Drosophila subobscura is a Palearctic species that has recently colonized the Americas. It was first found in 1978 in Puerto Montt, Chile, and in 1982 in Port Townsend, WA. The colonization and rapid expansion of the species in western South and North America provides distinctive opportunities for investigating the process of evolution in action. The inversion polymorphism in the O chromosome from populations of central California and northern Washington, separated by 1300 km, corresponds to a previously observed latitudinal cline, also observed in Europe. Recessive lethal genes are not randomly distributed among the chromosomal arrangements. The incidence of lethal allelism is high, yielding unrealistically low estimates of the effective size of these populations (on the order of 1000 individuals). The high incidence of lethal allelism is likely to be a consequence of the low number of the American colonizers (on the order of 10-100 individuals), but the persistence of the allelism over several years suggests that some lethal-carrying chromosomes may be heterotic owing to shared associations between lethal and other genes.

The NEXT experiment aims to observe the neutrinoless double beta decay of 136Xe 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 Qββ. 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 22Na 1275 keV gammas and electronpositron pairs produced by conversions of gammas from the 228Th 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.[Figure not available: see fulltext.]

NEXT is an experiment dedicated to neutrinoless double beta decay searches in xenon. The detector is a TPC, holding 100 kg of high-pressure xenon enriched in the 136Xe isotope. It is under construction in the Laboratorio Subterráneo de Canfranc in Spain, and it will begin operations in 2015. The NEXT detector concept provides an energy resolutionbetter than 1% FWHM and a topological signal that can be used to reduce the background. Furthermore, the NEXT technology can be extrapolated to a 1 ton-scale experiment. © 2014 J. J. Gómez Cadenas et al.

Abstract: NEXT-100 is an electroluminescent high-pressure xenon gas time projection chamber that will search for the neutrinoless double beta (0νββ) decay of 136Xe. The detector possesses two features of great value for 0νββ searches: energy resolution better than 1% FWHM at the Q value of 136Xe and track reconstruction for the discrimination of signal and background events. This combination results in excellent sensitivity, as discussed in this paper. Material-screening measurements and a detailed Monte Carlo detector simulation predict a background rate for NEXT-100 of at most 4 × 10−4 counts keV−1 kg−1 yr−1. Accordingly, the detector will reach a sensitivity to the 0νββ-decay half-life of 2.8 × 1025 years (90% CL) for an exposure of 100 kg·year, or 6.0 × 1025 years after a run of 3 effective years.[Figure not available: see fulltext.]

We investigate the potential of using deep learning techniques to reject background events in searches for neutrinoless double beta decay with high pressure xenon time projection chambers capable of detailed track reconstruction. The differences in the topological signatures of background and signal events can be learned by deep neural networks via training over many thousands of events. These networks can then be used to classify further events as signal or background, providing an additional background rejection factor at an acceptable loss of efficiency. The networks trained in this study performed better than previous methods developed based on the use of the same topological signatures by a factor of 1.2 to 1.6, and there is potential for further improvement.