Aiaru Smat

PHOEBE is commonly used for star spot modeling, since is natively supports parameters like spot latitude, size and temperature contrast. For accretion disks PHOEBE has only limited support - users often approximate the disk using third light components or external custom scripts, since the current version doesn’t fully model disk geometry or emission

AI and physics informed learning methods can greatly enhance the analysis of physic data by extracting patterns, embedding physical laws, accelerating simulations and improving data reconstructions and prediction. They combine data driven intelligence with physical principles to make modeling, prediction and discovery more accurate and efficient.

That is a fascinating topic. Be ^ 11 is such an interesting case with its one neutron halo and weak binding

hi! studying fluctuations in high energy nuclear collisions is a fascinating area, especially for understanding the qcd phase transition and the early universe

How are Bayesian analysis methods using Monte Carlo sampling applied to estimate the parameters of dark energy models based on observational data? I have come across references to Markov Chain Monte Carlo (MCMC) in the context of CMB and supernova analyses, but it is unclear how to interpret the resulting posterior distributions. I would appreciate any clarifications or references to tutorials or review articles.