Neutrino masses and mixing parameters in a left-right model with mirror fermions

In this work we consider a left-right model containing mirror fermions with gauge group SU(3)$_{C} \otimes SU(2)_{L} \otimes SU(2)_{R} \otimes U(1)_{Y^\prime}$. The model has several free parameters which here we have calculated by using the recent values for the squared-neutrino mass differences. Lower bound for the mirror vacuum expectation value helped us to obtain crude estimations for some of these parameters. Also we estimate the order of magnitude of the masses of the standard and mirror neutrinos.Comment: 13 pages, version submitted to European Physical Journal

SN 2016jhj at redshift 0.34: extending the Type II supernova Hubble diagram using the standard candle method

Although Type Ia supernova cosmology has now reached a mature state, it is important to develop as many independent methods as possible to understand the true nature of dark energy. Recent studies have shown that Type II supernovae (SNe II) offer such a path and could be used as alternative distance indicators. However, the majority of these studies were unable to extend the Hubble diagram above redshift z = 0.3 because of observational limitations. Here, we show that we are now ready to move beyond low redshifts and attempt high-redshift (z ≳ 0.3) SN II cosmology as a result of new-generation deep surveys such as the Subaru/Hyper Suprime-Cam survey. Applying the ´standard candle method´ to SN 2016jhj (z = 0.3398 ± 0.0002; discovered by HSC) together with a low-redshift sample, we are able to construct the highest-redshift SN II Hubble diagram to date with an observed dispersion of 0.27 mag (i.e. 12-13 per cent in distance). This work demonstrates the bright future of SN II cosmology in the coming era of large, wide-field surveys like that of the Large Synoptic Survey Telescope.Fil: de Jaeger, T.. University of California at Berkeley; Estados UnidosFil: Galbany, L.. University of Pittsburgh at Johnstown; Estados UnidosFil: Filippenko, A. V.. University of California at Berkeley; Estados UnidosFil: González Gaitán, S.. Universidad de Chile; ChileFil: Yasuda, N.. University of Tokio; JapónFil: Maeda, K.. University of Tokio; JapónFil: Tanaka, M.. University of Tokio; JapónFil: Morokuma, T.. University of Tokio; JapónFil: Moriya, T. J.. National Astronomical Observatory of Japan; JapónFil: Tominaga, N.. University of Tokyo; JapónFil: Nomoto, Ken’ichi. University of Tokyo; JapónFil: Komiyama, Y.. National Astronomical Observatory of Japan; JapónFil: Anderson, J. P.. European Southern Observatory; ChileFil: Brink, T. G.. University of California at Berkeley; Estados UnidosFil: Carlberg, R. G.. University of Toronto; CanadáFil: Folatelli, Gaston. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas; Argentina. University of Tokyo; JapónFil: Hamuy, M.. Universidad de Chile; ChileFil: Pignata, G.. Universidad Andrés Bello; ChileFil: Zheng, W.. University of California at Berkeley; Estados Unido

Results from the Supernova Photometric Classification Challenge

We report results from the Supernova Photometric Classification Challenge (SNPCC), a publicly released mix of simulated supernovae (SNe), with types (Ia, Ibc, and II) selected in proportion to their expected rate. The simulation was realized in the griz filters of the Dark Energy Survey (DES) with realistic observing conditions (sky noise, point-spread function and atmospheric transparency) based on years of recorded conditions at the DES site. Simulations of non-Ia type SNe are based on spectroscopically confirmed light curves that include unpublished non-Ia samples donated from the Carnegie Supernova Project (CSP), the Supernova Legacy Survey (SNLS), and the Sloan Digital Sky Survey-II (SDSS-II). A spectroscopically confirmed subset was provided for training. We challenged scientists to run their classification algorithms and report a type and photo-z for each SN. Participants from 10 groups contributed 13 entries for the sample that included a host-galaxy photo-z for each SN, and 9 entries for the sample that had no redshift information. Several different classification strategies resulted in similar performance, and for all entries the performance was significantly better for the training subset than for the unconfirmed sample. For the spectroscopically unconfirmed subset, the entry with the highest average figure of merit for classifying SNe~Ia has an efficiency of 0.96 and an SN~Ia purity of 0.79. As a public resource for the future development of photometric SN classification and photo-z estimators, we have released updated simulations with improvements based on our experience from the SNPCC, added samples corresponding to the Large Synoptic Survey Telescope (LSST) and the SDSS, and provided the answer keys so that developers can evaluate their own analysis.Comment: accepted by PAS

The correlation between C/O ratio, metallicity and the initial WD mass for SNe Ia

In this paper, we want to check whether or not the carbon abundance can be affected by initial metallicity. We calculated a series of stellar evolution. We found that when $Z\leq0.02$, the carbon abundance is almost independent of metallicity if it is plotted against the initial WD mass. However, when $Z>0.02$, the carbon abundance is not only a function of the initial WD mass, but also metallicity, i.e. for a given initial WD mass, the higher the metallicity, the lower the carbon abundance. Based on some previous studies, i.e. both a high metallicity and a low carbon abundance lead to a lower production of $^{\rm 56}$Ni formed during SN Ia explosion, the effects of the carbon abundance and the metallicity on the amount of $^{\rm 56}$Ni are enhanced by each other, which may account for the variation of maximum luminosity of SNe Ia, at least qualitatively. Considering that the central density of WD before supernova explosion may also play a role on the production of $^{\rm 56}$Ni and the carbon abundance, the metallicity and the central density are all determined by the initial parameters of progenitor system, i.e. the initial WD mass, metallicity, orbital period and secondary mass, the amount of $^{\rm 56}$Ni might be a function of the initial parameters. Then, our results might construct a bridge linking the progenitor model and the explosion model of SNe Ia.Comment: 7pages, 4 figures, accepted for publication in A&

Lepton mass generation and family number violation mechanism in the SU(6)⊗U(1)SU(6)\otimes U(1) model

Lepton family number violation processes arise in the $SU(6)_L \otimes U(1)_Y$ model due to the presence of an extra neutral gauge boson, Z$'$, with family changing couplings, and due to the fact that this model demands the existence of heavy exotic leptons. The mixing of the standard Z with Z$'$ and the mixing of ordinary leptons with exotic ones induce together family changing couplings on the Z and therefore nonvanishing rates for lepton family number violation processes, such as $Z \to e \bar{\mu}$, $\mu \to ee\bar{e}$ and $\mu \to e\gamma$. Additional contributions to the processes $\mu \to e \gamma$ and $\mu \to ee \bar{e}$ are induced from the mass generation mechanism. This last type of contributions may compete with the above one, depending on the masses of the scalars which participate in the diagrams which generate radiatively the masses of the charged leptons. Using the experimental data we compute some bounds for the mixings parameters and for the masses of the scalars.Comment: 12 pages, Latex, 7 figures. Accepted for publication in Int. Journ. of Mod. Phys.

Big data marketing during the period 2012–2019: a bibliometric review

The present study identifies the most significant trends in production of high impact scientific papers related to the Big Data Marketing variable during the period between the years 2012 and 2019 through a revision of the Scopus database, which manages to highlight the relevance of 113 indexed papers. For this purpose, the following descriptive bibliometric indicators are implemented: production volume, type of document, number of citations, and country of application. In the studied time period, the evidence suggests an annual growth in the production volume of papers related to the variable, but with a significant drop in 2017. The knowledge areas that showcases more researches about the Big Data Marketing variable are computer science, mathematics, decision-making, and engineering domain