Yerkebulan.B

Another common method for estimating dark energy parameters is Nested Sampling (e.g., the MultiNest algorithm): How it works: instead of a traditional chain, Nested Sampling builds a sequence of nested regions with increasing likelihood, gradually narrowing the parameter space while estimating the integral over the posterior. Advantages: works well for multimodal and high-dimensional distributions; directly provides the marginal likelihood (evidence), which is useful for model comparison. Application in cosmology: used with CMB, supernovae, and BAO data to simultaneously estimate dark energy parameters and compare different models

New AI-based methods for analyzing physical data include Neural ODEs for modeling dynamics, Graph Neural Networks for structured interactions, physics-informed Gaussian Processes for uncertainty-aware predictions, Reinforcement Learning for discovering patterns and strategies, and Symbolic Regression (like AI Feynman or PySR) for deriving analytical formulas from data.

That’s a fascinating topic! You might want to look into the Van der Waals–like approach to black hole thermodynamics — it’s widely used to study phase transitions and critical behavior, especially in AdS black holes. In the extended phase space, the cosmological constant is treated as a thermodynamic pressure, and the black hole mass corresponds to enthalpy. Analyzing the P–VP–VP–V or T–ST–ST–S diagrams can reveal first- and second-order phase transitions similar to real fluids. You could start with Kubizňák & Mann (2012), “P–V criticality of charged AdS black holes”, which is a key reference. It provides a solid framework for interpreting stability and criticality in this context.

Really interesting work! Try using the EEMD + matched filtering method — it works well with non-stationary noise. EEMD helps separate the signal into components and remove the background, while the matched filter enhances weak neutron pulses, improving the signal-to-noise ratio.