Machine Learning Applications in Cosmology and Dark Matter Research

The integration of machine learning techniques into cosmological research is rapidly advancing the field, particularly in the analysis of dark matter interactions and enhancing the precision of cosmological models. One prominent example is the development of a Physics-Informed Neural Network, or PINN, which addresses the Hubble tension by reconstructing the redshift-dependent Hubble parameter within the Tsallis Holographic Dark Energy model. This framework allows for simultaneous estimation of key cosmological parameters, including the Hubble constant and neutrino density, significantly reducing the statistical tension previously observed in standard models, as noted in a recent arXiv paper. The research demonstrated that the PINN-based approach could alleviate the Hubble tension from approximately five sigma down to a range of 0.5 to 2.2 sigma, showcasing its effectiveness compared to traditional Markov Chain Monte Carlo methods, according to findings published on arXiv.

Additionally, advancements in machine learning are also evident in the recovery of the 21-cm signal from neutral hydrogen, which is crucial for understanding early cosmic structures. A study utilizing Gaussian Process Regression, or GPR, performed a Bayesian model comparison to effectively discern between different modeling strategies for this faint signal. The most effective GPR models achieved remarkable accuracy in power spectrum recovery, demonstrating the potential of these machine learning techniques in overcoming challenges presented by astrophysical foregrounds, as highlighted in another arXiv submission.

Furthermore, machine learning applications extend to the search for dark matter through gravitational-wave interferometers. A recent study explored three types of dark matter—dilatons, dark photons, and tensor bosons—leveraging data from the fourth LIGO-Virgo-KAGRA observing run. While no direct signals were detected, the research established the most stringent upper limits to date on these dark matter candidates, underscoring the capability of gravitational-wave detectors as innovative tools for exploring new physics, as reported in an arXiv research paper.

The dynamic nature of dark energy is also being scrutinized using Bayesian models that incorporate machine learning techniques for evidence evaluation. A study analyzing data from the Dark Energy Spectroscopic Instrument collaboration found a significant preference for a dynamic dark energy model over the cosmological constant, although results varied when compared to traditional methods, emphasizing the nuanced interpretations required in cosmological modeling. These evolving methodologies not only improve the accuracy of cosmological models but also open new avenues for resolving long-standing tensions in cosmology, demonstrating the transformative impact of machine learning in the field.