Magnetars as Powering Sources of Gamma-Ray Burst Associated Supernovae, and Unsupervised Clustering of Cosmic Explosions

Magnetars as Powering Sources of Gamma-Ray Burst Associated Supernovae, and Unsupervised Clustering of Cosmic Explosions
Amit Kumar, Kaushal Sharma, Jozsef Vink’o, Danny Steeghs, Benjamin Gompertz, Joseph Lyman, Raya Dastidar, Avinash Singh, Kendall Ackley, Miika Pursiainen
arXiv:2403.18076v1 Announce Type: new
Abstract: We present the semi-analytical light curve modelling of 13 supernovae associated with gamma-ray bursts (GRB-SNe) along with two relativistic broad-lined (Ic-BL) SNe without GRBs association (SNe 2009bb and 2012ap), considering millisecond magnetars as central-engine-based power sources for these events. The bolometric light curves of all 15 SNe in our sample are well-regenerated utilising a $chi^2-$minimisation code, $texttt{MINIM}$, and numerous parameters are constrained. The median values of ejecta mass ($M_{textrm{ej}}$), magnetar’s initial spin period ($P_textrm{i}$) and magnetic field ($B$) for GRB-SNe are determined to be $approx$ 5.2 M$_odot$, 20.5 ms and 20.1 $times$ 10$^{14}$ G, respectively. We leverage machine learning (ML) algorithms to comprehensively compare the 3-dimensional parameter space encompassing $M_{textrm{ej}}$, $P_textrm{i}$, and $B$ for GRB-SNe determined herein to those of H-deficient superluminous SNe (SLSNe-I), fast blue optical transients (FBOTs), long GRBs (LGRBs), and short GRBs (SGRBs) obtained from the literature. The application of unsupervised ML clustering algorithms on the parameters $M_{textrm{ej}}$, $P_textrm{i}$, and $B$ for GRB-SNe, SLSNe-I, and FBOTs yields a classification accuracy of $sim$95%. Extending these methods to classify GRB-SNe, SLSNe-I, LGRBs, and SGRBs based on $P_textrm{i}$ and $B$ values results in an accuracy of $sim$84%. Our investigations show that GRB-SNe and relativistic Ic-BL SNe presented in this study occupy different parameter spaces for $M_{textrm{ej}}$, $P_textrm{i}$, and $B$ than those of SLSNe-I, FBOTs, LGRBs and SGRBs. This indicates that magnetars with different $P_textrm{i}$ and $B$ can give birth to distinct types of transients.arXiv:2403.18076v1 Announce Type: new
Abstract: We present the semi-analytical light curve modelling of 13 supernovae associated with gamma-ray bursts (GRB-SNe) along with two relativistic broad-lined (Ic-BL) SNe without GRBs association (SNe 2009bb and 2012ap), considering millisecond magnetars as central-engine-based power sources for these events. The bolometric light curves of all 15 SNe in our sample are well-regenerated utilising a $chi^2-$minimisation code, $texttt{MINIM}$, and numerous parameters are constrained. The median values of ejecta mass ($M_{textrm{ej}}$), magnetar’s initial spin period ($P_textrm{i}$) and magnetic field ($B$) for GRB-SNe are determined to be $approx$ 5.2 M$_odot$, 20.5 ms and 20.1 $times$ 10$^{14}$ G, respectively. We leverage machine learning (ML) algorithms to comprehensively compare the 3-dimensional parameter space encompassing $M_{textrm{ej}}$, $P_textrm{i}$, and $B$ for GRB-SNe determined herein to those of H-deficient superluminous SNe (SLSNe-I), fast blue optical transients (FBOTs), long GRBs (LGRBs), and short GRBs (SGRBs) obtained from the literature. The application of unsupervised ML clustering algorithms on the parameters $M_{textrm{ej}}$, $P_textrm{i}$, and $B$ for GRB-SNe, SLSNe-I, and FBOTs yields a classification accuracy of $sim$95%. Extending these methods to classify GRB-SNe, SLSNe-I, LGRBs, and SGRBs based on $P_textrm{i}$ and $B$ values results in an accuracy of $sim$84%. Our investigations show that GRB-SNe and relativistic Ic-BL SNe presented in this study occupy different parameter spaces for $M_{textrm{ej}}$, $P_textrm{i}$, and $B$ than those of SLSNe-I, FBOTs, LGRBs and SGRBs. This indicates that magnetars with different $P_textrm{i}$ and $B$ can give birth to distinct types of transients.