KilonovAE: Exploring Kilonova Spectral Features with Autoencoders

Kilonovae are likely a key site of heavy r -process element production in the Universe, and their optical/infrared spectra contain insights into both the properties of the ejecta and the conditions of the r -process. However, the event GW170817/AT2017gfo is the only kilonova so far with well-observe...

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Bibliographic Details
Main Authors: N. M. Ford, Nicholas Vieira, John J. Ruan, Daryl Haggard
Format: Article
Language:English
Published: IOP Publishing 2024-01-01
Series:The Astrophysical Journal
Subjects:
Online Access:https://doi.org/10.3847/1538-4357/ad0b7d
Description
Summary:Kilonovae are likely a key site of heavy r -process element production in the Universe, and their optical/infrared spectra contain insights into both the properties of the ejecta and the conditions of the r -process. However, the event GW170817/AT2017gfo is the only kilonova so far with well-observed spectra. To understand the diversity of absorption features that might be observed in future kilonovae spectra, we use the TARDIS Monte Carlo radiative transfer code to simulate a suite of optical spectra spanning a wide range of kilonova ejecta properties and r -process abundance patterns. To identify the most common and prominent absorption lines, we perform dimensionality reduction using an autoencoder, and we find spectra clusters in the latent space representation using a Bayesian Gaussian Mixture model. Our synthetic kilonovae spectra commonly display strong absorption by strontium _38 Sr ii , yttrium _38 Y ii , and zirconium _40 Zr i–ii , with strong lanthanide contributions at low electron fractions ( Y _e ≲ 0.25). When a new kilonova is observed, our machine-learning framework will provide context on the dominant absorption lines and key ejecta properties, helping to determine where this event falls within the larger “zoo” of kilonovae spectra.
ISSN:1538-4357