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POSSIS: predicting spectra, light curves, and polarization for multidimensional models of supernovae and kilonovae
Stockholm University, Faculty of Science, Department of Physics. Stockholm University, Faculty of Science, The Oskar Klein Centre for Cosmo Particle Physics (OKC).
Number of Authors: 12019 (English)In: Monthly notices of the Royal Astronomical Society, ISSN 0035-8711, E-ISSN 1365-2966, Vol. 489, no 4, p. 5037-5045Article in journal (Refereed) Published
Abstract [en]

We present POSSIS, a time-dependent three-dimensional Monte Carlo code for modelling radiation transport in supernovae and kilonovae. The code incorporates wavelength- and time-dependent opacities, and predicts viewing-angle dependent spectra, light curves, and polarization for both idealized and hydrodynamical explosion models. We apply the code to a kilonova model with two distinct ejecta components, one including lanthanide elements with relatively high opacities and the other devoid of lanthanides and characterized by lower opacities. We find that a model with total ejecta mass M-ej = 0.04M(circle dot) and half-opening angle of the lanthanide-rich component Phi = 30 degrees provides a good match to GW170817/AT 2017gfo for orientations near the polar axis (i.e. for a system viewed close to face-on). We then show how crucial is the use of self-consistent multidimensional models in place of combining one-dimensional models to infer important parameters, such as the ejecta masses. We finally explore the impact of M-ej and Phi on the synthetic observables and highlight how the relatively fast computation times of POSSIS make it well-suited to perform parameter-space studies and extract key properties of supernovae and kilonovae. Spectra calculated with POSSIS in this and future studies will be made publicly available.

Place, publisher, year, edition, pages
2019. Vol. 489, no 4, p. 5037-5045
Keywords [en]
gravitational waves, opacity, radiative transfer, methods: numerical, stars: neutron, supernovae: general
National Category
Physical Sciences
Identifiers
URN: urn:nbn:se:su:diva-175957DOI: 10.1093/mnras/stz2495ISI: 000489302400046OAI: oai:DiVA.org:su-175957DiVA, id: diva2:1369300
Available from: 2019-11-11 Created: 2019-11-11 Last updated: 2019-11-11Bibliographically approved

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Bulla, Mattia
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