arXiv subjects

Dhruv S. Bal

Publications and source records attributed to Dhruv S. Bal.

3 recordsLinked to original sources

arXiv2026

Probing Evolution of Long Gamma-Ray Burst Properties through Their Cosmic Formation History

The astrophysics of Long GRB (LGRB) progenitors as well as possible cosmological evolution in their properties still poses many open questions. Previous studies suggest that the LGRB rate density (LGRB-RD) follows the cosmic star formation rate density (SFRD) only at high-z and attribute this to the metallicity evolution of progenitor stars. For low z, opinions differ on whether the uptick in the LGRB RD is due to a distinct class of low-luminosity GRBs or perhaps even a different progenitor subclass. To investigate these questions, we utilize data from the Neil Gehrels Swift Observatory and ground-based observatories (redshift). To test the hypothesis that the observations can be mapped (with/without evolution) to the well-established cosmic SFRD, we consider three cases: no evolution, beaming angle evolution, and a simple power-law evolution. The comparison shows that the 'no evolution' case can be ruled out. Our study highlights that the beaming angle evolution or the simple power law evolution are also not sufficient to obtain a good match between the LGRB-RD and SFRD. Rather, the inclusion of multiple evolving properties of LGRBs in combination appears to be required to match the two rate densities in their entirety.

astro-ph.HE↗

arXiv2026

Probing evolution of Long GRB properties through their cosmic formation history aided by Machine Learning predicted redshifts

Gamma-ray Bursts (GRBs) are valuable probes of cosmic star formation reaching back into the epoch of reionization, and a large dataset with known redshifts ($z$) is an important ingredient for these studies. Usually, $z$ is measured using spectroscopy or photometry, but $\sim80\%$ of GRBs lack such data. Prompt and afterglow correlations can provide estimates in these cases, though they suffer from systematic uncertainties due to assumed cosmologies and due to detector threshold limits. We use a sample with $z$ estimated via machine learning models, based on prompt and afterglow parameters, without relying on cosmological assumptions. We then use an augmented sample of GRBs with measured and predicted redshifts, forming a larger dataset. We find that the predicted redshifts are a crucial step forward in understanding the evolution of GRB properties. We test three cases: no evolution, an evolution of the beaming factor, and an evolution of all terms captured by an evolution factor $(1+z)^δ$. We find that these cases can explain the density rate in the redshift range between 1-2, but neither of the cases can explain the derived rate densities at smaller and higher redshifts, which may point towards an evolution term different than a simple power law. Another possibility is that this mismatch is due to the non-homogeneity of the sample, e.g., a non-collapsar origin of some long GRB within the sample.

astro-ph.HE↗

arXiv2025

Redshift Classification of Optical Gamma-Ray Bursts using Supervised Learning

Gamma-ray bursts (GRBs) are among the most luminous explosions in the Universe and serve as powerful probes of the early cosmos. However, the rapid fading of their afterglows and the scarcity of spectroscopic measurements make photometric classification crucial for timely high-redshift identification. We present an ensemble machine learning framework for redshift classification of GRBs based solely on their optical plateau and prompt emission properties. Our dataset comprises 171 long GRBs observed by the Swift UVOT and more than 450 ground-based telescopes. The analysis pipeline integrates robust statistical techniques, including M-estimator outlier rejection, multivariate imputation using Multiple Imputation by Chained Equations, and Least Absolute Shrinkage and Selection Operator feature selection, followed by a SuperLearner ensemble combining parametric, semi-parametric, and non-parametric algorithms. The optimal model, trained on raw optical data with outlier removal at a redshift threshold of z equals 2.0, achieves a true positive rate of 74 percent and an area under the curve of 0.84, maintaining balanced generalization between training and test sets. At higher thresholds, such as z equals 3.0, the classifier sustains strong discriminative power with an area under the curve of 0.88. Validation on an independent GRB sample yields 97 percent overall accuracy, perfect specificity, and an ensemble area under the curve of 0.93. Compared to previous prompt- and X-ray-based classifiers, our optical framework offers enhanced sensitivity to high-redshift events, improved robustness against data incompleteness, and greater applicability to ground-based follow-up. We also publicly release a web application that enables real-time redshift classification, facilitating rapid identification of candidate high-redshift GRBs for cosmological studies.

astro-ph.HE↗