Radiative Transfer Effects in He I Emission Lines

We consider the effect of optical depth of the 2 ^{3}S level on the nebular recombination spectrum of He I for a spherically symmetric nebula with no systematic velocity gradients. These calculations, using many improvements in atomic data, can be used in place of the earlier calculations of Robbins. We give representative Case B line fluxes for UV, optical, and IR emission lines over a range of physical conditions: T=5000-20000 K, n_{e}=1-10^{8} cm^{-3}, and tau_{3889}=0-100. A FORTRAN program for calculating emissivities for all lines arising from quantum levels with n < 11 is also available from the authors. We present a special set of fitting formulae for the physical conditions relevant to low metallicity extragalactic H II regions: T=12,000-20,000 K, n_{e}=1-300 cm^{-3}, and tau_{3889} < 2.0. For this range of physical conditions, the Case B line fluxes of the bright optical lines 4471 A, 5876 A, and 6678 A, are changed less than 1%, in agreement with previous studies. However, the 7065 A corrections are much smaller than those calculated by Izotov & Thuan based on the earlier calculations by Robbins. This means that the 7065 A line is a better density diagnostic than previously thought. Two corrections to the fitting functions calculated in our previous work are also given.Comment: To be published in 10 April 2002 ApJ; relevant code available at ftp://wisp.physics.wisc.edu/pub/benjamin/Heliu

Observability of flashes from ejecta crashes in aspherical supernovae, with application to SN 2008D

A new class of transient, which has been hypothesized to accompany the explosion of an aspherical compact supernova, would arise when streams of ejecta collide outside the star. However, conditions that favour the prompt release of radiation from the collision, such as a diffuse stellar envelope, disfavour the creation of non-radial ejecta in the first place. To determine whether the collision can both occur and be visible, we simulate aspherical explosions using the HUJI-RICH moving-mesh hydrodynamics code and analyze them in terms of diffusion measures defined for individual fluid elements. While our simulations are highly idealized, they connect to realistic explosions via a single dimensionless parameter. Defining two measures of the importance of diffusivity (two versions of the inverse P'eclet number), we find that one varies in a way that indicates colliding ejecta can release a photon flash, while the other does not. Examining the x-ray transient XT 080109 associated with supernova SN 2008D, we find that its fluence and duration are consistent with the properties of an ejecta collision in the aspherical model that is most likely to emit a flash. Our results give tentative evidence for the possibility of collision-induced flashes for a narrow and radius-dependent range of asphericity, and motivate future radiation hydrodynamics simulations.Comment: 8 pages, 3 figures, submitted to MNRA

Improving Predictions for Helium Emission Lines

We have combined the detailed He I recombination model of Smits with the collisional transitions of Sawey & Berrington in order to produce new accurate helium emissivities that include the effects of collisional excitation from both the 2 (3)S and 2 (1) S levels. We present a grid of emissivities for a range of temperature and densities along with analytical fits and error estimates. Fits accurate to within 1% are given for the emissivities of the brightest lines over a restricted range for estimates of primordial helium abundance. We characterize the analysis uncertainties associated with uncertainties in temperature, density, fitting functions, and input atomic data. We estimate that atomic data uncertainties alone may limit abundance estimates to an accuracy of 1.5%; systematic errors may be greater than this. This analysis uncertainty must be incorporated when attempting to make high accuracy estimates of the helium abundance. For example, in recent determinations of the primordial helium abundance, uncertainties in the input atomic data have been neglected.Comment: ApJ, accepte

The 44Ti-powered spectrum of SN 1987A

SN 1987A provides a unique opportunity to study the evolution of a supernova from explosion into very late phases. Due to the rich chemical structure, the multitude of physical process involved, and extensive radiative transfer effects, detailed modeling is needed to interpret the emission from this and other supernovae. In this paper, we analyze the late-time (~8 years) HST spectrum of the SN 1987A ejecta, where 44Ti is the dominant power source. Based on an explosion model for a 19 Msun progenitor, we compute a model spectrum by calculating the degradation of positrons and gamma-rays from the radioactive decays, solving the equations governing temperature, ionization balance and NLTE level populations, and treating the radiative transfer with a Monte Carlo technique. We obtain a UV/optical/NIR model spectrum which is found to reproduce most of the lines in the observed spectrum to good accuracy. We find non-local radiative transfer in atomic lines to be an important process also at this late stage of the supernova, with ~30% of the emergent flux in the optical and NIR coming from scattering/fluorescence. We investigate the question of where the positrons deposit their energy, and favor the scenario where they are locally trapped in the Fe/He clumps by a magnetic field. Energy deposition into these largely neutral Fe/He clumps makes Fe I lines prominent in the emergent spectrum. Using the best available estimates for the dust extinction, we determine the amount of 44Ti produced in the explosion to 1.5\pm0.5 * 10^-4 Msun.Comment: 23 pages, 9 figures. 44Ti mass updated from 1.4E-4 to 1.5E-4 Msu

To which world regions does the valence–dominance model of social perception apply?

Over the past 10 years, Oosterhof and Todorov’s valence–dominance model has emerged as the most prominent account of how people evaluate faces on social dimensions. In this model, two dimensions (valence and dominance) underpin social judgements of faces. Because this model has primarily been developed and tested in Western regions, it is unclear whether these findings apply to other regions. We addressed this question by replicating Oosterhof and Todorov’s methodology across 11 world regions, 41 countries and 11,570 participants. When we used Oosterhof and Todorov’s original analysis strategy, the valence–dominance model generalized across regions. When we used an alternative methodology to allow for correlated dimensions, we observed much less generalization. Collectively, these results suggest that, while the valence–dominance model generalizes very well across regions when dimensions are forced to be orthogonal, regional differences are revealed when we use different extraction methods and correlate and rotate the dimension reduction solution.C.L. was supported by the Vienna Science and Technology Fund (WWTF VRG13-007); L.M.D. was supported by ERC 647910 (KINSHIP); D.I.B. and N.I. received funding from CONICET, Argentina; L.K., F.K. and Á. Putz were supported by the European Social Fund (EFOP-3.6.1.-16-2016-00004; ‘Comprehensive Development for Implementing Smart Specialization Strategies at the University of Pécs’). K.U. and E. Vergauwe were supported by a grant from the Swiss National Science Foundation (PZ00P1_154911 to E. Vergauwe). T.G. is supported by the Social Sciences and Humanities Research Council of Canada (SSHRC). M.A.V. was supported by grants 2016-T1/SOC-1395 (Comunidad de Madrid) and PSI2017-85159-P (AEI/FEDER UE). K.B. was supported by a grant from the National Science Centre, Poland (number 2015/19/D/HS6/00641). J. Bonick and J.W.L. were supported by the Joep Lange Institute. G.B. was supported by the Slovak Research and Development Agency (APVV-17-0418). H.I.J. and E.S. were supported by a French National Research Agency ‘Investissements d’Avenir’ programme grant (ANR-15-IDEX-02). T.D.G. was supported by an Australian Government Research Training Program Scholarship. The Raipur Group is thankful to: (1) the University Grants Commission, New Delhi, India for the research grants received through its SAP-DRS (Phase-III) scheme sanctioned to the School of Studies in Life Science; and (2) the Center for Translational Chronobiology at the School of Studies in Life Science, PRSU, Raipur, India for providing logistical support. K. Ask was supported by a small grant from the Department of Psychology, University of Gothenburg. Y.Q. was supported by grants from the Beijing Natural Science Foundation (5184035) and CAS Key Laboratory of Behavioral Science, Institute of Psychology. N.A.C. was supported by the National Science Foundation Graduate Research Fellowship (R010138018). We acknowledge the following research assistants: J. Muriithi and J. Ngugi (United States International University Africa); E. Adamo, D. Cafaro, V. Ciambrone, F. Dolce and E. Tolomeo (Magna Græcia University of Catanzaro); E. De Stefano (University of Padova); S. A. Escobar Abadia (University of Lincoln); L. E. Grimstad (Norwegian School of Economics (NHH)); L. C. Zamora (Franklin and Marshall College); R. E. Liang and R. C. Lo (Universiti Tunku Abdul Rahman); A. Short and L. Allen (Massey University, New Zealand), A. Ateş, E. Güneş and S. Can Özdemir (Boğaziçi University); I. Pedersen and T. Roos (Åbo Akademi University); N. Paetz (Escuela de Comunicación Mónica Herrera); J. Green (University of Gothenburg); M. Krainz (University of Vienna, Austria); and B. Todorova (University of Vienna, Austria). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.https://www.nature.com/nathumbehav/am2023BiochemistryGeneticsMicrobiology and Plant Patholog

To which world regions does the valence–dominance model of social perception apply?

Over the past 10 years, Oosterhof and Todorov’s valence–dominance model has emerged as the most prominent account of how people evaluate faces on social dimensions. In this model, two dimensions (valence and dominance) underpin social judgements of faces. Because this model has primarily been developed and tested in Western regions, it is unclear whether these findings apply to other regions. We addressed this question by replicating Oosterhof and Todorov’s methodology across 11 world regions, 41 countries and 11,570 participants. When we used Oosterhof and Todorov’s original analysis strategy, the valence–dominance model generalized across regions. When we used an alternative methodology to allow for correlated dimensions, we observed much less generalization. Collectively, these results suggest that, while the valence–dominance model generalizes very well across regions when dimensions are forced to be orthogonal, regional differences are revealed when we use different extraction methods and correlate and rotate the dimension reduction solution