The Effect of the Velocity Distribution on Kilonova Emission

Fryer, Chris L.; Hungerford, Aimee L.; Wollaeger, Ryan T.; Miller, Jonah M.; De, Soumi; Fontes, Christopher J.; Korobkin, Oleg; Kedia, Atul; Ristic, Marko; O’Shaughnessy, Richard

doi:10.3847/1538-4357/ad1036

Cited by 5 publications

(3 citation statements)

References 49 publications

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“…While the basic picture of KNe has remained unchanged for several decades since the semi-analytic work of Li & Paczyński (1998; for a review of KN physics, see also Metzger 2019), simulating KNe at high fidelity is an ever-developing field. Recent studies have explored detailed radiative transfer, atomic physics, non-local thermodynamic equilibrium (non-LTE), and multidimensional spatial ejecta; see, for instance, Fontes et al (2020), Tanaka et al (2020), Fontes et al (2023) on detailed LTE opacity, Hotokezaka et al (2021), Pognan et al (2022a), Pognan et al (2022bPognan et al ( , 2023 on detailed non-LTE opacity, and Heinzel et al (2021), Korobkin et al (2021), Bulla (2023), and Fryer et al (2024) on spatial distribution and multidimensional spatial effects (and see references therein). Each of these aspects brings a level of uncertainty into the simulations that otherwise might be encapsulated in free parameters (for example, gray opacity).…”

Section: Introductionmentioning

confidence: 99%

On a Spectral Method for β-particle Bound Excitation Collisions in Kilonovae

Wollaeger,

Fryer,

Chiodi

et al. 2024

ApJ

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The interaction of β-particles with the weakly ionized plasma background is an important mechanism for powering the kilonova (KN) transient signal from neutron star mergers. For this purpose, we present an implementation of the approximate fast-particle collision kernel, described by Inokuti following the seminal formulation of Bethe, in a spectral solver of the Vlasov–Maxwell–Boltzmann equation. In particular, we expand the fast-particle plane-wave atomic excitation kernel into coefficients of the Hermite basis, and derive the relevant discrete spectral system. In this fast-particle limit, the approach permits the direct use of atomic data, including optical oscillator strengths, normally applied to photon–matter interaction. The resulting spectral matrix is implemented in the MASS-APP spectral solver framework, in a way that avoids full matrix storage per spatial zone. We numerically verify aspects of the matrix construction, and present a proof-of-principle 3D simulation of a 2D axisymmetric KN ejecta snapshot. Our preliminary numerical results indicate that a reasonable choice of Hermite basis parameters for β-particles in the KN is a bulk velocity parameter u = 0, a thermal velocity parameter α = 0.5c, and a 9 × 9 × 9 mode velocity basis set (Hermite orders of 0–8 in each dimension). For interior-ejecta sample zones, we estimate that the ratio of thermalization from large-angle (≳2.°5) bound excitation scattering to total thermalization is ∼0.002–0.003.

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Section: Introductionmentioning

confidence: 99%

On a Spectral Method for β-particle Bound Excitation Collisions in Kilonovae

Wollaeger,

Fryer,

Chiodi

et al. 2024

ApJ

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“…The kilonova spectrum from the radiative-transfer process is shaped by the complicated interplay of ejecta properties, such as electron fraction (Y e ), velocity and density profiles, and temperature. These parameterized ejecta configurations have been widely studied (e.g., Tanaka & Hotokezaka 2013;Kasen et al 2017;Even et al 2020;Bulla et al 2021;Gillanders et al 2022Gillanders et al , 2024Wu et al 2022;Fontes et al 2023;Shingles et al 2023;Tak et al 2023;Fryer et al 2024). In Tak et al (2023), we delved into the influence of diverse ejection histories on the development of kilonova spectra and their associated light curves.…”

Section: Introductionmentioning

confidence: 99%

“…Specifically, we introduced representative velocity profiles of ejecta and explored how features in each velocity profile leave their imprints on both the kilonova spectrum and the light curve. Fryer et al (2024) also conducted a study with a similar focus, aiming to understand the impact of velocity distribution on light curves and spectra. As discussed by Shingles et al (2023) and Collins et al (2023), line transitions by Sr II commonly occur at the front end of the expanding line-forming region with higher velocities.…”

Section: Introductionmentioning

confidence: 99%

Impact of Ejecta Temperature and Mass on the Strength of Heavy Element Signatures in Kilonovae

Tak,

Uhm,

Gillanders

2024

ApJ

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A kilonova, the electromagnetic emission produced by compact binary mergers, is formed through a delicate interplay of physical processes, involving r-process nucleosynthesis and interactions between heavy elements and photons through radiative transfer. This complexity makes it difficult to achieve a comprehensive understanding of kilonova spectra. In this study, we aim to enhance our understanding and establish connections between physical parameters and observables through radiative-transfer simulations. Specifically, we investigate how ejecta temperature and element mass influence the resulting kilonova spectrum. For each species, the strength of its line features depends on these parameters, leading to the formation of a distinct region in the parameter space, dubbed the resonance island, where the line signature of that species is notably evident in the kilonova spectrum. We explore its origin and applications. Among explored r-process elements (31 ≤ Z ≤ 92), we find that four species—SrII, YII, BaII, and CeII—exhibit large and strong resonance islands, suggesting their significant contributions to kilonova spectra at specific wavelengths. In addition, we discuss potential challenges and future perspectives in observable heavy elements and their masses in the context of the resonance island.

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Kilonova Spectral Inverse Modelling with Simulation-based Inference: An Amortized Neural Posterior Estimation Analysis

Darc,

Bom,

Fraga

et al. 2024

ApJ

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Kilonovae represent a category of astrophysical transients, identifiable as the electromagnetic (EM) counterparts associated with the coalescence events of binary systems comprising neutron stars and neutron star–black hole pairs. They act as probes for heavy-element nucleosynthesis in astrophysical environments. These studies rely on an inference of the physical parameters (e.g., ejecta mass, velocity, composition) that describe kilonovae-based on EM observations. This is a complex inverse problem typically addressed with sampling-based methods such as Markov Chain Monte Carlo or nested sampling algorithms. However, repeated inferences can be computationally expensive, due to the sequential nature of these methods. This poses a significant challenge to ensuring the reliability and statistical validity of the posterior approximations and, thus, the inferred kilonova parameters themselves. We present a novel approach: simulation-based inference using simulations produced by KilonovaNet. Our method employs an ensemble of amortized neural posterior estimation (ANPE) with an embedding network to directly predict posterior distributions from simulated spectral energy distributions. We take advantage of the quasi-instantaneous inference time of ANPE to demonstrate the reliability of our posterior approximations using diagnostics tools, including coverage diagnostic and posterior predictive checks. We further test our model with real observations from AT 2017gfo, the only kilonova with multimessenger data, demonstrating agreement with previous likelihood-based methods while reducing inference time down to a few seconds. The inference results produced by ANPE appear to be conservative and reliable, paving the way for testable and more efficient kilonova parameter inference.

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The Effect of the Velocity Distribution on Kilonova Emission

Cited by 5 publications

References 49 publications

On a Spectral Method for β-particle Bound Excitation Collisions in Kilonovae

On a Spectral Method for β-particle Bound Excitation Collisions in Kilonovae

Impact of Ejecta Temperature and Mass on the Strength of Heavy Element Signatures in Kilonovae

Kilonova Spectral Inverse Modelling with Simulation-based Inference: An Amortized Neural Posterior Estimation Analysis

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