Circumstellar Interaction Signatures in the Low Luminosity Type II SN 2021Gmj
ASTROPHYSICAL JOURNAL(2024)
Univ Calif Davis | Univ Arizona | Inst Space Sci ICE | Gemini Observ | Univ Saskatchewan | WM Keck Observ | Univ Washington | Texas A&M Univ | Cumbres Observ | Univ N Carolina | Ctr Astrophys Harvard & Smithsonian | Rutgers State Univ | Inst Nacl Astrofis Opt & Elect INAOE CONACyT | Univ Calif Berkeley
Abstract
We present comprehensive optical observations of SN 2021gmj, a Type II supernova (SN II) discovered within a day of explosion by the Distance Less Than 40 Mpc survey. Follow-up observations show that SN 2021gmj is a low-luminosity SN II (LL SN II), with a peak magnitude M-V = -15.45 and an Fe ii velocity of similar to 1800 km s(-1) at 50 days past explosion. Using the expanding photosphere method, we derive a distance of 17.8-0.4+0.6 Mpc. From the tail of the light curve we obtain a radioactive nickel mass of (MNi)-Ni-56 = 0.014 +/- 0.001 M-circle dot. The presence of circumstellar material (CSM) is suggested by the early-time light curve, early spectra, and high-velocity H alpha in absorption. Analytical shock-cooling models of the light curve cannot reproduce the fast rise, supporting the idea that the early-time emission is partially powered by the interaction of the SN ejecta and CSM. The inferred low CSM mass of 0.025 M-circle dot in our hydrodynamic-modeling light-curve analysis is also consistent with our spectroscopy. We observe a broad feature near 4600 angstrom, which may be high-ionization lines of C, N, or/and He II. This feature is reproduced by radiation-hydrodynamic simulations of red supergiants with extended atmospheres. Several LL SNe II show similar spectral features, implying that high-density material around the progenitor may be common among them.
MoreTranslated text
Key words
Core-collapse supernovae,Type II supernovae,Circumstellar matter,Stellar mass loss,Red supergiant stars
PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Try using models to generate summary,it takes about 60s
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Related Papers
2007
被引用48 | 浏览
2004
被引用411 | 浏览
2015
被引用138 | 浏览
2016
被引用774 | 浏览
2013
被引用140 | 浏览
2015
被引用50 | 浏览
2012
被引用256 | 浏览
2017
被引用72 | 浏览
2021
被引用13 | 浏览
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
去 AI 文献库 对话