First Measurement of η Meson Production in Neutrino Interactions on Argon with MicroBooNE

First Measurement of ηMeson Production in Neutrino Interactions on Argon with MicroBooNE P. Abratenko,35 O. Alterkait,35 D. Andrade Aldana,15 J. Anthony,5L. Arellano,20 J. Asaadi,34 A. Ashkenazi,32 S. Balasubramanian,12 B. Baller,12 G. Barr,25 J. Barrow,21,32 V. Basque,12 O. Benevides Rodrigues,31 S. Berkman,12 A. Bhanderi,20 A. Bhat,39 M. Bhattacharya,12 M. Bishai,3A. Blake,17 B. Bogart,22 T. Bolton,16 J. Y. Book,14 L. Camilleri,10 Y. Cao,20 D. Caratelli ,4I. Caro Terrazas,9F. Cavanna,12 G. Cerati,12 Y. Chen,28 J. M. Conrad,21 M. Convery,28 L. Cooper-Troendle,39 J. I. Crespo-Anadón,6M. Del Tutto,12 S. R. Dennis,5P. Detje,5A. Devitt,17 R. Diurba,2Z. Djurcic,1 R. Dorrill,15 K. Duffy,25 S. Dytman,26 B. Eberly,30 P. Englezos,27 A. Ereditato,7,12 J. J. Evans,20 R. Fine,18 O. G. Finnerud,20 W. Foreman,15 B. T. Fleming,7N. Foppiani,14 D. Franco,7A. P. Furmanski,23 D. Garcia-Gamez,13 S. Gardiner,12 G. Ge,10 S. Gollapinni,33,18 O. Goodwin,20 E. Gramellini,12 P. Green,20,25 H. Greenlee,12 W. Gu,3R. Guenette,20 P. Guzowski,20 L. Hagaman,7O. Hen,21 R. Hicks,18 C. Hilgenberg,23 G. A. Horton-Smith,16 Z. Imani,35 B. Irwin,23 R. Itay,28 C. James,12 X. Ji,3L. Jiang,37 J. H. Jo,3,39 R. A. Johnson,8Y.-J. Jwa,10 D. Kalra,10 N. Kamp,21 G. Karagiorgi,10 W. Ketchum,12 M. Kirby,12 T. Kobilarcik,12 I. Kreslo,2M. B. Leibovitch,4I. Lepetic,27 J.-Y. Li,11 K. Li,39 Y. Li,3K. Lin,27 B. R. Littlejohn,15 W. C. Louis,18 X. Luo,4C. Mariani,37 D. Marsden,20 J. Marshall,38 N. Martinez,16 D. A. Martinez Caicedo,29 K. Mason,35 A. Mastbaum,27 N. McConkey,20,36 V. Meddage,16 K. Miller,7J. Mills,35 A. Mogan,9T. Mohayai,12 M. Mooney,9 A. F. Moor,5C. D. Moore,12 L. Mora Lepin,20 S. Mulleriababu,2D. Naples,26 A. Navrer-Agasson,20 N. Nayak,3 M. Nebot-Guinot,11 J. Nowak,17 N. Oza,10,18 O. Palamara,12 N. Pallat,23 V. Paolone,26 A. Papadopoulou,1,21 V. Papavassiliou,24 H. B. Parkinson,11 S. F. Pate,24 N. Patel,17 Z. Pavlovic,12 E. Piasetzky,32 I. D. Ponce-Pinto,7I. Pophale,17 S. Prince,14 X. Qian,3J. L. Raaf,12 V. Radeka,3A. Rafique,1M. Reggiani-Guzzo,20 L. Ren,24 L. Rochester,28 J. Rodriguez Rondon,29 M. Rosenberg,35 M. Ross-Lonergan,18 C. Rudolf von Rohr,2G. Scanavini,39 D. W. Schmitz,7 A. Schukraft,12 W. Seligman,10 M. H. Shaevitz,10 R. Sharankova,12 J. Shi,5E. L. Snider,12 M. Soderberg,31 S. Söldner-Rembold,20 J. Spitz,22 M. Stancari,12 J. St. John,12 T. Strauss,12 S. Sword-Fehlberg,24 A. M. Szelc,11 W. Tang,33 N. Taniuchi,5K. Terao,28 C. Thorpe,17 D. Torbunov,3D. Totani,4M. Toups,12 Y.-T. Tsai,28 J. Tyler,16 M. A. Uchida,5 T. Usher,28 B. Viren,3M. Weber,2H. Wei,19 A. J. White,7Z. Williams,34 S. Wolbers,12 T. Wongjirad,35 M. Wospakrik,12 K. Wresilo,5N. Wright,21 W. Wu,12 E. Yandel,4T. Yang,12 L. E. Yates,12 H. W. Yu,3G. P. Zeller,12 J. Zennamo,12 and C. Zhang3 (MicroBooNE Collaboration)* 1Argonne National Laboratory (ANL), Lemont, Illinois 60439, USA 2Universität Bern, Bern CH-3012, Switzerland 3Brookhaven National Laboratory (BNL), Upton, New York 11973, USA 4University of California, Santa Barbara, California 93106, USA 5University of Cambridge, Cambridge CB3 0HE, United Kingdom 6Centro de Investigaciones Energ´eticas, Medioambientales y Tecnológicas (CIEMAT), Madrid E-28040, Spain 7University of Chicago, Chicago, Illinois, 60637, USA 8University of Cincinnati, Cincinnati, Ohio 45221, USA 9Colorado State University, Fort Collins, Colorado 80523, USA 10Columbia University, New York, New York 10027, USA 11University of Edinburgh, Edinburgh EH9 3FD, United Kingdom 12Fermi National Accelerator Laboratory (FNAL), Batavia, Illinois 60510, USA 13Universidad de Granada, Granada E-18071, Spain 14Harvard University, Cambridge, Massachusetts 02138, USA 15Illinois Institute of Technology (IIT), Chicago, Illinois 60616, USA 16Kansas State University (KSU), Manhattan, Kansas 66506, USA 17Lancaster University, Lancaster LA1 4YW, United Kingdom 18Los Alamos National Laboratory (LANL), Los Alamos, New Mexico 87545, USA 19Louisiana State University, Baton Rouge, Louisiana 70803, USA 20The University of Manchester, Manchester M13 9PL, United Kingdom 21Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA 22University of Michigan, Ann Arbor, Michigan 48109, USA 23University of Minnesota, Minneapolis, Minnesota 55455, USA PHYSICAL REVIEW LETTERS 132, 151801 (2024) 0031-9007=24=132(15)=151801(7) 151801-1 Published by the American Physical Society 24New Mexico State University (NMSU), Las Cruces, New Mexico 88003, USA 25University of Oxford, Oxford OX1 3RH, United Kingdom 26University of Pittsburgh, Pittsburgh, Pennsylvania 15260, USA 27Rutgers University, Piscataway, New Jersey 08854, USA 28SLAC National Accelerator Laboratory, Menlo Park, California 94025, USA 29South Dakota School of Mines and Technology (SDSMT), Rapid City, South Dakota 57701, USA 30University of Southern Maine, Portland, Maine 04104, USA 31Syracuse University, Syracuse, New York 13244, USA 32Tel Aviv University, Tel Aviv, Israel, 69978 33University of Tennessee, Knoxville, Tennessee 37996, USA 34University of Texas, Arlington, Texas 76019, USA 35Tufts University, Medford, Massachusetts 02155, USA 36University College London, London WC1E 6BT, United Kingdom 37Center for Neutrino Physics, Virginia Tech, Blacksburg, Virginia 24061, USA 38University of Warwick, Coventry CV4 7AL, United Kingdom 39Wright Laboratory, Department of Physics, Yale University, New Haven, Connecticut 06520, USA (Received 30 May 2023; revised 4 January 2024; accepted 13 March 2024; published 10 April 2024) We present a measurement of ηproduction from neutrino interactions on argon with the MicroBooNE detector. The modeling of resonant neutrino interactions on argon is a critical aspect of the neutrino oscillation physics program being carried out by the DUNE and Short Baseline Neutrino programs. η production in neutrino interactions provides a powerful new probe of resonant interactions, complementary to pion channels, and is particularly suited to the study of higher-order resonances beyond the Δð1232Þ.We measure a flux-integrated cross section for neutrino-induced ηproduction on argon of 3.22 0.84ðstatÞ 0.86ðsystÞ10−41 cm2=nucleon. By demonstrating the successful reconstruction of the two photons resulting from ηproduction, this analysis enables a novel calibration technique for electromagnetic showers in GeV accelerator neutrino experiments. DOI: 10.1103/PhysRevLett.132.151801 Neutrino oscillation physics experiments have embarked on an expansive program aimed at performing precision measurements of neutrino oscillation parameters including measurements of the charge-parity violating phase in the lepton sector, δCP. These experiments additionally provide a unique environment to search for new physics through possible rare processes occurring along the beamline. This research program is in part enabled by the accelerator-based neutrino oscillation program which leverages GeV-scale neutrino beams and liquid argon time projection chamber (LArTPC) detectors through the short baseline neutrino (SBN) [1] program and deep underground neutrino experiment (DUNE) [2]. Uncertainties in modeling the neutrino interaction rate on argon impact the precision to which neutrino oscillation parameter measurements can be performed. Similarly, neutrino interactions constitute a background for beyond the standard model (BSM) processes [3]. In both cases, accurate modeling of the interaction rate and final-state particles produced in neutrino interactions is a crucial part of this experimental program. This has led to a broad program focused on studying neutrino interactions to support and enhance the upcoming neutrino oscillation and BSM physics programs [4]. Neutrinos interact with atomic nuclei with a broad range of interaction modes. An important process in the OðGeVÞ energy range is resonant interactions (RES) where a neutrino strikes a single nucleon (neutron or proton) exciting a baryon resonance. Uncertainties on the modeling of these processes contribute to the overall systematics on neutrino event rates. RES interactions and their modeling uncertainty play a particularly important role in both shortand long-baseline experiments due to the production of final states which mimic signatures of νμ→νeoscillation and BSM observables. Constraints on resonant interactions, particularly on argon, can contribute to validations and improvements of such interaction models. Moreover, resonant interactions are one of the dominant interaction modes for the long-baseline DUNE neutrino experiment. A broad category of baryon resonances can be excited when neutrinos strike a nucleon [5,6]. Most resonances decay to a nucleon and a charged or neutral pion, and this final state has been the most frequently studied to date in RES neutrino-nucleus interactions. These interactions are dominated by the excitation and decay of the Δð1232Þ resonant state. However, higher order resonances, while Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI. Funded by SCOAP3. PHYSICAL REVIEW LETTERS 132, 151801 (2024) 151801-2 subdominant, contribute at the ∼10% level to the total event rate. If not properly accounted for, these resonances can lead to mismodeled backgrounds in precision oscillation measurements and BSM searches. Yet, testing their modeling in neutrino interactions is made difficult by the lack of experimental measurements. Resonances such as the Nð1535Þ,Nð1650Þ, and Nð1710Þ states have sizable (though with large uncertainties) branching fractions to ηproduction of 30%–55%, 15%–35%, and 10%–50%, respectively [7]. For context, roughly 1%–2% of all neutrino interactions in DUNE will lead to ηmesons in the final state. Measuring ηproduction in neutrino interactions is a promising way to study RES interactions targeting resonant states that cannot be easily probed through measurements of pion production. The BEBC WA59 Collaboration reported a measurement of η production on a Ne-H2target [8], and 13 candidate ηevents were seen by the ICARUS experiment operating at LNGS in an unpublished study [9]. Both measurements were performed in the multi-GeV neutrino beams of the SPS at CERN. Theoretical calculations for the cross section for η production in neutrino interactions are reported in Refs. [10–12]. We present the first measurement of the cross section for ηproduction in neutrino interactions on argon. The measurement uses 6.79 ×1020 protons on target (POT) of neutrino data collected on axis on the booster neutrino beamline (BNB) [13] by MicroBooNE during the first three years of operation, 2016 to 2018. The analysis leads to the largest sample of ηmeson candidates observed in neutrinoargon interactions and is the first measurement of their production on any target in a beam of sub-GeV mean energy. Being the first quantitative measurement of η production on argon, this measurement opens a completely new area for probing neutrino interactions. In addition to the important impact on cross section modeling, the ability to observe ηdecays in a LArTPC can find broader application. We identify three additional ways in which ηparticle measurements in LArTPCs can have a significant impact on neutrino, nuclear, and BSM physics searches: (1) The ability to observe ηdecays in a LArTPC opens the door for searches of proton-decay in the p→eþþηand p→μþþηchannels with the DUNE experiment. This is a channel that has already been used for proton-decay searches by Super-K [14] with competitive limits of ∼1034 years. This decay channel complements the primary focus of DUNE on the Kþþ¯ νdecay mode. (2) Measurements of ηparticles through their decay to photon pairs provide a novel tool for the calibration of the electromagnetic (EM) energy scale, a critical component of the νelepton energy determination for the extraction of δCP and other oscillation parameters. Decays to photon pairs from ηparticles provide a sample of higher energy showers which complement the Oð50–200 MeVÞphotons from π0 decay [15]. Photons from ηdecay, in particular, have greater overlap with the energy of electrons expected from the νeflux component of SBN and will allow for a datadriven validation of shower energy-scale reconstruction linearity up to GeV energies. (3) Finally, the large uncertainty in current experimental measurements of baryon resonance decays to the η[7] can be constrained through precise measurements of ηproduction in neutrino interactions. These items indicate the large impact this and future measurements of ηproduction in a LArTPC can have across different areas of particle physics. The MicroBooNE detector [16] comprises a TPC with 85 ton of liquid argon active mass accompanied by a photon detection system made up of 32 photomultiplier tubes (PMTs). Neutrino interactions on the argon target are recorded through the ionization and scintillation light signatures produced by final-state charged particles traversing the detector volume. Ionization charge is recorded on three wire planes allowing the experiment to obtain millimeter-resolution three-dimensional images of neutrino interactions. Scintillation light collected on the PMT array provides the timing resolution necessary to identify neutrino interactions in-time with the BNB and to reject cosmic-ray backgrounds. The simulation of neutrino interactions and particle propagation through the MicroBooNE detector is carried out within the LArSoft framework [17]. The BNB neutrino flux at the MicroBooNE detector is simulated leveraging the flux simulation developed by the MiniBooNE Collaboration [18] accounting for MicroBooNE’s position along the beamline. Neutrino interactions in the detector are simulated with the GENIE v3.0.6 (G18_10a_02_11a) event generator [19] that was tuned to CC0πdata from the T2K Collaboration [20] as described in Ref. [21]. Resonances are modeled according to the description of Rein and Sehgal [5] and are allowed to decay based on tabulated branching ratios from the Particle Data Group [7]. Decays of resonances above the Δð1232Þare treated as isotropic. While multiple resonances can contribute to ηproduction, only a few do so at a meaningful rate. In particular, the Nð1535Þis predicted to contribute the dominant rate of ηproduction (87%) according to the GENIE generator simulation used in this analysis. It is important to note however that the GENIE simulation does not account for interference between the different resonances, and is further subject to the large uncertainty in the branching fractions of these resonant states. While based on simulation, this observation suggests that studies of ηproduction in the BNB can serve as a unique selector of a pure sample of events from a single non-Δ resonant state. This provides new handles for detailed studies and model constraints for RES interactions. Particle propagation through the detector is carried out via the GEANT 4simulation [22], and propagation of ionization and scintillation signals is carried out through dedicated algorithms that model the detector’s response. Simulated neutrino interactions are overlayed with data PHYSICAL REVIEW LETTERS 132, 151801 (2024) 151801-3 events collected with an unbiased trigger in anticoincidence with the beam which allows for data-driven cosmic-ray and detector noise modeling. PMT signals from MicroBooNE’s data are used to apply an online trigger that rejects events with little visible light collected in coincidence with the 1.6μs BNB neutrino spill. Offline, PMT signals from both data and simulation are processed through reconstruction algorithms that measure the photoelectrons (PE) on each PMT associated to the interaction in-time with the BNB spill. Both data and simulated events undergo the same reconstruction workflow. Noise filtering [23] and signalprocessing [24,25] algorithms are applied to TPC wire signals to measure energy deposits on each wire plane. The Pandora multi-algorithm pattern recognition framework [26] is used to reconstruct three-dimensional particle trajectories and a particle flow hierarchy and to identify the Oð10Þinteractions (mostly cosmic rays) occurring in each recorded event. MicroBooNE’s TPC and PMT signals are calibrated to account for position and time-dependent variations in detector response. PMT gains are calibrated for each PMT independently on a weekly basis, and the overall light yield in the detector is calibrated through a single time-dependent correction factor. MicroBooNE’s TPC signal calibration accounts for position and timedependent variations in the detector’s ionization production, transport, and signal formation. These calibrations account for the variation in the detector’s positiondependent electric field [27,28] and for the relative and absolute charge-scale calibration [29]. Electromagnetic shower energy calibration is performed through the methods described in Ref. [15] leading to a shower energy correction of ×1.20 to account for energy deposited by the shower not collected by the reconstruction. The calibration of the detector’s calorimetric response is particularly relevant to this analysis which relies on calorimetry to measure the energy of EM showers. The ηmeson has multiple decay modes with comparable branching fractions. The dominant channels are η→2γ, η→3π0, and η→π0þπþþπ−, with branching ratios of 40%, 33%, and 23%, respectively [7]. This analysis targets the decay to two photons given that it is the dominant decay mode, and it leads to the cleanest final-state signature. The very low rate expected for ηproduction in MicroBooNE (<1% of all νinteractions) makes the 2γsignature particularly attractive due to the powerful background rejection that can be achieved by selecting for a 2γinvariant mass consistent with 548 MeV=c2, the mass of the η meson. The signal for this analysis is defined as events in which an ηparticle is produced as a result of the neutrino-argon interaction and where there are two photons and no π0present in the final state. No other activity from charged particles at the vertex is required to identify the candidate event. While muon neutrinos make up ∼95% of the BNB flux, neutrinos, and anti-neutrinos of all flavors are included in the signal definition. Finally, this analysis does not apply selection cuts on the presence of an outgoing lepton in the interaction and, therefore, targets ηproduction from both charged current (CC) and neutral current (NC) processes. The interaction process being sought can therefore be described as νCCþNC →ηþ0π0þ X→2γþ0π0þXwith Xdenoting any additional particles of any multiplicity. Neutrino interactions are identified using both scintillation light and TPC signals. Interactions which are out-oftime with respect to the in-time TPC drift window are rejected. Remaining TPC interactions which are inconsistent with the in-time scintillation light signal collected by the PMTs are discarded. At this stage, a comparable rate of selected neutrino to cosmic-ray interactions is achieved with partially contained in-time cosmic-ray interactions comprising the bulk of selected backgrounds. This yields an 83% efficiency for identifying neutrino interactions. After isolating neutrino interactions, cuts are applied to isolate the 2γtopology being sought. The selection is implemented leveraging the tools developed in Ref. [30]. Neutrino candidates are required to have an interaction vertex in the TPC fiducial volume and a Pandora topological neutrino score greater than 0.1 [31]. Diphoton events are selected by requiring exactly two reconstructed showers with greater than 50 MeV of reconstructed energy in each shower. The requirement that exactly two showers are reconstructed serves to reject events with an ηand additional π0as well as events where the ηdecays via the three π0mode. Two quality cuts are further applied to reconstructed showers: a minimum distance from the reconstructed neutrino interaction vertex of 2 cm is required and showers must have a reconstructed direction that is aligned with the direction connecting the shower to the interaction vertex (cos θshower >0.9). At this stage the selection efficiency is 19.5% and the purity 3.5% with backgrounds dominated by π0events. To reject π0events and select ηcandidates, events with a diphoton mass smaller than 250 MeV=c2and larger than 750 MeV=c2are rejected. This requirement brings the efficiency to 18.2% with a one order of magnitude increase in purity (30.2%). Diphoton pairs from π0candidates are used to validate and refine the energy scale calibration for EM showers leading to an additional energy scale correction of 5.2% [32]. Residual backgrounds consist of misreconstructed π0 events and interactions with two or more π0s in the final state. These residual backgrounds are rejected by relying on the kinematics of the η→2γdecay. Given two neutral particles of different mass but equivalent total energy decaying to two photons, the lighter particle will produce a more highly boosted diphoton pair. To leverage this kinematic constraint, we require that selected diphoton pairs have an opening angle such that cos θγγ <0.5. The 2γ decay allows us to define a kinematically minimal mass for a diphoton pair with minimum opening angle θγγ, PHYSICAL REVIEW LETTERS 132, 151801 (2024) 151801-4 Mmax ¼Eγγ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1 2ð1−cos θγγÞ r;ð1Þ where Eγγ is given by the sum of the energy of the two photons. This quantity provides a powerful discriminant for particles of different mass and relies only on the opening angle between the two photons and the sum of the shower energies. Therefore, the dependence on the accuracy of the reconstructed energy for each individual shower is reduced. A cut requiring that events have a value of Mmax > 400 MeV=c2is applied bringing the final selection purity and efficiency to 49.9% and 13.6%, respectively. Importantly, while relying on event kinematics, this cut is tailored to cause minimal bias in selecting signal events leading to a flat efficiency for ηparticles with energies in the 0.5–1.0 GeV range. Distributions for cos θγγ and Mmax which show the separation between signal and background achieved through the use of these variables are provided in the Supplemental Material [32]. A total of 93 events are selected in the dataset used in this analysis. A candidate η event from this dataset is shown in Fig. 1. While the analysis is inclusive of CC and NC processes, the selection is dominated by CC interactions according to the simulated prediction. This is a consequence of the larger relative content of 3∶1for CC:NC events in the simulation as well as a larger selection efficiency for CC ηproduction (15.4%, compared to 8.9% for NC). Dedicated measurements of NC and CC ηproduction will be pursued in future work. This analysis measures a single-bin, flux-integrated cross section for ηproduction. The measurement is carried out by calculating the expression σ¼N−B ϵ×Ntarget ×Φν ;ð2Þ with Nand Bthe selected number of data events and expected number of background events, respectively, ϵthe efficiency for signal events (13.6%), Ntarget the number of target nucleons (4.057 ×1031), and Φνthe integrated neutrino flux (5.01 ×1011 ν=cm2). Backgrounds from 1π0and multi-π0events are constrained in a datadriven way to improve the accuracy and to reduce the overall uncertainty on the extracted ηproduction cross section. The Supplemental Material describes how this constraint is carried out [32]. A fake-data study is performed using events generated via the NuWro event generator treated as data. The fake-data study included the full sideband constraint procedure and led to an extracted cross section within 1σof the NuWro truth value. Figure 2shows the distribution of Mγγ for ηcandidates after applying the full event selection. The simulated prediction (stacked histogram in Fig. 2) shows a peak for the signal sample in the 450–550 MeV=c2bin consistent with the ηmass of 548 MeV=c2. Systematic uncertainties for the measurement are assessed by studying the impact of model variations on the extracted cross section. The constrained uncertainty due to modeling of the neutrino flux, cross section model, and particle reinteractions in the detector leads to an uncertainty of 14.2% for 1π0and multi-π0events. This uncertainty to the cross section contributed by non-π0backgrounds is found to be 10.4% and is left unconstrained. As detailed in Ref. [33], detector systematic uncertainties account for discrepancies between data and simulation in charge and light response. Detector modeling leads to a 17.7% systematic uncertainty on the extracted cross section. Additional uncertainties on the extracted cross section are due to simulation sample statistics (7.6%), uncertainty FIG. 1. Event display of candidate ηevent. FIG. 2. Distribution of Mγγ for selected ηcandidates showing data (data points with statistical uncertainties denoted by the error bar) and the predicted event rate (stacked histogram). Different colors denote different topologies, as described in the legend. The gray error band denotes the systematic uncertainty on the predicted event rate. PHYSICAL REVIEW LETTERS 132, 151801 (2024) 151801-5 on the number of argon targets (1.0%), POT exposure (2.0%), and the impact of sample statistics on the selection efficiency (2.0%). The total systematic uncertainty is calculated to be 26.3%. The data statistical uncertainty is 25.6%. While this analysis reports a cross section inclusive of CC and NC interactions, we highlight the differences in efficiency for these two channels and the magnitude of systematic uncertainties on their modeled ratio and efficiency. The efficiencies for CC and NC interactions are 14.32.8% and 8.90.4%, respectively, where uncertainties denote the uncertainty due to cross section model variations. The selection efficiency, including all systematic uncertainties, is 13.62.4%. Finally, the cross section modeling uncertainty on the predicted CC to NC ratio is 20%. The impact of these uncertainties will be meaningful in future high statistics measurements. The measured cross section per nucleon for a final state with two photons and no π0in the final state tagged by the selection is found to be σν→1ηþX→2γþ0π0þX¼1.27  0.33ðstatÞ0.34ðsystÞ10−41 cm2=nucleon. Because of its >10−19 second lifetime, the ηdecays almost always outside of the struck nucleus, and while final-state interactions can affect the propagation of the ηparticle as it exits the nucleus, they do not impact the particular decay mode chosen. The measured cross section can then be corrected for the well measured ηbranching ratio to two photons of 39.41% 0.20% [7]. This leads to a total cross section for ηproduction (σν→1ηþX)of3.22 0.84ðstatÞ0.86ðsystÞ 10−41 cm2=nucleon. The reported cross section is integrated over all contributions to the MicroBooNE flux from νμ(93.7%), ¯ νμ(5.8%), νe(0.5%), and ¯ νe(0.05%). In simulation, 98.6% of selected signal events originate from νμinteractions, 0.9% from ¯ νμ, and 0.5% from νe. The extracted cross section (σν→1ηþX) can be compared to that for different neutrino interaction generators. For the GENIE generator, a cross section of 4.63 and 4.61 ×10−41 cm2=nucleon is calculated for this signal definition for the GENIE v2_12_10 and GENIE v3_00_06 G18_10a_02_11a models, respectively. The NuWro 19.02.1 [34] generator gives a cross section of 5.45 ×10−41 cm2=nucleon, and NEUT v5.4.0 [35] gives a cross section of 11.9×10−41 cm2=nucleon. Both versions of GENIE, as well as NuWro, give a cross section which is larger than observed but still within 1−2σof the measured value accounting for uncertainties. The NEUT cross section is found to be significantly larger than what is observed in data. The Supplemental Material shows a figure comparing the data result to various generator predictions [32]. The sample of ηcandidate events is additionally employed to reconstruct the invariant mass of the hadronic system to probe the excited resonance. This is calculated using additional information from the hadronic system produced in the interaction. If protons are identified as exiting the neutrino vertex, then the leading proton is combined with the 4-vector of the ηto calculate the mass W of the hadronic system. Protons are identified through the particle identification methods presented in Ref. [36]. The reconstructed Wis shown in Fig. 3for the events selected by the analysis. The data and simulation show good agreement, and the distribution peaks at ∼1.5GeV in agreement with the expectation that most ηparticles are produced though an excitation of the Nð1535Þresonance. In absolute terms, there are over 1 order of magnitude more π0candidates than selected ηcandidate events. The π0dominated distribution shows a clear separation from that for ηcandidates, peaking at ∼1.2GeV as expected for events produced through an excitation of the Δð1232Þresonance. Isolating ηproduction events allows us to suppress the large rate of Δð1232Þ events which would otherwise swamp higher resonances making their study challenging. This represents the first demonstration of the ability to identify higher-order resonances other than the Δð1232Þin neutrino-nucleus interactions and provides a new powerful tool for the study of RES interactions. In summary, this Letter presents the first cross section measurement of ν-Ar ηproduction. Future measurements of ηproduction in MicroBooNE will benefit from additional data for higher statistics measurements. The measurement of ηproduction in LArTPCs launched through this work will further flourish with the SBND [37] and DUNE-ND [38] detectors which will leverage significantly larger neutrino flux in order to report results with ≳103 candidate events. These will have a significant impact on measurements of resonant interaction processes and, in particular, a unique ability to constrain higher-order FIG. 3. Reconstructed invariant mass of the hadronic system utilizing the four-momenta of the reconstructed ηand leading proton (if identified) in the event. The black solid line and data points show the distribution for ηcandidate events predicted and observed, respectively. The distributions in red show the same reconstructed quantity for events from the MicroBooNE data compared to prediction from the π0sideband, normalized to the same number of events from the prediction for the ηselection. PHYSICAL REVIEW LETTERS 132, 151801 (2024) 151801-6 resonances above the Δð1232Þup to uncertainties in their branching ratios. Future high statistics cross section measurements of ηproduction will nonetheless have to confront challenges in constraining the sizable singleand multi-π0 background processes which are subject to large modeling uncertainties, with particular attention needed in how sideband constraints are used to extrapolate background predictions into the signal region. In addition, these samples will provide a new tool for the calibration of EM showers that are of particular importance to the oscillation and BSM physics programs that are being carried out with these detectors. This document was prepared by the MicroBooNE Collaboration using the resources of the Fermi National Accelerator Laboratory (Fermilab), a U.S. Department of Energy, Office of Science, HEP User Facility. Fermilab is managed by Fermi Research Alliance, LLC (FRA), acting under Contract No. DE-AC02-07CH11359. MicroBooNE is supported by the following: the U.S. Department of Energy, Office of Science, Offices of High Energy Physics and Nuclear Physics; the U.S. National Science Foundation; the Swiss National Science Foundation; the Science and Technology Facilities Council (STFC), part of the United Kingdom Research and Innovation; the Royal Society (United Kingdom); and the UK Research and Innovation (UKRI) Future Leaders Fellowship. Additional support for the laser calibration system and cosmic ray tagger was provided by the Albert Einstein Center for Fundamental Physics, Bern, Switzerland. We also acknowledge the contributions of technical and scientific staff to the design, construction, and operation of the MicroBooNE detector as well as the contributions of past collaborators to the development of MicroBooNE analyses, without whom this work would not have been possible. *[email protected] [1] M. Antonello et al. (MicroBooNE, LAr1-ND, ICARUSWA104 Collaborations), arXiv:1503.01520. [2] B. Abi et al. (DUNE Collaboration), arXiv:2002.03005. [3] N. M. Coyle, S. W. Li, and P. A. N. Machado, J. High Energy Phys. 12 (2022) 166. [4] A. B. Balantekin et al.,arXiv:2209.06872. [5] D. Rein and L. M. Sehgal, Ann. Phys. (N.Y.) 133, 79 (1981). [6] T. Leitner, O. Buss, L. Alvarez-Ruso, and U. Mosel, Phys. Rev. C 79, 034601 (2009). [7] R. L. Workman et al. (Particle Data Group), Prog. Theor. Exp. Phys. 2022, 083C01 (2022). 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