††thanks: Now at Los Alamos National Laboratory††thanks: Now at University of
Minnesota

# Data Preservation at MINERvA

R. Fine  University of Rochester, Rochester, New York 14627 USA  B. Messerly
University of Pittsburgh, Pittsburgh, Pennsylvania 15260, USA  and K.S.
McFarland  University of Rochester, Rochester, New York 14627 USA  on behalf
of the MINERvA Collaboration  Please direct correspondence to<EMAIL_ADDRESS><EMAIL_ADDRESS>
(August 31, 2020)

###### Abstract

Over the past ten years, MINERvA has collected an accelerator neutrino
interaction dataset that is uniquely relevant to the energy range of DUNE.
These are the only currently available data at intermediate and high momentum
transfers for multiple nuclear targets in the same beam. MINERvA is
undertaking a campaign to preserve these data and make them publicly available
so that they may be analyzed beyond the end of the MINERvA collaboration. We
encourage the community to consider the development of centralized resources
to enable long-term access to these data and analysis tools for the entire HEP
community.

##  I Technical Strategy

The MINERvA data preservation project consists of three components: (1)
preservation of MINERvA data into a single ROOT tuple that incorporates low-
and high-level reconstructed objects; (2) the MINERvA Analysis Toolkit (MAT) –
a broadly applicable HEP software toolkit for calculating systematic
uncertainties using event tuple objects; (3) a software package built on the
MAT for reproducing MINERvA published results, which includes templates for
performing new analyses.

To date, each published MINERvA analysis has employed its own tailored
strategy for preparing ROOT tuples that feed into the macro (event-loop) stage
of analysis. They are prepared using the Gaudi framework Barrand  _et al._
(2001), and apply analysis-specific reconstruction techniques to commonly
calibrated and prepared low-level data. Historically, this has served MINERvA
well, as it enables parallel development of distinct reconstruction techniques
and decentralizes the production of analysis tuples. The MINERvA analysis
program has reached a level of maturity at which we can now summarize the
reconstruction for a broad variety of final states in a unified analysis
tuple. This structure will support a large number of analyses using a smaller
disk footprint than would be used by separately preserving tuples which are
specialized to individual analyses. This approach also obviates the need to
develop new Gaudi routines, which further reduces the computational resources
needed to perform future analyses. We will include in these tuples low-level
reconstruction objects that could, in principle, be used for novel
reconstructions.

It is a common analysis strategy in HEP experiments to estimate systematic
uncertainties by simulating the experiment many times in a multitude of
systematic universes . In MINERvA analyses, systematic universes incorporate
the effects of systematic uncertainties by inserting variations directly into
physics distributions at all stages of the analyses. For example, a typical
analysis at MINERvA combines the distributions of signal and background events
as functions of some kinematic variable, $S(x),B(x)$, the efficiency of
performing that selection, $\epsilon(x)$, and the integrated flux, $\Phi$, to
extract a differential cross section,
$\frac{d\sigma}{dx}\propto\frac{S(x)-B(x)}{\epsilon(x)\Phi}$. Each of these
inputs is calculated independently in $\mathcal{O}(100)$ distinct systematic
universes, and is stored in a modified version of ROOT’s TH{1,2}D class. Using
these objects, it is straightforward to calculate the uncertainty arising from
any subset of the systematic variations for any of these physics
distributions, or any distribution derived from them. Similarly, any analysis
technique used in the extraction of a cross section, such as the construction
of background sideband constraints, is performed independently in each
systematic universe. A suite of custom C++ classes facilitates the execution
of this strategy, and streamlines the evaluation of systematic uncertainty
across all MINERvA analyses. Collectively, we refer to these classes as the
MINERvA Analysis Toolkit (MAT). As part of our data preservation effort, we
intend to make the MAT publicly available and we encourage its adoption by
other neutrino experiments.

Using the methods provided by the MAT, writing event loops is straightforward.
Within the loop over a ROOT tuple, there is a loop over systematic universes.
In each systematic universe, cuts are applied and histograms corresponding to
various kinematic variables are filled. This preserves the event-by-event
effects of the systematic variations across all bins of each histogram
constructed. Whereas a systematics-agnostic user would fill a TH1D, for
example, in this event loop, the user instead fills an “MnvH1D”, which maps a
TH1D to each systematic variation. Downstream, the MnvH1D supports the
standard operations available for a TH1D and executes them across all
systematic universes. In general, a systematic variation may modify the value
of a kinematic variable, and alter an event selection through a cut placed on
that kinematic variable. By incorporating the handling of systematic universes
into the event loop, this approach guarantees that kinematic variables are
shifted appropriately and that the effects of those shifts are propagated to
all downstream aspects of the analysis. As part of our data preservation
campaign, we are developing software using this approach that will easily
reproduce MINERvA analyses and enable future modifications to them. Future
use-cases may include the modification of an event selection, the construction
of a new observable, or the implementation of a new signal definition. Future
users will be able to make any of these modifications and re-extract cross
sections. For example, a future user may be interested in adding a final state
neutron requirement to $\nu_{\mu}$ CCQE-like final states, defining the
transverse angle between that neutron’s direction and the summed $p_{T}$ of
the leading proton and muon, and then extracting a differential cross-section
with respect to that new variable.

##  II Applications of MINERvA Data

MINERvA completed data-taking in 2019, and expects the number of new analyses
undertaken by the collaboration to dramatically curtail beginning in 2021.
Once the DUNE near detector begins to collect data (later in this decade
Acciarri  _et al._ (2016)), its data set (FHC) will likely exceed the size of
MINERvA’s within one year of operations Marshall  _et al._ (2020). Thus, there
will be at least five years during which the MINERvA data set provides the
community’s only opportunity to study neutrino interactions at intermediate
and high momentum transfers for multiple nuclear targets in the same beam. The
MINERvA collaboration is likely to continue in a less active configuration
during this interim period, but recognizes that it will have insufficient
resources available to address all questions that may arise. Therefore, we
believe it is important to maintain access to the data MINERvA has collected
to continue probing the interaction models that will be used by DUNE in the
measurement of CP violation and other neutrino phenomena.

In recent years, MINERvA has led the field in probing these models in the few-
GeV regime. To date we have published 31 cross section and flux measurements
using our “Low Energy” (LE; $\langle E_{\nu}\rangle\sim 3$ GeV) data, and we
expect that our “Medium Energy” (ME; $\langle E_{\nu}\rangle\sim 6$ GeV) data
would support at least as many publications (a number of which are currently
underway by the collaboration). Together, these data sets include over 4
million charged-current $\nu_{\mu}$ interactions in the active ($CH$) region
of the detector, and roughly half as many $\bar{\nu}_{\mu}$ interactions.
There are an additional $\mathcal{O}(1\text{ million})$ interactions in the
passive nuclear target region of the detector, which includes $He$, $H_{2}O$,
$C$, $Fe$, and $Pb$. These data can be analyzed to support the construction of
interaction models that span nuclei both larger than and smaller than argon.
Though we have used and continue to use these data, current efforts are
limited by the human resources available to analyze them.

In the coming years, neutrino interaction models must be improved to ensure
the success of DUNE’s ambitious physics program. Until then, MINERvA will
offer the largest and most relevant neutrino interaction dataset, against
which such models can be tested. New, discriminating analysis techniques are
continually being invented and refined, and the MINERvA data have the
flexibility to be used for studying new observables. For example, consider the
analysis technique in which transverse kinematic imbalance is used to probe
intranuclear dynamics in neutrino interactions Lu  _et al._ (2016); Furmanski
and Sobczyk (2017); Abe  _et al._ (2018); Dolan  _et al._ (2018); Lu  _et al._
(2018); Dolan (2018); Lu and Sobczyk (2019); Harewood and Gran (2019); Cai
_et al._ (2019, 2020); Coplowe  _et al._ (2020) or the absence thereof Lu  _et
al._ (2015); Duyang  _et al._ (2018, 2019); Munteanu  _et al._ (2020);
Hamacher-Baumann  _et al._ (2020). This technique has provided a new handle on
probing nuclear effects, but has only been utilized in the analysis of modern
data sets. Evidently, the infrastructure is not available to re-analyze
historical data at this level of detail. We believe that both access to the
data and to an infrastructure to facilitate its analysis are necessary
components of a successful preservation campaign. As described in Section I,
our data preservation strategy includes support for the calculation of new
kinematic variables and access to software which will enable reproduction of a
wide range of current MINERvA analyses. We expect this to serve as a launching
point for the reanalysis of existing selections to include new kinematic
variables or to test against new interaction models. We also expect that
future analyzers may modify existing selections to measure additional classes
of neutrino interactions. For example, a future analyzer may wish to further
constrain our $\nu_{\mu}$ CCQE-like selection or to perform an exclusive
$\nu_{e}$ analysis. Given the recent advances in machine learning, we also
plan to provide tools for turning our events into images that can be used in
machine learning research.

##  III Data Preservation and Snowmass

We intend to make all aspects of our data preservation product publicly
available and documented sufficiently that a trained experimental neutrino
physicist could, in principle, use them. However, we acknowledge that such a
goal has not yet been realized by any modern neutrino experiments, and we
worry that future analysis of our data may not be viable without some
involvement from MINERvA collaborators. As a practical matter, MINERvA has
always had the concept of limited authorship wherein temporary collaborators
use our data to perform a specific measurement. We expect support for this
analysis approach to extend beyond the current phase of our collaboration, so
we expect our data to be useful to the community in the few-year time frame.
For our data to be useful farther into the future, or for it to be usable
without involvement from current MINERvA collaborators, we believe that
additional resources will be required.

Because MINERvA is a scintillator-based experiment, the disk footprint for
storing the entirety of our data is small compared to some more recent
neutrino interaction experiments. We currently expect the total size of our
data set (FHC+RHC, LE+ME, Data+Simulation) will be $\sim 10$ TB. The
corresponding computational resources required to loop through these data vary
with the complexity of the analysis, and in particular its dimensionality. For
reference, a one-dimensional analysis filling $\mathcal{O}(10)$ histograms can
run over the entire FHC ME data set in $\sim 1$ hour using the default 2GB
allocations of Fermilab’s batch computing system. We believe that we will be
able to maintain access to the data via Fermilab computing resources on the
time scale of DUNE, but there is not an obvious longer-term storage option.
Additionally, hosting the data at Fermilab will, presumably, restrict access
to Fermilab users. We encourage the community to consider longer-term
solutions for hosting not only preserved MINERvA data but future data
collected and preserved by other neutrino experiments.

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