The NOνA experiment is a two-detector, long-baseline neutrino experiment operating in the recently upgraded NuMI muon neutrino beam. Simulating neutrino interactions and backgrounds requires many steps including: the simulation of the neutrino beam flux using FLUKA and the FLUGG interface; cosmic ray generation using CRY; neutrino interaction modeling using GENIE; and a simulation of the energy deposited in the detector using GEANT4. To shorten generation time, the modeling of detector-specific aspects, such as photon transport, detector and electronics noise, and readout electronics, employs custom, parameterized simulation applications. We will describe the NOνA simulation chain, and present details on the techniques used in modeling photon transport near the ends of cells, and in developing a novel data-driven noise simulation. Due to the high intensity of the NuMI beam, the Near Detector samples a high rate of muons originating in the surrounding rock. In addition, due to its location on the surface at Ash River, MN, the Far Detector collects a large rate (˜ 140 kHz) of cosmic muons. We will discuss the methods used in NOνA for overlaying rock muons and cosmic ray muons with simulated neutrino interactions and show how realistically the final simulation reproduces the preliminary NOνA data

Aurisano, A.

Backhouse, C.

Hatcher, R.

Mayer, N.

Musser, J.

Patterson, R.

Schroeter, R.

Sousa, A.

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The NOvA simulation chain
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2015 J. Phys.: Conf. Ser. 664 072002
(http://iopscience.iop.org/1742-6596/664/7/072002)
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The NOνA simulation chain
A Aurisano1, C Backhouse2, R Hatcher3, N Mayer4, J Musser5,
R Patterson2, R Schroeter6, and A Sousa1
1University of Cincinnati, Cincinnati OH, USA
2California Institute of Technology, Pasadena CA, USA
3Fermi National Accelerator Laboratory, Batavia IL, USA
4Tufts University, Medford MA, USA
5Indiana University, Bloomington IN, USA
6Harvard University, Cambridge, MA, USA
E-mail: aurisaam@ucmail.uc.edu, bckhouse@caltech.edu, rhatcher@fnal.gov,
nathan.mayer@tufts.edu, jmusser@indiana.edu, rbpatter@caltech.edu,
rschroet@physics.harvard.edu, Alex.Sousa@uc.edu
Abstract. The NOνA experiment is a two-detector, long-baseline neutrino experiment
operating in the recently upgraded NuMI muon neutrino beam. Simulating neutrino interactions
and backgrounds requires many steps including: the simulation of the neutrino beam flux using
FLUKA and the FLUGG interface; cosmic ray generation using CRY; neutrino interaction
modeling using GENIE; and a simulation of the energy deposited in the detector using
GEANT4. To shorten generation time, the modeling of detector-specific aspects, such as
photon transport, detector and electronics noise, and readout electronics, employs custom,
parameterized simulation applications. We will describe the NOνA simulation chain, and
present details on the techniques used in modeling photon transport near the ends of cells,
and in developing a novel data-driven noise simulation. Due to the high intensity of the NuMI
beam, the Near Detector samples a high rate of muons originating in the surrounding rock.
In addition, due to its location on the surface at Ash River, MN, the Far Detector collects
a large rate ( ∼140 kHz) of cosmic muons. We will discuss the methods used in NOνA for
overlaying rock muons and cosmic ray muons with simulated neutrino interactions and show
how realistically the final simulation reproduces the preliminary NOνA data.
1. Introduction
NOνA [1] is a two-detector, long-baseline neutrino oscillation experiment located 14 mrad off-
axis from the NuMI neutrino beam and is designed to measure the oscillation probabilities for
νµ → νe and ν¯µ → ν¯e. The probabilities will allow us to study the mass ordering of the three
neutrino species as well as constrain the charge-parity violating phase in the leptonic sector.
NOνA can also measure the oscillation probabilities for νµ → νµ and ν¯µ → ν¯µ to improve the
precision with which we know |∆m232| and θ23.
The NuMI beam, originally built to supply neutrinos to the MINOS experiment, is produced
by the NuMI facility [2] shown in Fig. 1. The beam begins with 120 GeV protons from the
Main Injector striking a graphite target. The resulting particles are focused by two magnetic
focusing horns. Particles then travel down the decay pipe to allow pions to decay to muons
and neutrinos. Muons and any remaining hadrons are absorbed in rock leaving a beam of only
neutrinos. The focusing horns can run in two modes: in forward horn current mode, the horns
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focus pi+ and defocus pi− resulting in a nearly pure νµ beam; reversing the horn current focuses
pi− resulting in a ν¯µ enriched beam. After upgrades in preparation for the NOνA experiment,
the NuMI beam now runs in medium energy mode with a peak energy of ∼8 GeV.
Figure 1. A schematic of the NuMI facility. The NuMI beam can run in a νµ mode or a
ν¯µ-enriched mode by changing the direction of the current in the magnetic focusing horns.
The NOνA detectors are low Z tracking calorimeters composed of alternating vertical and
horizontal planes of liquid scintillator filled PVC cells. Light is transported out of the cells
using wave-length shifting fiber loops that are read out by avalanche photodiodes (APDs). Far
detector cells are 15.6 m long while near detector cells are 4.2 m long. Cells in both the near and
far detectors have a 4 cm×6 cm cross-section. The far detector (Fig. 2) is 15.6 m×15.6 m×60 m
with a mass of 14 kton and ∼344,000 cells. The near detector (Fig. 3) is 4.2 m× 4.2 m× 15.8 m
with a mass of 0.3 kton and ∼20,000 cells. The near detector is designed to be functionally
equivalent to the far detector to allow for systematic uncertainty cancellation. To handle the
high rate environment of the near detector cavern, the near detector is outfitted with 4x faster
electronics than the far detector. As seen in Fig. 4 the far detector is located near Ash River,
Minnesota, 810 km from the NuMI target. The near detector is located on the Fermilab campus
1 km from the NuMI target. Both detectors are 14 mrad off-axis to create a 2 GeV narrow-band
beam.
Figure 2. The NOνA far detector seen from
the top.
Figure 3. The NOνA near detector seen
from the front.
The steps to simulate neutrino interactions and backgrounds are illustrated in Fig. 5. The
simulation of neutrino interactions starts with modeling hadron production within the target,
21st International Conference on Computing in High Energy and Nuclear Physics (CHEP2015) IOP Publishing
Journal of Physics: Conference Series 664 (2015) 072002 doi:10.1088/1742-6596/664/7/072002
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Figure 4. The locations of the
near and far detector, and the path
of the NuMI beam. The detector
are located off the path of the
beam (off-axis) to select neutrinos
with energies narrowly distributed
around 2 GeV.
focusing in the horns, and downstream tertiary production to determine the production rate
and energy spectrum of each neutrino flavor from the decay of pions, kaons, and muons in the
decay pipe using the FLUKA simulation package [3, 4] and the FLUGG GEANT4 geometry
interface [5]. The resulting simulated neutrinos are stored in flux files along with information
about their parentage, and they are used as inputs to the neutrino event generation stage
performed with GENIE [6]. Factorizing out the simulation of the neutrino flux from the rest of
the simulation minimizes the number of times this computationally intensive step needs to be
run as well as allowing for after the fact tunings of hadron production or focusing parameters.
Since the far detector is on the surface, cosmic rays are a significant background; we generate
cosmic ray events with CRY [7]. The particle lists generated by either GENIE or CRY are then
passed to GEANT4 [8, 9], which propagates particles through the detector and produces energy
deposits in active material. Finally, the list of energy deposits in active material are passed
to a parameterized front-end simulation which converts energy deposits into scintillation light,
transports scintillation light to the APD, and simulates the readout electronics response. The
final output is formatted like raw data.
Due to the high beam intensity at the near detector, many neutrinos interact in the rock
in front of the detector. Simulating these interactions requires allowing GEANT4 to propagate
muons through a very large rock volume which is a slow process, and only a few of these
muons will make it into our detector. To correctly account for this, we simulate many neutrino
interactions with the mother volume including a large rock volume in front of the detector,
and only keep those that leave energy in the detector. During normal simulation, with the
mother volume only including the detector and the immediate detector hall, we overlay these
rock ’singles’ at a rate determined during the generation of flux files after the GEANT4 stage.
2. Photon Transport
While GEANT4 is capable of simulating optical photon processes, generating scintillation light
and propagating it through the cell, up the fiber, and to the APD is very time consuming.
Instead, we observe that the NOνA detectors are composed of many identical readout cells as
shown in Fig. 6, so if we can generate templates to parameterize photon transport once, we
can use them everywhere. The processes we must be able to parameterize are: the collection
of scintillation photons by the fiber, the transport of light up the fiber, and the response of the
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Figure 5. Steps in the simulation chain for the NOνA experiment
APD to the captured light.
To understand the collection of scintillation photons by the fiber, we developed a ray tracing
simulation which uses the measured scintillation spectrum of the liquid scintillator, the average
measured reflectivity of the PVC cell walls, and the absorption spectrum of the wavelength
shifting fibers. Fig. 7 shows the resulting collection rate as a function of the distance along the
length of the cell between where the photon was collected relative to the its origin (∆Z) and
the time when the photon was collected relative to its emission (∆T ).
Figure 6. A schematic of the
readout cell, the basic building
block of the NOνA detectors.
Figure 7. The collection rate of scintillation
photons by a wavelength shifting fiber loop as
estimated by a custom ray tracing simulation.
In the final simulation, this distribution is shifted to the position along the cell where
GEANT4 has placed an energy deposit and is multiplied by the visible deposited energy and
21st International Conference on Computing in High Energy and Nuclear Physics (CHEP2015) IOP Publishing
Journal of Physics: Conference Series 664 (2015) 072002 doi:10.1088/1742-6596/664/7/072002
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a factor to convert energy to scintillation photons. The upper manifold and the lower end cap
of the readout cell have low reflectivity. We approximate this by truncating any portion of the
template laying outside of the cell. At this point, we have the mean number of photons captured
as a function of ∆Z.
Next, we must transport the photons up the fiber to determine the number of photons
absorbed by the APD as a function of time. For a given bin in ∆Z, we transport half of the
collected photons up the long path around the fiber loop while the other half are transported
up the short path. The mean number of photons surviving after being transported around the
fiber is determined using an attenuation curve shown in Fig. 8 constructed from the average
over the quality control tests of many spools of wavelength shifting fiber. After accounting for
the quantum efficiency of the APD and Poisson sampling, we have the number of photoelectrons
absorbed by the APD for a given ∆Z bin. The timing of photons is determined by the sum of
the time of the GEANT4 energy deposit, the ∆T due to propagation in the scintillator drawn
from the 1D projection of the photon collection template at a fixed ∆Z, and the propagation
time up the fiber. Since the apparent speed of photon propagation in the fiber depends on the
angle of propagation (and hence the true path length), we draw from a template constructed
from the travel time up the fiber divided by the travel distance as calculated by a fiber ray
tracing simulation. Once photons from all ∆Z bins have been propagated to the APD, we have
the number of photo-electrons as a function of time for a given energy deposit.
Figure 8. The attenuation curve,
used to convert photons collected
by the fiber to photons transported
to the APD, shown in linear and
log scales. As can be seen from
the log scale version, the curve can
be adequately approximated as a
double exponential.
In addition to the Poisson variation induced by the number of photoelectrons (Npe) captured
by the APD, APDs have an excess noise factor (F) that expands the variance of the detected
signal from Npe to F ∗Npe. The theoretical distribution of the APD response is known [10], but it
is difficult to randomly sample. Since the avalanche amplification process in an APD consists of
many random charge multiplications, and the log-normal distribution can be considered the limit
of the product of many independent random variables, we model the theoretical distribution as
21st International Conference on Computing in High Energy and Nuclear Physics (CHEP2015) IOP Publishing
Journal of Physics: Conference Series 664 (2015) 072002 doi:10.1088/1742-6596/664/7/072002
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Npe Poisson sampled with a mean value of N¯pe and multiplied by a scale factor sampled from
a log-normal distribution a mean of 1 and a variance of (F − 1)/Npe. This preserves the mean
of the Poisson distribution while correctly expanding the variance to account for excess noise.
As seen in Fig. 9, the log-normal expanded photoelectron distribution matches the theoretical
distribution well.
Figure 9. A comparison of APD response distribution from theory to simulation using a log-
normal distribution.
3. Electronics Simulation
The goal of the electronics simulation is to convert the distribution of excess noise expanded
photoelectrons as a function of time from the photon transport stage into digital signals similar
to those collected from the detector. The front-end boards that this step emulates contain three
chips: an ASIC which performs pulse shaping, and ADC which converts the shaped analog
signals into digital signals, and an FPGA which performs real-time zero suppression.
The pulse shaping in the ASIC is performed by a CR-RC circuit. Therefore, we create analog
traces in units of photoelectrons using the equation:
f(t) = Npe
F
F −R
(
e−(t−t0)/F − e−(t−t0)/R
)
(1)
where t0 is the time that the photoelectron pulse was collected by the APD, F is the fall time
of the CR-RC circuit, and R is the rise time of the CR-RC circuit.
Next, we add electronics noise. In cells with energy deposits, we add a noise trace modeled
by the sum of two Gaussian Markov chains representing current and voltage noise sources to
the physics trace. The voltage noise component is of the form:
Vi =
Tread
R+ Tread
Zi +
R
R+ Tread
Vi−1 (2)
where Tread is the time between digitization (500 ns for the far detector and 125 ns for the near
detector) and Zi is random number drawn from the unit Gaussian distribution. The current
noise component is of the the form:
Ci =
Tread
F + Tread
Zi +
F
F + Tread
Ci−1 (3)
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Journal of Physics: Conference Series 664 (2015) 072002 doi:10.1088/1742-6596/664/7/072002
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and the final noise trace is aVi + bCi where a and b are determined through a fit to pedestal
scans. To save time for cells with no energy deposits, we distribute unclustered hits extracted
from real data.
At this stage, we convert the trace from units of photoelectrons to ADC using a factor
determined through charge injection studies. We add a baseline drawn from the distribution of
modes seen in pedestal scan, and truncate to integers. Finally, we emulate the real-time zero
suppression performed by the FPGA by looking for peaks above threshold in the dual correlated
sampling trace defined as:
dcsi = ADCi −ADCi−3 (4)
where ADCi is the value of the current digitization sample and ADCi−3 is the value of the
digitization sample three time slices earlier than the current one. In the real readout, thresholds
are set on a channel-by-channel basis from periodic pedestal scans. In the simulation, we can
draw thresholds from a distribution of thresholds seen at the real detector or use the actual
thresholds recorded in the run conditions database.
4. Data/Monte Carlo Comparisons
Using the complete simulation, we can now compare low-level quantities in data and Monte
Carlo. To keep our signal regions blind, we only look at cosmic ray data by ignoring any data
taken during a beam spill trigger. The photoelectron spectrum for data and Monte Carlo can
be seen in Fig. 10 for the near detector and Fig. 11 for the far detector. It is important to note
that no oﬄine calibrations have been applied, and the only tunings applied are the overall light
level (photons per MeV) needed in the photon transport stage and the noise coefficients needed
in the digitization stage. The peaks of the distributions agree well, and the effect of baseline
variation on saturated hits can be seen at large photoelectron values.
Figure 10. A comparison of the photoelec-
tron spectrum in cosmic rays at the near de-
tector in data and Monte Carlo.
Figure 11. A comparison of the photoelec-
tron spectrum in cosmic rays at the far detec-
tor in data and Monte Carlo.
5. Summary
Simulation at NOνA is a multi-stage process. For beam events this requires simulating the beam
flux with FLUKA and the FLUGG GEANT geometry interface, generating neutrino interactions
with GENIE, and simulating the energy deposited in active material with GEANT4. Cosmic
rays, a major background at the far detector, are simulated using the CRY event generator.
21st International Conference on Computing in High Energy and Nuclear Physics (CHEP2015) IOP Publishing
Journal of Physics: Conference Series 664 (2015) 072002 doi:10.1088/1742-6596/664/7/072002
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The modeling of the collection and transport of scintillation light, the response of the APD, and
the front-end boards is done by custom, parametrized modules. Preliminary results show that
low-level quantities in cosmic ray data and Monte Carlo match well.
Acknowledgements
The author acknowledges support for this research was carried out by the Fermilab scientific
and technical staff. Fermilab is operated by Fermi Research Alliance, LLC under contract
No. De-AC02-07CH11359 with the United States Department of Energy.
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21st International Conference on Computing in High Energy and Nuclear Physics (CHEP2015) IOP Publishing
Journal of Physics: Conference Series 664 (2015) 072002 doi:10.1088/1742-6596/664/7/072002
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