Deep Learning on Type Ia Supernovae

Erscheinungsjahr 2026
ISBN: 978-3-032-13031-0
Verlag: Springer

Buch, Englisch, 126 Seiten, Format (B × H): 155 mm x 235 mm

Reihe: Springer Theses

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.

Zielgruppe

Research

Autoren/Hrsg.

Chen, Xingzhuo

Fachgebiete

• Interdisziplinäres Wissenschaften Wissenschaften: Allgemeines
• Naturwissenschaften Astronomie Kosmologie, Urknalltheorie
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Angewandte Mathematik, Mathematische Modelle
• Naturwissenschaften Astronomie Astronomische Beobachtung: Observatorien, Instrumente, Methoden

Inhaltsverzeichnis

Introduction and literature review.- Artificial intelligence assisted inversion on type ia supernovae.- Artificial intelligence assisted inversion (aiai): quantifying the spectral features of 56Ni of type Ia supernovae.- An integral-based technique (ibt) to accelerate the monte-carlo radiative transfer computation for supernovae.

Über Autor(innen)

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.