identical with the exception of the aforementioned noise peak.

(a) dc calculation with photoelectron exclusion.

(b) Area without (black) and with photoelectron exclusion (gray).

Figure 7.5: Photoelectron exclusion results.

###  7.3 Implementation, configuration and automation

A subtle feature of data analysis is algorithm parametrization. It has to
scale-up along the experiment life cycle and must allow flexibility and ease
of configuration. This parametrization can become a tedious and error-prone
process having in mind the increase of the number of signals. Hence, some
mechanisms were implemented in order to reduce manual parameter introduction,
avoid data replication and automate processes where possible.

######

In this section the analysis software invocation is covered and all features
such as configuration or automation are described.

####  7.3.1 Algorithm parametrization

All algorithms used in analysis software need parametrization as seen above.
Some of this parameters can be extracted from the DAQ configuration (see
Section 6.2.2), others from SER characterization (3.5) and the remaining ones
from characterization algorithm process (7.1).

##### DAQ parameters

The parameters read from DAQ configuration are sampling rate and least
significant bit value (the voltage value of digitization units). Both of them
are used to get the result of the algorithms in physical units. Sampling rate
is read from the digitizer configuration and the vertical resolution is
deduced from the configured board: 250 $\mu$V/point for 12-bit MATACQ and 125
$\mu$V/point for 14-bit MATACQ.

######

Critical parameters from DAQ configuration are hardware threshold and
coincidence window both of them configured in CAEN N843 constant fraction
discriminator. This module does not allow either parameter reading or remote
configuration and for this reason the values have to be changed carefully and
in a documented way, modifying configuration parameters as described in the
next section.

##### Input parameters

This section covers all input parameters required by the analysis software
which can be configured via configuration file. It describes all possible
valid values for the parameters and their meaning.

######  inipulse:

The number of points considered as pretrigger. It is used to calculate the
baseline level. See Section 7.1.1.

######  len_of_interest:

This parameter is mostly used in unconventional acquisitions. It defines (in
nanoseconds) the time windows to integrate the area and to obtain other
parameters like areap, fm, sm or tm. It is used when the acquisition windows
is wider than the width of the expected signal. A special case for the use of
this parameter is the SER characterization given the width of a single
photoelectron (see Section 3.5). The use of len_of_interest prevents
considering extra baseline when needed. The default value is all the pulse
depth, in nanoseconds, using all points of the waveform.

######  trigger:

This parameter configures the _trigger software_ parameter. It must match the
hardware trigger level value. It is expressed in millivolt.

######  lp_trigger:

This parameter configures the _trigger software_ for the last peak algorithm.
It is very useful to distinguish this value from the trigger value because
using the same value will cause trigger bias in the SER extraction (see
Section 8.6).

######  skw_thr:

This value is used as skewness threshold of refined dc calculation as
described in Section 7.2. Setting this parameter to 0 disables this refinement
(default value)

######  rms_thr:

This parameter is used combined with the previous skw_thr. See Section 7.2.

######  adche_thr:

This parameter is used as threshold for counting the time since a very
energetic event occurred. The output variable hetick is set to 0 when this
threshold is exceeded, otherwise is set to clock distance to the last event
above this threshold. See Section 7.1.4.

######  nrms:

This value is used as threshold of peak identification described in Section
7.1.2. This threshold is calculated as dc-nrms$\cdot$rms and all peaks below
it are discarded. It uses 3 as default value.

######  trigger_mode:

It allows to change the default algorithm to determine t0 using (or not) the
aforementioned trigger value. It is useful in case of using different hardware
trigger methods and it allows software algorithm to behave in the same way.
The valid algorithms are: [itemize] & relative: This is the default value of
this parameter and the most frequently used. It calculates t0 as described in
Section7.1. The constant fraction discriminator has a relative trigger and
this algorithm has to be used in this case. absolute: This algorithm does not
use extracted dc value as estimation of the baseline level. It uses trigger as
an absolute level. This configuration is useful in the case of using a
threshold discriminator. rms: The rms method uses dc-nrms$\cdot$rms as trigger
level. It is worth to note that this algorithm uses different level for every
pulse. This trigger level has the advantage of adapting the level to the noise
of the baseline and it can be useful in noisy runs. It has the disadvantage of
being different of the hardware one. t0_fixed: This is a special method that
assigns t0 to inipulse value. It can be useful in the case of having the
signal of interest in a fixed time relating to digitizer trigger. It can be
used in special cases in conjunction with len_of_interest value.

##### Configuration file

All the above parameters can be configured via configuration file. It uses
YAML format in the same way as DAQ program (see Section 6.2.2). It has a
section to override the default values in a global way, set values to a set of
signals or in a per signal way. This scheme was designed in order to avoid to
set all parameters of all signals but allowing individual configuration. This
last feature is needed by subtle differences between signals given the PMT
parameter dispersion (see Section 3.6).

######

The file consists of several sections. Every section can configure all
parameters mentioned in the previous section. The name of the section
determines the signals affected by the configuration. There are three
different ways to configure the parameters: [itemize] & default section: In
this section the override of the default parameters can be done and it applies
to all signals unless it is override by a more specific section. regular
expression section: This section overrides the previous section in the case
that the name of the signal matches with a regular expression [220]. A regular
expression section can be used to configure a set of signals that shares
similar properties. It has been useful in the case of test signals and
especially with attenuated signals as those used in ANAIS-25 and ANAIS-37 set-
ups. signal name section: This section configures the parameter used to a
particular signal overriding all other possible applying sections.

######

This mechanism can be observed in the Listing 5. The example uses default and
regular expression sections only. It sets skewness threshold, trigger level
and inipulse by default, and it overrides trigger level and skewness threshold
for signals that matches p[2-3].? regular expression. The result of this
matching can be p20, p21, p30 and p31 taking into account signal naming
conventions as it can be seen in Section 7.3.2.

Listing 5: Analysis configuration example.

⬇

- default:

trigger: -1.2

skw_thr: .5

inipulse: 580.

- p[2-3].?:

trigger: -10

skw_thr: 0

This parametrization is critical for obtaining accurate results. Knowing the
settings used in this analysis step is very convenient and hence all
configuration parameters are written in the output file with the obtained
parameters. This file is described in the next section.

####  7.3.2 Output file and Naming conventions

All parameters extracted and calculated from the DAQ data are written in an
output file. This file includes all DAQ data excluding the waveform data for
performance reasons. The new file has all information related to an event: DAQ
and analysis parameters and the path to pulse information. This path is used
for the Pulse Inspection Tool (see Section 7.3.4). This file also contains the
analysis configuration mentioned in the previous section. Table 7.1 lists all
analysis parameters added to the DAQ parameters mentioned in Tables 6.1 and
6.2 as summary of all algorithms described in Section 7.1.

Name |  Description |  Type
---|---|---
max |  Maximum pulse value |  Signal parameter
high |  Minimum pulse value |  Signal parameter
tmax |  Minimum pulse time |  Signal parameter
dc |  baseline level |  Signal parameter
mode |  baseline statistical mode |  Signal parameter
rms |  baseline standard deviation |  Signal parameter
skw |  baseline skewness |  Signal parameter
t0 |  pulse onset time |  Signal parameter
area |  pulse area |  Signal parameter
abe |  low energy area |  Signal parameter
fm |  first momentum from tmax |  Signal parameter
sm |  second momentum from tmax |  Signal parameter
tm |  third momentum from tmax |  Signal parameter
p1,p2,p3,p4 |  Signal area ratios in different time-bases |  Signal parameters
pnm_peaks |  Identified peaks of n detector m signal stored in a TPolyMarker |  Peaks
n |  Number of peaks |  Peak based parameter
areap |  Area around peaks |  Peak based parameter
arealp |  Last peak area |  Peak based parameter
hlp |  Last peak height |  Peak based parameter
poslp |  Last peak time (position in pulse) |  Peak based parameter
drt |  Delta Real Time |  Event Parameter
ddt |  Delta Dead Time |  Event Parameter
vts |  Time in seconds since veto event |  Event Parameter
heticks |  Time in seconds since a very energetic event |  Detector Parameter
p1ns,p2ns,p3ns,p4ns |  Detector n area ratios in different time-bases |  Detector Parameters
Table 7.1: Naming convention for analysis parameters.

Following the naming convention described in Section 6.3.1 all pulse
parameters are followed by its detector and signal number. For example, the
area of the detector 2 signal 0 is identified as area20. All parameters also
allows the _sum notation_ (e.g. n3s for total peaks in detector 3, an alias
for n30+n31).

######

It is worth to note the special nature of p parameters _sum notation_. In
these parameters the sum ratio is defined as shown in expression 7.5 with
areas being the sum of the areas of the two signals from a detector. For this
reason, these parameters are defined as _Detector Parameters_ in Table 7.1.

####  7.3.3 Automation and report notification

Launching the analysis software and checking the results can be a tedious and
error prone task in a multisignal multidetector experiment. Hence, the process
has been automated as much as possible.

######

This automation relies on the data synchronization system (see Section 6.2.1).
The analysis is automatically triggered when a new run has been synchronized
via inotify Linux feature [221] and incrond daemon [222]. The new run is
analyzed, several tests are performed and reports are generated in order to
detect possible malfunction and undesirable conditions such as unexpected
noise. The implementation of all stability checks described in Section 9.3 in
an automatic way is ongoing.

######

Some other reports are currently performed for its visual inspection such as
baseline parameter distribution (dc, rms, skw, allowing the discovery of
baseline anomalies as those reported in Section 4.5.5), QDC and area stability
along one run and the pulse onset difference. Two report examples are
described in this section: the delta trigger time distribution and the correct
coincidence tagging crosschecked with the pulse onset difference.

##### Delta trigger time distribution

Both radioactive background and possible dark matter signal are Poissonian
processes. For such processes, the probability of measuring $x$ events having
a mean rate $r$ in a time $t$ is:

$P(x,rt)=\frac{(rt)^{x}}{x!}e^{-rt}$ (7.6)

The probability of having two consecutive events in $dt$ is:

$P_{c}dt=P(0,rt)P(1,rdt)=re^{-rt}dt$ (7.7)

which corresponds with an exponential decay with constant $r$. Therefore, all
Poissonian processes will contribute with an exponential distribution and any
departure of this behavior should be considered as effect of other kind of
spurious events such as electric noise.

######

The typical delta time distribution can be seen in Figure 7.6(a) for a
particular ANAIS-37 run. It exhibits a clear exponential decay with a mean
rate of 1.8 Hz for this particular run. Additionally, it can be seen another
constant at the beginning of the distribution caused by the long
phosphorescence constants of the NaI(Tl) crystals [180] explaining the 2.1 Hz
trigger rate measured in the run.

(a) Delta trigger time in normal operation.

(b) Delta trigger time distribution with a triggering 50 Hz noise.

Figure 7.6: Delta trigger time distributions.

######

Typical noises have constant frequency distributions and can be easily
detected in such a way. For example, the noise effect can be seen in Figure
7.6(b). A noise with base frequency of 50 Hz can be deduced from this figure
and it corresponds to HV power supply noise, now filtered as described in
Section 4.5.5.

##### T0 vs. Pattern Unit

The information given by the t0 parameter can be used to infer PMT coincidence
trigger. For this reason, comparison between t0 and the Pattern Unit can be a
good crosscheck for both extraction algorithm and hardware module. The trigger
from each detector is produced when the signals from both PMTs are above the
threshold in the defined coincidence window. This condition can be translated
in terms of t0 and coincidence window. The crosscheck can be done in two ways:
try to detect signals in both PMTs that do not give signal in the Pattern Unit
(pu) or signals marked by the pattern unit but without a valid t0.

######

The search of events that fulfills these conditions gives information about
the t0 algorithm quality (including improper settings like threshold_sw),
Pattern unit hardware problems and differences between trigger and
digitization lines, including noises in either line. A high number of
anomalous events can aid to identify acquisition problems: pu without t0 in
the waveform can reveal noises in trigger signal and t0 coincidences without
pu tagging could be caused by noises in digitization signal only.

####  7.3.4 Analysis tools

A selection of tools developed in order to aid analysis process are covered in
this section.

##### Pulse inspection tool

A direct inspection of the waveform is not trivial due to the ROOT file
format. For this reason a tool to visualize the signals was developed. In
addition, a set of features was implemented for adding visual information and
for parameter extraction check. An example of a low energy event displayed
with the Pulse inspection tool can be seen in Figure 7.7. The parameters are
drawn in the pulse with vertical and horizontal lines. The default values
displayed are t0 (green), tmax (vertical red) and dc (horizontal red). The
identified peaks are displayed as an option.

Figure 7.7: Pulse inspection tool.

######

This tool is based in a similar tool for the previous ANAIS DAQ. It was
adapted to match the different data format and it evolved to cover other
features and to scale-up with the increase of signals. The most important
feature was the calculation of mean pulse of a previous selected list. The
mean pulse can be calculated centering all pulses in the same parameter (t0,
tmax or poslp) useful to obtain SER mean pulses (as it can be seen in Section
8.6) or scintillation constants [186]. Another feature is an optional
parameter to specify the detector to display, very useful in an increasing
number of signals scenario. The tool also supports exporting the resultant
pulses to allow external data processing.

##### ScatInspector

Another closely related tool is ScatInspector. This tool is designed to
inspect scatter plots in a very useful way. The user can select any point in a
scatter plot and the waveform of each PMT is drawn using the aforementioned
pulse inspector tool. In addition, ScatInspector highlights the same event in
the other canvases as it can be seen in Figure 7.8. This feature is very
useful to explore unknown populations displaying the same event in several
scatter plots.

Figure 7.8: ScatInspector tool. Selecting an event in a scatter plot
highlights it in other plots and shows the pulses.

##### ANAIS data browser

A web application was designed and implemented using the django framework
[223] in order to aid the ANAIS data browsing. The graphic user interface uses
a hierarchical tree with set-ups and runs in every set-up, showing all
automatic information of every run in both text and graphical ways as it can
be seen in Figure 7.9 and the results of the automatic tests described in
Section 7.3.3. It also allows to follow the stability of the data along a set-
up by plotting the parameters described in Section 9.3. This interface could
be expanded to give public access to the ANAIS data in the future.

Figure 7.9: ANAIS Data Browser web application.

######

Other non-visual tools have been created such as a skim file creator in order
to store filtered events together allowing to manage data cuts in a fast and
easy way and making persistent lists of events, or the event counter per live
time in regular time bins described in Section 8.3.1.

###  7.4 Low energy NaI event selection

The previously described analysis algorithms are essential for the energy
estimation, the event selection and the noise removal. The most significant
sources of noise at low energy masking the bulk NaI(Tl) scintillation are the
scintillation of the surrounding materials such as quartz [224] or PMT glass
and dark events in the photomultiplier. Additionally, electric noise can also
be harmful despite its usual almost zero area (energy) because of their more
symmetric nature (see Section 4.5.5). The current selection cuts of low energy
bulk scintillation events, based on previous works [173, 160] taking advantage
of the experience of the operation of previous prototypes, are presented in
this section.

######

The different temporal behavior of these noises as compared with the bulk NaI
scintillation is used as strategy to remove them. Very fast scintillation
tends to pile-up several photoelectrons and the peak detection algorithm does
not resolve them. This fact allows to impose the condition to number of peaks
n in PMT signal (n>2 in each signal). The cut also removes most of the dark
random coincidences (see Section 8.8) in addition to the fast scintillation.

######

Next, cuts based on the specific temporal parameters p1s and p2s taking into
account the NaI scintillation constant are applied. A third kind of cuts is
sensitive to the symmetry of light collection between the PMT signals in a
detector. The spurious events tend to be much more asymmetrical than the bulk
scintillation. The applied cut is performed in the ratio between the area
difference between signals versus total area (area0-area1/area0+area1).

######

The distribution of these parameters and the efficiency of the cuts have been
characterized with genuine ${}^{109}Cd$ events in very long calibrations
storing the very low energy events. This process takes advantage of the
conditional data storing described in the previous chapter.

######

In addition to the noise removal, other cuts are performed in the low energy
region in the search for dark matter interaction. The events correlated with
muons and other high energy events must be avoided because of their non-WIMP
related nature (see Chapter 5, especially Section 5.5.3, for further
information about this kind of events). A time after any muon interaction will
be rejected from the dark matter analysis because of this reason. The
afterglow caused for very energetic events is also rejected. These two cuts
are easily performed by using the vts and heticks described earlier.

######

These cuts will be reviewed with the understanding of the PMT events. This
understanding will be highly improved with the ongoing data taking of a blank
module (see Section 2.4) allowing to characterize and reject as much PMT
events as possible and helping to reach an energy threshold of 1 keVee before
filtering.

##  Chapter 8 Data acquisition characterization

The whole data acquisition system was tested during its development at the
University of Zaragoza and has been characterized with the ANAIS-25 and
ANAIS-37 prototypes at the LSC. The characterization of the time parameters
such as real time, live time and duty cycle are presented in Sections 8.1, 8.2
and 8.3 respectively.

######

In addition, the hardware trigger efficiency has been studied. First, the
energy calibration is presented in Section 8.4. Next, the single electron
response (SER) has been characterized onsite and compared with the
characterized at the University of Zaragoza PMT test bench in Section 8.5.
Additionally the SER trigger efficiency has been estimated. The light
collection has been measured for ANAIS-25 and ANAIS-37 combining the SER with
the calibration. Finally, the hardware trigger efficiency has been simulated
with different coincidence time windows taking into account the previously
measured light collection and SER trigger efficiency. The simulation result
has been crosschecked with measurements for very low energy events as it can
be seen in Section 8.7. The effect in random coincidence triggers due to the
coincidence window reduction can be seen in Section 8.8.

###  8.1 Real time clocks

Real time clocks play a key role in the ANAIS experiment. The time binning
needed to test the annual modulation and some other stability parameters (see
Section 8.3.1) rely on real time clocks. The DAQ system has two clocks: a
hardware 50 ns tick clock (see Section 4.4.1) and the software clock with 1 ns
nominal resolution. The accuracy and stability of these clocks are
characterized in this section. PC clocks are known to be irregular and
unreliable. The Network Time Protocol [225] (NTP) was used to mitigate the
effect allowing clocks to be synchronized within a few milliseconds.

####  8.1.1 32-bit latched vs. 64-bit non-latched counter

The real time clock is stored in two different scalers: a 32-bit latched
scaler and a 64-bit non-latched scaler. The real time and live time
measurement strategy has been presented in Section 4.4.1. The improvement of
the use of a latched scaler is reviewed in this section as well as the reasons
to keep the old 64-bit scaler.

######

The time difference of the two scalers can be observed in Figure 8.1. The
latched scaler stores via hardware the counter value at the trigger as opposed
to the value of the non-latched scaler that keep increasing the value. The
time difference accounts the time needed to wake-up the DAQ software and the
time needed to download the scaler value. This time difference is always
positive showing the non-latched nature of the 64-bit scaler. Additionally,
the time difference distribution shows a mean of 43 $\mu$s and exhibits a
binning pattern of 1 $\mu$s revealing a 1 MHz clock somewhere in the
communication process with a minimum value of the order of 20 $\mu$s, fully
compatible with the IRQ latency measured in Section 8.2.2.

Figure 8.1: Non-latched/latched scalers time difference.

######

The more precise latched measurement of the trigger is chosen for the real
time event tagging, and its value is referred as real time clock in the
following. Nevertheless, the non-latched 64-bit scaled was maintained in
operation because it gives information about the DAQ software response.
Additionally, its higher bit depth is useful to hold the long temporal
information in the 32 most significant bits.

####  8.1.2 NTP system

The NTP architecture is shown in Figure 8.2. The PC behaves as client for NTP
servers. Packets are exchanged between the client and server using NTP On-Wire
Protocols. These protocols are resistant to lost, replayed or spoofed packets.
The information obtained passes sanity tests111It checks packet, date and time
format, packet duplication and distance threshold among other tests.. This
packet interchange obtains timestamps (see Figure 8.2(b)): the origin
timestamp T1 upon departure of the client request, the receive timestamp T2
upon arrival at the server, the transmit timestamp T3 upon departure of the
server reply, and the destination timestamp T4 upon arrival at the client.
These timestamps are used to calculate the clock offset and roundtrip delay
samples:

$\displaystyle offset=[(T2-T1)+(T3-T4)]/2$ (8.1a) $\displaystyle
delay=(T4-T1)-(T3-T2)$ (8.1b)

This offset variable is a good approximation to the clocks distance between
client and server if roundtrip delay is symmetric, due to the cancellation
given its definition. The delay variable is a good approximation to the
roundtrip delay if the clock offset is small.

######

Therefore, the offset calculation quality depends on the quality of the
network between client and server because it assumes symmetric propagation
delays. Asymmetric routes, network congestion or long route paths can cause a
deterioration of synchronization accuracy as it can be seen in next section.

(a) NTP global architecture.

(b) NTP Timestamps.

Figure 8.2: NTP architecture. From [226].

######

This calculations for one or more servers are processed by mitigation
algorithms selecting the offset and delay samples most likely to produce
accurate results and choosing one of them as the system peer and producing the
final offset. This offset is used by clock discipline algorithm to adjust the
PC clock time and frequency. The clock offset and frequency are recorded in
the loopstats file and it is used in this work as quality reference.

Figure 8.3: NTP Clock Discipline Algorithm. From [226].

######

It is important to note that NTP was conceived to minimize both offset and
network use. For this reason, the clock adjustment is not performed in a naive
way by subtracting the offset: it tries to regulate the local clock taking
into account the past data and guessing its future behavior. A block diagram
of this clock discipline algorithm is shown in Figure 8.3. The timestamp of a
reference clock of remote server is compared with the timestamp of the system
clock, represented as a variable frequency oscillator (VFO), to produce a raw
offset sample $V_{d}$. Offset samples are processed by the _clock filter_ to
produce a filtered update $V_{s}$. The _loop filter_ implements a type-2
proportional-integrator controller (PIC). The PIC can minimize errors in both
time and frequency using predictors $x$ and $y$, respectively. The _clock
adjust_ process samples these predictors once each second to produce the
system clock update $V_{c}$.

####  8.1.3 Real Time clocks characterization

The reliability of the PC System Clock can be estimated by collecting NTP
statistics. The loopstats file contains date in Modified Julian Calendar
(MJC), time in seconds (millisecond precision) from the beginning of the day
and clock and frequency offsets used to compensate clock drifts and frequency
wander (in seconds with nanosecond precision and parts per million
respectively). The offset distribution and its behavior over time can be seen
in Figure 8.4. The top Figures show the offset distribution (8.4(a)) and
offset over time (8.4(b)) with default Linux distribution NTP servers. It can
be seen that the offset distribution gives a FWHM of 2.7 ms with many offsets
in the order of ten milliseconds, worse than NTP claim of few milliseconds
[225]. As seen in previous section, the accuracy of NTP synchronization is
dependent on the quality of network. A new NTP configuration was used with
Spanish NTP pool (es.pool.ntp.org). A closer pool of servers could improve
network quality connection. The result can be seen in the bottom figures:
offset distribution (8.4(c)) and offset over time (8.4(d)). Offset
distribution shows a better FWHM of 1.5 ms with much more statistical
significance and confirming the effect of long route paths between client and
server in NTP protocol. The frequency offset is almost constant giving a value
of 30 ppm due to the slow nature of the PC system clock. It is worth to note
that this offset is usually less than a very few milliseconds, so the PC clock
can be used as reliable clock in the long term using NTP synchronization and
the offset logs are stored to detect bigger differences due to network
congestion or network failures.

(a) NTP offset (Default servers).

(b) NTP offset over time (Default servers).

(c) NTP offset (Spanish servers).

(d) NTP offset over time (Spanish servers).

Figure 8.4: NTP Offset statistics: default NTP servers (top) vs. Spanish pool
NTP servers (bottom).

######

The Hardware clock behavior using NTP controlled PC clock as reference can be
seen in Figure 8.5(a). It shows a noticeable drift. This drift is very linear,
in this case with a slope of 1.72 $\mu$s/s: a systematic drift of 1.7 ppm.
Such a drift can be discounted in order to see the relative clock oscillations
as can be seen zoomed in Figure 8.5(b). The peak-to-peak oscillation is in the
order of few milliseconds and it is compatible with the NTP corrections as it
can be observed in NTP statistical log (Figure 8.4(c)). These data were
combined with typical Real Time data from DAQ in order to explain such an
oscillation. The result of this combination can be observed in Figure 8.5(b).
It can be seen the offset represented as an arrow. The length of this arrow is
the NTP offset (in $\mu$s) and it points up or down if it is negative or
positive respectively. The figure shows that the slope changes are correlated
with the offsets in both time and amplitude. This offset driven behavior also
confirms the PIC nature of the Clock Discipline Algorithm seen in Section
8.1.2. Therefore, the most important component of the oscillation of the
difference between real time clocks can be attributed to NTP. It shows a good
stability for the hardware clock and its instabilities must be below the
millisecond range.

(a) Hardware clock drift linear fit.

(b) NTP offset effect.

Figure 8.5: NTP Clock vs. Hardware Clock: clock differences, drift estimate,
difference subtracting drift and the effect of NTP offset.

######

This can be used to monitor the hardware real time clocks in the long term as
it can be seen in Figure 8.6. This figure shows run drifts over time using the
data from eight months runs. It can be observed two big shifts in the measured
drift. Both changes happened after maintenance tasks involving adding or
removing VME bus modules.

Figure 8.6:  Real time drift over time.

######

This clock characterization gives as conclusions: [itemize] & Hardware clock
has a systematic drift of very few ppm PC clock is synchronized via NTP with a
precision of few milliseconds Both of them are stable enough for their
purpose: Hardware clock can be used as timestamp of events and for computing
Live Time within a run. The typical drift in a week run will be of the order
of one second (1.02 s/week given 1.7 $\mu$s/s) PC clock can be used as
absolute clock with millisecond precision They can be monitoring each other in
order to control long term stability

###  8.2 Dead time characterization

The system dead time has been characterized by using the live time and dead
time counters previously described in Section 4.4.1. The counters behavior was
crosschecked using a pulser as trigger. The input frequency was varied in
order to observe the acquisition frequency behavior. The acquisition frequency
$F_{a}$ was modeled as:

$F_{a}=\frac{1}{\left\lceil DT/T_{i}\right\rceil T_{i}}$ (8.2)

taking into account the periodic nature of the input signal of period $T_{i}$.
This expression considers the acquisition dead time as a fixed time, in which
the acquisition does not accept any new event: $\left\lceil
DT/T_{i}\right\rceil$ are the number of periods in which the input trigger
will be rejected because of the dead time $DT$, giving a total acquisition
period of $\left\lceil DT/T_{i}\right\rceil$. The comparison between the
predicted and the measured acquisition frequency is observed in Figure 8.7
using the mean of the measured dead time as $DT$. It shows a good agreement
with a subtle difference explained by the non-constant nature of the dead
time.

Figure 8.7: Measured acquisition frequency behavior (squares) and fixed dead
time model (triangles, see text) versus input trigger frequency.

Once tested, the dead time measurements were used to compare several set-ups
and configurations in order to minimize it.

####  8.2.1 Synchronous vs. asynchronous data storing

The two different strategies used to store the data seen in Section 6.2.2 were
tested with the aforementioned pulser trigger in order to measure the dead
time differences. Figure 8.8 shows these measurements displaying up to 10%
less dead time at high acquisition frequencies for the asynchronous data
storing. The use of the asynchronous data storing isolates the data
acquisition from the unwanted latencies of the filesystem writing.

######

The asynchronous strategy was selected to its use in ANAIS experiment given
their better performance at high trigger frequency. In this way, the advantage
will have a positive impact at calibration time and in the afterglow episodes
after a very high energy events.

Figure 8.8: Dead time for synchronous and asynchronous data storing at
different acquisition frequencies.

####  8.2.2 IRQ waiting, board order reading and dead time

The IRQ trigger strategy was preferred over polling because of the noise
associated to the latter strategy (see Section 4.5.5). However, the process of
waiting to the interrupt could increase the dead time. An evaluation of the
order of magnitude of the cost of this process was performed by instrumenting
the kernel module from CAEN and calculating the time difference between the
interrupt time of arrival to kernel space and the time when the kernel will be
passed to the userspace. This time was always between 10-15 $\mu$s [227].
Having in mind the MATACQ board conversion time ($\simeq 650\mu s$), the IRQ
waiting latency can be absorbed in this time.

######

The order of the negotiation and data reading of the modules were rearranged
for this optimization and IRQ and polling strategies gave similar dead time
results. This rearrangement was also used to make use of the MATACQ conversion
time to read the data of the other boards, giving as a result a lower dead
time.

####  8.2.3 Live time clock versus dead time measured with latched scaler

The previous results were obtained using the non-latched counter and the live
time clock measuring the live time. The latched scaler was acquired and the
comparison of the two methods was performed in order to choose the better.
Figure 8.9(a) shows the difference between the live time measured with non-
latched counter using the live time clock and the dead time measured with the
latched scaler as described in Section 4.4.1, and taking into account that
$RealTime=LiveTime+DeadTime$. The systematic (very low, 0.5 parts per million)
drift of the non-latched counter can be explained with a skipped clock tick
starting the live time again. This can be observed in Figure 8.9(b): it shows
the live time difference between clocks divided by the number of the event.
The mean of such a distribution tends to be 25 ns, a half of the clock tick
(50 ns), probing the equal probability of lose or count the starting live time
clock tick. This test evinces a good agreement between the two methods
revealing a very subtle systematic effect using the live time separate counter
clock. Therefore, the latched scaler measurement was considered as live
time/dead time reference and it is used in the following.

(a)

(b)

Figure 8.9: Time difference between live time measured using live time
counter.

####  8.2.4 Dead time measurements and prospects

The dead time has been measured thanks to the latched scaler for every
individual event. The distribution of this dead time for an ANAIS-25 run can
be seen in Figure 8.10(a) with a mean value of 3.5 ms. The dead time has
overall low dispersion but exhibits some higher values due to the non-
deterministic nature of a non-realtime kernel but with very little
contribution to the total.

######

(a)

(b)

Figure 8.10: Event dead time distribution for ANAIS-25 (a) and ANAIS-37 (b)
set-ups.

The mean value of the dead time for ANAIS-37 is 4.5 ms. The increase of dead
time is due to the additional MATACQ board and the subsequent penalty for its
data reading. It is worth to note that these prototypes have twice digitizer
channels per detector than the final set-up due to the high energy signals
digitization. The dead time for the ANAIS full experiment will increase with
two additional MATACQ boards and it will be of the order of 5 ms taking into
account the use of 14-bit MATACQs which allow the use of 64-bit block mode
transfer and the use of a VME/PCI bridge with CONET2 protocol reducing the
data reading time.

######

The 5 ms dead time per event would give a 5% of dead time with a 10 Hz
acquisition frequency. Two strategies will be used in the final experiment to
reduce its impact: reduce the coincidence window and configure conditional
acquisition.

######

The change to the coincidence window from 200 ns to 100 ns would reduce the
dark rate random coincidence by half. The rate reduction using such a
coincidence window is studied in Section 8.8. The study of the hardware
trigger efficiency in Section 8.7 also quantifies the expected effect of a 100
ns window in the hardware trigger efficiency of the NaI scintillation events.

######

The conditional acquisition stores data depending on configurable runtime
conditions as described in Section 6.2.2. This technique can be applied to
reduce the dead time by avoiding the transfer of irrelevant data such as
baselines from non-triggered detectors. The selection can be done by checking
the trigger pattern (via Pattern Unit module) and read the triggered data
only. This strategy would be feasible due to the high hardware trigger
efficiency (see Section 8.7). The conditional data acquisition has been
already tested in long ${}^{109}Cd$ calibrations performed to have statistical
significance at low energies with big improvements in both dead time and disk
usage terms. It has been also tested in very high acquisition rate test run
with radon inside the shielding (see Section 9.2.3). The condition for trigger
was used in order to save disk space and have less dead time. The distribution
of the dead time with such conditions is shown in Figure 8.11. It can be
observed a multimodal distribution accounting for the events that transfer
data for one, two or three MATACQ boards. The vast majority of the events are
_single-hit_ events and for this reason the mean dead time is roughly the dead
time for one MATACQ (and the rest of modules such as pattern unit, QDC and
counters) giving a value of 2.4 ms.

Figure 8.11: Event dead time distribution with trigger condition for transfer
and data storing.

######

This dead time should be reduced by installing the new 14-bit MATACQ board
which allows the faster A32/D64 VME transfer mode giving a mean below 2 ms
even for the full ANAIS experiment taking into account the fact that most
triggers are _single-hit_ events and hence only one MATACQ data board has to
be transferred.

######

Anyway, the final experiment dead time should be of the order of the 2% taking
a conservative 2.4 ms per event and a trigger rate of 1 Hz per detector (see
Figure 9.10 to see the data supporting the last assumption).

###  8.3 Live time, dead time and down time

The live time is a key parameter in the ANAIS experiment because of its impact
in the exposure. In this section, the evolution of the live time along a
typical series of runs is studied in order to test the validity of the whole
system and routine, including the scheduled stops for system calibration.

######

The evolution of the live time of ANAIS-25 and ANAIS-37 set-ups can be seen in
Figure 8.12 showing also the dead time and the down time contribution to total
time. The most important down time in the ANAIS-25 set-up (see Figure 8.12(a))
was due to the maintenance labors in the LSC electrical facility. The other
downtimes are due to the installation of the veto system and to long pulse
calibrations. Anyway, the total live time percentage in this run was over 95%
of the time and the dead time a 0.8% of the total time.

(a)

(b)

Figure 8.12: Down time, dead time and live time over system time in weekly
binned basis for ANAIS-25 (a) and ANAIS-37 (b).

######

The currently running ANAIS-37 set-up (see Figure 8.12(b)) shows only a big
stop due to the change of D2 PMT covers. The total live time percentage was
about 91%, the down time a 5% and dead time of a 3%. This dead time increase
is due primarily to the rate increase caused by the PMT light exposure and for
a LSC $N_{2}$ nitrogen flux cut (see Section 9.2.3). It is worth to note that
the reduction of live time for events related to muon and high energy events
has not been accounted in this analysis, but this reduction can be estimated
in the order of 1% cutting one second after every such event.

######

Anyway, these live time percentages are above the live time reported by DAMA
[5] ranging from 58% to 83% depending on the year. The data also show a good
homogeneous distribution of the live time along the set-up time, mandatory for
a proper modulation analysis.

####  8.3.1 Live time measurements

It is essential to compute the live time in arbitrary regular time intervals
to properly calculate the evolution of the rate of events of interests such as
alpha rate or single hit low energy events. This live time binning algorithm
uses the two software and hardware clocks to account the real and live time of
a particular time bin. Additionally, it can take a list of events and count
them in every bin.

######

The NTP controlled PC clock is used at the beginning of every run in order to
establish the absolute time and the time event tagging is used to accumulate
the real and live times to each bin. The algorithm allows to establish the bin
time width as parameter and can be used to test the temporal behavior of
especial events as well as compute time statistics as seen in this section.

###  8.4 ANAIS-25 and ANAIS-37 calibration

The ANAIS-25 energy calibration consisted of ${}^{22}Na$ and ${}^{57}Co$ data
taking with QDCs and occasional pulse calibration with radioactive sources in
order to calibrate the area energy estimator [160].

######

The calibration system changed in ANAIS-37 (see Section 2.4) and the
calibration routine was also changed in order to calibrate with ${}^{109}Cd$
only. This calibration allows to quickly calibrate at low energies due the
intensity of 22.6 keV line. In addition to this line, the calibration spectrum
features a peak due to the presence of a cover of heat-shrink tube with $Br$
content. The tube is used to ensure the junction of the sources to the wire.
Figure 8.13 shows the calibration results with and without the cover proving
its origin. The line corresponds to 11.87 keV, the $Br$ K-shell X-rays mean.

Figure 8.13: ${}^{109}Cd$ source without (gray) and with (black) heat-shrink
tube showing the K-shell Br line.

######

Additionally, all calibrations are performed with pulse acquisition for area
calibration in order to have routinely calibrated the two more used energy
estimators, area and QDC. The area spectra for ${}^{109}Cd$ is shown in Figure
8.14 for all ANAIS-37 PMT signals (signal ij corresponds to PMT j of detector
Di).

Figure 8.14: ANAIS-37 area calibration with ${}^{109}Cd$ source. It features
the 22.6 keV and 88.0 keV peaks as well as the 11.9 keV of the $Br$ K-shell
X-ray mean (see text).

######

In this set-up the operating voltages of the D0 and D1 detectors were
increased in order to better study the low energy region and hence the SER
values are higher. The voltage of the new detector (D2) was selected to have a
$10^{6}$ gain value in both PMTs in order to explore the high energy region
being able to study the $\alpha$ rate contribution to the background (as seen
in Sections 2.3 and 2.4). These gain differences can be observed in Figure
8.14. The global low energy calibration is performed by adding two internal
lines, ${}^{40}K$ and ${}^{22}Na$ seen in Section 2.3 as shown in Figure 8.15.
The accurate energy calibrations of ANAIS-25 and ANAIS-37 prototypes supports
the idea of the scale-up of the same system and sources for the ANAIS full
experiment.

(a)

(b)

Figure 8.15: Calibration fit and residuals using ${}^{109}Cd$ external source
and ${}^{40}K$ and ${}^{22}Na$ internal lines.

######

The very good resolution of the Alpha Spectra modules was measured in the
first ANAIS-25 set-up [160] and it can be reported again by measuring the
${}^{109}Cd$ 22.6 keV width peak. The results for the ANAIS-25 set-up after
the replacing of D1 R11065SEL PMTs for R12669SEL2 (Table 8.2) and ANAIS-37
(Table 8.2) were calculated for every signal checking possible asymmetries.
The global detector resolution was calculated with the sum signal.

PMT |  | PMT
---
$\sigma$/E (%)
| Detector
---
$\sigma$/E (%)
00 | $10.93\pm 0.03$ | $8.51\pm 0.03$
01 | $11.24\pm 0.04$
10 | $11.33\pm 0.05$ | $8.59\pm 0.04$
11 | $10.90\pm 0.05$
Table 8.1: ANAIS-25 resolution at 22.6 keV.

PMT |  | PMT
---
$\sigma$/E (%)
| Detector
---
$\sigma$/E (%)
00 | $10.97\pm 0.04$ | $8.73\pm 0.03$
01 | $11.18\pm 0.04$
10 | $11.40\pm 0.03$ | $8.80\pm 0.03$
11 | $11.02\pm 0.03$
20 | $11.46\pm 0.07$ | $8.99\pm 0.05$
21 | $11.40\pm 0.08$
Table 8.2: ANAIS-37 resolution at 22.6 keV.

###  8.5 Onsite SER extraction

The PMTs SER was measured at the University of Zaragoza test bench using an UV
LED illumination of very low intensity, and triggering with the excitation LED
signal as described in Section 3.5.2. This characterization was very useful to
compare and crosscheck to the measured SER of the same PMT at the LSC. This
onsite SER extraction was performed using the peak identification algorithm
(see Section 7.1.2) with the data taken in the normal operation of the ANAIS
detectors at the LSC.

######

The SER was built by selecting peaks at the end of the pulse of each PMT in
order to avoid trigger bias in pulses having a low number of peaks to prevent
the pile-up of several photoelectrons (phe). An example of a pulse fulfilling
these conditions can be seen in Figure 8.16(a) and the mean pulse of a
selection of this kind of events is shown in Figure 8.16(b).

(a)

(b)

Figure 8.16: Pulse with a low number of phe; peaks identified by the applied
algorithm are shown with triangles (a). The last peaks of such kind of pulses
reconstructs the Single Electron Response (SER). The SER mean pulse (b).

######

The photoelectron area (proportional to charge) is integrated in a fixed time
window around the peak maximum (see Section 7.1.3)) in order to obtain the
single electron response charge distribution. The SER charge distribution
extracted for the same PMT by these two methods is compared in Figure 8.17
showing full agreement between both distributions.

Figure 8.17: SER charge distributions derived at PMT test bench (gray) and
along normal operation (black).

####  8.5.1 SER trigger efficiency

The onsite single electron response extracted with the aforementioned last
peak technique is compared with the distribution of triggered events in Figure
8.18. Both distributions are represented (conveniently scaled) in area for the
ANAIS-37 set-up. The plots are constructed with the last peak algorithms for
both populations. The SER is constructed by requiring non-triggering in order
to avoid the trigger bias.

######

The figures clearly show the previously mentioned higher voltage in D0 and D1
compared to the D2 lower value in ANAIS-37 set-up. It can be noted in Figure
8.18 the almost full trigger efficiency of the photoelectron signals of the
two first modules. The third module exhibits a high but not full photoelectron
trigger efficiency. The method followed to quantify the trigger efficiency is
to compare the area of the scaled distributions. The triggered distribution is
integrated directly and the SER distribution is fitted to a Gaussian, the most
important SER contribution as seen in Section 3.5.

PMT | % SER triggered
---|---
00 | 80%
01 | 89%
10 | 88%
11 | 97%
20 | 71%
21 | 75%
Table 8.3: ANAIS-37 SER trigger efficiency.

The SER trigger efficiency values seen in Table 8.3 are high considering that
the hardware trigger level must be set above the baseline noise. It has to be
noted the PMT 11 SER efficiency value seems to be overestimated because of the
triggering of a dark count population as it can be observed in Figure 8.18
(Signal 11). This effect is under study.

######

Additionally, the statistical distribution of the scintillation photons (see
Section 3.4) and a good light collection (see next section) can give near 100%
trigger efficiency down to 1 keV as it can be seen in Section 8.7.

Figure 8.18: Single electron area distribution (black) compared with the area
distribution of triggered events (gray).

###  8.6 Light collection

The light collection is another key parameter in a low threshold scintillation
experiment because it has a direct influence on both energy threshold and
resolution. For this reason, careful studies has been performed in ANAIS
prototypes [160].

######

First, the single electron response (SER) charge distribution was determined
as described earlier. Next, the light collection (in photoelectrons per keV)
can be inferred with the response to a known calibration line. In this
section, SER extraction and light collection measurements for ANAIS-25 and
ANAIS-37 set-ups are covered.

######

The light collected by each of the PMTs coupled to the ANAIS-25 modules was
calculated by dividing the charge distribution associated to a known energy
deposition in the NaI crystal and the SER charge distribution derived as
described in Section 8.5. The 22.6 keV line from a ${}^{109}Cd$ calibration
source was used for this study. The result of the SER charge spectrum and the
${}^{109}Cd$ line Gaussian fits can be seen in Table 8.4 (PMT ij corresponds
to PMT j of detector Di). These results and the global light collection of the
two ANAIS-25 detectors are summarized in Table 8.5. The results confirm the
prototypes outstanding light collection and its impact in resolution as seen
earlier in Table 8.2. The very good optical performance of the Alpha Spectra
modules evidenced by these figures is very promising to reach a very low
energy threshold of 1 keVee.

PMT |  | SER mean
---
mV·ns
| SER $\sigma$
---
mV·ns
| 22.6 keV mean
---
mV·ns
| 22.6 keV $\sigma$
---
mV·ns
00 | $35.47\pm 0.35$ | $21.73\pm 0.25$ | $6122\pm 2$ | $669\pm 2$
01 | $29.42\pm 0.21$ | $18.81\pm 0.24$ | $5057\pm 2$ | $568\pm 2$
10 | $41.20\pm 0.25$ | $28.30\pm 0.21$ | $7139\pm 4$ | $809\pm 4$
11 | $44.52\pm 0.29$ | $24.36\pm 0.24$ | $7570\pm 4$ | $825\pm 3$
Table 8.4: ANAIS-25 values for SER charge distribution and ${}^{109}Cd$ 22.6 keV line Gaussian fits. PMT |  | PMT
---
phe/keV
| Detector
---
phe/keV
00 | $7.64\pm 0.08$ | $15.24\pm 0.09$
01 | $7.61\pm 0.05$
10 | $7.67\pm 0.05$ | $15.19\pm 0.07$
11 | $7.52\pm 0.05$
Table 8.5: ANAIS-25 light collection.

######

The same procedure was repeated with ANAIS-37 set-up data. The results of the
SER charge distribution and the ${}^{109}Cd$ 22.6 keV line Gaussian fits can
be seen in Table 8.6. The light collection for every PMT and detector can be
observed in Table 8.8. The newly extracted values for D0 and D1 are compatible
with those obtained for ANAIS-25. Good values for the new D2 (more than 15
phe/keV) have also been measured having again a good impact in terms of energy
threshold and resolution, crucial for the sensitivity to WIMPs annual
modulation.

PMT |  | SER mean
---
mV·ns
| SER $\sigma$
---
mV·ns
| 22.6 keV mean
---
mV·ns
| 22.6 keV $\sigma$
---
mV·ns
00 | $61.47\pm 0.36$ | $35.02\pm 0.32$ | $10257\pm 5$ | $1126\pm 4$
01 | $58.40\pm 0.71$ | $43.06\pm 0.51$ | $10425\pm 5$ | $1166\pm 4$
10 | $83.24\pm 0.55$ | $46.52\pm 0.51$ | $12820\pm 5$ | $1463\pm 4$
11 | $73.91\pm 0.74$ | $42.04\pm 0.52$ | $12740\pm 5$ | $1404\pm 4$
20 | $42.70\pm 2.10$ | $25.42\pm 1.79$ | $7928\pm 5$ | $909\pm 6$
21 | $44.57\pm 2.10$ | $26.67\pm 1.95$ | $8155\pm 6$ | $930\pm 6$
Table 8.6: ANAIS-37 values from SER charge distribution and ${}^{109}Cd$ 22.6
keV line Gaussian fits.

PMT |  | PMT
---
phe/keV
| Detector
---
phe/keV
00 | $7.38\pm 0.04$ | $15.26\pm 0.10$
01 | $7.88\pm 0.09$
10 | $6.81\pm 0.05$ | $14.44\pm 0.09$
11 | $7.62\pm 0.08$
20 | $8.21\pm 0.40$ | $16.31\pm 0.56$
21 | $8.09\pm 0.38$
Table 8.7: ANAIS-37 light collection.

PMT |  | QE
---
%
| Incident
---
phe/keV
| Incident
---
phe/keV
ZK5902 | 34.9 | 21.2 | 44.1
ZK5908 | 34.4 | 22.9
FA0018 | 33.86 | 20.1 | 41.1
FA0060 | 36.42 | 21.0
FA0034 | 35.91 | 22.9 | 43.7
FA0090 | 38.97 | 20.8
Table 8.8: ANAIS-37 Incident light.

######

In addition to the light collected, the effect of the PMT quantum efficiency
have been discounted in Table 8.8 in order to estimate the incident light to
the PMT and test the optical homogeneity of the modules. The results show good
similar values among PMTs and among modules.

######

In summary, the PMTs single electron response was characterized onsite and
compared with the previous PMTs measurements showing a full compatibility
among them. Using this extraction, an excellent light collection for the three
ANAIS detectors, of the order of $\sim$15 phe/keV, has been measured. Thanks
to this, an energy threshold for the ANAIS experiment at 1 keVee is at reach,
if populations at this level are understood and filtered. This would
significantly improve the sensitivity of the ANAIS Project in the search for
the annual modulation effect in the WIMPs signal (see Section 2.5).

###  8.7 Hardware trigger efficiency

The SER trigger efficiency seen in Section 8.5 and the computed light
collection presented in Section 8.6 can be combined to simulate the hardware
trigger efficiency behavior and can be compared later with the experimental
data.

######

The scintillation emission has been modeled as a Poissonian process of 40
photons per keV as seen in Section 3.1. The light collection was simulated by
considering the probability of every photon to reach the PMT with the measured
photoelectrons per keV. Once the phe number is obtained, the time of arrival
of photoelectrons was simulated to follow the scintillation exponential
distribution in every PMT. Next, the SER trigger efficiency was applied
marking triggered photoelectrons using the measured values for $D0$, $D1$ and
$D2$. Simulated signals of 0.9 keV can be seen in Figure 8.19 with non-
triggered photoelectrons marked as red.

######

(a)

(b)

Figure 8.19: Simulation of the photoelectron time of arrival for 0.9 keV
events. The event (a) would trigger with a 200 ns coincidence window, the
event (b) does not.

Finally, a time window was applied to every event in order to test the trigger
condition taking into account the dead time of the CAEN N843 constant fraction
discriminator. The CFD does not retrigger for at least a minimum
(configurable) dead time of 150 ns in which it does not generate another
trigger window. The simulated event shown in Figure 8.19(a) triggers whereas
the event in Figure 8.19(b) do not fulfill the trigger condition with a 200 ns
window. Different coincidence windows were tested as seen in Table 8.9 for 0.9
keV events again.

Detector |  | Trigger efficiency
---
(200 ns)
| Trigger efficiency
---
(100 ns)
$D0$ | $92.8\%$ | $83.8\%$
$D1$ | $94.4\%$ | $86.4\%$
$D2$ | $89.8\%$ | $79.4\%$
$D_{\textrm{perf}}$ | $95.8\%$ | $89.3\%$
Table 8.9: Simulated hardware trigger efficiency with different coincidence
windows for 0.9 keV events.

######

The resulting values show a very high hardware trigger efficiency for events
of such low energy. Additionally, the efficiency of $D_{\textrm{perf}}$ use a
perfect (100%) SER trigger efficiency and it shows the irreducible fraction of
non-triggering events due to the 0-photon Poissonian probability combined with
the events with non-matched photons within the coincidence window.

######

These simulated results were crosschecked with real ${}^{22}Na$ events using
the coincidence with 1274 keV events in adjacent modules (see Section 2.3).
The comparison was done considering the event rate of ${}^{22}Na$ with and
without triggering and requiring peaks in both signals in order to avoid
random coincidences with PMT noises. On one hand we consider the triggered
events in coincidence by using the Pattern Unit information. On the other
hand, we select all events acquired with a 1274 keV event in an adjacent
detector with one peak or more in both PMT signals. The results of such a
selection can be seen in Figure 8.20. The triggered events are displayed in
red and all events with peaks in blue.

(a)

(b)

(c)

Figure 8.20: Events with peaks in both signals recorded in coincidence
${}^{22}Na$ (black) and triggered (gray) in both detectors for D0 (a), D1 (b)
and D2 (c).

######

The comparison of these triggering data with the simulation is shown in Table
8.10. It is worth to note that these data are not the trigger efficiency
because it does not take into account the events that do not have
photoelectrons in one or both signals. The results are compatible given the
statistical error. It can be observed a slight discrepancy in the D1
efficiency, caused by the SER trigger overestimation in signal 11 seen in
Section 8.5.1. Anyway, the overall compatibility supports the input parameters
of the simulation: light collection and SER trigger efficiency.

Detector | Measured efficiency | Simulated efficiency
---|---|---
$D0$ | $93.5\pm 2.0\%$ | $94.1\%$
$D1$ | $92.3\pm 1.6\%$ | $95.8\%$
$D2$ | $89.4\pm 1.7\%$ | $91.1\%$
Table 8.10: Trigger efficiency in events with peaks.

######

###  8.8 Effect of noise reduction with a 100 ns coincidence window

The previous section has concluded that ANAIS will have a very good hardware
trigger efficiency, slightly reduced if the coincidence window is lowered from
200 ns to 100 ns. The noise reduction is studied in this section in order to
see the full benefits of such a reduction.

######

The temporal behavior of different kind of effects and the effect of the
reduction can be observed in Figure 8.21. It shows the distribution of the
onset time difference between the signals of a module. Additionally, it plots
those events having more than two peaks in both signals (filled with
horizontal pattern) and those with less than or equal to two peaks in some of
the signals (diagonal pattern). The selection of having two or more peaks in
both signal is the first quality cut described in Section 7.4 qualifying the
diagonal region as noise. It can be seen that the noise accounts the vast
majority of the plateau events. The effect of the coincidence window reduction
can be quantified in such a way to computing the number of the events outside
the (-100 ns,100 ns) interval and comparing with all noise events. This gives
a noise reduction of 36%, 35% and 35% for D0, D1 and D2 respectively,
accounting around a 23 % of all triggered events. This reduction can almost
reach the 50% in case of the activation due to $N_{2}$ flux cut (see Section
9.2.3) due to the random nature of such coincidences.

Figure 8.21: Onset time difference between signals 0 and 1 in one detector.
Noise (diagonal) and genuine (horizontal) distributions.

######

It should be stressed that the reduction would be a 50% if the noise was a
totally random noise. It is clear that it is not the case because of the
temporal distribution of such a population featuring a visible bump for lower
pulse onset difference. The origin of such noises can be attributed to
scintillation of some materials and other light emissions excluding dark
noises. Anyway, a good reduction of random coincidences would be beneficial in
terms of live time and disk space weighting the aforementioned low energy
trigger efficiency loss.

##  Chapter 9 Environmental and data acquisition stability

The system stability is a key feature for an annual modulation experiment as
seen in Section 2.1. Therefore, the temporal evolution of some relevant
parameters is reviewed in this chapter. The slow control system, responsible
for the monitoring and storage of environmental parameters, is presented in
Section 9.1 and its data evolution are show in Section 9.2.

######

The evolution of some critical data acquisition parameters are also studied
for ANAIS-25 and ANAIS-37 set-ups. The gain stability is checked in Section
9.3.1. The evolution of the trigger level with the time and the temperature
are shown in Section 9.3.2. Finally, the window coincidence has been measured
and a temperature dependency can be reported (see Section 9.3.3). The
influence of such a dependency in trigger efficiency at very low energy has
been computed.

###  9.1 Slow Control

The precise monitoring of environmental parameters is needed to check the
stability of the system as described in Section 2.1. This monitoring must save
data from all relevant parameters such as temperature or nitrogen flux that
can have influence in the data. The system must also give alarms in the case
of abnormal parameter values in order to restore the standard conditions as
soon as possible.

######

The list of parameters to monitor are:

###### Temperatures:

The temperature instability can cause a large number of effects in all the
data acquisition stages. Therefore, the temperature must be monitored at
different locations: inside the shielding, inside the hut and near the
electronic chain.

###### Nitrogen flux:

The shielding is continuously flushed with boil-off $N_{2}$ flux in order to
keep the experimental space as free as possible from radon and other possible
atmospheric radioactive isotopes. This flux must be monitored registering any
variation over the typical flux, including supply cut.

###### Radon concentration:

The radon activity is one of the most common sources of radioactive background
as seen in Section 1.4.2. The ambient concentration must be monitored in order
to detect its variations despite the fact that above mentioned continuous
boil-off $N_{2}$ flux reduces the internal activity at least a factor 100
[186]. Therefore, measuring the radon activity inside the shielding would
require a much more sensitive detector, far from the current sensitivity of
commercial radon detectors.

###### PMT high voltage and intensity:

The PMT electrical signal is very dependent on the HV value as described in
Section 3.5.2. This value must be monitored in order to assure the gain
stability of all PMTs. The current can also be monitored in order to detect
any other problems in the PMT supply if overcurrent or current cut happens.

###### Crate health parameters:

The VME and NIM crates house the most of the electronic modules and hence
monitoring these crates is crucial. The CAEN crates used in ANAIS allow to
monitor several temperatures (power supply and module section temperature) as
well as fan behavior and voltages and intensity of all crate power supply.

######

A system to monitor temperature and $N_{2}$ flux was designed and a LabVIEW
program was developed [190]. Its graphic user interface can be seen in Figure
9.1. It monitors and stores the values from three PT100 temperature probes
(inside the shielding, ambient temperature in Hall B and near the VME crate)
and from an AWM3300 Honeywell flow sensor for $N_{2}$.

######

The radon concentration is measured by a Genitron (now Saphymo) AlphaGuard
system that also records the pressure, humidity and temperature environmental
parameters. Additionally, some utilities to monitor the intensity and voltage
of the CAEN high voltage mainframe was later developed.

Figure 9.1: Slow control graphical interface.

###  9.2 Slow Control Data

####  9.2.1 Temperatures

The overall temperature at the LSC Hall B is stable in two degree range as it
can be seen in Figure 9.2(a). The temperature taken by the AlphaGuard system
shows the most notable variation at the beginning of the 2015 year due to LSC
climate control system testing. The rest of the year, the Hall B room
temperature ranges from 18 to 21 ºC. It can be noted a change in the
thermostat behavior at the beginning of June, the time when the refrigeration
was needed to keep the thermostat in its setpoint. Additionally, the weekly
behavior can be observed with temperature variations (see Figure 9.2(c))
associated to working activity (Monday to Friday) and more stable weekends.
The room temperature variations are below 1 ºC in a typical day. These changes
have very little impact inside the shielding due the thermal inertia, as shown
in Figure 9.2(d). The crate temperatures are also measured but their low
precision make them suitable for health checking and as crosscheck purposes
only.

(a) 2015 temperature at Hall measured by the AlphaGuard detector.

(b) Slow control probe temperatures in the ANAIS-37 set-up.

(c) Temperature in a typical week.

(d) Temperature during a typical working day.

Figure 9.2: Slow control temperatures.

######

These data have been used to test temperature stability of the trigger level
(Section 9.3.2) and the stability of the coincidence window (Section 9.3.3).

####  9.2.2 $Rn$ concentration, humidity and pressure

Other environmental parameters measured by the AlphaGuard device are $Rn$
concentration, humidity and pressure. The temporal behavior of these
parameters along the ANAIS-25 and ANAIS-37 set-ups can be seen in Figure 9.3.
The daily and monthly means are plotted over the time. It can be noted the
seasonal variations of these parameters.

(a) .

(b) .

(c) .

(d)

(e)

(f)

Figure 9.3:  Radon Density (a and b), humidity (c and d) and pressure (e and
f) daily mean values (a, c and e) and monthly mean values with modulation fit
(b, d and f).

A simple fit following a cosine function ($V_{0}+A\cdot
cos(\frac{2\pi}{T}(t-t_{M})$) has been performed using the monthly means and
letting free the four parameters. Table 9.1 shows the extracted fit
parameters.

Parameter | Radon concentration | Humidity | Pressure
---|---|---|---
$V_{0}$ |  $82.98\pm 0.06$ $(mBq/m^{3})$ |  $43.35\pm 0.01$ $(\%)$ |  $879.85\pm 0.01$ $(mbar)$
$A$ |  $7.06\pm 0.09$ $(mBq/m^{3})$ |  $10.35\pm 0.01$ $(\%)$ |  $3.90\pm 0.01$ $(mbar)$
$T$ |  $385\pm 1$ days |  $364\pm 0$ days |  $350\pm 1$ days
$t_{M}$ |  | 30th May 2012
---
($\pm$ 5 days)
| 10th August 2015
---
($\pm$ 0.1 days)
| 25th July 2014
---
($\pm$ 0.2 days)
Table 9.1: Environmental parameters fit.

The humidity data neatly modulates in a seasonal way, giving an almost exact
year period. The other fits show less clear seasonal variations, with
extracted periods near to one year. The same fit was applied to temperature
data resulting compatible with no modulation due to the controlled nature of
the parameter.

######

Despite the seasonal external Rn variation, it does not imply radon inside the
ANAIS shielding because of the flushing of boil-off $N_{2}$ system. There have
been established upper limits to the radon inside in the ANAIS background
prototypes [160, 186] and the new calibration system (see Section 8.4) should
have a good impact in the reduction of the residual radon content.

####  9.2.3 $N_{2}$ Flux

The ANAIS prototypes have seen changes in the acquisition rate associated to
flux cuts. The effect of the lack of nitrogen flux can be observed in Figure
9.4(a). A clear rate increase can be noted in both detectors correlated with
the nitrogen cut. It is worth to note that the rate decrease is not immediate
to flux restitution. Several lines appear in the spectrum over time almost
simultaneously with the flux cut as can be observed in Figure 9.4(b).
Therefore, the behavior of the whole rise and decay rate cannot be attributed
directly to these lines, to ${}^{222}Rn$ or daughters, because the rate
returns to the stable level at least three days after the flux restitution.
The most important fraction of the rate increase is due to photoelectron
random coincidence as it can be seen in the next two figures.

(a) Detector 0 (red), 1 (green) DAQ rate during $N_{2}$ flux cut (black).

(b) High energy over time showing appearing lines with flux cut.

(c) Event rate with more than three peaks in both signals.

(d) Pulse onset difference in a normal period (blue) and with activation for
$N_{2}$ flux cut (red).

Figure 9.4: $N_{2}$ cut activation effect.

######

Figure 9.4(c) shows the rate of events having three or more peaks, the first
quality cut for the low energy event selection (see Section 7.4). It shows an
increase of those type of events that does not explain the full rate behavior,
showing a fast decay after the flux restitution as opposed to the total rate
(note the different vertical scale and shapes of Figures 9.4(a) and 9.4(c)).
Additionally, it can be seen the different temporal behavior of this kind of
events. The onset pulse difference between the two PMT signals can be observed
in Figure 9.4(d) in normal operation (blue) and with $N_{2}$ flux cut (red).
The almost random temporal nature of the later suggests a de-activation
mechanism of an activation caused by the radon. The origin of the
photoelectron increase during nitrogen flux cut is under study but activation
of the PMTs has been discarded because the blank module (a module with only
PMTs, see Section 2.4) was not able to reproduce the effect.

####  9.2.4 PMT power supply monitoring

The PMT power supply monitoring can detect malfunction and instabilities. The
typical high voltage and current values for PMTs can be seen in Figure 9.5.
The current and voltage fluctuations in the normal operation are very small
and they are in the limits of the least significant bit of the ADC. For this
reason, the figures show a step behavior.

Figure 9.5: PMT Power supply monitoring: HV (left) and current (right).

###  9.3 Data taking stability

The evolution of critical acquisition parameters is studied in this section:
the gain, the trigger level and the coincidence window. Variations in these
parameters could mimic an annual modulation. For this reason, their temporal
behavior must be monitored. Some other important parameters have been studied
in the previous chapter, such as the single electron response and the light
collection, which automatic stability control is ongoing.

####  9.3.1 Gain stability

Monitoring the calibration of the energy estimators is a key feature needed
for a long running experiment and even more critical in an annual modulation
search. Therefore, the evolution of the calibration and background lines have
been studied. Figure 9.6 shows some calibration and background peaks over
time. The evolution of the gain in the ANAIS-25 set-up is shown in Figure
9.6(a) (D0) and Figure 9.6(b) (D1). These figures show the percentage over
their mean values of a series of parameters: two ${}^{109}Cd$ lines (22.6 keV
and 88.0 keV) and the ${}^{210}Pb$ background peak (sum of a 46.5 keV gamma
plus a $\beta^{-}$ accounting a mean value around 50 keV).

(a)

(b)

(c)

(d)

(e)

Figure 9.6: Evolution of calibration and background lines (see text).

######

The calibration values have been calculated with QDC values and the background
lines with the area energy estimator. The plotted values are extracted from a
fixed window in the spectra around the maxima, avoiding in such a way the bias
of a Gaussian fit with non-Gaussian peaks (some peaks are a weighted sum of
several contributions). It is fair to mention the use of only one signal to
the D0 calibration because of a temporal malfunction of the qdc00 channel.
Anyway, a very good stability can be reported, with a slight drift of the
order of the 2% of the mean value in both detectors and in both estimators,
QDC and area. The ANAIS-25 routine was changed as seen in Section 8.4
digitizing also the calibration signals providing redundancy in energy
estimators.

######

The same procedure was applied for ANAIS-37 data with some little variations:
all values are calculated with the area energy estimator and the background in
D2 was calculated with the ${}^{125}I$ cosmogenic line, because of the reduced
${}^{210}Pb$ contamination of this module (see Section 2.4). The ${}^{125}I$
decays with 100% probability by electron capture to an excited state of the
$Te$ daughter nucleus and the considered peak accounts this excited level
(35.5 keV) and the binding energy of the K-shell of $Te$ (31.8 keV) resulting
an energy deposition of 67.3 keV. The evolution of the calibration and
background peaks can be seen in Figures 9.6(c) (D0), 9.6(d) (D1) and 9.6(e)
(D2) showing again a good stability with variations below the 2% except a
slightly higher variation in D2 due to the PMT cover replacement in June. This
set-up modification also had impact in D1 gain to a lesser extent.

######

This study has shown a good stability of the gain and a good correspondence in
the temporal behavior among calibration and background peaks. This will allow
to control the gain evolution in the long run.

####  9.3.2 Trigger level stability

The trigger stability is mandatory in a low threshold modulation experiment as
well as a controlled gain. The single peak triggered population is first
studied and compared with the onsite single electron response in order to
verify the trigger level. Next, a study of the evolution of this trigger over
the set-up taking data is presented.

######

Figure 9.7: Single electron amplitude distribution (black) compared with the
amplitude distribution of triggered events (gray).

Figure 9.7 illustrates the effect of the trigger level in the amplitude
discrimination. All PMT discriminators were configured with the same value
giving a very similar discrimination curve. The stability of the trigger level
has been studied by locating the transition where the triggered population
clearly reduces. The criterion to locate the transition was chosen at one half
of the maximum of the (1 mV, 5 mV) interval, the trigger transition zone seen
in Figure 9.7.

######

The stability of the trigger transition can be observed in Figure 9.8 using
the previously described criterion. The results show a fairly constant
transition point along the ANAIS-25 (Figure 9.8(a)) and ANAIS-37 (Figure
9.8(b)) set-ups moving only 0.25 mV, the 12bit MATACQ digitizer vertical
resolution.

######

There is a clear exception at the beginning of the ANAIS-37 set-up data taking
corresponding to the detector D2. The D2 trigger was configured at higher
level in the two first runs because of the exposure to light of their two PMTs
vastly increasing the trigger rate at photoelectron level. Next, the trigger
levels were set at the same values than the other detectors.

(a)

(b)

Figure 9.8: Trigger level evolution along the time for ANAIS-25 (a) and
ANAIS-37 (see text for details).

##### Trigger level and temperature

The ANAIS temperature at the LSC is fairly constant in both inside the
shielding and near the crates (see Section 9.2.1). However, some changes were
made in the LSC air conditioning system and a short interval of ambient
temperature instability happened. This interval was studied in order to
describe its impact. It is worth to note that the effect of the temperature in
the trigger at photoelectron level was studied in a somewhat reduced set-up in
the Laboratory of the University of Zaragoza (see Section 4.5.4). A similar
(subtle) DC drift in the pulse digitization can be seen during the temperature
instability interval as it can be observed in Figure 9.9(a). The study of the
amplitude distributions of the triggered events gives no significant
variations in the trigger level, far below 1% in both mean and standard
deviation (see Figure 9.9(b)). Additionally, no significant variation in the
trigger level was measured.

(a) DC level (black) and temperatures (red shielding internal, blue VME crate)
vs. time.

(b) Triggered amplitude at high ($\sim$20 ºC, blue) and low temperature
($\sim$17 ºC, red).

Figure 9.9: DC level and triggered height with temperature variations.

######

The very little measured trigger level variations over time or with the
temperature have a very little impact in the SER trigger efficiency having in
mind the SER amplitude distribution seen in Figure 9.7. This impact is even
lower for final conditions with higher gains (such as D0 and D1 in ANAIS-37)
and taking into account the effect of the SER efficiency in the total hardware
trigger efficiency with the excellent light collection of the ANAIS modules
(see Section 8.7).

##### Data acquisition rate stability

The previously seen trigger level stability should mean a stable trigger rate
in the long run since this rate is dominated by random photoelectron
coincidences. As a general comment, the rate is unstable at the beginning of
the set-ups due to the PMT light exposure and the radon entrance to the
shielding, but once both effects decay, the other source of trigger
instability is the $N_{2}$ flux cut as seen in Section 9.2.3. The ANAIS-25
set-up (see Figure 9.10) exhibits all these features in its rate evolution
with four $N_{2}$ flux cuts (the second and longest cut was deliberate in
order to study the rate effect) and one anti-radon box aperture. The muon flux
has influence in the data acquisition rate as seen in Section 5.5.3. This
effect is too fast to be observed in Figure 9.10 but it can be seen in Figure
5.16. A seasonal modulation could have impact in this rate depending on its
amplitude.

Figure 9.10: Trigger rate evolution for ANAIS-25.

######

Despite the effect of the initial PMT activation and the Nitrogen flux cut, a
good trigger stability has been achieved and an almost constant trigger rate
is expected for the full experiment.

####  9.3.3 Coincidence window stability

Variations in the coincidence windows can affect the trigger efficiency at low
energy as it can be seen in Section 8.7, the most harmful energy region for a
low threshold experiment. For this reason, a measure of this coincidence
window along the data taking would monitor the influence of this systematic
effect.

######

The coincidence window can be measured from the ANAIS data in an indirect way
by calculating the pulse onset difference between the two signals of a
detector in triggering events. This measurement is possible thanks to the
separate digitization of the two PMT waveforms. The distribution of the onset
difference for one detector can be seen in Figure 9.11.

Figure 9.11: Onset time difference between signals 0 and 1 in one detector and
coincidence windows extraction from discriminator 0 (W0) and discriminator 1
(W1).

######

The CFD trigger window (W0 and W1 in Figure 9.11) can be deduced offline with
the ANAIS data in such a way. The coincidence window evolution was measured
considering the end of the time difference distribution where the counts are a
half of the plateau. The data for all runs of ANAIS-25 can be seen in Figure
9.12(a) showing some non-constant features with similar behavior in all
windows. The evolution of the coincidence windows correlates with the
temperature measured at the rack of the electronics as it can be seen in
Figure 9.12(a). The correlation exhibits variations up to 6 ns in the
temperature range 17 ºC - 23 ºC. The same procedure was applied for all
background runs of ANAIS-37 set-up and the results can be seen in Figure
9.12(b). These results along this set-up were quite stable with differences
not exceeding 3 ns with a more stable environmental temperature.

(a)

(b)

Figure 9.12: Coincident windows and electronics temperature for ANAIS-25 (a)
and ANAIS-37 (b) set-ups.

######

The impact of such coincidence window variations was studied by simulating
again the trigger efficiency as described in Section 8.7 with offsets of 6ns.
Table 9.2 shows the results for the simulated trigger efficiency for 0.9 keV
events considering the D0 light collection and its SER trigger efficiency for
each PMT signal showing an effect below the 1% in trigger efficiency for 200
ns but in the order of the expected amplitude of the annual modulation for 100
ns.

######

Coincidence window time (ns) | 200 | 206 | 100 | 106
---|---|---|---|---
Trigger efficiency (%) | 92.8 | 93.0 | 83.8 | 84.7
Table 9.2: Simulated trigger efficiency for different coincidence windows at
0.9 keV.

It must be noted that the efficiency variations are highly dependent on the
light collection and the coincidence window width and could mimic an annual
modulation effect at low energies. Therefore, the monitoring of the
coincidence windows in both direct and indirect ways is mandatory to guarantee
the stability in the trigger efficiency. It is also worth to note that the
trigger efficiency increases very quickly with the energy given the very high
light collection of the ANAIS modules. In addition, a temperature
stabilization system for the ANAIS front-end is under study.

## Summary and conclusions

There is an overwhelming number of hints pointing to the existence of dark
matter. These evidences, ranging from galactic to cosmological scales,
restrict the nature of this dark matter. It has to be very weakly interacting
with other matter and it has to be non-relativistic (_cold dark matter_).
These facts exclude all known particles from the Standard Model of Particle
Physics.

######

The aforementioned properties suggest methods to detect dark matter in both
indirect and direct ways as well as the possibility of its creation in
accelerators. The indirect detection of dark matter would imply to detect the
byproducts of their co-annihilation or decay. The direct detection would be
through the measurement of the direct interaction with detectors. A
hypothetical WIMP (Weakly Interacting Massive Particle) is expected to
interact producing a recoil with the subatomic particles composing the
detector that can be measured with different techniques.

######

Several detectors from the nineties have been devoted to the dark matter
direct detection with different targets and techniques. The detector targets
have ranged from cryogenic semi-conductors to noble gases TPCs. DAMA/LIBRA, an
experiment carried out in the Laboratori Nazionali del Gran Sasso (LNGS), has
claimed to have detected a signal of annual modulation in _single-hit_ events
in the 2-6 keVee energy window. The annual modulation is a predicted effect
considering a WIMP halo surrounding the Milky Way and the different relative
velocity of the Earth with respect to this halo along the year. The DAMA/LIBRA
result has not been confirmed by other experiments with nominal better
sensitivity, although the comparison among different targets is controversial.

######

The ANAIS project has been a long time effort devoted to carry out an
experiment to detect dark matter annual modulation with very low background
NaI(Tl) detectors. This experiment could confirm the DAMA/LIBRA positive
signal with the same target and technique. Such an experiment has a very
stringent requirements in order to have enough sensitivity to an annual
modulation at very low energy. These requirements are: very low energy
threshold, a background as low as possible in the region of interest and a
high enough target mass. In addition to these fundamental requirements, very
good stability and control of environmental parameters have to be accomplished
in order to avoid systematic effects to mimic the effect of the annual
modulation. An experiment of more than one hundred kilograms of ultrapure
NaI(Tl) has been conceived and it is being commissioned at the Canfranc
Underground Laboratory (LSC).

######

This work was devoted to the design, implementation and characterization of a
data acquisition system suitable for the ANAIS experiment, having in mind the
previously mentioned requirements. It has described the Photomultiplier Tubes
(PMTs) used by the ANAIS modules and the algorithms and protocol developed in
order to pass quality tests to all units. It has presented the design and
implementation of the electronic front-end for the ANAIS modules and muon
tagging system along with the data acquisition software. The analysis software
was adapted from the software of the previous prototypes to allow easy scale-
up to the full experiment. Finally, the test of the optical performance of the
Alpha Spectra modules and the tests of data acquisition and monitoring of
environmental parameters were performed.

######

The Hamamatsu R12669SEL2 PMTs, the selected model for the ANAIS full
experiment, were characterized. First, the features needed for ANAIS were
reviewed: background as low as possible, high quantum efficiency and few dark
counts. Theory and methods to characterize the single electron response (SER)
have been reviewed. Additionally, algorithms to detect the incident light of a
pulsed ultra violet LED and deconvolute the SER were developed, tested and
used to characterize the gain and the relative quantum efficiency. The
limitations of this method given the instabilities of the electronic circuit
were pointed out and further improvements have been suggested. The dependency
of the dark counts with the temperature and the de-excitation after ambient
light exposure of the PMTs were measured. A suitable protocol to test all PMT
units has been proposed and the results of gain (and gain/voltage curve),
peak-to-valley, dark counts and relative quantum efficiency have been
presented. All units tested have fulfilled the requirements in these
parameters and in low background measurements performed in a low background
HPGe detector at the LSC. All units are stored at the LSC and the quality
control of the remaining ones is ongoing for their use in the full experiment.

######

The electronic front-end requirements have been reviewed following their
related experimental goals. The conceptual design and the VME and NIM modules
to be used were presented. A low noise linear preamplifier was also developed.
The critical elements of the electronic chain such as the preamplifier, the
constant fraction discriminator (CFD) and the MATACQ digitizer were thoroughly
tested with special care in crucial aspects such as the quality of the
digitized signal and the low trigger level. In addition, the baseline
characterization and improvements were reviewed, showing specific problems to
the data acquisition coming from the scale-up process. In particular, several
crosstalk effects among signals coming across the HV power supply and the
preamplifier were detected, addressed and solved.

######

The design and implementation of the muon detection system were also reviewed
from its conception to the first preliminary data analysis. The rate of the
detector noise was important at underground conditions and a specific offline
pulse shape analysis was designed and tested. Total and coincidence counts
were computed giving hints of the asymmetry of the muon flux due to the
mountain profile. The muon effect in the NaI crystal data was studied
observing both long afterglows with direct muon interaction and low energy
events generated by the muon indirect interaction. This system will allow to
tag the muon related events in order to study its influence and discard them
from an annual modulation analysis.

######

A data acquisition program was designed and implemented for the ANAIS
experiment. It was conceived with scalability, stability and performance
requirements as guidelines. It was implemented in C++ in Linux and the output
data is written in ROOT format making easier the debug and the analysis
phases. The more important features of the program are the ease to add new
detector and signals without having to rewrite/recompile neither acquisition
nor analysis programs, the data acquisition configuration storage together
with the acquired data, the asynchronous data storing allowing to absorb the
disk latency and the runtime conditions evaluated to avoid reading and/or
storing some values or events with significant disk and dead time savings. It
is worth to note that both NaI and plastic scintillator data acquisition
systems use the same program showing its versatility. Additionally, a monitor
system was implemented to check the health of the acquisition system by
testing the acquisition rate and sending daily reports. The DAQ software is
currently running and ready for the full experiment.

######

The data analysis software has also been adapted for the growing number of
signals and also tested with three detectors. All algorithms developed along
the operation of the previous modules were reviewed and modified for the new
data format and prepared for the final experiment. A new configuration system
was developed in order to easily set the input parameters of the analysis and
store this parametrization with the analyzed data. In addition to the data
acquisition and the data analysis software, a system to synchronize,
automatically analyze and pass quality test to the data and notify warnings in
case of malfunction or unusual behavior was also implemented in order to
quickly react to incidences and keep the duty cycle and the consequent
exposure as high as possible.

######

The whole data acquisition system was characterized in order to know its
performance in very relevant parameters for the ANAIS experiment such as dead
time or hardware trigger efficiency. The characterization of the real time
clock used in ANAIS was performed with a crosscheck with the PC system time
synchronized via the Network Time Protocol (NTP) over Internet. This
crosscheck showed a slight drift in the real time clock, but good stability
and accuracy for the ANAIS purposes can be reported. Additionally, the NTP
system was configured with Spanish servers with a much better time
synchronization for the PC clock. The dead time was studied for several
configurations. The acquisition process was configured for optimal operation
with asynchronous data storing due to its lower impact in dead time.
Additionally, the IRQ trigger strategy was preferred over polling due to the
noise produced by the latter. The additional latency of the IRQ waiting was
absorbed by reordering the acquisition and using the digitizer (MATACQ board)
dead time to wait and download the rest of the modules. The dead time was also
measured in the ANAIS-25 and ANAIS-37 prototypes noticing the increase of the
dead time with the added digitizers. A strategy to keep the dead time
controlled was designed by configuring the acquisition in order to store the
triggered waveforms only, with a significant saving in both dead time and
storage terms, giving as a result an almost fixed dead time of 2.4 ms with a
foreseen improvement due to hardware improvements (the use of CONET2 VME/PCI
bridge and 14bit-MATACQs). The accumulation of dead time was computed and
considering the down time (which includes routine calibration and maintenance
time), the live time was calculated for the ANAIS-25 and ANAIS-37 prototypes
obtaining a notable duty cycle over the 95% of the total time. These numbers
should be achievable for the ANAIS full experiment given the foreseen dead
time of the order of the 2% with similar calibration and maintenance
protocols.

######

The hardware trigger efficiency at low energy was also characterized. The
single electron response (SER) was extracted onsite and the light collection
was measured by using calibration lines, giving as a result an excellent light
collection of the order of 14 photoelectron per keV. The trigger efficiency of
the SER was computed for all PMTs. Using these results and the 200 ns
coincidence window, a simulation was performed to obtain the trigger
efficiency at very low energy. The result of the simulation was crosschecked
with real 0.9 keV from ${}^{22}Na$ contamination in the bulk showing good
agreement, giving an efficiency above 90% at this energy. Additionally, a
study of the coincidence window reduction to 100 ns and its impact in terms of
noise reduction (35% less noise) and trigger efficiency (of the order of 10%
lower) was performed.

######

The environmental and data acquisition stability has also been studied for the
ANAIS-25 and ANAIS-37 prototypes. The slow control system was presented and
the data for almost four years in the LSC Hall B were analyzed. Some
parameters exhibit seasonal variations and they fit to a sinusoidal function
with a near yearly period. Additionally, an increase in the rate correlated
with the lack of Nitrogen flux was noted, pointing to an excitation of the NaI
crystal as responsible of the emission of light with very slow constants
triggered as random coincident photons. All these effects will be carefully
monitored and mitigated where possible during the full experiment.

######

In addition to the environmental monitoring, the stability of data acquisition
parameters that could mimic an annual modulation was studied along the
ANAIS-25 and ANAIS-37 set-ups. First, the gain was checked along the time
showing a good stability of the order of the 2% in both calibration and
background peaks. Next, the stability of the trigger level with time and
temperature was studied finding no significant shift in it. The coincidence
window was measured taking advantage of the separate signal digitization of
both PMTs in every module. A dependency with the temperature was found in
ANAIS-25 set-up (6 ns with a temperature shift of 6 ºC) that would be
significant in the trigger efficiency at low energy. The ANAIS-37 set-up did
exhibit a very little shift due to a more stable temperature conditions. A
simulation confirms that 6 ns variations would be dangerous for 100 ns
coincidence windows at very low energy, where annual modulation is expected.
For this reason, the coincidence window will be monitored in detail and a
temperature stabilization system for the ANAIS front-end is under study.

######

As a result of this work, a data acquisition system for the ANAIS experiment
has been constructed and characterized. The system has been tested up to three
detectors showing its versatility, scalability, reliability and good
performance and makes it ready for the installation of six more detectors
during the next months. Additionally, the stability of the environmental and
data acquisition parameters has been studied showing good behavior. The
protocols to monitor the full experiment have been developed. These good
properties together with the very good performance of the modules in terms low
background and light collection imply a very good potential to verify a
possible dark matter annual modulation signal.

## Resumen y conclusiones

Existen numerosos indicios, a muy distintas escalas, desde la galáctica a la
cosmológica, de la existencia de una gran cantidad de materia oscura en el
Universo. Dichas evidencias además acotan las características de las
partículas que la componen: deben interaccionar muy débilmente con el resto de
la materia o de lo contrario el resultado de esas interacciones podría ser
observado. Se trata de materia oscura no relativista (_materia oscura fría_)
que no encuentra candidatos a su composición en el modelo estándar de la
física de partículas. Por lo tanto, la detección de materia oscura tendría
implicaciones a todas las escalas y es un tema muy candente en la física tanto
experimental como teórica.

######

Las propiedades de la materia oscura sugieren métodos para su detección tanto
directa como indirectamente así como la posibilidad de su creación en
aceleradores. La detección indirecta de materia oscura se realizaría mediante
la observación de los productos de su aniquilación o desintegración. La
detección directa se realizaría mediante la medida de su interacción con
detectores. Un hipotético WIMP (_weakly interacting massive particle_ ,
partícula masiva que interacciona débilmente) interaccionaría produciendo un
retroceso de alguna partícula subatómica del detector medible mediante
diferentes técnicas.

######

Estos efectos se han tratado de observar desde los noventa en experimentos
cada vez más sensibles con distintas técnicas y materiales blanco, desde
detectores criogénicos basados en semiconductores hasta grandes TPCs de gases
nobles. DAMA/LIBRA, un experimento llevado a cabo en el Laboratorio Nacional
de Gran Sasso (LNGS), ha obtenido un resultado positivo de modulación anual en
el rango energético de 2-6 keV en eventos en anticoincidencia atribuido a
materia oscura. La modulación anual es un efecto predicho dado el cambio
estacional de la velocidad relativa de la tierra respecto al flujo de materia
oscura. El resultado de DAMA/LIBRA no ha sido confirmado por ningún otro
experimento, algunos de ellos con mejor sensibilidad nominal, aunque la
comparación entre distintos materiales blanco es objeto de controversia.

######

El proyecto ANAIS, iniciado en los noventa, se ha dedicado a desarrollar un
experimento de materia oscura con ioduro de sodio en el Laboratorio
Subterráneo de Canfranc (LSC) y podría confirmar el resultado positivo de
DAMA/LIBRA usando la misma técnica y el mismo material blanco. Un experimento
de estas características posee unos requisitos muy estrictos para tener
sensibilidad suficiente a la modulación anual: tener el menor fondo
radioactivo posible en la zona de interés, poseer un umbral energético muy
bajo y tener suficiente masa. Además de estos requisitos fundamentales es
necesaria una muy buena estabilidad en la adquisición de datos y un buen
control de los parámetros ambientales para evitar que posibles efectos
sistemáticos puedan ser tomados por modulación anual de materia oscura. El
experimento ANAIS constará de más de cien kilogramos de ioduro de sodio
ultrapuro en proceso de fabricación y que serán instalados en los próximos
meses en el LSC.

######

Esta tesis ha estado enfocada al diseño, implementación y caracterización de
un sistema de adquisición de datos adecuado para el experimento ANAIS teniendo
en cuenta los requisitos antes mencionados. Se han estudiado los
fotomultiplicadores (PMTs) elegidos para la detección de la luz del centelleo
del ioduro de sodio. Además se han desarrollado los algoritmos y protocolos
necesarios para hacer el control de calidad de todas la unidades. Se ha
descrito el diseño de la electrónica necesaria para la adquisición de datos de
los módulos de ioduro y los centelladores plásticos usados como detector de
muones junto con el software de adquisición y de análisis de datos. También se
ha medido la recogida de luz de tres módulos de Alpha Spectra, los dos
primeros de que constó el prototipo ANAIS-25 y un tercero que unido a los dos
anteriores formaron ANAIS-37. Por último, se ha hecho un estudio de la
estabilidad de los parámetros ambientales y de parámetros cruciales para la
adquisición de datos.

######

Los fotomultiplicadores (PMTs) Hamamatsu R12669SEL2 fueron los elegidos para
el experimento ANAIS dado que cumplen los requisitos de tener un bajo fondo
radioactivo, una alta eficiencia cuántica y un número bajo de cuentas oscuras.
Se han presentado la teoría y los métodos utilizados para la caracterización
de la respuesta a un solo fotoelectrón (_single electron response_ , SER).
Además, se han implementado y testado algoritmos para detectar la luz emitida
por un LED ultravioleta excitado por pulsos y deconvolucionar la SER de los
PMTs. Posteriormente se usan estos algoritmos para determinar la ganancia y la
eficiencia cuántica relativa. Se pudieron observar las limitaciones de este
método debido a inestabilidades en la electrónica de excitación y se proponen
mejoras para los test futuros. Se han medido las cuentas oscuras y su
dependencia con la temperatura y con el tiempo posterior a la exposición a la
luz ambiente del fotomultiplicador. Se ha propuesto un protocolo a seguir para
la caracterización de los fotomultiplicadores y se han presentado los datos de
las medidas de las primeras unidades. Estas medidas han consistido en la curva
de ganancia frente a voltaje, la relación pico-valle, las cuentas oscuras y la
medida de eficiencia cuántica relativa. Todos los PMTs han pasado el control
de calidad en dichos parámetros y en cuanto a radiopureza medida en un
detector de germanio ultrapuro de bajo fondo en el LSC. Los
fotomultiplicadores están actualmente almacenados en el Laboratorio
Subterráneo de Canfranc y el control de calidad de las unidades restantes que
van a ser usadas en el experimento completo está en proceso.

######

Se han revisado los requerimientos que debe cumplir el sistema electrónico
para ANAIS teniendo en cuenta los requisitos del experimento. Se han descrito
el diseño de dicho sistema, basado en módulos NIM y VME, y el preamplificador
de bajo ruido diseñado específicamente para ANAIS. Se han realizado tests
exhaustivos de las partes más críticas de este sistema como el
preamplificador, el discriminador de fracción constante (CFD) y la
digitalizadora MATACQ teniendo especial cuidado en los aspectos más sensibles
para el experimento como la calidad de la señal digitalizada y el bajo umbral
de disparo. Además la linea base del experimento se ha caracterizado y
mejorado. Se han detectado y resuelto problemas de ruido e interferencias
entre señales, algunos de ellos provenientes del incremento en el número de
módulos, observadas a distintos niveles como la fuente de alto voltaje o el
preamplificador.

######

Se ha diseñado e implementado un sistema de detección de muones y se han
presentado los primeros datos preliminares. Se descubrió un importante ritmo
de sucesos de ruido en el LSC que hacía imposible el disparo en umbral y se
diseñó un sistema de análisis de forma de pulso para descartar ruido en tiempo
de análisis. Se han presentado los datos de esta discriminación así como el
criterio para ampliar el montaje a los dieciséis plásticos centelleadores de
que constará el experimento completo. Se ha medido el ritmo total de muones
así como en coincidencia de varias caras, observando una asimetría que sugiere
un flujo no simétrico en el ángulo azimutal en consonancia con el perfil
asimétrico de la montaña. Se ha observado el efecto de los muones en los
cristales de ioduro que podrían afectar al análisis de detección de materia
oscura. En particular, se ha podido correlacionar con eventos en los plásticos
dos tipos de efectos: eventos a baja energía y episodios de activación debido
a interacciones directas de los muones. Este sistema permitirá etiquetar estos
eventos para estudiar su influencia y descartarlos de un análisis de materia
oscura.

######

Se diseño e implementó un programa de adquisición de datos para el experimento
ANAIS. Fue concebido con los requisitos de escalabilidad, estabilidad y
rendimiento como guía. Fue implementado en C++ sobre Linux. Se eligió ROOT
como formato de salida para facilitar la depuración y el análisis de datos. El
programa permite añadir con facilidad nuevas señales y detectores a la
adquisición sin recompilar ni el software de adquisición ni el software de
análisis guardando la configuración de hardware junto a los datos adquiridos.
Además usa un almacenamiento asíncrono que permite absorber las latencias del
sistema de ficheros reduciendo el tiempo muerto. Por último, se pueden
configurar condiciones evaluadas en tiempo de ejecución que permiten leer y/o
guardar solamente los datos relevantes en función de otros valores adquiridos
con gran ahorro de espacio en disco y reducción en el tiempo muerto. Hay que
hacer notar que el software de adquisición de datos de los ioduros y de los
centelleadores plásticos es el mismo ejecutado en dos ordenadores distintos
con dos configuraciones distintas demostrando así la versatilidad para la que
fue diseñado. Además, se implementó un sistema de monitorización que comprueba
el ritmo de adquisición y envía informes diarios para su inspección. Toda esta
infraestructura se encuentra actualmente en ejecución y está preparada para el
experimento completo.

######

El software de análisis de datos también ha sido preparado para el incremento
del número de detectores y testeado con tres detectores. Todos los algoritmos
desarrollados a lo largo de la operación de los prototipos anteriores fueron
revisados y adaptados a los nuevos formatos de datos y preparados para un gran
incremento en el número de señales. Se desarrolló un sistema de configuración
del análisis para poder parametrizarlo fácilmente y guardar dicha
parametrización junto a los datos analizados. Además del software de
adquisición de datos y de análisis, se diseñó un sistema para sincronizar y
analizar automáticamente los datos y ejecutar una serie de test de calidad
permitiendo enviar avisos de funcionamiento anómalo para poder reaccionar
rápidamente y maximizar el tiempo de exposición.

######

El sistema de adquisición fue caracterizado en su conjunto para conocer
parámetros relevantes como el tiempo muerto o la eficiencia de trigger
hardware. La caracterización del reloj de tiempo real usado en ANAIS fue
realizada mediante su verificación con el reloj del PC sincronizado por
internet vía NTP (Network Time Protocol). Esta verificación mostró una ligera
deriva en el reloj de tiempo real, demostrando no obstante estabilidad y
precisión suficientes para los propósitos de ANAIS. Además el sistema NTP se
configuró con servidores españoles consiguiendo una sincronización mejor del
reloj del PC. Se estudió el comportamiento del tiempo muerto para distintas
configuraciones y como resultado el sistema de adquisición se configuró con
almacenamiento de datos asíncrono. También se configuró para realizar la
adquisición esperando a una IRQ en lugar de haciendo una consulta continua
debido a que dicha consulta, a pesar de reducir el tiempo muerto, genera ruido
en la digitalización. El mayor tiempo muerto en la espera de la interrupción
es absorbido dentro del tiempo muerto de conversión de la digitalizadora
(MATACQ) durante el cual se espera dicha interrupción permitiendo además
descargar los datos de los otros módulos si la lectura de datos se reordena
convenientemente. Se midió el tiempo muerto de los prototipos ANAIS-25 y
ANAIS-37 constatando el aumento de tiempo muerto con el aumento de tarjetas
digitalizadoras. Se diseñó una estrategia para mantenerlo controlado basada en
la descarga y almacenamiento condicionado al disparo del correspondiente
detector. Como resultado el tiempo muerto medido para esta configuración es de
2.4 ms por evento. Este valor se verá reducido con la introducción de algunas
mejoras en el hardware (tarjeta de comunicaciones con protocolo CONET2 y
digitalizadora MATACQ de 14 bits). Teniendo en cuenta también el tiempo de
parada de adquisición (que incluye tiempos de calibración, mantenimiento,
etc.) a lo largo de los distintos montajes se obtuvo una notable tasa de
tiempo vivo a lo largo de ANAIS-25 y ANAIS-37 superior en ambos casos al 95%.
Esta tasa debería ser alcanzable en el experimento final dado que el tiempo
muerto previsto es del orden del 2% y se usarán protocolos similares de
calibración y mantenimiento.

######

La eficiencia del trigger hardware también fue caracterizada. Se extrajo la
respuesta a un sólo electrón (_single electron response_ , SER) con los datos
de ANAIS-25 y ANAIS-37. Con los datos de la SER y con las señales de fuentes
de calibración se calculó la recogida de luz con un resultado excelente del
orden de 14 fotoelectrones por keV. Una vez caracterizada, se computó la
eficiencia de trigger de la SER para todos los fotomultiplicadores. Usando
estos datos y una ventana de coincidencia de 200 ns, se realizó una simulación
para obtener la eficiencia de trigger a baja energía. Posteriormente se
compararon estos datos con eventos reales de 0.9 keV de ${}^{22}Na$ y se
encontró una buena concordancia entre ambos resultados. Se determinó que la
eficiencia del trigger hardware con eventos de 0.9 keV es superior al 90%.
Además se estudió el impacto de la reducción de la ventana de coincidencia a
100 ns en términos de reducción eventos de ruido (un 35% menos) y de reducción
en la eficiencia de trigger hardware (del orden de un 10% menos).

######

También han sido estudiados los parámetros ambientales y la estabilidad de
parámetros de adquisición como la ganancia, el nivel de trigger o la ventana
de coincidencia para los montajes ANAIS-25 y ANAIS-37. El sistema de
adquisición de parámetros ambientales y de monitorización (también llamado
slow control) toma datos de temperaturas, concentración de radon, flujo de
nitrógeno para ventilar el blindaje, presión, humedad, temperatura y voltaje y
corriente de los fotomultiplicadores. Los datos de casi cuatro años en el Hall
B del Laboratorio Subterráneo de Canfranc han sido analizados y se ha
observado una periodicidad estacional en datos de presión, humedad y
concentración de radon. Se realizó un ajuste a un seno más una constante y se
obtuvo como resultado una modulación con un periodo muy cercano al año. Además
se investigó el efecto del aumento en el ritmo de disparo de los prototipos de
ANAIS con la falta de flujo de nitrógeno. Este efecto apunta a una excitación
en el cristal que emite con posterioridad (con constantes muy largas) fotones
que son detectados por la coincidencia de los fotomultiplicadores como
coincidencias fortuitas. Todos estos efectos se monitorizarán y mitigarán en
lo posible en el experimento completo.

######

Además de los parámetros ambientales, se ha estudiado la estabilidad de
parámetros de adquisición. Una inestabilidad en estos parámetros podría
inducir una modulación anual a baja energía y atribuirse erróneamente a
materia oscura. La ganancia ha permanecido bastante estable a lo largo de cada
uno de los montajes con variaciones del orden del 2% compatibles en los picos
de fondo y de calibración. Dicha calibración permite corregir estas pequeñas
variaciones en la ganancia. El análisis del nivel de trigger no ha apreciado
variaciones substanciales (por debajo de 0.25 mV) ni con el tiempo ni con la
temperatura. La ventana de coincidencia se midió en los datos de fondo
aprovechando la digitalización de las dos vías de cada módulo. Se encontró una
dependencia con la temperatura en ANAIS-25 (6 ns con 6 ºC de diferencia) que
podría tener un impacto significativo en la eficiencia del trigger hardware.
Por el contrario en ANAIS-37 el efecto fue muy inferior debido a una mayor
estabilidad de la temperatura. Una simulación confirmó que el efecto de una
variación de 6 ns podría ser peligrosa usando una ventana de coincidencia de
100 ns en eventos de muy baja energía, donde se espera la modulación anual.
Por este motivo una monitorización tanto directa como indirecta de la ventana
de coincidencia a lo largo de año es obligatoria y está estudiándose la
posibilidad de un sistema de control de temperatura para la electrónica de
ANAIS.

######

Como resultado del trabajo recogido en esta tesis se ha construido y
caracterizado un sistema de adquisición de datos para ANAIS. Además este
sistema ha sido probado con tres detectores, demostrando su versatilidad,
escalabilidad, fiabilidad y buen rendimiento que lo hacen estar preparado para
la instalación de seis detectores más en los próximos meses. Además se ha
estudiado e implementado la monitorización de los parámetros ambientales, que
han mostrado un buen comportamiento y se han desarrollado los protocolos
necesarios para la monitorización del experimento completo. Estas
prestaciones, junto con la buena calidad de los detectores en términos de
radiopureza y recolección de luz implican un buen potencial para verificar una
posible señal de modulación anual de materia oscura.

## Agradecimientos

En primer lugar me gustaría dar las gracias a mis directores de tesis por su
guía y apoyo. A José Ángel Villar le agradezco especialmente toda la confianza
que puso y ha puesto en mí. A Carlos Pobes que me acercase a este mundo a la
vez que toda la paciencia, didáctica y ayuda antes y durante la realización de
esta tesis.

######

También querría agradecer a María Luisa Sarsa todo lo que he podido aprender
de su vastísimo conocimiento de la detección directa de materia oscura en
todas sus ramificaciones. Este trabajo tampoco habría sido posible sin la
sabiduría y dedicación de Julio César Amaré, sin quien nada de lo que aquí se
habla de electrónica analógica y filtrado de ruido eléctrico se podría haber
llevado a cabo. También agradecer a María Martínez sus conocimientos de los
aspectos teóricos y prácticos de la detección directa y su disponibilidad para
aclararme dudas. Lo mismo podría decir de Susana Cebrián y su excepcional
trato. También Clara Cuesta, con la que comencé aquí ya hace unos años, me
introdujo en el experimento ANAIS y me explicó con paciencia todos los
detalles. Y por supuesto debo (y quiero) agradecer a Alfonso Ortiz de
Solórzano muchísimas cosas: los viajes a Canfranc y las cenas en Jaca, el
trabajo con él que nunca es rutina. He podido disfrutar de su dedicación, de
su optimismo y de la pasión que pone en lo que hace. Agradezco también la
ayuda que me han brindado Eduardo García y Jorge Puimedón cada vez que lo he
necesitado, a Patricia Villar por todo su apoyo, especialmente con las medidas
de los fotomultiplicadores, a Javier Mena por el soporte y a Ysrael Ortigoza
por ser como es.

######

Por supuesto toda la gente del Laboratorio Subterráneo de Canfranc tiene
también mi agradecimiento por su trato. En particular me gustaría dar las
gracias a Iulian, Sivia, Alberto y Sergio que son con los que más he trabajado
y que me han ayudado también dentro y fuera del Laboratorio. Y al Servicio de
Instrumentación Electrónica de la Universidad de Zaragoza, a Pedro y Chema,
por su ayuda, consejos y aportaciones y por su excelente disponibilidad.

######

No me querría olvidar de toda la gente que hacía del café y la comida un sitio
distinto: Clara, Asun, David, Héctor, Laura, Carlos, María, Javier, Ángel,
Carmen, Paco, Alicia y Diana y de aquellos con los que he compartido (o casi)
despacho: Alfredo, Javi, JuanAn, Theopisti y Xavi. También ha sido muy
importante para mí el ánimo de mis amigos, especialmente de Emiliano, Rosa,
Pedro e Íñigo.

######

Por último me gustaría acordarme de mi familia, de mis padres por haberme
hecho como soy, de todos mis hermanos y familias, de Mario, Miguel, Carolina y
Pili, por su constante apoyo, y de Jorge, Elena y Blanca por la comprensión y
el ánimo y por todo lo que habéis hecho y dejado de hacer por mí estos últimos
tiempos y no podré agradeceros suficiente.

## Acknowledgments

I have to thank some other people that have been very helpful for this work.
The CAEN staff has been very supportive with every question, problem or issue.
I have to thank specially to Gianni Di Maio and Alberto Luchessi for their
quick responses and their helpfulness. I’m also very grateful to Marta Trueba
and the ATI team for their good treatment.

######

This work was supported by the Spanish Ministerio de Economía y Competitividad
and the European Regional Development Fund (MINECO-FEDER) (FPA2011-23749,
FPA2014-55986-P), the Consolider-Ingenio 2010 Programme under grant CPAN
CSD2007-00042 and the Gobierno de Aragón (Group in Nuclear and Astroparticle
Physics, GIFNA).

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