3He-Rich Solar Energetic Particles in Helical Jets on the Sun

Particle acceleration in stellar flares is ubiquitous in the Universe, however, our Sun is the only astrophysical object where energetic particles and their source flares can both be observed. The acceleration mechanism in solar flares, tremendously enhancing (up to a factor of ten thousand) rare elements like 3He and ultra-heavy nuclei, has been puzzling for almost 50 years. Here we present some of the most intense 3He- and Fe-rich solar energetic particle events ever reported. The events were accompanied by non-relativistic electron events and type III radio bursts. The corresponding high-resolution, extreme-ultraviolet imaging observations have revealed for the first time a helical structure in the source flare with a jet-like shape. The helical jets originated in relatively small, compact active regions, located at the coronal hole boundary. A mini-filament at the base of the jet appears to trigger these events. The events were observed with the two Solar Terrestrial Relations Observatories STEREO on the backside of the Sun, during the period of increased solar activity in 2014. The helical jets may be a distinct feature of these intense events that is related to the production of high 3He and Fe enrichments.Comment: accepted for publication in The Astrophysical Journa

Comprehensive Characterization of Solar Eruptions with Remote and In-Situ Observations, and Modeling : The Major Solar Events on 4 November 2015

Solar energetic particles (SEPs) are an important product of solar activity. They are connected to solar active regions and flares, coronal mass ejections (CMEs), EUV waves, shocks, Type II and III radio emissions, and X-ray bursts. These phenomena are major probes of the partition of energy in solar eruptions, as well as for the organization, dynamics, and relaxation of coronal and interplanetary magnetic fields. Many of these phenomena cause terrestrial space weather, posing multiple hazards for humans and their technology from space to the ground. Since particular flares, shocks, CMEs, and EUV waves produce SEP events but others do not, since propagation effects from the low corona to 1 AU appear important for some events but not others, and since Type II and III radio emissions and X-ray bursts are sometimes produced by energetic particles leaving these acceleration sites, it is necessary to study the whole system with a multi-frequency and multi-instrument perspective that combines both in-situ and remote observations with detailed modeling of phenomena. This article demonstrates this comprehensive approach and shows its necessity by analyzing a trio of unusual and striking solar eruptions, radio and X-ray bursts, and SEP events that occurred on 4 November 2015. These events show both strong similarities and differences from standard events and each other, despite having very similar interplanetary conditions and only two flare sites and CME genesis regions. They are therefore major targets for further in-depth observational studies, and for testing both existing and new theories and models. We present the complete suite of relevant observations, complement them with initial modeling results for the SEPs and interplanetary magnetic connectivity, and develop a plausible scenario for the eruptions. Perhaps controversially, the SEPs appear to be reasonably modelled and evidence points to significant non-Parker magnetic fields. Based on the very limited modeling available, we identify the aspects that are and are not understood, and we discuss ideas that may lead to improved understanding of the SEP, radio, and space-weather events.Peer reviewe

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]

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Assessing changes in Cd phytoavailability to tomato in amended calcareous soils.

A plot study was conducted to assess changes in Cd phytoavailability to a tomato cultivar in an agricultural soil in Southeastern Spain amended in two different ways (A and B), under controlled conditions. The experimental soil corresponded to a fine-loamy carbonatic thermic Calcidic Haploxeroll (Soil Survey Staff, 1998). A) Soil was amended with a single application of sewage sludge from a municipal source that had a total Cd concentration of 0.5 mg kg-1 at a rate that represented a final average concentration in the mixture of soil and sludge of less than 50 µg Cd kg-1. B) The amendment consisted of the addition of a mineral fertiliser with the same amount of NPK as in the sewage sludge application. The final levels of Cd were supposed to be negligible. A plot series without amendments was also performed (C). DTPA plus triethanolamine, and ammonium acetate extractable fractions in soils were analysed for all the plots. The time-dependent Cd accumulation in different parts of the tomato plants was studied by means of a Cd salt treatment. For each block (A, B, and C) four levels of Cd (0, 3, 30, 100 mg kg-1) were added as CdCl2. There was a significant increase in plant Cd after the initial cropping. Tomato stems, leaves and fruits were analysed separately for Cd determination. Differential Cd distribution and accumulation in tomato parts was detected

Solar Electron Beam -- Langmuir Wave Interactions and How They Modify Solar Electron Beam Spectra: Solar Orbiter Observations of a Match Made in the Heliosphere

International audienceSolar Orbiter's four in-situ instruments have recorded numerous energetic electron events at heliocentric distances between 0.5 and 1au. We analyse energetic electron fluxes, spectra, pitch angle distributions, associated Langmuir waves, and type III solar radio bursts for 3 events to understand what causes modifications in the electron flux and identify the origin and characteristics of features observed in the electron spectrum. We investigate what electron beam properties and solar wind conditions are associated with Langmuir wave growth and spectral breaks in the electron peak flux as a function of energy. We observe velocity dispersion and quasilinear relaxation in the electron flux caused by the resonant wave-particle interactions in the deca-keV range, at the energies at which we observe breaks in the electron spectrum, co-temporal with the local generation of Langmuir waves. We show, via the evolution of the electron flux at the time of the event, that these interactions are responsible for the spectral signatures observed around 10 and 50keV, confirming the results of simulations by Kontar & Reid (2009). These signatures are independent of pitch angle scattering. Our findings highlight the importance of using overlapping FOVs when working with data from different sensors. In this work, we exploit observations from all in-situ instruments to address, for the first time, how the energetic electron flux is modified by the beam-plasma interactions, and results into specific features to appear in the local spectrum. Our results, corroborated with numerical simulations, can be extended to a wider range of heliocentric distances

Polarization of Langmuir waves observed by RPW-TDS instrument on Solar Orbiter during Type III radio bursts

International audienceWe investigate the polarization of Langmuir waves observed by the Time Domain Sampler (TDS) module of the Radio and Plasma Waves instrument on Solar Orbiter during several extensive Type III burst events. During its two-year-long cruise phase, Solar orbiter often crossed the source region of the Type III radio emission and observed the Langmuir waves generated by solar energetic electrons. The waves are known to exhibit complex modulation and often non-trivial elliptical polarization which sometimes rapidly changes on the timescales of tens of milliseconds. We show that the observed waveforms are typically composed of multiple sub-packets with a relatively short coherence length. We investigate the correlation between the polarization of the waves, simultaneously observed energetic electrons beams and other plasma properties

A randomized trial of planned cesarean or vaginal delivery for twin pregnancy

Background: Twin birth is associated with a higher risk of adverse perinatal outcomes than singleton birth. It is unclear whether planned cesarean section results in a lower risk of adverse outcomes than planned vaginal delivery in twin pregnancy.\ud \ud Methods: We randomly assigned women between 32 weeks 0 days and 38 weeks 6 days of gestation with twin pregnancy and with the first twin in the cephalic presentation to planned cesarean section or planned vaginal delivery with cesarean only if indicated. Elective delivery was planned between 37 weeks 5 days and 38 weeks 6 days of gestation. The primary outcome was a composite of fetal or neonatal death or serious neonatal morbidity, with the fetus or infant as the unit of analysis for the statistical comparison.\ud \ud Results: A total of 1398 women (2795 fetuses) were randomly assigned to planned cesarean delivery and 1406 women (2812 fetuses) to planned vaginal delivery. The rate of cesarean delivery was 90.7% in the planned-cesarean-delivery group and 43.8% in the planned-vaginal-delivery group. Women in the planned-cesarean-delivery group delivered earlier than did those in the planned-vaginal-delivery group (mean number of days from randomization to delivery, 12.4 vs. 13.3; P = 0.04). There was no significant difference in the composite primary outcome between the planned-cesarean-delivery group and the planned-vaginal-delivery group (2.2% and 1.9%, respectively; odds ratio with planned cesarean delivery, 1.16; 95% confidence interval, 0.77 to 1.74; P = 0.49).\ud \ud Conclusion: In twin pregnancy between 32 weeks 0 days and 38 weeks 6 days of gestation, with the first twin in the cephalic presentation, planned cesarean delivery did not significantly decrease or increase the risk of fetal or neonatal death or serious neonatal morbidity, as compared with planned vaginal delivery