.1 Single-turn fast extraction

As for single-turn injection, kicker magnets and septa are used for single-
turn fast extraction. To reduce the required kicker magnet strength, the beam
is sometimes moved close to the septum by a closed orbit bump. An example of a
typical fast extraction layout is shown in Fig. 16. The kicker magnet deflects
the entire beam into the septum in a single turn. The septum then bends the
beam into the transfer line. The smallest deflection angles are required for a
phase advance of $\pi/2$ between the kicker and the septum. The septum
deflection may also be in the other plane from the kicker deflection. Losses
around the ring or in the extraction channel at the moment of the extraction
process can originate from orbit errors (closed orbit bump errors), kicker
failures, kick synchronization errors or particles in the extraction gap
during which the extraction kicker field rises (uncaptured beam). An example
of a kicker rising in an extraction gap populated with particles is shown in
Fig. 17 for the CERN Neutrino to Gran Sasso fast extraction from the CERN SPS.

Figure 16: Travelling from right to left, the beam is deflected by the kicker
magnet into the septum, which bends the beam into the transfer line. Figure
17: One turn takes 23 for the SPS beam at 400. For the CERN Neutrino to Gran
Sasso beam, two batches, each of 10, fill the SPS. The batches are extracted
one after the other at intervals of 50. The extraction kickers have to rise
between the batches. The beam in the gaps between the batches is lost on the
extraction septum blade or in the SPS.

###  4.2 Non-resonant multiturn extraction

This type of extraction is used to fill a larger machine from a smaller one
over several turns. The beam is sliced into equal parts by a fast orbit bump
that puts the whole beam onto the septum. The beam is extracted in a few turns
with the machine tune rotating the beam. This type of extraction is an
intrinsically high-loss process.

###  4.3 Resonant multiturn extraction

Multipole fields from sextupoles or octupoles are used to distort the circular
normalized phase-space and define a stable area delimited by unstable fixed
points. The beam is slowly extracted by approaching the respective resonance
increasingly closely and going across it. By adjusting the tune change speed,
the extracted intensity can be controlled and a constant particle spill can be
achieved.

###  4.4 Resonant low-loss multiturn extraction

An alternative to the non-resonant multiturn extraction with lower losses is
the resonant low-loss multiturn extraction. Non-linear fields (sextupole and
octupole) are used to create islands of stability in phase-space. The
particles are driven into the islands through tune variations. The islands are
separated further in phase-space by varying the non-linear fields. The islands
are then extracted in a similar manner as for the non-resonant multiturn
extraction with a fast bump turn after turn.

##  5 Machine protection for beam transfer

The typical concepts for machine protection systems for beam transfer will be
introduced with the example of the SPS to LHC injection process and the LHC
beam dumping system. The LHC is filled with 12 injections from the SPS. The
extraction system in the SPS is a fast extraction using a large closed orbit
bump of ${\approx}35\Umm$ amplitude in the horizontal plane and horizontal
kickers and septa. The LHC transfer lines are $3\Ukm$ long. The LHC injection
system is a single-turn injection consisting of horizontal septa magnets and
vertical kicker magnets that deflect the beam on the LHC closed orbit. The LHC
beam dump system is a fast extraction system consisting of a horizontal kicker
system and vertical septum magnets. The beam is deflected into a $900\Um$ dump
channel, at the end of which the beam dump block is located.

The design parameters of the LHC proton beams in the SPS at the moment of
extraction and after acceleration in the LHC are summarized in Table 1. The
equipment damage limit has been calculated and measured to be of the order of
$100\UJ/\UcmZ^{3}$, which corresponds to only $\approx 5\%$ of the injected
intensity in the LHC.

Table 1: LHC nominal beam parameters | Energy | Number of bunches | Bunch intensity | Stored energy
---|---|---|---|---
| [] |  |  | []
SPS extraction | 450 | 288 | $1.7\times 10^{11}$ | $2.4$
LHC top energy | 7000 | 2808 | $1.7\times 10^{11}$ | $360$

The basics of injection and extraction protection can be summarized as:

  * •

Ensure correct settings: energy tracking systems, power supply surveillance;

  * •

Ensure kicker synchronization: particle-free gaps (gap cleaning), locking
extraction kicker triggers to RF revolution frequency, ;

  * •

Provide passive protection system: in the form of collimators and absorbers
against fast possible failure mechanisms and unavoidable kicker failures.

###  5.1 Monitoring, permits, and kickers

Different interlocking systems are installed in the SPS, the LHC, the LHC
injection region, and the SPS extraction region and transfer lines. A number
of systems are monitored and a Boolean status from these systems is
transferred to the interlock system. The result of the combination of the
different inputs is called a permit, injection permit, extraction permit, SPS
beam permit, These permits are each connected to a kicker system. The SPS beam
permit and LHC beam permit are connected to the dump kicker systems. If the
beam permit becomes false, the dump kickers fire and dump the beam on the
respective beam dump blocks. Without an extraction permit in the SPS, the beam
is not extracted from the SPS. The kickers will not fire. Similarly, the LHC
injection kickers will not fire without an injection permit.

All permits involved in the SPS to LHC transfer are connected hierarchically.
The beam from the SPS must not be extracted if the LHC injection permit is not
true. The SPS extraction permit therefore depends on the LHC injection permit.
Also, the beam should not be injected into the LHC if the LHC is not ready (if
the LHC beam permit is false). The LHC beam permit is input to the LHC
injection permit. Figure 18 shows an overview of the permit hierarchy of the
SPS to LHC transfer. The hardware systems in the SPS and LHC to which the
different monitoring systems are connected and which provide permits are LHC-
type beam interlock controllers (BICs) [5]. The interlocks of equipment of the
different areas in the transfer lines are combined in local BICs (TT60A, TI2U
for transfer lines TI 2 and TT60, respectively), see Fig. 19.

Figure 18: Layout of the SPS to LHC transfer with the extraction kicker MKE in
the SPS, the transfer line (TT60 and TI2) and injection kicker MKI in the LHC,
with corresponding extraction and injection permits. Figure 19: Monitoring
systems associated with equipment in the different areas involved in the SPS
to LHC transfer are combined into sub-results, which are input to the
extraction permit. Each circle in the drawing represents a ‘beam interlock
controller’. INJ1 is the injection permit of LHC beam 1.

####  5.1.1 Which systems are monitored?

Almost every element in the SPS to LHC transfer regions is monitored and
interlocked:

  * •

the orbit at the SPS extraction point, measured using a number of dedicated
beam position monitors;

  * •

power supply currents, measured $2\Ums$ before SPS extraction for all
extraction and transfer line circuits, including trajectory correcting dipole
magnets;

  * •

septa currents, kicker state, and kicker charging voltage;

  * •

vacuum valves and optical transition radiation (OTR) screens, which both have
to be out for high-intensity beams;

  * •

the settings and gaps of passive protection devices;

  * •

the beam loss reading of the beam loss monitors – if one shot generates losses
above threshold, the next shot will be inhibited;

  * •

the trajectory via the reading of beam position monitors.

Figure 20 shows the graphical user interface display for the beam interlock
controllers installed in the first part of the LHC transfer line TI 8, called
TT40.

Figure 20: Graphical user interface displays of two beam interlock controllers
in TT40, the upstream part of transfer line TI 8. The vacuum system, the warm
magnet interlock system (WIC), the extraction kicker MKE4 state, the septa MSE
and MST states, the TT40 power supply currents, the septa currents, the orbit
bump corrector magnet currents, the fast magnet current change monitors
(FMCM), which survey fast changes in case of failures instead of absolute
current values, the optical transition radiation (OTR) screen positions, the
beam loss monitors (BLM) and the extraction orbit with BPM LSS4 are all input
to these interlock controllers.

####  5.1.2 Masking of interlocks

Appropriate settings (of corrector magnet power converters or collimators that
have to be aligned around the beam trajectory) can frequently only be
established with the beam. For the first set-up, these types of interlock
should, therefore, not be taken into account when generating permits. In LHC
jargon, the disabling of interlocks is called ‘masking’. A number of systems
can have maskable interlocks in the beam interlock controller matrix. For the
LHC-type beam interlock controllers, masking is only allowed at low intensity,
the so-called set-up intensity. The set-up intensity is derived from the SPS
or LHC beam current transformers and is distributed across the machines in the
form of the set-up intensity flag. A dedicated distribution system has been
put in place – the Safe Machine Parameter System [6]. The interlock masks are
automatically ignored if the measured intensity is above the set-up intensity
limit. Figure 21 shows the graphical user interface of one of the beam
interlock controllers involved in the SPS to LHC transfer. A number of inputs
are maskable. Several masks have been set. As the set-up beam intensity flag
is false, the masked interlocks are nevertheless taken into account.

Figure 21: Graphical user interface display of a beam interlock controller in
TT60, which is the upstream part of LHC transfer line TI 2. Inputs 8–14 are
maskable and can be disabled in case of intensity below the set-up beam flag.
In this example, the set-up beam flag is false and the masks do not have an
effect, in the column ‘ matrix’ the masked inputs return false (red), despite
the mask.

####  5.1.3 Set-up intensity and other concepts

The set-up intensity has to be reasonably high, for testing under
representative conditions, but must be below the equipment damage limit. For
the LHC, the damage limit has been simulated and measured as ${\approx}2\times
10^{12}$ protons at 450 with a normalized emittance of 3.5[7]. Based on this
limit, the set-up intensity has been defined as $1\times 10^{12}$ protons at
injection energy. Its energy dependence has been obtained from simulation:

$\left(\frac{E\ [\UGeV]}{450\ [\UGeV]}\right)^{1.7}\times I\ [\mathrm{p}]\leq
1\times 10^{12}.$ (6)

The LHC injection protection concept cannot rely only on monitoring systems
and interlocking monitored systems. The LHC consists of thousands of sub-
systems, which all have to be in the correct state to receive the beam. Only a
fraction of these are directly connected to the interlock system. The SPS
extraction permit for a high-intensity beam is therefore only given if there
is already a beam circulating in the LHC. The so-called probe intensity has to
be injected first, to check whether everything really is ready for the beam.
The probe intensity is ${<}5\times 10^{9}$ protons, almost a factor of 100
below the intensity of 1 nominal bunch. The probe intensity information is
generated from the SPS beam current transformers and the beam presence
information is generated from the sum signal of the beam position monitors in
the LHC. Both are distributed in the form of flags over the Safe Machine
Parameter System. The condition that is hard-coded in the beam interlock
controller that generates the SPS extraction permit, Fig. 22, is

$\text{SPSprobebeamflag}\lor\\{\text{beampresence}\land[\lnot(\text{LHCset-
upbeamflag})\lor\text{SPSset-upbeamflag}]\\}\ .$ (7)

This condition translates as: ‘high-intensity extraction from the SPS above
the probe intensity is only allowed if there is a beam circulating in the
LHC’. The set-up beam flag in the LHC has to be forced to false to remove
potential masks if the SPS intensity is above the set-up intensity.

Figure 22: Graphical user interface of the beam interlock controller
generating the SPS extraction permit for LHC beam 1. At the very top, the SPS
set-up beam flag state can be seen, labelled ‘Safe Beam Flag I’. The last
three inputs to the controller are the SPS probe beam flag, the LHC beam
presence flag and the LHC set-up beam flag.

###  5.2 Protection against fast failures

Beam transfer using fast extraction systems or single-turn injection cannot be
aborted once the beam transfer is triggered. All involved systems have to be
checked and give the OK before the trigger arrives. For the power supplies in
the SPS to LHC transfer, the last current verification happens
${\approx}2\Ums$ before the extraction kickers fire. Some of the circuits,
however, have very low time constants for the current decay in the case of a
power supply failure. The magnetic field can, therefore, still be
significantly wrong at the moment of the beam passage, even if the power
supply only fails at the very last moment. An example is the extraction septum
circuit, with a time constant of only $23\Ums$ and large bending angles of
$12\Umrad$. The trajectory would be changed by roughly 40$\sigma$ within
$1\Ums$ in the case of a power supply failure. The aperture in the transfer
line is only 10$\sigma$. Instead of relying only on the classic power
converter current surveillance, a faster system had to be put in place for the
septa circuits and several other circuits. This system is the so-called fast
magnet current change monitor (FMCM). Instead of measuring small currents, it
measures changes in the voltage of a critical power supply. It can detect
relative current changes of $10^{-4}$ in less than $1\Ums$. It was
successfully deployed for septa current surveillance, with a reaction time of
50 for a current change of $0.1\%$ [8]. Figure 23 shows the FMCM system.

Figure 23: The fast current change monitor measures the voltage of a power
supply and calculates real-time current changes using a low-pass filter
followed by a high-pass filter. The low-pass filter removes the noise and
calculates the current; the high-pass filter removes the d.c. part and shows
only current changes.

Kicker failures can develop on microsecond time-scales. No surveillance system
would be fast enough to stop the beam in time. If a failure occurs during the
kick pulse, other means of protecting downstream equipment from the beam
impact have to be envisaged. These means are passive protection systems, in
the form of absorbers and collimators.

####  5.2.1 Passive protection

The design criteria for a passive protection system can be obtained by
answering a number of questions.

  * •

Against which failure should the passive protection devices protect? This will
identify where to put the devices and how many will be needed. Examples will
be given later on.

  * •

What is the damage level, quench level, or allowed radiation level of
downstream equipment? This will define the required attenuation by the passive
protection device.

  * •

What is the maximum possible particle density that can impact the protection
device according to the failure scenario? At which repetition rate can the
failure happen? This will define the required robustness of the device and
thus the maximum possible material density. Together with the required
attenuation, this gives the required length of the device.

  * •

What is the aperture that has to be protected? If it has to be set a few
$\sigma$ from the beam centre, it will have to be made movable. The required
protection settings need to be evaluated.

The design of passive protection devices involves several different
disciplines in accelerator physics and material science. Simulations for
particle tracking, energy deposition in material, and thermomechanical
response are required.

####  5.2.2 Examples of passive protection devices in the LHC injection
protection system

If absorbers are to protect against failures of one particular dipole error
source, they are placed at 90∘ phase advance downstream of the error location.
This is the case of the TDI absorber, which protects against LHC injection
kicker failures. It has to withstand the high-intensity LHC beams and is
therefore made of low-intensity materials, to be sufficiently robust. It
consists of a sandwich of materials, starting with the low-density BN5000
($\rho=1.92\Ug/\UcmZ^{3}$) over a length of 2.85, then 0.6 of , and finally
0.7 of – alloy. A large quantity of secondary showers still escapes the TDI if
there is an impact. The TCDD mask protects the next magnetic element, the
superconducting D1 separation dipole, from the shower particles. Figure 24
shows an overview of the injection region at LHC point 2 with the injection
equipment, the TDI protection device, and the TCDD mask.

The setting of a protection device, the distance from the nominal orbit or
trajectory and thus the amplitude cut in phase-space, has to be chosen so as
to include a margin for orbit variations, beta beat, mechanical tolerances,
set-up tolerances, The extent of the particle distribution in phase-space also
has an impact on the required setting. This is illustrated in Fig. 25. If the
phase-space is cut at a single location, a fraction of the surviving particles
will still have oscillation amplitudes larger than the absorber setting. This
effect can be reduced by adding another absorber a few tens of degrees further
downstream. In the LHC injection region, the TDI absorber is complemented by
two shorter graphite auxiliary collimators of 1 length at $\pm 20^{\circ}$
phase advance from the TDI, with two jaws each.

Figure 24: A top view of the LHC injection region with the horizontal MSI
injection septum, the vertical inject kickers MKI, followed 90∘ downstream by
the TDI injection protection device and the TCDD mask. The red line indicates
the beam direction. Part of the transfer line can also still be seen. Figure
25: A fraction of the surviving beam will still have larger oscillation
amplitudes than the absorber setting. This has to be taken into account when
choosing an appropriate absorber protection setting.

The LHC transfer lines are equipped with a generic passive protection system
to protect against any failure during SPS extraction or transfer. It provides
full phase-space coverage in the horizontal and vertical planes. In a single
pass, this can only be achieved by installing collimators at several phase-
space locations. In the case of the LHC transfer lines, three collimators per
plane with 60∘ phase advance between two neighbouring collimators are chosen.
They are located at the ends of the lines, to cover as many failures as
possible. The maximum amplitude escaping such a system for 60∘ between the
collimators can be calculated from the collimator setting $n[\sigma]$:

$a_{\mathrm{max}}\ [\sigma]=\frac{n\ [\sigma]}{\cos(60^{\circ}/2)}\ .$ (8)

An illustration of the phase-space cut for the LHC transfer line collimation
system with a setting of 4.5$\sigma$ and 1.4$\sigma$ set-up accuracy is shown
in Fig. 26.

Figure 26: The phase-space coverage of the LHC transfer line collimation with
three collimators per plane and 60∘ phase advance between two collimators. The
nominal setting is 4.5$\sigma$ and the set-up accuracy from collimator and
trajectory in a single pass is 1.4$\sigma$. The maximum escaping amplitudes
for primary beam are, therefore, 6.9$\sigma$.

###  5.3 Beam dumping

The beam dump system is one of the main components of the machine protection
system. It is connected to the machine’s beam permit and is triggered when the
permit disappears. Fast-rising kicker magnets (and sometimes septa) are used,
as for a fast extraction system, to steer the beam onto a beam dump block. The
beam dump block can be installed in the circular machine, if it is an internal
beam dump, or at the end of a transfer line, if it is an external beam dump.
The system has to work for all energies and beam types of the accelerator. As
kicker systems are involved, the beam dump system itself can cause very fast
and hence very severe failures. The design has to take the failure scenarios
in account and foresee sufficient redundancy and surveillance. In the LHC, the
allowable failure rate is $10^{-7}/\UhZ$ to $10^{-8}/\UhZ$, which corresponds
to safety integrity level 3.

In contrast with a pure fast extraction system, where the pulse forming
networks only have to be charged a few milliseconds before the extraction
request, the beam dump pulse forming networks are always charged. The charging
voltage follows the energy of the accelerator. A system to derive the energy
from the synchrotron is required. In the LHC and SPS, this system is called
the beam energy tracking system (BETS). The energy information is derived from
the main dipole currents [9]. The LHC BETS surveys the correct energy tracking
of the pulse forming networks and other involved equipment, such as the dump
septa currents. If a tracking error occurs, the beam dump is triggered
immediately before the error becomes too large.

The LHC beam dump system is installed in long straight section 6. The distance
between the kicker magnets and the dump block is 975. An overview of long
straight section 6, the dump equipment and dump lines is shown in Fig. 27.

Figure 27: The two beam dump systems installed in long straight section 6 of
the LHC for beams 1 and 2

The beam dump block itself is made of 0.7 and 3.5 lengths of graphite with a
density of 1.73/3, interspersed with 3.5 lengths of lower-density graphite
($\rho=1.1\Ug/\Ucm^{3}$). This is followed by 1 of and 2 of at the end. The
core is in a steel cylinder and in inert 2 gas. It is surrounded by about 900t
of radiation shielding blocks. The beam dump is designed such that no
structural damage is to be expected during 20 of operation with ultimate LHC
intensities ($1.7\times 10^{11}$ protons per bunch in 3.5 emittance and 2808
bunches). Nevertheless, the dump block is only sufficiently robust for 7
protons with beam dilution.

A set of vertical and horizontal dilution kickers is installed in the
extraction channel, to sweep the beam onto the beam dump block. Not all
bunches, therefore, will end up on the same spot. The resulting LHC beam dump
sweep shape is shown in Fig. 28, and the waveforms of the horizontal and
vertical dilution kickers are shown in Fig. 29.

Figure 28: Simulated swept beam density on the front face of the LHC beam dump
block. The sweep is the result of the excitation of horizontal and vertical
dilution kickers with waveforms as illustrated in Fig. 29. Figure 29: LHC
horizontal and vertical dilution kicker waveforms

####  5.3.1 Failure scenarios of the LHC beam dump system

The LHC dump kicker magnets have a rise time of 3. No particles are allowed in
the so-called abort gap, whose length corresponds to the kicker rise time,
Fig. 30. Even if the gap is kept particle-free, the kickers can still trigger
spontaneously or asynchronously. Passive protection is therefore installed at
the front face of the septum magnets (TCDS) and 90∘ downstream of the MKD dump
kickers to protect the LHC circulating aperture (TCDQ). The TCDQ is a 9 long
single-sided movable absorber made of graphite. A fixed mask in front of the
superconducting Q4 quadrupole protects the Q4 from damage from secondary
showers generated in the TCDQ in the case of an impact.

Figure 30: No particles are allowed in the abort gap of the dump kickers. The
black line indicates the particle bunch trains. The smallest gaps between the
trains correspond to the injection kicker rise time in the SPS; the $1\Uus$
gaps come from the LHC injection kicker rise time requirements. There is one
large gap of $3\Uus$ for the dump kicker rise time. The beam must not be
injected into the gap. Mechanisms are arranged to prevent this and any
uncaptured beam is cleaned out of the gap after transverse feedback. The red
line indicates the dump kicker (MKD) waveform.

The different failure scenarios of the LHC beam dump system have been analyzed
and divided into three categories. LHC beam dump failure scenario: beyond
design This category of failures should not happen in the LHC lifetime, as
they would result in severe damage to the machine. Several layers of
redundancy are built into the system to cover against them:

  * •

not receiving the trigger from the beam interlock system after a failure in
the LHC;

  * •

failure of beam energy tracking and extracting the entire beam at the wrong
angle;

  * •

fewer than 14 of the 15 dump kicker magnets firing during a dump;

  * •

fewer than 3 of the 10 dilution kickers firing during a dump.

LHC beam dump scenario: active surveillance The beam dump system and clean
dump action depend on a number of conditions that must be monitored. If these
conditions degrade, a dump is issued while the conditions are still sufficient
for a clean dump. Examples are:

  * •

failures of general services (electricity, vacuum, cooling, ethernet, …);

  * •

bad beam position in dump region;

  * •

magnet powering failure of extraction equipment.

LHC beam dump scenario: passive protection This category of failures cannot be
prevented by surveillance systems and involve failures of the kicker systems.
Redundancy has been built into the number of required dump kickers to ensure
correct extraction. These failures can be tolerated and will not cause damage:

  * •

one missing extraction kicker magnet;

  * •

missing dilution kicker magnets;

  * •

erratic behaviour of a dilution kicker magnet.

Finally there is a set of possible kicker failures where passive protection is
required. They cannot be tolerated without correct settings of the passive
protection element TCDQ:

  * •

erratic behaviour of an extraction kicker magnet (spurious asynchronous
trigger).

##  6 Final remarks

A number of additional concepts have been invented around and for the machine
protection of LHC beam transfer systems, which have not been introduced in
this lecture. Some of these will be covered in the lecture by J. Wenninger on
Machine Protection and Operation for the LHC. A very important issue concerns
the automatic post-operational checks and post-mortem system, which verify the
correct functioning of the relevant systems after each beam transfer by
launching a quick analysis of a number of recorded datasets from equipment and
beam instrumentation [10, 11]. Degradation of the systems and increased
likelihood of a failure are detected by these checks. This allows experts to
intervene before an actual failure occurs.

## References

  * [1] M. Bei, Workshop on Beam-induced Quenches, CERN, 15–16 September 2014, http://indico.cern.ch/event/323249/.
  * [2] CERN Accelerator School, General Accelerator Physics, Introductory Level, http://cas.web.cern.ch/cas/Granada-2012/Granada-after.html.
  * [3] M. Barnes et al., Injection and extraction magnets: septa, in Proceedings of the CAS-CERN Accelerator School: Magnets, Bruges, Belgium, 16-25 June 2009, edited by D. Brandt, CERN-2010-004 (CERN, Geneva, 2010), pp. 167-184, http://dx.doi.org/10.5170/CERN-2010-004.167
  * [4] M. Barnes et al., Injection and extraction magnets: kicker magnets, CERN Accelerator School: Magnets, Bruges, Belgium, 16-25 June 2009, edited by D. Brandt, CERN-2010-004 (CERN, Geneva, 2010), pp. 141-166, http://dx.doi.org/10.5170/CERN-2010-004.141
  * [5] B. Puccio et al., The CERN beam interlock system: principle and operational experience, Proc. IPAC, 2010, http://accelconf.web.cern.ch/AccelConf/IPAC10/index.htm
  * [6] V. Kain et al., Functional specification: safe machine parameters, LHC-CI-ES-0004 rev 1.0 (2009), https://ab-div-bdi-bl-blm.web.cern.ch/ab-div-bdi-bl-blm/Specification/LHC-CI-ES-0004-10-00.pdf
  * [7] V. Kain et al., Material damage test with $450\UGeV$ LHC-type beam, Proc. PAC, 2005, http://accelconf.web.cern.ch/AccelConf/p05/INDEX.HTML
  * [8] M. Werner et al., A fast magnet current change monitor for machine protection in HERA and the LHC, Proc. ICALEPS, 2005, http://accelconf.web.cern.ch/AccelConf/ica05/
  * [9] E. Carlier et al., The beam energy tracking system of the LHC beam dumping system, Proc. ICALEPS, 2005, http://accelconf.web.cern.ch/AccelConf/ica05/
  * [10] N. Magnin et al., External post-operational checks for the LHC beam dumping system, Proc. ICALEPS, 2011, http://accelconf.web.cern.ch/AccelConf/icalepcs2011/index.htm
  * [11] L. Drosdal et al., Automatic injection quality checks for the LHC, Proc. ICALEPS, 2011, http://accelconf.web.cern.ch/AccelConf/icalepcs2011/index.htm


# Evaluating Webcam-based Gaze Data as an
Alternative for Human Rationale Annotations

###### Abstract

Rationales in the form of manually annotated input spans usually serve as
ground truth when evaluating explainability methods in NLP. They are, however,
time-consuming and often biased by the annotation process. In this paper, we
debate whether human gaze, in the form of webcam-based eye-tracking
recordings, poses a valid alternative when evaluating importance scores. We
evaluate the additional information provided by gaze data, such as total
reading times, gaze entropy, and decoding accuracy with respect to human
rationale annotations. We compare WebQAmGaze, a multilingual dataset for
information-seeking QA, with attention and explainability-based importance
scores for 4 different multilingual Transformer-based language models (mBERT,
distil-mBERT, XLMR, and XLMR-L) and 3 languages (English, Spanish, and
German). Our pipeline can easily be applied to other tasks and languages. Our
findings suggest that gaze data offers valuable linguistic insights that could
be leveraged to infer task difficulty and further show a comparable ranking of
explainability methods to that of human rationales.

Keywords: explainability, eye-tracking, rationales

Evaluating Webcam-based Gaze Data as an
Alternative for Human Rationale Annotations

Stephanie Brandl1, Oliver Eberle2, Tiago Ribeiro3,
---
Anders Søgaard1, Nora Hollenstein1,4
1 University of Copenhagen, Denmark
2 Technische Universität Berlin, Germany
3 IT University of Copenhagen, Denmark
4 University of Zurich, Switzerland
<EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS>
<EMAIL_ADDRESS><EMAIL_ADDRESS>

Abstract content

## 1\. Introduction

In order to build reliable and trustworthy NLP applications, it is crucial to
be able to explain and interpret a model’s decision. These Explainable AI
(XAI) approaches often rely on human-annotated rationales for evaluation
DeYoung et al. (2020). They are usually expensive and biased towards
annotation guidelines Hansen and Søgaard (2021); Parmar et al. (2023) or the
annotators’ demographic Al Kuwatly et al. (2020). While collecting lab-
recorded human gaze data is at least as expensive as collecting rationales, it
provides a more intuitive annotation process as annotators can naturally solve
the task while reading the text, eliminating the need for additional post-
processing annotations after task completion. Recording human gaze during
annotation tasks has been suggested in the past Zaidan et al. (2007); Tokunaga
et al. (2013), while studies in computer vision have shown promising results
when including gaze into models for attribute prediction Murrugarra-Llerena
and Kovashka (2017). Webcam-based eye-tracking recordings, where data is
collected via a standard webcam, on the other hand, are much more cost-
effective and are catching up in data quality Ferhat and Vilariño (2016).

In this paper, we analyse whether and to what extent webcam-based eye-tracking
can pose a valid alternative to human rationales when evaluating XAI methods
in NLP. In the first part of this paper, we focus on the eye-tracking dataset
itself, analysing the data quality across languages, looking for indicators of
task difficulty, and evaluating to what extent gold-label rationales can be
decoded directly from the eye-tracking signal. In the second part, we extend
our analysis to attention-based explanations as well as Layer-Wise Relevance
Propagation (LRP) Ali et al. (2022) and Gradient$\,\times\,$Input Baehrens et
al. (2010); Shrikumar et al. (2017). We perform this analysis for question
answering (QA) in English, Spanish, and German, and 4 multilingual Transformer
models.

Figure 1: One sample from the WebQAmGaze corpus with ground-truth _rationales_
, average _eye-tracking_ pattern across participants and model-based
_relevance_ scores computed with LRP based on mBERT. The correct answer is
shown in the rationale (_upper_). We see that both the gaze pattern and the
model-based explanation scores focus on the first part of the answer more than
on the second.

#### Contributions.

This work is the first analysis of webcam-based eye-tracking as an alternative
for human-annotated rationales on text. We investigate (i) possible gaze-based
indicators for task difficulty and (ii) factors that influence data quality in
webcam-based eye-tracking. Furthermore, we (iii) fine-tune 4 multilingual
Transformer models (mBERT, distilMBert, XLMR, and XLMR-L) on question
answering in English, Spanish, and German to (iv) evaluate model explanations
both in reference to human rationales and gaze.

We build upon the eye-tracking data and analysis of Ribeiro et al. (2023) by
computing entropy and decoding accuracy scores of the collected fixation
patterns. We furthermore perform an XAI-based analysis to investigate gaze
data as a possible alternative to human rationales for the evaluation of model
explanations.

Results presented in Section 3 show that gaze provides valuable additional
linguistic information that can potentially be used to infer task difficulty.
Decoding accuracy based on gaze data varies across languages, resulting in
promising results, especially for German. Gaze data further shows a similar
ranking for explainability methods as human rationales, posing a potential
alternative when evaluating XAI methods as shown in Section 4. Our code is
available at: github.com/stephaniebrandl/rationales-eyetracking-xai.

## 2\. Related Work

#### XAI for Transformers.

Attention modules allow us to directly interpret attention tensors to
understand or visualize the inner model workings Bahdanau et al. (2015).
However, growing evidence suggests that raw attention scores may not provide a
faithful explanation of the model prediction Jain and Wallace (2019); Serrano
and Smith (2019); Ali et al. (2022). Besides the naive aggregation of raw
attention weights, more elaborate explanation mechanisms have been proposed
such as attention flow and attention rollout Abnar and Zuidema (2020) that
consider the layered model structure to assign importance scores.

Alternatively, gradient-based methods such as Gradient$\,\times\,$Input Voita
et al. (2019); Wu and Ong (2020) and integrated gradients Wallace et al.
(2019) have been used to explain Transformer model predictions. However, the
naive computation of model gradients in Transformers suffers from
instabilities that can be mitigated via a modified layer-wise relevance
propagation (LRP) scheme guided by the principle of relevance conservation Ali
et al. (2022); Eberle (2022). This approach results in more faithful model
explanations when compared to other Transformer explanations.

In this work, we consider a variety of methods and include both attention-
based (first and last-layer attention, attention rollout) and gradient-based
(Gradient$\times$Input, LRP) explanations.

#### Evaluation XAI.

The automated quantitative evaluation of Explainable AI approaches has
received growing attention Zhang et al. (2019); Rosenfeld (2021); Samek et al.
(2021); Zhou et al. (2021); Hedström et al. (2023).

In evaluating explanations, it is useful to distinguish between approaches
that evaluate how well a method explains the model prediction process and
approaches that focus on explaining a particular ground truth. The former is
most commonly assessed using faithfulness, sufficiency, or complexity metrics
Swartout and Moore (1993), while the latter typically involves rationale
annotations or measurements to assess human alignment with the model’s
decision strategy Miller (2019); DeYoung et al. (2020).

We here focus on evaluating explanations based on human-annotated gold label
rationales and open up the question of whether human gaze poses a valid
alternative in this evaluation process.

#### Evaluation with human signals.

Capturing the model prediction faithfully does not necessarily align with
human annotations, since different task-solving strategies can emerge in
models and humans Rudin (2019); DeYoung et al. (2020); Atanasova et al.
(2020). Previous work has directly compared human and expert annotations of
input data to model explanations Schmidt and Bießmann (2019); Camburu et al.
(2018); DeYoung et al. (2020), and compared alignment between psychophysical
signals during task-solving to model-based explanations Das et al. (2016);
Klerke et al. (2016); Barrett et al. (2018); Zhang and Zhang (2019);
Hollenstein et al. (2021). Another line of work has analysed the alignment
between human gaze and model explanations. Overall, they found a clear
correlation between first-layer attention, attention flow Abnar and Zuidema
(2020) and gradient-based explanations with human gaze in English normal
reading Hollenstein and Beinborn (2021) as well as task-specific reading
Eberle et al. (2022); Ikhwantri et al. (2023), and also in multilingual
settings Morger et al. (2022); Brandl and Hollenstein (2022); Bensemann et al.
(2022). It further has been found that higher alignment between models and
gaze does not necessarily lead to higher task performance Sood et al. (2020)
or higher faithfulness Eberle et al. (2022).

#### Webcam-based eye-tracking.

Recording human gaze via webcams enables the collection of larger datasets and
has been applied in both NLP and computer vision Xu et al. (2015); Papoutsaki
et al. (2017); Hutt et al. (2023). While less accurate than professional eye-
tracking devices, results comparable to lab studies have been reported
Semmelmann and Weigelt (2018). Results demonstrate that well-known phenomena
can be replicated from online data, but are slightly less accurate and with
higher variance compared to in-lab recordings.

We will focus our analysis on WebQAmGaze Ribeiro et al. (2023), a multilingual
dataset for information-seeking QA. There, we compared a subset of the
recorded data set with respective lab-recorded counterparts. We report
Spearman correlations of greater than 0.5 for most texts. Here, we extend this
and look into data quality of webcam-based eye-tracking and how this relates
to decoding accuracies and evaluation in comparison to traditional XAI
methods.

Figure 2: Toy example to visualize decoding accuracies (ROC-AUC scores) of
ground-truth rationales for three different eye-tracking patterns (v1-v3). The
correct ranking as in _v1_ leads to a perfect score of $1$. In _v2_ only one
of the _correct_ tokens (_Narges_) appears in the top-2 of the reading
patterns which leads to a lower ROC-AUC score as shown on the right, similar
for _v3_ where the relevant tokens only appear within the top-5. For the
analysis with real gaze patterns, we only use one pattern per text in each set
after averaging across participants.

## 3\. Gaze-based Analysis

In the following, we evaluate gaze data based on human-annotated rationales in
English, Spanish, and German on a subset of the XQuAD dataset.

### 3.1. Data

#### XQUAD.

XQuAD Artetxe et al. (2019) contains professional translations of question-
answer pairs from a subset of SQuAD v1.1 Rajpurkar et al. (2016) into 11
languages. For each context paragraph, there is a set of questions that is
annotated with the correct answer, i.e., the span of where it can be found in
the text. The correct answers have been crowdsourced by annotators and
selected based on a majority vote.

#### WebQamGaze.

WebQamGaze Ribeiro et al. (2023) is a multilingual webcam-based eye-tracking
dataset collected with WebGazer where participants read texts from XQuAD.
Participants perform two different tasks, normal reading and information-
seeking, each for 4 and 5 different texts of XQuAD, respectively. In the
normal reading task, each text is followed by a comprehension question. In the
information-seeking (IS) task, the question is asked before showing the text
but also while and after reading the respective paragraph. We focus our
analysis on the information-seeking part of XQuAD for English ($N{=}126$),
Spanish ($N{=}51$) and German ($N{=}19$), where $N$ represents the number of
participants. Self-reported language fluency in the respective language were
4.6 - 4.9 on average per language. We show further statistics in Table 1. We
extract total reading times (TRT) by summing over all fixations per word and
participant, i.e., how long someone looks at a specific word including
regressions. We furthermore compute relative fixation duration (RFD), i.e.,
reading patterns, for individual participants by dividing TRT per word by the
sum over all TRTs in the respective context, similar to Hollenstein and
Beinborn (2021). Finally, we average RFD across participants. Figure 1 shows
an example of ground-truth annotations, gaze and model explanation.

Lang. | Texts | Tokens | Age
---|---|---|---
| n | min/max | avg | answer | avg
EN | 71 | 31/130 | 97 | 2.6 | 37
ES | 42 | 35/131 | 96 | 2.9 | 33
DE | 25 | 26/112 | 80 | 1.6 | 30

Table 1: Statistics for the IS task in the WebQAmGaze dataset. Each row shows
the number of texts, the minimum, maximum, and average number of tokens per
text, followed by the average number of tokens per answer and the average age
of the participants.

### 3.2. Analyses

We first carry out an in-depth analysis of WebQAmGaze. We therefore look into
data quality, which varies across languages for this dataset Ribeiro et al.
(2023). We therefore compute entropy across texts, which is known to be an
indicator for task difficulty, and decoding accuracy with respect to human
rationales to find out to what extent we can extract rationales from fixation
patterns. This analysis aims to assess what kind of additional information the
fixation patterns contain that can be beneficial for evaluating XAI.

Figure 3: Entropy and decoding accuracy separated by all languages. Medians
are displayed within the boxplots as a straight line whereas means are shown
as white dots. Data has been filtered based on the WebGazer accuracy with a
threshold of 20% (orange) and additionally we removed wrong answers (purple).

#### Data quality.

In Ribeiro et al. (2023), we report WebGazer accuracies across languages as an
indicator for overall data quality. The WebGazer accuracy is calculated in the
calibration phase and indicates how accurately fixations are recorded on
average, i.e., to what extent fixations on a particular point on the screen
are detected by the software. We see that both median and mean across
participants increase from English (23.6%, 30.6%) to Spanish (34.6%, 38.4%)
and German (41.7%, 39.0%). This accuracy is based on individual webcams and
should not be language-dependent.

#### Entropy.

Gaze entropy, i.e., entropy calculated on fixation patterns, has been found to
be an indicator for task difficulty in previous eye-tracking studies Di Stasi
et al. (2016); Wu et al. (2020); Mejia-Romero et al. (2021). We calculate
entropy across texts, results are shown in Figure 3 (left), where we see a
decrease in entropy of the relative fixation patterns from English to Spanish
and German based on all samples, i.e., question-answer pairs. Based on the
previous finding of different data quality, we filter the dataset based on
WebGazer accuracy with a threshold of 20% and keep the samples above that. We
also remove wrong answers in the IS task. We find that for all languages,
higher data quality and filtering out wrong answers lead to overall lower
entropy values. This effect is strongest for English.

#### Error prediction.

Based on the aforementioned literature on gaze entropy and task difficulty and
workload, we look into a possible connection between error prediction in the
QA task and gaze entropy. In Ribeiro et al. (2023), we show that TRT in
WebQAmGaze differs significantly between participants who respond correctly
vs. incorrectly which here is the only available proxy for task difficulty.
Our correlation analysis extends on these findings, and we observe significant
negative correlation between TRT on the given text and task accuracy, i.e.,
the longer a person reads the text the more likely the given answer to be
wrong. We find this effect to be significant in all three languages ($p<0.05$)
with correlation coefficients ranging from $-0.23$ (en) to $-0.54$ (de). We
further find task accuracy to correlate with entropy values ($p<0.05$) for
individual samples averaged across participants for Spanish ($-0.41$) and
German ($-0.49$). This suggests that higher entropy (more sparse reading
patterns) correlates with a lower task accuracy, i.e., more difficult tasks,
which is in line with Di Stasi et al. (2016); Wu et al. (2020) who find higher
entropy to be an indicator of higher workload in surgical tasks. Mejia-Romero
et al. (2021) on the other hand, find a higher workload in driving to be
connected to lower gaze entropy. All mentioned studies use 2-dimensional gaze
coordinates whereas we use reading times where gaze has been allocated to
specific words prior to calculating gaze entropy.

Figure 4: ROC-AUC scores for decoding rationales from attention-based and
gradient-based model explanations, i.e., decoding accuracies, across all 3
languages. Results for Gaze are model-agnostic. Individual samples with an
F1-scores below 50 have been filtered out per model and language.

#### Decoding.

We compute decoding accuracies to quantify how much information about the
ground-truth rationale is contained in eye-tracking and model signal. This
approach is related to cognitive neuroscience, where brain activity is mapped
to the original stimuli (here the ground-truth rationales from XQuAD) in order
to get a better understanding of human language processing Huth et al. (2016).

We compute “area under the ROC curve” (ROC-AUC) to assess the discriminatory
ability of the gaze-based fixation patterns in detecting the correct
rationales (true-positives) compared to the incorrectly detected tokens
(false-positives). A ROC-AUC score of 0.5 indicates discrimination at chance
level. Our toy example in Figure 2 presents three reading patterns (eye-
tracking v1-3) that lead to different ROC-AUC scores as shown on the right.
Here, the correct ranking is crucial, i.e., a perfect score of 1 is reached
when the ranking of the top-k tokens fully agrees with the rationale tokens,
here _Narges_ and _Mohammadi_.

In Figure 3 (right), we show decoding accuracies for all languages based on
all samples (previously averaged across participants) and observe that
rationales can indeed be decoded from gaze data with mean decoding accuracies
around 60% for English and Spanish and around 70% for German. Here, we only
observe a marginal increase after applying the same filtering with respect to
data quality and task accuracy.

Figure 5: Comparison of gaze-based and rationale-based ranking of explanation
methods for English (EN), Spanish (ES), and German (DE) – top to bottom. Ranks
1 to 5 indicate model explanations most to least aligned with human importance
scores. Spearman rank correlation $r_{s}$ at $p\leq 0.01$ (∗∗), $p\leq 0.05$
(∗), or not significant (ns). Results are based on text samples filtered by
correct human answers.

## 4\. XAI-based Analysis

We will look at the evaluation of XAI methods based on gold label rationales
from two different sides. In a first step, we extend the decoding analysis
from Section 3 to also analyse if ground truth annotations can be decoded from
model-based explanations. In a second step, we evaluate model explanations
based on ground truth rationales. This results in a ranking of XAI methods. In
the following, we investigate if these rankings can similarly be obtained from
gaze-based signals.

We focus our analysis on mBERT, distil-mBERT, XLMR and XLMR-L, covering a
range of widely-used multilingual encoder-only models. Explanation methods
based on BERT-like models have previously been shown to correlate with human
gaze Eberle et al. (2022); Brandl and Hollenstein (2022) as well as human
rationale annotations Thorn Jakobsen et al. (2023).

### 4.1. Fine-tuning Models

We fine-tune 4 multilingual pre-trained language models (mBERT, distil-mBERT,
XLMR, XLMR-L) individually for each of the three languages (en, es, de) on
XQuAD after filtering out the language-specific text samples that have been
used in WebQAmGaze. We split the remaining data into train and validation set
(90/10) and use the samples from WebQAmGaze as the test set. This results in
training datasets of 818-990 samples and evaluation datasets of 91-111 samples
for the three languages respectively. For the fine-tuning, we use a span
classification head on top of the encoder and train with AdamW with a learning
rate of 2e-5, a batch size of 16, weight decay of 0.01 for 7 epochs. We fine-
tune all models for 3 different seeds, evaluate all of them, and report
average scores. We further filter out the samples with an F1 score below 0.5
in the QA task.

### 4.2. Model Explanations

#### Attention-based.

In order to extract model explanations, we include both attention-based and
gradient-based methods. We compute averages over first-layer attention, last-
layer attention tensors Hollenstein and Beinborn (2021) and attention rollout
Abnar and Zuidema (2020).

#### Gradient-based.

We further compare to ‘Gradient$\times$Input’ Baehrens et al. (2010);
Shrikumar et al. (2017) and ‘Layer-wise Relevance Propagation’ (LRP, Bach et
al. 2015). To compute faithful LRP explanations, we apply specific propagation
rules that are designed to reconstitute the conservation of relevance Ali et
al. (2022). For the models considered here, this was implemented by detaching
specific model components that occur in the self-attention mechanism and the
normalization layers from the gradient computation. We note that this does not
affect the model predictions.

### 4.3. Analyses and Experiments

#### Decoding.

As presented in Section 3.2, annotations could be recovered from gaze patterns
with averaged ROC-AUC scores ranging from 59% to 73%. In Figure 4, we show
results for the same analysis where we also decode the annotations with
respect to model-based explanation for all 3 languages. We find that human
rationales can be effectively decoded from explanations, in particular from
LRP-based relevance with ROC-AUC scores ranging from around 67% up to 96%
across models.

Both first-layer and last-layer attention and rollout scores show mixed
decoding abilities with ROC-AUC scores mostly below 65% where XLMR shows the
highest accuracies across all 3 languages.

#### Ranking.

We now turn to the question of how well human gaze patterns can be used to
evaluate XAI methods in direct comparison to commonly used human rationale
annotations.

To compare the agreement between annotation and explanation, we rank tokens
according to their importance scores as assigned by an explanation method. We
then compare the accumulated importance scores against the accumulated
evidence assigned by human rationales or gaze fixations. By computing the area
under the curve (AUC), we measure how well evidence from human annotations
aligns with the most relevant tokens from an XAI perspective. AUC scores
closer to zero indicate that model-based and gaze/rationale-based importance
scores identify different tokens as most relevant for the task, AUC scores of
0.5 indicate aligned importance scores. Scores greater than 0.5 signify higher
importance attribution by humans (gaze/rationales) to the most relevant tokens
based on model explanation. Similar approaches have been used for the
evaluation of explanation methods Bach et al. (2015); Ancona et al. (2018).

The computed AUC scores are used to rank the different models, with rank 1 to
5 indicating the model explanations that are most to least aligned with human
signals. The ranking across methods is shown in Figure 5 for mBERT, distil-
mBERT, XLMR, and XLMR-L. First focusing on rationales, we find that in line
with previous work, gradient-based approaches rank favorably in comparison to
attention-based methods (first/last-layer attention, first-layer rollout).
While the respective ranking based on gaze data is less consistent, we do
observe an overall comparable ranking of explanation methods, in particular,
for mBERT, distil-mBERT, and XLMR. The deeper XLMR-L in comparison tends to
identify first-layer-attention as the most human-aligned explanation for both
gaze-based attribution and rationales. Further analysis of AUC scores suggests
that for XLMR-L, AUC scores are generally lower, indicating a differently
selected set of most relevant tokens. We find that first-layer attention tends
to rank favorably when compared to gaze in contrast to rationales. This effect
is in line with previously observed high correlation between early-layer
attention and gaze-based attention Morger et al. (2022); Brandl and
Hollenstein (2022). While the ranking of explanation methods can differ across
models, we see that for 9/12 models (across all languages) the rankings based
on gaze and rationales correlate with Spearman rank correlation scores ranging
from 0.52 to 0.97 ($p\leq 0.05$). This suggests that these gaze signals can be
considered as an alternative to rationales for the creation of cost-effective
large evaluation datasets for XAI.

## 5\. In-depth Analysis

We look into different factors that potentially drive WebGazer accuracy (data
quality) and decoding accuracies based on linguistic features in the text. We
also show results on additional recordings for a subset of the English
dataset.

Figure 6: Comparison of WebGazer and decoding accuracies on sets 02 and 18 for
different groups of workers.

#### Control setup.

Given the lower webcam accuracy for English, we further analyse two additional
datasets that were collected for a subset of the original WebQAmGaze. The
first dataset volunteers was recorded by 19 collaborators who were given the
link online and were not paid for this study. The second dataset control has
been recorded at the University of Copenhagen with 10 members on-site. Those
participants were also not paid for this study. For the control dataset, we
used the same laptop, a MacBook Pro 13-inch (M1, 2020), across all
participants in a controlled setup where we used artificial light in a
relatively dark room but no further equipment. Figure 6 shows the results,
i.e., WebGazer and decoding accuracies, for the two new datasets and for the
same subset of the original dataset collected via mturk. We see that WebGazer
accuracy still varies but is on average higher than in the same subset of the
original dataset (23.8% vs. 40.5% and 43.4%). Regarding decoding accuracy, the
median increases for all groups when we filter based on data quality (the same
holds for the mean except for mturk) but not the same holds for filtering out
wrong answers. We see the highest decoding accuracies for volunteer with
median accuracies of 77-81%, which is also the group with the highest WebGazer
accuracy. Overall these results suggest that better webcam accuracy also leads
to higher decoding accuracies.

#### Vision.

As Figure 6 shows, even in a controlled setup with the same lightning
condition and laptop, the WebGazer accuracy varies between 11% and 70% with an
average of 40%. For this dataset, we collected information about participants
wearing glasses during the experiment. When considering that factor, we see
clear differences between people with and without glasses. For participants
with glasses (4/10), the average WebGazer accuracy drops to 20% whereas the
average accuracy for people without glasses is at 54%. We are not the first to
see differences in data quality with participants wearing glasses, Greenaway
et al. (2021) report that for 7 out of 9 participants, the webcam-based eye-
tracker was not able to successfully mesh the participants faces due to lens
reflections. Unfortunately, we do not have any information about participants’
vision in the remaining datasets.

Figure 7: Decoding accuracy for the eye-tracking data with respect to ground-
truth rationales based on the relative position of the answer in the text. We
therefore group the dataset into 4 equally-sized bins based on where in the
text the answer can be found. The x-axis shows the percentage of the upper
bound of the respective bin.

#### Relative position of answers in text.

We hypothesize that in the information-seeking tasks, participants stop
reading after finding the correct answer which might affect decoding
accuracies. We therefore split the English part of the original dataset into 4
equally sized bins based on the relative position of the answer in the text.
We then compare the decoding accuracy for the ground truth rationales with
respect to the eye-tracking data, similar to Figure 3 (right) for individual
bins and show results in Figure 7. We clearly see a drop in median (and mostly
also mean) accuracy the later in the text the answer can be found. This might
be due to a more dispersed reading pattern, assuming that participants stop
reading earlier, resulting in more sparse and presumably easier-to-decode
reading patterns for answers located earlier in the text.

Figure 8: Results for short and long texts. English data was split into
equally sized bins based on the length of the text. Figure 9: Results for
short and long answers. English data was split into equally sized bins based
on the length of the gold label answer.

#### Length of text and answer.

We also look into how the length of both text and answer, measured by the
number of tokens, influences the decoding accuracy for eye-tracking. For both
analyses, we split the English part of the original dataset into two equally
sized bins. For the text length, we set the threshold at 87 tokens, i.e., the
median length and show results for decoding accuracies with respect to gaze in
Figure 8. We see a slightly higher decoding accuracy for longer texts (median:
57.1% vs. 62.1%). Similarly, we apply the median length of all answers (2
tokens) as a threshold to split the data and look at decoding accuracies for
both gaze and models in Figure 9. We find that models overall show slightly
higher accuracies for shorter than for longer answers, while the effect for
gaze is even stronger with a gap of 10%. This effect also holds when we split
the data into 4 bins.

## 6\. Discussion

This work presents a first look into the possibilities of low-cost gaze data
as an alternative to human rationale annotations. We have compared human gaze
in an information-seeking QA task with model-based explanations in 3 languages
(en, es, de) for 4 multilingual Transformer-based language models.

We see that data quality, measured with the WebGazer accuracy, largely varies
between recordings, even when data is collected with the same camera and
lighting conditions. One reason for this might be the use of glasses, which
have been shown to affect the accuracy in webcam-based eye-tracking due to
lens reflections. Unfortunately, this information is typically not available
and thus we recommend including it the questionnaire for future data
collection.

We use the error rate across participants as a proxy for task difficulty and
look into possible indicators. We find TRT (all languages) and gaze entropy
(Spanish and German) to strongly correlate with the error rate with negative
coefficients. Spatial gaze entropy has been found to be an indicator for
workload in surgical and driving tasks before.

Rationales and gaze provide complementary information to assess if human
signals and model explanations are well-aligned. By decoding rationales from
model explanations, we could clearly see that some explanations contain
relevant signal to achieve high accuracies. We find that explanation methods
that were found to be more faithful, in particular gradient-based
explanations, are able to reach higher ROC-AUC scores than attention-based
explanations. This clearly shows how the alignment of rationales and model
explanations depends on the choice of appropriate XAI methods.

We see various factors that might influence decoding accuracies both for
models and gaze. The relative position of the answer in the text as well as
the text length and the number of tokens in the correct answer seem to
potentially influence the ability to decode the gold label answer where longer
texts and shorter answers lead to higher accuracies. This might be due to the
fact that it takes more time, and thus more fixations are collected, to detect
a shorter answer in a longer text which might lead to more accurate gaze
patterns.

We further have explored the potential use of webcam-based gaze patterns as a
more accessible alternative to rationale annotations. While rankings of XAI
methods that result from a comparison to (i) rationales and (ii) gaze-based
attention show comparable rankings, we observe that the agreement between
rankings can depend on the specific model and data. Although evaluating and
aligning models using webcam-data can currently not yet fully replace high-
quality rationale annotations, we argue that they do provide useful
information, in particular, when collecting rationales is not feasible.

## 7\. Conclusion.

We showed that eye-tracking, even in lower quality than lab-quality
recordings, provides useful linguistic information, e.g., in the form of
reading times and entropy values in English, Spanish, and German. Thus,
although webcam-based eye-trackers catch up in data quality, we do not need
lab-quality to benefit from the additional signals in gaze. Further research
is needed to investigate entropy as a possible indicator for task difficulty
in reading patterns, similar to prior work on spatial gaze patterns in
workload tasks. This integrated approach of recording both human gaze and
rationales could readily be extended to other tasks and languages, which may
vary not only in linguistic but also in computational characteristics.

## 8\. Limitations.

This work focuses on analysing gaze data as an alternative for human rationale
annotations when evaluating explainability methods. We apply our analysis to a
subset of the dataset including three Indo-European languages: English,
Spanish, and German. Thus, this analysis does not cover a wide variety of
languages and, furthermore, we have only a small sample size for German (19
participants). We focus on one dataset/task and on multilingual BERT-like
language models. To draw more general conclusions this analysis needs to be
extended to more language families, datasets, and language models.

## 9\. Acknowledgements

We are particularly thankful for the volunteers at the University of
Copenhagen for generously taking the time to participate in the small control
study for this paper. We further thank Laura Cabello and Ilias Chalkidis for
their valuable comments on the manuscript. SB is funded by the European Union
under the Grant Agreement no. 10106555, FairER. OE received funding by the
German Ministry for Education and Research (under refs 01IS18056A and
01IS18025A) and BIFOLD. Views and opinions expressed are those of the
author(s) only and do not necessarily reflect those of the European Union or
European Research Executive Agency (REA). Neither the European Union nor REA
can be held responsible for them.

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