Determining Data Quality for NOvA experiment

1. NO!A Data Quality Performance and Efficiency
The Near Detector successfully made 79.7% of good runs and the Far Detector successfully made 86.2% of good runs so far.! The bad
subruns due to different failure modes:
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Bottom: Left: 6 Months of Near Detector Data and failure modes within that time period with overall/segmented efficiency Right: 1 year of Far
Detector data and failure modes within that time period, with overall/segmented efficiency
1.8%
6.3%
0.06%
0.8%
0.4%
0.09%
8.3%
0.01%
1. The NO!A Experiment
15.8%
3.1%
< 0.1%
14.9%
0.0%
26.1%
14.1%
14.9%
4.9%
0.2%
wire (beam direction)
• Two functionally identical low-z highly active
detectors located 14 milliradians off the NuMi
neutrino beam axis, separated by 809km
• The Far Detector has 344,064 channels and the
Near Detector has 20,192 individual channels.
• The Far Detector is located on the surface while the
Near Detector is ~100m underground to help
remove cosmic rays.
• The NOvA experiment is designed to run 365 days a
year, 24/7. Constant monitoring and data quality
determination is needed to immediately fix any
potential problems
• The NOvA detectors are segmented in the following
way:
wire (beam direction)
wire (beam direction)
2. Far Detector Data Quality
Determining Data Quality for the NO!A Experiment
Chatura Kuruppu On behalf of NO!A Collaboration
University of South Carolina, ckuruppu@email.sc.edu
The Far and Near Detectors• The quality of the data is based on a periodic zero bias trigger that contains
150Hz of cosmics.
• The Far Detector is unique in the fact that it can be compartmentalized on a
subrun-by-subrun level, using detector masks to ignore any parts of the
detector that are considered bad to maximize the amount of the good POT
which we keep for analyzes.
• Far Detector has 64 subruns in a run, with each subrun averaging about 2-3
minutes of data
3. Near Detector Data Quality
• The quality of data is determined by using neutrino beam data.
• The Near Detector provides spill level data quality cuts to filter out low quality
spills that are missed by subrun level quality cuts and also focuses on the
quality of the beam received from Fermilab
• Near Detector has 24 subruns in a single run, with a subrun averaging to about
an hour of data.
FD Hardware Level Matrix
In the Far Detector, hit rates are monitored on a channel (pixel) and FEB level in
each DCM and DiBlock. Each DCM is assigned individual cuts over all DiBlocks
for both channels and FEBs. These cuts allow the Far Detector environment to be
segmented and allow certain parts of the detector to be masked off on a subrun-
by-subrun level.
FD Subrun Level Cuts
The Far Detector data quality is determined using a subruns worth of data. These
subrun level cuts look at overall hardware performance, the health of the DAQ by
monitoring trigger levels, and by looking at basic reconstruction products, such as
hit clustering (slicing), in the detector.
ND Spill Level Quality
Since subruns are comparatively long in the Near Detector, spill level quality metrics
are needed to maximize POT received. Some spill quality metrics we look at are:
number of noisy channels, number of hits that are out of beam window time, and
number of “bad” or missing DCMs
ND Subrun Level Cuts
The subrun level cuts for the Near Detector look at the overall hardware performance,
beam conditions and performance, reconstruction products, and trigger levels which
could indicate beam or DAQ performance.
Left: The average hit rate in the Near Detector per 1000 spills of the neutrino beam. The
red arrows indicate the values that are cut on. This metric tracks the the number of physics
hits normalized by the number of POT. This can help indicate when the detector
environment changes, including gain changes.
Right: The subrun averaged neutrino beam timing peak start. The red arrows indicate the
values that are cut on. This metric helps ensure that the Near Detector is synchronized in
time and is receiving beam spills correctly.
ND Beam Quality
The beam quality is closely monitored using the Intensity Frontier Database
(IFDB). NOvA receives beam metrics from this data, such as NuMI horn
current, POT, positioning, width, and timing of the beam. These metrics help
determine if a beam is considered good or bad which is rolled into our data
quality metrics.
Examples of beam quality metrics, where the dashed red line shows the cut
between good and bad beam for that metric
Left: A year of Far Detector data showing which DiBlocks have been masked off (red)
and shows a ~99.4% efficiency for our whole detector.
Right: The averaged hit rate over several runs for channels in the top of the detector.
NOvA expects to see different rates in different parts of the detector because of
differences in the cosmic rate throughout the Far Detector. In addition, DCMs near the
top of the detector are more likely to get hit more than those on the side of the detector.
Left: The overall hit rate in the detector on a subrun level over the
past year. This is to track any changes in gain due to cooling
failures, as an example. In the past year, there has been a permanent
change in the gain from ~100 to ~150 only in the Far Detector. This
has caused the hit rate to increase overall, due to increasing our
sensitivity.
Right: A metric that tracks trigger levels in the Far Detector. The red
arrow indicates where a cut is made to determine subrun quality.
This metric is cut on to ensure that slicing rate is constant. Changes
in slicing rate can indicate reconstruction failures due to
synchronization issues or excess noise.
• 32 channels per Front End Board (FEB)
• 64 FEBs per Data Concentrator Module (DCM)
• 12 DCMs per DiBlock (DB) for Far Detector
• ~4 DCMs per DiBlocks for Near Detector A real time plot demonstrating FEB hit rates in the Far Detector
An event display showing a non-reporting DCM in a beam Spill event
Far Detector
Near Detector
NDOS Prototype