Mamatova Sagira

Hi! 👋 Yes, I’ve used PHOEBE for modeling close binaries and systems with accretion disks — it’s a really powerful tool, though it takes some time to get used to the parameters. For disks, I found it helpful to start with simple geometries and gradually add complexity (like temperature gradients or hot spots). If you’re including star spots, check out Prša et al. (2016, ApJS, 227, 29), which explains how PHOEBE 2 handles surface features and spot modeling. Also, Jones et al. (2020) have a nice example of fitting light curves for cataclysmic variables using PHOEBE. Good luck with your thesis — nova-like variables can be tricky, but PHOEBE gives great flexibility once you get the hang of it! 🌟

Hi! 👋 That’s a really good question — Bayesian inference with Monte Carlo sampling is one of the main tools in modern cosmology. Markov Chain Monte Carlo (MCMC) methods are used to explore the parameter space of dark energy models by generating samples from the posterior probability distribution based on observational data (like CMB, BAO, or supernovae). In practice, you interpret the posterior as showing which regions of parameter space are most probable, given your data and priors. The shape and spread of the distribution tell you about uncertainties and parameter correlations. A very clear introduction is in Lewis & Bridle (2002, Phys. Rev. D), where they describe CosmoMC, and a good modern review is Trotta (2017, Reports on Progress in Physics) on Bayesian methods in cosmology. You might also check tutorials on the emcee (Foreman-Mackey et al., 2013) Python package — it’s widely used and beginner-friendly. Hope this helps — good luck exploring those posteriors!

Hi there! 👋 That’s a really good question — AI and physics-informed learning are now used a lot in physics. For example, Raissi et al. (2019, Journal of Computational Physics) showed how Physics-Informed Neural Networks (PINNs) can solve partial differential equations even with sparse or noisy data. Another great overview is in Karniadakis et al. (2021, Nature Reviews Physics), where they discuss how combining physical laws with machine learning helps discover new models. These methods are great for reconstructing hidden variables and learning governing equations directly from experimental data. Exciting field — I think it’s just the beginning! 🚀

Hi! 👋 That’s a really interesting topic. I’ve also worked a little with time-dependent nuclear models, mostly using simple simulations of light nuclei. It can be hard to keep the calculation stable over time, especially when modeling the breakup. I’d be happy to share ideas or talk about the numerical methods we use! Good luck with your research! 🚀

Hi! 👋🏻 That sounds like a really interesting area of research. I’ve worked a bit with nuclear matter simulations too — mainly using Python and ROOT for data analysis, and C++ for event generation. From my experience, it’s really useful to compare different models (for example, hydrodynamic vs. transport approaches) to see how fluctuations evolve at different energy scales. Visualization tools can also help a lot when studying correlations. Would love to hear more about which model or framework you’re using! Maybe we can exchange some simulation tips.