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One of the main problems of lung transplantation is the shortage of organs as well as reduced survival rates. In the absence of an international standardized model for lung donor-recipient allocation, we set out to develop such a model based on the characteristics of past experiences with lung donors and recipients with the aim of improving the outcomes of the entire transplantation process. This was a retrospective analysis of 404 lung transplants carried out at the Reina Sofía University Hospital (Córdoba, Spain) over 23 years. We analyzed various clinical variables obtained via our experience of clinical practice in the donation and transplantation process. These were used to create various classification models, including classical statistical methods and also incorporating newer machine-learning approaches. The proposed model represents a powerful tool for donor-recipient matching, which in this current work, exceeded the capacity of classical statistical methods. The variables that predicted an increase in the probability of survival were: higher pre-transplant and post-transplant functional vital capacity (FVC), lower pre-transplant carbon dioxide (PCO2) pressure, lower donor mechanical ventilation, and shorter ischemia time. The variables that negatively influenced transplant survival were low forced expiratory volume in the first second (FEV1) pre-transplant, lower arterial oxygen pressure (PaO2)/fraction of inspired oxygen (FiO2) ratio, bilobar transplant, elderly recipient and donor, donor-recipient graft disproportion requiring a surgical reduction (Tailor), type of combined transplant, need for cardiopulmonary bypass during the surgery, death of the donor due to head trauma, hospitalization status before surgery, and female and male recipient donor sex. These results show the difficulty of the problem which required the introduction of other variables into the analysis. The combination of classical statistical methods and machine learning can support decision-making about the compatibility between donors and recipients. This helps to facilitate reliable prediction and to optimize the grafts for transplantation, thereby improving the transplanted patient survival rate.

Objective One of the main problems of lung transplantation is the shortage of organs as well as reduced survival rates. In the absence of an international standardized model for lung donor-recipient allocation, we set out to develop such a model based on the characteristics of past experiences with lung donors and recipients with the aim of improving the outcomes of the entire transplantation process. Methods This was a retrospective analysis of 404 lung transplants carried out at the Reina Sofía University Hospital (Córdoba, Spain) over 23 years. We analyzed various clinical variables obtained via our experience of clinical practice in the donation and transplantation process. These were used to create various classification models, including classical statistical methods and also incorporating newer machine-learning approaches. Results The proposed model represents a powerful tool for donor-recipient matching, which in this current work, exceeded the capacity of classical statistical methods. The variables that predicted an increase in the probability of survival were: higher pre-transplant and post-transplant functional vital capacity (FVC), lower pre-transplant carbon dioxide (PCO2) pressure, lower donor mechanical ventilation, and shorter ischemia time. The variables that negatively influenced transplant survival were low forced expiratory volume in the first second (FEV1) pre-transplant, lower arterial oxygen pressure (PaO2)/fraction of inspired oxygen (FiO2) ratio, bilobar transplant, elderly recipient and donor, donor-recipient graft disproportion requiring a surgical reduction (Tailor), type of combined transplant, need for cardiopulmonary bypass during the surgery, death of the donor due to head trauma, hospitalization status before surgery, and female and male recipient donor sex. Conclusions These results show the difficulty of the problem which required the introduction of other variables into the analysis. The combination of classical statistical methods and machine learning can support decision-making about the compatibility between donors and recipients. This helps to facilitate reliable prediction and to optimize the grafts for transplantation, thereby improving the transplanted patient survival rate.

A bstract The NEXT experiment aims to observe the neutrinoless double beta decay of 136 Xe in a high-pressure xenon gas TPC using electroluminescence (EL) to amplify the signal from ionization. One of the main advantages of this technology is the possibility to reconstruct the topology of events with energies close to Q ββ . This paper presents the first demonstration that the topology provides extra handles to reject background events using data obtained with the NEXT-DEMO prototype. Single electrons resulting from the interactions of 22 Na 1275 keV gammas and electronpositron pairs produced by conversions of gammas from the 228 Th decay chain were used to represent the background and the signal in a double beta decay. These data were used to develop algorithms for the reconstruction of tracks and the identification of the energy deposited at the end-points, providing an extra background rejection factor of 24 . 3 ± 1 . 4 (stat.)%, while maintaining an efficiency of 66 . 7 ± 1 . % for signal events.

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. For photon emission below 1000 nm, the NBrS yield increases from about10−2photon/e−cm−1bar−1at pressure-reduced electric field values of50Vcm−1bar−1to above3×10−1photon/e−cm−1bar−1at500Vcm−1bar−1. Above1.5kVcm−1bar−1, values that are typically employed for electroluminescence, it is estimated that NBrS is present with an intensity around1photon/e−cm−1bar−1, which is about 2 orders of magnitude lower than conventional, excimer-based electroluminescence. Despite being fainter than its excimeric counterpart, our calculations reveal that NBrS causes luminous backgrounds that can interfere, in either gas or liquid phase, with the ability to distinguish and/or to precisely measure low primary-scintillation signals (S1). In particular, we show this to be the case in the “buffer” region, where keeping the electric field below the electroluminescence threshold does not suffice to extinguish secondary scintillation. The electric field leakage in this region should be mitigated to avoid intolerable levels of NBrS emission. Furthermore, we show that this new source of light emission opens up a viable path toward obtaining S2 signals for discrimination purposes in future single-phase liquid TPCs for neutrino and dark matter physics, with estimated yields up to20–50photons/e−cm−1.

A bstract The Neutrino Experiment with a Xenon TPC (NEXT) searches for the neutrinoless double-beta (0 νββ ) decay of 136 Xe using high-pressure xenon gas TPCs with electroluminescent amplification. A scaled-up version of this technology with about 1 tonne of enriched xenon could reach in less than 5 years of operation a sensitivity to the half-life of 0 νββ decay better than 10 27 years, improving the current limits by at least one order of magnitude. This prediction is based on a well-understood background model dominated by radiogenic sources. The detector concept presented here represents a first step on a compelling path towards sensitivity to the parameter space defined by the inverted ordering of neutrino masses, and beyond.

A bstract 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 136 Xe. 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 228 Th 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 analyses.

A bstract If neutrinoless double beta decay is discovered, the next natural step would be understanding the lepton number violating physics responsible for it. Several alternatives exist beyond the exchange of light neutrinos. Some of these mechanisms can be distinguished by measuring phase-space observables, namely the opening angle cos θ among the two decay electrons, and the electron energy spectra, T 1 and T 2 . In this work, we study the statistical accuracy and precision in measuring these kinematic observables in a future xenon gas detector with the added capability to precisely locate the decay vertex. For realistic detector conditions (a gas pressure of 10 bar and spatial resolution of 4 mm), we find that the average cos θ ¯ and T 1 ¯ values can be reconstructed with a precision of 0.19 and 110 keV, respectively, assuming that only 10 neutrinoless double beta decay events are detected.

The imaging of individual Ba 2+ ions in high pressure xenon gas is one possible way to attain background-free sensitivity to neutrinoless double beta decay and hence establish the Majorana nature of the neutrino. In this paper we demonstrate selective single Ba 2+ ion imaging inside a high-pressure xenon gas environment. Ba 2+ ions chelated with molecular chemosensors are resolved at the gas-solid interface using a diffraction-limited imaging system with scan area of 1 × 1 cm 2 located inside 10 bar of xenon gas. This form of microscopy represents key ingredient in the development of barium tagging for neutrinoless double beta decay searches in 136 Xe. This also provides a new tool for studying the photophysics of fluorescent molecules and chemosensors at the solid-gas interface to enable bottom-up design of catalysts and sensors. Barium tagging is a key ingredient for future detectors of neutrinoless double beta decay in low-background environments. Here, the authors demonstrate fluorescence imaging of single Ba2+ ions in high pressure Xenon gas, by comparing activity between Ba2+ chelated and unchelated samples of crown-ether chemosensors.

A bstract The NEXT-White detector, a high-pressure gaseous xenon time projection chamber, demonstrated the excellence of this technology for future neutrinoless double beta decay searches using photomultiplier tubes (PMTs) to measure energy and silicon photomultipliers (SiPMs) to extract topology information. This analysis uses 83 m Kr data from the NEXT-White detector to measure and understand the energy resolution that can be obtained with the SiPMs, rather than with PMTs. The energy resolution obtained of (10.9 ± 0.6)%, full-width half-maximum, is slightly larger than predicted based on the photon statistics resulting from very low light detection coverage of the SiPM plane in the NEXT-White detector. The difference in the predicted and measured resolution is attributed to poor corrections, which are expected to be improved with larger statistics. Furthermore, the noise of the SiPMs is shown to not be a dominant factor in the energy resolution and may be negligible when noise subtraction is applied appropriately, for high-energy events or larger SiPM coverage detectors. These results, which are extrapolated to estimate the response of large coverage SiPM planes, are promising for the development of future, SiPM-only, readout planes that can offer imaging and achieve similar energy resolution to that previously demonstrated with PMTs.

If neutrinoless double beta decay is discovered, the next natural step would be understanding the lepton number violating physics responsible for it. Several alternatives exist beyond the exchange of light neutrinos. Some of these mechanisms can be distinguished by measuring phase-space observables, namely the opening angle cos θ among the two decay electrons, and the electron energy spectra, T1 and T2. In this work, we study the statistical accuracy and precision in measuring these kinematic observables in a future xenon gas detector with the added capability to precisely locate the decay vertex. For realistic detector conditions (a gas pressure of 10 bar and spatial resolution of 4 mm), we find that the average c̅o̅s̅ ̅θ̅ and T̅₁̅ values can be reconstructed with a precision of 0.19 and 110 keV, respectively, assuming that only 10 neutrinoless double beta decay events are detected.