SearcharxivSearch

arXiv subjects

Christopher Cook

Publications and source records attributed to Christopher Cook.

2 recordsLinked to original sources

Insights for Early Dark Energy with Big Bang Nucleosynthesis

Big Bang Nucleosynthesis (BBN), as one of the earliest processes in the universe accessible to direct observation, offers a powerful and independent probe of the cosmic expansion history. With recent advances in both theory and observation, including efficient and flexible BBN codes, percent-level measurements of primordial deuterium and helium-4 abundances, refined measurements of nuclear reaction rates, and precise determinations of the baryon density from the cosmic microwave background, particularly keen insights can be gained from BBN. In this work, we leverage these developments to place model-independent constraints on deviations from the Standard Model expansion history during BBN. Using the latest abundance data, we apply principal component analysis to identify the most constrained and physically meaningful modes of expansion history variation. This approach allows us to impose the most general constraints on early dark energy during the epoch of BBN. We further examine whether general modifications to the expansion rate could alleviate the long-standing lithium problem. Our results demonstrate that BBN, sharpened by modern data and statistical techniques, remains an indispensable probe of dark energy and new physics in the early universe.

astro-ph.CO

GRB Redshift Classifier to Follow-up High-Redshift GRBs Using Supervised Machine Learning

Gamma-ray bursts (GRBs) are intense, short-lived bursts of gamma-ray radiation observed up to a high redshift ($z \sim 10$) due to their luminosities. Thus, they can serve as cosmological tools to probe the early Universe. However, we need a large sample of high$-z$ GRBs, currently limited due to the difficulty in securing time at the large aperture Telescopes. Thus, it is painstaking to determine quickly whether a GRB is high$z$ or low$-z$, which hampers the possibility of performing rapid follow-up observations. Previous efforts to distinguish between high$-$ and low$-z$ GRBs using GRB properties and machine learning (ML) have resulted in limited sensitivity. In this study, we aim to improve this classification by employing an ensemble ML method on 251 GRBs with measured redshifts and plateaus observed by the Neil Gehrels Swift Observatory. Incorporating the plateau phase with the prompt emission, we have employed an ensemble of classification methods to enhance the sensitivity unprecedentedly. Additionally, we investigate the effectiveness of various classification methods using different redshift thresholds, $z_{threshold}$=$z_t$ at $z_{t}=$ 2.0, 2.5, 3.0, and 3.5. We achieve a sensitivity of 87\% and 89\% with a balanced sampling for both $z_{t}=3.0$ and $z_{t}=3.5$, respectively, representing a 9\% and 11\% increase in the sensitivity over Random Forest used alone. Overall, the best results are at $z_{t} = 3.5$, where the difference between the sensitivity of the training set and the test set is the smallest. This enhancement of the proposed method paves the way for new and intriguing follow-up observations of high$-z$ GRBs.

astro-ph.HE