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Mohanraj Madheshwaran

Publications and source records attributed to Mohanraj Madheshwaran.

2 recordsLinked to original sources

Statistical inference of fast radio burst environments using galaxy number density

Fast radio bursts (FRBs) are bright, millisecond-duration radio transients of unknown origin. They are categorized as repeaters and non-repeaters, possibly indicating distinct progenitor types. However, validating this distinction is difficult because of the limited number of localized FRBs. Large-scale galactic environments can provide insight into the nature of the host galaxies of FRBs and their progenitors. High-number-density regions are typically associated with old galaxies, whereas low-number-density regions are linked to young star-forming or less massive quiescent galaxies. In this study, we use galaxy number density to statistically assess the environments of 19 repeaters and 253 non-repeaters from CHIME Catalog 1, using galaxies from the WISE x PS1 catalog. A Kolmogorov-Smirnov (KS) test showed no significant difference between the two populations ($p_{\rm KS} = 0.673$). This result indicates that the statistical significance of the difference could depend on small-number statistics, highlighting the necessity of future FRB samples. Intriguingly, a comparison of FRBs with random galaxy fields suggests that FRBs may preferentially occur in underdense galactic environments, with a median $p$-value ($p_{\rm KS}$) of $2.84 \times 10^{-2}$ compared to random galaxy apertures.

astro-ph.GA↗

Machine learning classification of baseband data of CHIME FRBs

Fast Radio Bursts (FRBs) are bright millisecond radio pulses. Their origin is still unknown in the field of astronomy. A notable distinction among FRBs is that some sources repeat, while others appear to be non-repeating events. Interestingly, repeating FRBs tend to exhibit broader temporal widths and narrower spectral bandwidths compared to non-repeat events, suggesting they may arise from different physical mechanisms. However, current radio telescopes have limited coverage and sensitivity, which hinders a complete survey with continuous long-term monitoring. This issue makes it difficult to confirm repeat activity and potentially leads to misclassification of repeaters as non-repeaters; these are referred to as repeater candidates. To address this, machine learning techniques have emerged as a useful tool for classifying distinct FRB types in previous studies. In this study, we utilize the CHIME/FRB baseband catalog with three orders of magnitude better time resolution than the intensity catalog. Measured fluences are available in the baseband catalog, while only upper limits are reported in the intensity catalog. We apply machine learning to the baseband catalog to evaluate classification outcomes. We identify 15 repeater candidates among 122 non-repeating FRBs in the baseband catalog. Additionally, our classification identifies 31 sources previously categorized as repeater candidates as non-repeaters, highlighting a significant difference from the prior work. Of these repeater candidates, 14 overlap with previous findings, while 1 is newly identified in this work. Notably, one of our candidates was confirmed as a repeater by CHIME/FRB. Follow-up observations for the 14 candidates are highly encouraged.

astro-ph.HE↗