2019 Fiscal Year Research-status Report
Studying Massive Star Evolution from Progenitor to Supernova Remnant using Long-term Hydrodynamical Simulations and Machine Learning
| Project/Area Number |
19K03913
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| Research Institution | Kyoto University |
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Principal Investigator |
李 兆衡 京都大学, 理学研究科, 講師 (50611844)
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| Project Period (FY) |
2019-04-01 – 2024-03-31
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| Keywords | Supernova remnants / Massive stars / Stellar evolution / Multi-wavelength / Cosmic rays |
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| Outline of Annual Research Achievements |
This year our team has made progress in a number of areas concerning massive stars undergoing core-collapse supernova (SN) explosions:
1) A systematic survey of supernova remnant (SNR) emission using extensive numerical models
We have embarked on a numerical study to survey a broad parameter space exploring the broadband emission from various types of SNRs in a variety of circumstellar medium (CSM). By self-consistently including physics such as acceleration of particles and their inelastic interactions with the surrounding environment coupled with hydrodynamic simulations, we predict the long-term evolution of dynamics and the multi-wavelength emission up to 5000 yrs after explosion. These predictions can be compared with current as well as future observations such as Cherenkov Telescope Array, a soon-coming ground-based very-high-energy gamma-ray observatory. Doctoral student Haruo Yasuda and I have published a refereed paper in the Astrophysical Journal to report our results.
2) A prediction of non-thermal signal from very early stage of core-collapse supernovae
Our group has developed a new hydrodynamical code to calculate the non-thermal emission from supernovae interacting with dense pre-SN wind immediately after the explosion. In particular, we predict future detection of strong sub-mm emission by instruments like ALMA for core-collapse SNe which possess a dense and confined CSM. These future detections will help reveal the late-time mass loss history of massive stars prior to their explosions. A refereed paper has been published in the Astrophysical Journal.
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| Current Status of Research Progress |
Current Status of Research Progress
1: Research has progressed more than it was originally planned.
Reason
The main motivation of this project is to understand the late-stage evolution of massive stars from progenitor all the way to the SNR phase using self-consistent and coherent numerical models. In this first year, we have already explored two very important epochs to help further our understanding of these fascinating phenomena, namely, the young SNR phase up to a few 1000 years after SNe, and the first few months right after the explosion. As mentioned above, we discovered that multi-wavelength non-thermal emission from these two epochs are crucial to probe the CSM and hence the late-time mass loss history of the progenitor stars of core-collapse SNe. While recording the mass loss history directly from observations of massive stars, especially for the final phase right before core-collapse, is challenging, we discovered that observing and modeling non-thermal emission from supernova remnants after the explosions due to the interaction of the SN blastwave with the surrounding CSM (created by the pre-SN stellar wind) is a novel and powerful way to constrain the mass loss and late evolution of massive stars.
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| Strategy for Future Research Activity |
In the next fiscal year, we will continue our numerical survey of SNR models, pushing the simulations to explore the rest of the evolution stages. For example, the radiative SNR phase up to 100,000 years after explosion. By such we will be able to provide a consistent picture of the full evolution of SNRs from young to old stages in various CSM environments. Calculation of the accompanying broadband electromagnetic signals can confront future observations by instruments such as the Cherenkov Telescope Array and others to constrain our models to deepen our understanding of the Galactic and extragalactic SNR populations and their environments as well as the detailed physics of particle acceleration and so on.
Another line of research we will begin to approach, as already described in the proposal, is the development of a machine learning (ML) technique to explore an extensive grid of numerical models which follows the evolution of massive stars from progenitor to SNR phase. We aim at providing a robust predictive/interpretative tool for constraining the progenitor and environments of SN/SNR from observational data. As a first step, we will manufacture a large number of hydrodynamical models for different SN progenitors and environments to calculate the thermal X-ray spectra at different ages. By training the machine using these large grid of spectral models, we aim at creating a model that can be used to extract important physical information from SNRs observed by current and future X-ray telescopes.
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| Causes of Carryover |
A trip to the USA in March 2020 (destination: Center for Astrophysics at Harvard University) for research collaboration was cancelled due to the current COVID-19 situation in the world. The tentative plan now is that the incurred amount from this fiscal year will be used for realizing this trip in the next fiscal year.
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