Name: | Monalisa Dubey |
Affiliation: | Aryabhatta Research Institute of Observational Sciences (ARIES), Nainital |
Conference ID : | ASI2024_826 |
Title : | Unraveling the progenitor characteristics of long-plateau core-collapse supernovae via light curve modelling |
Authors : | Monalisa Dubey (1,2), Kuntal Misra (1), Bhavya Ailawadhi (1, 3), Naveen Dukiya (1, 2), Raya Dastidar (4, 5) |
Authors Affiliation: | 1 Aryabhatta Research Institute of Observational Sciences, Manora Peak, Nainital 263 001, India
2 Department of Applied Physics, Mahatma Jyotiba Phule Rohilkhand University, Bareilly, 243006,
India
3 Department of Physics, Deen Dayal Upadhyaya Gorakhpur University, Gorakhpur-273009, India
4 Instituto de Astrofísica, Universidad Andres Bello, Fernandez Concha 700, Las Condes, Santiago RM,
Chile
5 Millennium Institute of Astrophysics (MAS), Nuncio Monsenor Sòtero Sanz 100, Providencia, Santiago
RM, Chile |
Mode of Presentation: | Oral |
Abstract Category : | High Energy Phenomena, Fundamental Physics and Astronomy |
Abstract : | The diversity in the light curve of Type II core-collapse supernova (CCSN) is mainly due to the variety in the characteristics of the progenitor, the morphology of the circumstellar medium (CSM), and the energy of the explosion. Using analytical (Nagy and Vinkó, 2016; Jäger et al., 2020) and hydrodynamical (MESA+STELLA) modelling of the light curves, the progenitor characteristics (like initial mass, explosion energy, nickel (56) mass, mass loss history, etc.) can be revealed. We perform analytical and hydrodynamical modelling in a sample of core-collapse SNe, which exhibits long plateau durations. Although hydrodynamical modelling is more robust and provides accurate results, analytical modelling is computationally less intensive. Our results indicate that the values of progenitor mass and explosion energy from analytical modelling are lower than those obtained from hydrodynamical modelling. The possible progenitors are red supergiants with masses greater than 20 M⊙ with excessive mass loss rate (dm ≃ 10-2 M⊙ yr-1). |