Sayora

👋🏼 Wow, that’s really cool! 🌌 I’ve read about PHOEBE — amazing that you’re using it for nova-like stars! 👋🏼 That’s awesome! 🙌 I’m also using ROOT for my analysis — I run it on Ubuntu. 🐧 It’s a really powerful tool once you get used to it! We’ve recently published our latest results in the following paper: https://arxiv.org/abs/2312.17573 Hello Yerkebulan 🙂 Exactly! Both fields explore how matter behaves in extreme environments — really cool parallel! 🚀 By the way, Daniyar also replied to my post here — his research topic is quite similar, so I think you two could really exchange ideas or even collaborate. 🤝 You really seem to know this topic well! 😊 I’d be glad to read more about your work.

Hi! 🖐🏼 Here’s my Scopus link if you’d like to learn more about my research. commercial link removed by moderator Hello. Totally agree! 🚀 Our research feels like a time machine — we’re traveling back to the moment of the Big Bang! 🌌✨

Hi! 👋 I’m studying the thermodynamic properties of black holes — like temperature, entropy, and pressure — especially near critical points. From what I’ve learned, many researchers treat the cosmological constant as pressure and study black holes in an extended phase space. This lets you use ideas similar to normal thermodynamics (like the Van der Waals gas) to analyze phase transitions. The usual approach is to look at the equation of state P=P(T,r+)P = P(T, r_+)P=P(T,r+), find critical points where ∂P/∂v=∂2P/∂v2=0, and then study Gibbs free energy to see which phase is stable. For simple examples, the Reissner–Nordström–AdS black hole is often used — it shows small/large black hole transitions, similar to liquid–gas ones. If you want to read more, I’d recommend: Kubizňák & Mann (2012) – P–V criticality of charged AdS black holes Dolan (2011) – Pressure and volume in the first law of black hole thermodynamics

Hey! 👋 Yeah, GW data can get really noisy 😅 I’ve used bandpass + wavelet denoising (with scipy.signal and pywt) before making spectrograms — that helped a lot. For training, I used CNNs in PyTorch, with extra noise-augmented data to make the model more stable. Some folks also try autoencoders for pre-cleaning, but honestly, simple filtering + normalization often works great. Good luck with your analysis! 🚀✨

Hey! 👋 Yeah, I’ve worked with noisy detector signals before. 👉 First, try background subtraction and simple smoothing (moving average or Savitzky–Golay). 👉 If noise changes over time — wavelet filtering works well. 👉 For tracking slow signal changes — use a Kalman filter. 👉 If you have enough data, you can test ML denoising (autoencoder), but it’s more complex. Also check shielding, grounding, and detector calibration — sometimes that helps the most ⚛️

👋 Hi everyone! I’m not working in this field myself, but I know researchers who study solar radio emission using a non-stationary approach 🌗. My work is more about cosmic ray dynamics 💻. If you are interested, I can share contacts.

👋 Hi! PHOEBE — it’s a ready-made program (package) designed for modeling eclipsing binaries 🌗. 🧠 It already includes physics models, calculation algorithms, and a user interface. 💫 I study cosmic ray motion, and unfortunately, PHOEBE isn’t suitable for our research 🚫. 💻 So we write our own programs in C++ to do the modeling ourselves! ⚙️

👋 Hi everyone! I’m not working in this field myself 🧑‍🔬, but I know some scientists from the INP 🔬. I can share the article 📄 they’re using for their research 💡 if anyone’s interested! 😊 1808.08490

Hi 👋🏼 I worked briefly on nuclear halo modeling. 💻 How I got started Choose an approach: For simple systems, use few-body models (e.g., a nucleus + 2 neutrons). For more realistic systems, use TDHF or TDDFT. I've mastered the following software: 👉 Sky3D is an open-source program for TDHF modeling. 👉 TDDFT codes (there are adaptations for nuclear systems). 👉 For few-body problems, you can write your own code (Python, Fortran, C++). I've learned to visualize data: see how density and energy change over time. Here are some basic reviews that were very helpful during my work: K. Yabana & T. Nakatsukasa — Time-dependent approaches in nuclear dynamics D. Lacroix, C. Simenel — Introduction to TDHF for nuclear reactions P. G. Hansen & B. Jonson — The neutron halo of light nuclei If you have any specific questions, I can ask my supervisors. 🤝🏼

As I understand it, MCMC helps to find the parameters of dark energy that best fit the observational data, and the posterior distributions show which parameter values are most likely. Isn’t that so?🤔 Moderator NoteAttachment removed. Please provide relevant discussion material in the post.

Hey! 👋 Great question! AI and physics-informed learning can really help in studying physical data. 🤖⚙️ By adding physics rules into machine learning models, we can get better results even if the data is noisy or small. The model “knows” what’s physically possible, so it avoids nonsense predictions. This approach is already used in areas like fluid flow, materials, and space research. 🌌 It’s a cool way to mix data and theory to find new physical laws! 🔍

Hi Symbat 👋 I'm new to this experiment and just learning how to process the data 😊 We're using Monte Carlo simulations and GEANT4.

Hi everyone!👋🏻 I’m studying fluctuations in high-energy nuclear collisions — how matter behaves under extreme conditions, like after the Big Bang. I use C++ and computer simulations for this. Has anyone else worked with nuclear matter simulations? Happy to chat and share ideas! 😊