Kilonovae, the potential electromagnetic counterparts to neutron star mergers, offer crucial insights into high-energy transient phenomena and provide a unique opportunity to probe the properties of the sources powering these events. However, significant uncertainties exist in kilonova modeling, which hinder the accurate prediction and interpretation of observational data. These modeling uncertainties, if not properly accounted for, can lead to misinterpretations of key astrophysical parameters. In this thesis, we present a novel data-driven approach for quantifying time-dependent and filter-dependent systematic uncertainties in kilonova models. By incorporating interpolation schemes that account for the non-stationary behavior of systematic errors, our methodology enhances the reliability of parameter estimation for kilonova events. Through a series of synthetic injection-recovery tests using the Ka2017 and Bu2019lm models, we validate the effectiveness of our approach in recovering injected parameters within credible intervals. We apply this method to the observed kilonova AT2017gfo, performing parameter estimation with different time node configurations. Our results show that a combination of time- and filter-dependent systematic uncertainties leads to the most reliable recovery of the source parameters. Notably, we find a systematic error of less than 1 magnitude between 1 and 5 days after the merger. Additionally, the model reveals significant insights into the temporal and spectral evolution of systematic uncertainties, indicating the need for both early follow-up observations and improved modeling techniques for later phases of kilonovae. These findings highlight the importance of properly accounting for systematic uncertainties in kilonova modeling, contributing to more precise multi-messenger astronomy and offering a robust framework for future transient event studies.

Find the full thesis here: SahilJhawar_MSc_Thesis.pdf

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