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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2024-01-01
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| Series: | The Astrophysical Journal |
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| Online Access: | https://doi.org/10.3847/1538-4357/ad0b7d |
| _version_ | 1797352213378498560 |
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| author | N. M. Ford Nicholas Vieira John J. Ruan Daryl Haggard |
| author_facet | N. M. Ford Nicholas Vieira John J. Ruan Daryl Haggard |
| author_sort | N. M. Ford |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T13:13:04Z |
| format | Article |
| id | doaj.art-26bb6e326c48484688736f3e8ec5c78a |
| institution | Directory Open Access Journal |
| issn | 1538-4357 |
| language | English |
| last_indexed | 2024-03-08T13:13:04Z |
| publishDate | 2024-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | The Astrophysical Journal |
| spelling | doaj.art-26bb6e326c48484688736f3e8ec5c78a2024-01-18T10:40:56ZengIOP PublishingThe Astrophysical Journal1538-43572024-01-01961111910.3847/1538-4357/ad0b7dKilonovAE: Exploring Kilonova Spectral Features with AutoencodersN. M. Ford0https://orcid.org/0000-0001-8921-3624Nicholas Vieira1https://orcid.org/0000-0001-7815-7604John J. Ruan2https://orcid.org/0000-0001-8665-5523Daryl Haggard3https://orcid.org/0000-0001-6803-2138Department of Physics, McGill University , 3600 rue University, Montreal, Québec, H3A 2T8, Canada ; nicole.ford@mail.mcgill.ca; Trottier Space Institute, 3550 Rue University , Montréal, Québec, H3A 2A7, CanadaDepartment of Physics, McGill University , 3600 rue University, Montreal, Québec, H3A 2T8, Canada ; nicole.ford@mail.mcgill.ca; Trottier Space Institute, 3550 Rue University , Montréal, Québec, H3A 2A7, CanadaDepartment of Physics and Astronomy, Bishop's University , 2600 rue College, Sherbrooke, Québec, J1M 1Z7, CanadaDepartment of Physics, McGill University , 3600 rue University, Montreal, Québec, H3A 2T8, Canada ; nicole.ford@mail.mcgill.ca; Trottier Space Institute, 3550 Rue University , Montréal, Québec, H3A 2A7, CanadaKilonovae 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.https://doi.org/10.3847/1538-4357/ad0b7dNeutron starsR-processRadiative transfer simulationsSpectral line identificationDimensionality reduction |
| spellingShingle | N. M. Ford Nicholas Vieira John J. Ruan Daryl Haggard KilonovAE: Exploring Kilonova Spectral Features with Autoencoders The Astrophysical Journal Neutron stars R-process Radiative transfer simulations Spectral line identification Dimensionality reduction |
| title | KilonovAE: Exploring Kilonova Spectral Features with Autoencoders |
| title_full | KilonovAE: Exploring Kilonova Spectral Features with Autoencoders |
| title_fullStr | KilonovAE: Exploring Kilonova Spectral Features with Autoencoders |
| title_full_unstemmed | KilonovAE: Exploring Kilonova Spectral Features with Autoencoders |
| title_short | KilonovAE: Exploring Kilonova Spectral Features with Autoencoders |
| title_sort | kilonovae exploring kilonova spectral features with autoencoders |
| topic | Neutron stars R-process Radiative transfer simulations Spectral line identification Dimensionality reduction |
| url | https://doi.org/10.3847/1538-4357/ad0b7d |
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