Simultaneous Fitting of the Spectral Energy Density, Energy-dependent Size, and X-ray Spectral Index vs. Radius of The Young Pulsar Wind Nebula PWN G0.9+0.1. (arXiv:1905.07222v1 [astro-ph.HE])
<a href="http://arxiv.org/find/astro-ph/1/au:+Rensburg_C/0/1/0/all/0/1">Carlo van Rensburg</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Venter_C/0/1/0/all/0/1">Christo Venter</a>
We have constructed and calibrated a spherically-symmetric,
spatially-dependent particle transport and emission code for young pulsar wind
nebulae (PWNe). This code predicts the spectral energy distribution (SED) of
the radiation spectrum at different positions in a PWN, thus yielding the
surface brightness vs. radius and hence the nebular size as function of energy.
It also predicts the X-ray spectral index at different radii from the central
pulsar, depending on the nebular B-field profile and particle transport
properties. We apply the code to PWN G0.9+0.1 and fit these three functions
concurrently, thus maximizing the constraining power of the data. We use a
Markov-chain-Monte-Carlo (MCMC) method to find best-fit parameters with
accompanying errors. This approach should allow us to better probe the spatial
behaviour of the bulk-particle motion, the $B$-field and diffusion coefficient,
and break degeneracies between different model parameters. Our model will
contribute to interpreting results by the future Cherenkov Telescope Array
(CTA) that will yield many more discoveries plus morphological details of
very-high-energy Galactic PWNe.
We have constructed and calibrated a spherically-symmetric,
spatially-dependent particle transport and emission code for young pulsar wind
nebulae (PWNe). This code predicts the spectral energy distribution (SED) of
the radiation spectrum at different positions in a PWN, thus yielding the
surface brightness vs. radius and hence the nebular size as function of energy.
It also predicts the X-ray spectral index at different radii from the central
pulsar, depending on the nebular B-field profile and particle transport
properties. We apply the code to PWN G0.9+0.1 and fit these three functions
concurrently, thus maximizing the constraining power of the data. We use a
Markov-chain-Monte-Carlo (MCMC) method to find best-fit parameters with
accompanying errors. This approach should allow us to better probe the spatial
behaviour of the bulk-particle motion, the $B$-field and diffusion coefficient,
and break degeneracies between different model parameters. Our model will
contribute to interpreting results by the future Cherenkov Telescope Array
(CTA) that will yield many more discoveries plus morphological details of
very-high-energy Galactic PWNe.
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