Insights from modelling magnetar-driven light curves of stripped-envelope supernovae

This work presents the semi-analytical light curve modelling results of 11 stripped-envelope SNe (SESNe), where millisecond magnetars potentially drive their light curves. The light-curve modelling is performed utilizing the χ 2 -minimization code MINIM considering millisecond magnetar as a central engine powering source. The magnetar model well regenerates the bolometric light curves of all the SESNe in the sample and constrains numerous physical parameters, including magnetar’s initial spin period ( P i ) and magnetic field ( B ), explosion energy of supernova ( E exp ), progenitor radius ( R p ), etc. Within the sample, the superluminous SNe 2010kd and 2020ank exhibit the lowest B and P i values, while the relativistic Ic broad-line SN 2012ap shows the highest values for both parameters. The explosion energy for all SESNe in the sample (except SN 2019cad), exceeding ≳ 2 × 10 51 erg, indicates there is a possibility of a jittering jet explosion mechanism driving these events. Additionally, a correlation analysis identifies linear dependencies among parameters derived from light curve analysis, revealing positive correlations between rise and decay times, P i and B , P i and R p , and E exp and R p , as well as strong anti-correlations of P i and B with the peak luminosity. Principal Component Analysis is also applied to key parameters to reduce dimensionality, allowing a clearer visualization of SESNe distribution in a lower-dimensional space. This approach highlights the diversity in SESNe characteristics, underscoring unique physical properties and behaviour across different events in the sample. This study motivates further study on a more extended sample of SESNe to look for millisecond magnetars as their powering source. • Semi-analytical modelling of SESNe shows magnetars power their light curves. • Most SESNe show high explosion energies, hinting at jittering jet-driven mechanisms. • Correlation analysis reveals key dependencies among various parameters. • PCA reduces dimensionality, offering clearer visualization of SESNe distributions.