Deep Learning on Type Ia Supernovae

Name: Deep Learning on Type Ia Supernovae
Brand: Springer
SKU: 9783032130310
Price: 15452.0 INR
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by Xingzhuo Chen

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ISBN13: 9783032130310
Binding: Hardcover
Subject: N/A
Publisher: Springer
Publisher Imprint: Springer
Publication Date: April 2, 2026
Pages: 143
Original Price: EUR 141.69
Language: English
Edition: N/A
Item Weight: 306 grams
BISAC Subject(s): Space Science / Cosmology

About The Book

The thesis presents the design of an Artificial Intelligence Assisted Inversion (AIAI) method to estimate type Ia supernova (SN Ia) ejecta structure based on the observed optical spectral time sequence. The research applied neural networks to 126 SNe Ia and found a correlation between the 3700 Å spectral feature and the 56Ni elemental abundance. To further adapt the AIAI method to the SNe Ia 3D structure estimate, the author developed an integral-based technique to significantly increase the signal-to-noise ratio in the polarized time-dependent 3D radiative transfer computations. To understand the SNe Ia progenitors, the spatially resolved SN Ia host galaxy spectra from MUSE and MaNGA were employed to estimate the delay time distribution (DTD). By using a grouping algorithm based on k-means and earth mover's distances, the research separated the host galaxy stellar population age distributions into spatially distinct regions and used the maximum likelihood method to constrain the DTD. It was found that the DTD is consistent to the double-degenerate progenitor models.

About The Author

Dr. Xingzhuo Chen is a theoretical astrophysicist working in the Texas A&M University Institute of Data Science. His research focuses on radiative transfer simulation on supernovae and scientific machine learning on magnetohydrodynamic simulations. He received his Ph.D in Astronomy from Texas A&M University. During his Ph.D, he studied the ejecta structure of type Ia supernovae using deep learning and radiative transfer simulations.

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