Where Are They Now? Where Are We Now?

In this paper we, a research team comprising one professor of education and four graduate students document our reflections on questions we have about the challenges of documenting the impact of teacher education coursework and on our collective research. This paper is organized into three, separate sections. In the first section we present data that Patricia collected while observing Renee teach the same group of prospective English students over two semesters. These courses, C&I 301 (Introduction to Teaching in a Diverse Society) and C&I 302 (Teaching Diverse Middle Grades Students), are the first two courses in a four course sequence that integrate methods of teaching English with critical analysis of schooling and with reflection on one’s own transition from student to teacher. For the two subsequent courses (C&I 303, Teaching Diverse High School Students; C&I 304, Assessing Secondary School Students) the students were taught by different instructors and, during C&I 304, were student teaching. The term, “diversity” is included in the course titles because the teacher education program emphasizes that multicultural education is not a separate course, but that celebrating and working productively with a diverse student population is embedded in everything we do as teachers of adolescents (and adults). In this paper we respond to two recommendations Renee and Patricia have raised in previous works (Clift & Brady, 2003; Clift, 2004) in that we are exploring the ways in which longitudinal study can be incorporated into self-study; we are also using friendly critics (Patricia, Raul, Jason, and Soo Joung) as we analyze Renée’s teaching and the potential impact of her courses (as well as that of the larger teacher education program) on thirteen teacher education graduates’ developing practice. (These graduates have all been out of the teacher education program for two years now.) As our work proceeded we realized that as a team we were grappling with issues of power, authority, and voice in both the selfstudy and larger study. We have shaped this paper to allow others to glimpse our process and the questions it continues to raise for our work

Supernova Model Discrimination with Hyper-Kamiokande

[EN] Core-collapse supernovae are among the most magnificent events in the observable universe. They produce many of the chemical elements necessary for life to exist and their remnants-neutron stars and black holes-are interesting astrophysical objects in their own right. However, despite millennia of observations and almost a century of astrophysical study, the explosion mechanism of core-collapse supernovae is not yet well understood. Hyper-Kamiokande is a next-generation neutrino detector that will be able to observe the neutrino flux from the next galactic core-collapse supernova in unprecedented detail. We focus on the first 500 ms of the neutrino burst, corresponding to the accretion phase, and use a newly-developed, high-precision supernova event generator to simulate Hyper-Kamiokande's response to five different supernova models. We show that Hyper-Kamiokande will be able to distinguish between these models with high accuracy for a supernova at a distance of up to 100 kpc. Once the next galactic supernova happens, this ability will be a powerful tool for guiding simulations toward a precise reproduction of the explosion mechanism observed in nature.We thank MacKenzie Warren, Ken'ichiro Nakazato, Tomonori Totani, Adam Burrows, David Vartanyan, and Irene Tamborra for access to the supernova models used in this work and for answering various related questions. This work was supported by MEXT Grant-in-Aid for Scientific Research on Innovative Areas titled "Exploration of Particle Physics and Cosmology with Neutrinos" under grant No. 18H05535, 18H05536, and 18H5537. In addition, participation of individual researchers has been further supported by funds from JSPS, Japan; the European Union's Horizon 2020 Research and Innovation Programme H2020 grant Nos. RISE-GA822070-JENNIFER2 2020 and RISEGA872549-SK2HK; SSTF-BA1402-06, NRF grant Nos. 20090083526, NRF-2015R1A2A1A05001869, NRF-2016R1D1A 1A02936965, NRF-2016R1D1A3B02010606, NRF-2017R1 A2B4012757, and NRF-2018R1A6A1A06024970 funded by the Korean government (MSIP); JSPS-RFBR Grant #20-5250010/20 and the Ministry of Science and Higher Education under contract #075-15-2020-778, Russia; Brazilian Funding agencies, CNPq and CAPES; STFC ST/R00031X/2, ST/T002891/1, ST/V002872/1, Consolidated Grants, UKRI MR/S032843/1 and MR/S034102/1, UK. Software: BONSAI.(Smy 2007), sntools. (Migenda et al. 2021), WCSim, 124. matplotlib.(Hunter 2007), NumPy.(van der Walt et al. 2011), SciPy.(Virtanen et al. 2020)Abe, K.; Adrich, P.; Aihara, H.; Akutsu, R.; Alekseev, I.; Ali, A.; Ameli, F.... (2021). Supernova Model Discrimination with Hyper-Kamiokande. The Astrophysical Journal. 916(1):1-17. https://doi.org/10.3847/1538-4357/abf7c4117916

Measurement of the Xe 136 two-neutrino double -decay half-life via direct background subtraction in NEXT

[EN] We report a measurement of the half-life of the 136Xe two-neutrino double-ß decay performed with a novel direct-background-subtraction technique. The analysis relies on the data collected with the NEXT-White detector operated with 136Xe-enriched and 136Xe-depleted xenon, as well as on the topology of double-electron tracks. With a fiducial mass of only 3.5 kg of Xe, a half-life of 2.34+0.80(stat)+0.30(sys)×1021 yr is derived from ¿0.46 ¿0.17 the background-subtracted energy spectrum. The presented technique demonstrates the feasibility of unique background-model-independent neutrinoless double-ß-decay searches.The NEXT Collaboration acknowledges support from the following agencies and institutions: the European Research Council (ERC) under Grant No. 951281-BOLD; the European Union's Framework Programme for Research and Innovation Horizon 2020 (2014-2020) under Grant No. 957202-HIDDEN; the MCIN/AEI/10.13039/501100011033 of Spain and ERDF "A way of making Europe" under Grant No. RTI2018-095979, the Severo Ochoa Program Grant No. CEX2018-000867-S, and the Maria de Maeztu Program Grant No. MDM-2016-0692; the Generalitat Valenciana of Spain under Grants No. PROMETEO/2021/087 and No. CIDEGENT/2019/049; the Portuguese FCT under Project No. UID/FIS/04559/2020 to fund the activities of LIBPhys-UC; the Pazy Foundation (Israel) under Grants No. 877040 and No. 877041; the U.S. Department of Energy under Contracts No. DE-AC02-06CH11357 (Argonne National Laboratory), No. DE-AC02-07CH11359 (Fermi National Accelerator Laboratory), No. DE-FG02-13ER42020 (Texas A&M), No. DE-SC0019054 (Texas Arlington), and No. DE-SC0019223 (Arlington, TX); the U.S. National Science Foundation under Grant No. CHE 2004111; and the Robert A. Welch Foundation under Grant No. Y-203120200401. D.G.D. acknowledges support from the Ramon y Cajal program (Spain) under Contract No. RYC-2015-18820. Finally, we are grateful to the Laboratorio Subterraneo de Canfranc for hosting and supporting the NEXT experiment.Novella, P.; Sorel, M.; Usón, A.; Adams, C.; Almazán, H.; Álvarez-Puerta, V.; Aparicio, B.... (2022). Measurement of the Xe 136 two-neutrino double -decay half-life via direct background subtraction in NEXT. Physical Review C (Online). 105(5):055501-1-055501-8. https://doi.org/10.1103/PhysRevC.105.055501055501-1055501-8105

Neutral Bremsstrahlung Emission in Xenon Unveiled

[EN] We present evidence of non-excimer-based secondary scintillation in gaseous xenon, obtained using both the NEXT-White time projection chamber (TPC) and a dedicated setup. Detailed comparison with first-principle calculations allows us to assign this scintillation mechanism to neutral bremsstrahlung (NBrS), a process that is postulated to exist in xenon that has been largely overlooked.The NEXT Collaboration acknowledges support from the following agencies and institutions: the European Research Council (ERC) under Advanced Grant No. 339787-NEXT; the European Unions Framework Program for Research and Innovation Horizon 2020 (20142020) under Grant Agreements No. 674896, No. 690575, and No. 740055; the Ministerio de Economa y Competitividad and the Ministerio de Ciencia, Innovacin y Universidades of Spain under Grants No. FIS2014-53371-C04 and No. RTI2018-095979, the Severo Ochoa Program Grants No. SEV-2014-0398 and No. CEX2018-000867-S, and the Mara de Maeztu Program MDM-2016-0692; the Generalitat Valenciana under Grants No. PROMETEO/2016/120 and No. SEJI/2017/011; the Portuguese FCT under Project No. PTDC/FIS-NUC/3933/2021 and under Project No. UIDP/04559/2020 to fund the activities of LIBPhys-UC; the U.S. Department of Energy under Contracts No. DE-AC02-06CH11357 (Argonne National Laboratory), No. DE-AC02-07CH11359 (Fermi National Accelerator Laboratory), No. DE-FG02-13ER42020 (Texas A&M), and No. DE-SC0019223/DE-SC0019054 (University of Texas at Arlington); and the University of Texas at Arlington (USA). D. G.-D. acknowledges Ramon y Cajal program (Spain) under Contract No. RYC- 2015-18820. J. M.-A. acknowledges support from Fundacin Bancaria la Caixa (ID 100010434), Grant No. LCF/BQ/PI19/11690012. We would like to thank Lorenzo Muniz for insightful discussions on the subtleties of electron transport in gases.Henriques, C.; Amedo, P.; Teixeira, JMR.; González-Díaz, D.; Azevedo, C.; Para, A.; Martín-Albo, J.... (2022). Neutral Bremsstrahlung Emission in Xenon Unveiled. Physical Review X. 12(2):021005-1-021028-23. https://doi.org/10.1103/PhysRevX.12.021005021005-1021028-2312

Radiogenic backgrounds in the NEXT double beta decay experiment

[EN] Natural radioactivity represents one of the main backgrounds in the search for neutrinoless double beta decay. Within the NEXT physics program, the radioactivity- induced backgrounds are measured with the NEXT-White detector. Data from 37.9 days of low-background operations at the Laboratorio Subterraneo de Canfranc with xenon depleted in Xe-136 are analyzed to derive a total background rate of (0.84 +/- 0.02) mHz above 1000 keV. The comparison of data samples with and without the use of the radon abatement system demonstrates that the contribution of airborne-Rn is negligible. A radiogenic background model is built upon the extensive radiopurity screening campaign conducted by the NEXT collaboration. A spectral fit to this model yields the specific contributions of Co-60, K-40, Bi-214 and Tl-208 to the total background rate, as well as their location in the detector volumes. The results are used to evaluate the impact of the radiogenic backgrounds in the double beta decay analyses, after the application of topological cuts that reduce the total rate to (0.25 +/- 0.01) mHz. Based on the best-fit background model, the NEXT-White median sensitivity to the two-neutrino double beta decay is found to be 3.5 sigma after 1 year of data taking. The background measurement in a Q(beta beta)+/- 100 keV energy window validates the best-fit background model also for the neutrinoless double beta decay search with NEXT-100. Only one event is found, while the model expectation is (0.75 +/- 0.12) events.The NEXT collaboration acknowledges support from the following agencies and institutions: the European Research Council (ERC) under the Advanced Grant 339787-NEXT; the European Union's Framework Programme for Research and Innovation Horizon 2020 (2014-2020) under the Marie Sklodowska-Curie Grant Agreements No. 674896, 690575 and 740055; the Ministerio de Economia y Competitividad and the Ministerio de Ciencia, Innovacion y Universidades of Spain under grants FIS2014-53371-C04, RTI2018-095979, the Severo Ochoa Program SEV-2014-0398 and the Maria de Maetzu Program MDM-2016-0692; the GVA of Spain under grants PROMETEO/2016/120 and SEJI/2017/011; the Portuguese FCT under project PTDC/FIS-NUC/2525/2014, under project UID/FIS/04559/2013 to fund the activities of LIBPhys, and under grants PD/BD/105921/2014, SFRH/BPD/109180/2015 and SFRH/BPD/76842/2011; the U.S. Department of Energy under contracts number DE-AC02-06CH11357 (Argonne National Laboratory), DE-AC02-07CH11359 (Fermi National Accelerator Laboratory), DE-FG02-13ER42020 (Texas A&M) and DE-SC0019223/DE-SC0019054 (University of Texas at Arlington); and the University of Texas at Arlington. DGD acknowledges Ramon y Cajal program (Spain) under contract number RYC-2015-18820. We also warmly acknowledge the Laboratori Nazionali del Gran Sasso (LNGS) and the Dark Side collaboration for their help with TPB coating of various parts of the NEXT-White TPC. Finally, we are grateful to the Laboratorio Subterraneo de Canfranc for hosting and supporting the NEXT experiment.Novella, P.; Palmeiro, B.; Sorel, M.; Usón, A.; Ferrario, P.; Gómez-Cadenas, JJ.; Adams, C.... (2019). Radiogenic backgrounds in the NEXT double beta decay experiment. Journal of High Energy Physics (Online). (10):1-26. https://doi.org/10.1007/JHEP10(2019)051S12610KamLAND-Zen collaboration, Search for Majorana Neutrinos near the Inverted Mass Hierarchy Region with KamLAND-Zen, Phys. Rev. Lett.117 (2016) 082503 [arXiv:1605.02889] [INSPIRE].GERDA collaboration, Improved Limit on Neutrinoless Double-β Decay of76Ge from GERDA Phase II, Phys. Rev. Lett.120 (2018) 132503 [arXiv:1803.11100] [INSPIRE].NEXT collaboration, NEXT-100 Technical Design Report (TDR): Executive Summary, 2012JINST7 T06001 [arXiv:1202.0721] [INSPIRE].M. Redshaw, E. Wingfield, J. McDaniel and E.G. Myers, Mass and double-beta-decay Q value of Xe-136, Phys. Rev. Lett.98 (2007) 053003 [INSPIRE].EXO-200 collaboration, Improved measurement of the 2νββ half-life of136Xe with the EXO-200 detector, Phys. Rev.C 89 (2014) 015502 [arXiv:1306.6106] [INSPIRE].KamLAND-Zen collaboration, Measurement of the double-β decay half-life of136Xe with the KamLAND-Zen experiment, Phys. Rev.C 85 (2012) 045504 [arXiv:1201.4664] [INSPIRE].NEXT collaboration, Initial results on energy resolution of the NEXT-White detector, 2018JINST13 P10020 [arXiv:1808.01804] [INSPIRE].NEXT collaboration, Energy Calibration of the NEXT-White Detector with 1% Resolution Near Qββof136Xe, arXiv:1905.13110 [INSPIRE].NEXT collaboration, Near-Intrinsic Energy Resolution for 30 to 662 keV Gamma Rays in a High Pressure Xenon Electroluminescent TPC, Nucl. Instrum. Meth.A 708 (2013) 101 [arXiv:1211.4474] [INSPIRE].NEXT collaboration, Characterisation of NEXT-DEMO using xenon KαX-rays, 2014JINST9 P10007 [arXiv:1407.3966] [INSPIRE].NEXT collaboration, First proof of topological signature in the high pressure xenon gas TPC with electroluminescence amplification for the NEXT experiment, JHEP01 (2016) 104 [arXiv:1507.05902] [INSPIRE].NEXT collaboration, Demonstration of the event identification capabilities of the NEXT-White detector, arXiv:1905.13141 [INSPIRE].A.D. McDonald et al., Demonstration of Single Barium Ion Sensitivity for Neutrinoless Double Beta Decay using Single Molecule Fluorescence Imaging, Phys. Rev. Lett.120 (2018) 132504 [arXiv:1711.04782] [INSPIRE].P. Thapa et al., Barium Chemosensors with Dry-Phase Fluorescence for Neutrinoless Double Beta Decay, arXiv:1904.05901 [INSPIRE].NEXT collaboration, Ionization and scintillation response of high-pressure xenon gas to alpha particles, 2013 JINST8 P05025 [arXiv:1211.4508] [INSPIRE].NEXT collaboration, Initial results of NEXT-DEMO, a large-scale prototype of the NEXT-100 experiment, 2013 JINST8 P04002 [arXiv:1211.4838] [INSPIRE].NEXT collaboration, Operation and first results of the NEXT-DEMO prototype using a silicon photomultiplier tracking array, 2013 JINST8 P09011 [arXiv:1306.0471] [INSPIRE].NEXT collaboration, Description and commissioning of NEXT-MM prototype: first results from operation in a Xenon-Trimethylamine gas mixture, 2014 JINST9 P03010 [arXiv:1311.3242] [INSPIRE].NEXT collaboration, Ionization and scintillation of nuclear recoils in gaseous xenon, Nucl. Instrum. Meth.A 793 (2015) 62 [arXiv:1409.2853] [INSPIRE].NEXT collaboration, An improved measurement of electron-ion recombination in high-pressure xenon gas, 2015 JINST10 P03025 [arXiv:1412.3573] [INSPIRE].NEXT collaboration, Accurate γ and MeV-electron track reconstruction with an ultra-low diffusion Xenon/TMA TPC at 10 atm, Nucl. Instrum. Meth.A 804 (2015) 8 [arXiv:1504.03678] [INSPIRE].NEXT collaboration, The Next White (NEW) Detector, 2018 JINST13 P12010 [arXiv:1804.02409] [INSPIRE].NEXT collaboration, Sensitivity of NEXT-100 to Neutrinoless Double Beta Decay, JHEP05 (2016) 159 [arXiv:1511.09246] [INSPIRE].V. Alvarez et al., Radiopurity control in the NEXT-100 double beta decay experiment: procedures and initial measurements, 2013 JINST8 T01002 [arXiv:1211.3961] [INSPIRE].NEXT collaboration, Radiopurity assessment of the tracking readout for the NEXT double beta decay experiment, 2015 JINST10 P05006 [arXiv:1411.1433] [INSPIRE].NEXT collaboration, Radiopurity assessment of the energy readout for the NEXT double beta decay experiment, 2017 JINST12 T08003 [arXiv:1706.06012] [INSPIRE].NEXT collaboration, Measurement of radon-induced backgrounds in the NEXT double beta decay experiment, JHEP10 (2018) 112 [arXiv:1804.00471] [INSPIRE].NEXT collaboration, Electron drift properties in high pressure gaseous xenon, 2018 JINST13 P07013 [arXiv:1804.01680] [INSPIRE].NEXT collaboration, Calibration of the NEXT-White detector using83m Kr decays, 2018JINST13 P10014 [arXiv:1804.01780] [INSPIRE].NEXT collaboration, Background rejection in NEXT using deep neural networks, 2017JINST12 T01004 [arXiv:1609.06202] [INSPIRE].NEXT collaboration, Application and performance of an ML-EM algorithm in NEXT, 2017JINST12 P08009 [arXiv:1705.10270] [INSPIRE]

Demonstration of background rejection using deep convolutional neural networks in the NEXT experiment

[EN] Convolutional neural networks (CNNs) are widely used state-of-the-art computer vision tools that are becoming increasingly popular in high-energy physics. In this paper, we attempt to understand the potential of CNNs for event classification in the NEXT experiment, which will search for neutrinoless double-beta decay in Xe-136. To do so, we demonstrate the usage of CNNs for the identification of electron-positron pair production events, which exhibit a topology similar to that of a neutrinoless double-beta decay event. These events were produced in the NEXT-White high-pressure xenon TPC using 2.6 MeV gamma rays from a Th-228 calibration source. We train a network on Monte Carlo-simulated events and show that, by applying on-the-fly data augmentation, the network can be made robust against differences between simulation and data. The use of CNNs offers significant improvement in signal efficiency and background rejection when compared to previous non-CNN-based analysesThis study used computing resources from Artemisa, co-funded by the European Union through the 2014-2020 FEDER Operative Programme of the Comunitat Valenciana, project DIFEDER/2018/048. This research used resources of the Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357. The NEXT collaboration acknowledges support from the following agencies and institutions: Xunta de Galicia (Centro singularde investigacion de Galicia accreditation 2019-2022), by European Union ERDF, and by the "Maria de Maeztu" Units of Excellence program MDM-2016-0692 and the Spanish Research State Agency"; the European Research Council (ERC) under the Advanced Grant 339787-NEXT; the European Union's Framework Programme for Research and Innovation Horizon 2020 (2014-2020) under the Grant Agreements No. 674896, 690575 and 740055; the Ministerio de Economia y Competitividad and the Ministerio de Ciencia, Innovacion y Universidades of Spain under grants FIS2014-53371-C04, RTI2018-095979, the Severo Ochoa Program grants SEV-20140398 and CEX2018-000867-S; the GVA of Spain under grants PROMETEO/2016/120 and SEJI/2017/011; the Portuguese FCT under project PTDC/FIS-NUC/2525/2014 and under projects UID/FIS/04559/2020 to fund the activities of LIBPhys-UC; the U.S. Department of Energy under contracts number DE-AC02-07CH11359 (Fermi National Accelerator Laboratory), DE-FG02-13ER42020 (Texas A&M) and DE-SC0019223/DE SC0019054 (University of Texas at Arlington); and the University of Texas at Arlington. DGD acknowledges Ramon y Cajal program (Spain) under contract number RYC-2015 18820. JMA acknowledges support from Fundacion Bancaria "la Caixa" (ID 100010434), grant code LCF/BQ/PI19/11690012. We also warmly acknowledge the Laboratori Nazionali del Gran Sasso (LNGS) and the Dark Side collaboration for their help with TPB coating of various parts of the NEXT-White TPC. Finally, we are grateful to the Laboratorio Subterraneo de Canfranc for hosting and supporting the NEXT experiment.Kekic, M.; Adams, C.; Woodruff, K.; Renner, J.; Church, E.; Del Tutto, M.; Hernando Morata, JA.... (2021). Demonstration of background rejection using deep convolutional neural networks in the NEXT experiment. Journal of High Energy Physics (Online). (1):1-22. https://doi.org/10.1007/JHEP01(2021)189S1221NEXT collaboration, The Next White (NEW) Detector, 2018 JINST 13 P12010 [arXiv:1804.02409] [INSPIRE].NEXT collaboration, Energy calibration of the NEXT-White detector with 1% resolution near Qββ of 136Xe, JHEP 10 (2019) 230 [arXiv:1905.13110] [INSPIRE].NEXT collaboration, Demonstration of the event identification capabilities of the NEXT-White detector, JHEP 10 (2019) 052 [arXiv:1905.13141] [INSPIRE].NEXT collaboration, Radiogenic Backgrounds in the NEXT Double Beta Decay Experiment, JHEP 10 (2019) 051 [arXiv:1905.13625] [INSPIRE].G. Carleo et al., Machine learning and the physical sciences, Rev. Mod. Phys. 91 (2019) 045002 [arXiv:1903.10563] [INSPIRE].A. Aurisano et al., A Convolutional Neural Network Neutrino Event Classifier, 2016 JINST 11 P09001 [arXiv:1604.01444] [INSPIRE].MicroBooNE collaboration, Convolutional Neural Networks Applied to Neutrino Events in a Liquid Argon Time Projection Chamber, 2017 JINST 12 P03011 [arXiv:1611.05531] [INSPIRE].MicroBooNE collaboration, Deep neural network for pixel-level electromagnetic particle identification in the MicroBooNE liquid argon time projection chamber, Phys. Rev. D 99 (2019) 092001 [arXiv:1808.07269] [INSPIRE].N. Choma et al., Graph Neural Networks for IceCube Signal Classification, in proceedings of the 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando, FL, U.S.A., 17–20 December 2018, pp. 386–391 [arXiv:1809.06166] [INSPIRE].E. Racah et al., Revealing Fundamental Physics from the Daya Bay Neutrino Experiment using Deep Neural Networks, in proceedings of the 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA), Anaheim, CA, U.S.A., 18–20 December 2016, pp. 892–897 [arXiv:1601.07621] [INSPIRE].EXO collaboration, Deep Neural Networks for Energy and Position Reconstruction in EXO-200, 2018 JINST 13 P08023 [arXiv:1804.09641] [INSPIRE].H. Qiao, C. Lu, X. Chen, K. Han, X. Ji and S. Wang, Signal-background discrimination with convolutional neural networks in the PandaX-III experiment using MC simulation, Sci. China Phys. Mech. Astron. 61 (2018) 101007 [arXiv:1802.03489] [INSPIRE].P. Ai, D. Wang, G. Huang and X. Sun, Three-dimensional convolutional neural networks for neutrinoless double-beta decay signal/background discrimination in high-pressure gaseous Time Projection Chamber, 2018 JINST 13 P08015 [arXiv:1803.01482] [INSPIRE].NEXT collaboration, Background rejection in NEXT using deep neural networks, 2017 JINST 12 T01004 [arXiv:1609.06202] [INSPIRE].NEXT collaboration, Sensitivity of NEXT-100 to Neutrinoless Double Beta Decay, JHEP 05 (2016) 159 [arXiv:1511.09246] [INSPIRE].D. Nygren, High-pressure xenon gas electroluminescent TPC for 0-ν ββ-decay search, Nucl. Instrum. Meth. A 603 (2009) 337 [INSPIRE].NEXT collaboration, Calibration of the NEXT-White detector using 83mKr decays, 2018 JINST 13 P10014 [arXiv:1804.01780] [INSPIRE].J. Martín-Albo, The NEXT experiment for neutrinoless double beta decay searches, Ph.D. Thesis, University of Valencia, Valencia Spain (2015) [INSPIRE].GEANT4 collaboration, GEANT4 — a simulation toolkit, Nucl. Instrum. Meth. A 506 (2003) 250 [INSPIRE].A. Krizhevsky, I. Sutskever and G.E. Hinton, Imagenet classification with deep convolutional neural networks, Commun. ACM 60 (2017) 84.N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever and R. Salakhutdinov, Dropout: A simple way to prevent neural networks from overfitting, J. Mach. Learn. Res. 15 (2014) 1929.S. Ioffe and C. Szegedy, Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, arXiv:1502.03167 [INSPIRE].C. Guo, G. Pleiss, Y. Sun and K.Q. Weinberger, On calibration of modern neural networks, arXiv:1706.04599.K. He, X. Zhang, S. Ren and J. Sun, Deep Residual Learning for Image Recognition, arXiv:1512.03385 [INSPIRE].K. He, X. Zhang, S. Ren and J. Sun, Identity mappings in deep residual networks, arXiv:1603.05027.X. Li, S. Chen, X. Hu and J. Yang, Understanding the Disharmony Between Dropout and Batch Normalization by Variance Shift, in proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, U.S.A., 15–20 June 2019, pp. 2677–2685.J. Deng, W. Dong, R. Socher, L. Li, K. Li and L. Fei-Fei, ImageNet: A large-scale hierarchical image database, in proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, U.S.A., 20–25 June 2009, pp. 248–255.B. Graham and L. van der Maaten, Submanifold sparse convolutional networks, arXiv:1706.01307.L. Dominé and K. Terao, Scalable deep convolutional neural networks for sparse, locally dense liquid argon time projection chamber data, Phys. Rev. D 102 (2020) 012005 [arXiv:1903.05663] [INSPIRE].C. Shorten and T.M. Khoshgoftaar, A survey on image data augmentation for deep learning, J. Big Data 6 (2019) 60.G.J. Székely and M.L. Rizzo, Testing for equal distributions in high dimension, InterStat 5 (2004) 1.G. Székely and M.L. Rizzo, Energy statistics: A class of statistics based on distances, J. Stat. Plann. Infer. 8 (2013) 1249.R.A. Fisher, The Design of Experiments, Oliver and Boyd (1935).NEXT collaboration, Sensitivity of a tonne-scale NEXT detector for neutrinoless double beta decay searches, arXiv:2005.06467 [INSPIRE].NEXT collaboration, Initial results of NEXT-DEMO, a large-scale prototype of the NEXT-100 experiment, 2013 JINST 8 P04002 [arXiv:1211.4838] [INSPIRE].NEXT collaboration, Operation and first results of the NEXT-DEMO prototype using a silicon photomultiplier tracking array, 2013 JINST 8 P09011 [arXiv:1306.0471] [INSPIRE]

Measurement of the cosmic ray spectrum above 4×10184{\times}10^{18} eV using inclined events detected with the Pierre Auger Observatory

A measurement of the cosmic-ray spectrum for energies exceeding $4{\times}10^{18}$ eV is presented, which is based on the analysis of showers with zenith angles greater than $60^{\circ}$ detected with the Pierre Auger Observatory between 1 January 2004 and 31 December 2013. The measured spectrum confirms a flux suppression at the highest energies. Above $5.3{\times}10^{18}$ eV, the "ankle", the flux can be described by a power law $E^{-\gamma}$ with index $\gamma=2.70 \pm 0.02 \,\text{(stat)} \pm 0.1\,\text{(sys)}$ followed by a smooth suppression region. For the energy ($E_\text{s}$) at which the spectral flux has fallen to one-half of its extrapolated value in the absence of suppression, we find $E_\text{s}=(5.12\pm0.25\,\text{(stat)}^{+1.0}_{-1.2}\,\text{(sys)}){\times}10^{19}$ eV.Comment: Replaced with published version. Added journal reference and DO

Energy Estimation of Cosmic Rays with the Engineering Radio Array of the Pierre Auger Observatory

The Auger Engineering Radio Array (AERA) is part of the Pierre Auger Observatory and is used to detect the radio emission of cosmic-ray air showers. These observations are compared to the data of the surface detector stations of the Observatory, which provide well-calibrated information on the cosmic-ray energies and arrival directions. The response of the radio stations in the 30 to 80 MHz regime has been thoroughly calibrated to enable the reconstruction of the incoming electric field. For the latter, the energy deposit per area is determined from the radio pulses at each observer position and is interpolated using a two-dimensional function that takes into account signal asymmetries due to interference between the geomagnetic and charge-excess emission components. The spatial integral over the signal distribution gives a direct measurement of the energy transferred from the primary cosmic ray into radio emission in the AERA frequency range. We measure 15.8 MeV of radiation energy for a 1 EeV air shower arriving perpendicularly to the geomagnetic field. This radiation energy -- corrected for geometrical effects -- is used as a cosmic-ray energy estimator. Performing an absolute energy calibration against the surface-detector information, we observe that this radio-energy estimator scales quadratically with the cosmic-ray energy as expected for coherent emission. We find an energy resolution of the radio reconstruction of 22% for the data set and 17% for a high-quality subset containing only events with at least five radio stations with signal.Comment: Replaced with published version. Added journal reference and DO

Measurement of the Radiation Energy in the Radio Signal of Extensive Air Showers as a Universal Estimator of Cosmic-Ray Energy

We measure the energy emitted by extensive air showers in the form of radio emission in the frequency range from 30 to 80 MHz. Exploiting the accurate energy scale of the Pierre Auger Observatory, we obtain a radiation energy of 15.8 \pm 0.7 (stat) \pm 6.7 (sys) MeV for cosmic rays with an energy of 1 EeV arriving perpendicularly to a geomagnetic field of 0.24 G, scaling quadratically with the cosmic-ray energy. A comparison with predictions from state-of-the-art first-principle calculations shows agreement with our measurement. The radiation energy provides direct access to the calorimetric energy in the electromagnetic cascade of extensive air showers. Comparison with our result thus allows the direct calibration of any cosmic-ray radio detector against the well-established energy scale of the Pierre Auger Observatory.Comment: Replaced with published version. Added journal reference and DOI. Supplemental material in the ancillary file