Sagira Mamatova

Hi there! 👋 Yes, neutron detector data can be quite tricky — especially in reactor environments where background radiation and electronic noise overlap with the signal. I’ve worked with similar flux measurements, and wavelet-based denoising combined with moving average smoothing gave good results for removing short-time fluctuations without losing physical information. If your data are time-dependent, Kalman filtering works great for tracking real signal variations while suppressing random noise. For more complex patterns, autoencoders or 1D CNNs trained on clean vs. noisy samples can perform surprisingly well — see Zhang et al. (2021, Nuclear Instruments and Methods in Physics Research A) for an example. Also, don’t forget to characterize the noise spectrum first — that helps you choose the right cutoff frequencies for Fourier or wavelet filters. Good luck with your analysis — sounds like an exciting project!

Hi! ☀️ That’s a fascinating topic — solar radio emissions are such a rich source of plasma physics insights. I’ve been working with time-dependent plasma simulations to study Type II and Type III radio bursts, focusing on how electron beams and shock fronts evolve in the corona. If you haven’t seen it yet, Reid & Ratcliffe (2014, Research in Astronomy and Astrophysics) is a great review on the physics of solar radio bursts. For modeling, you might also look at Li et al. (2022, ApJ), where they use time-dependent kinetic simulations to reproduce burst structures. I’d love to exchange ideas about numerical approaches — especially how you handle plasma density gradients and emission mechanisms in your model.

Hi everyone! 👋 I’m studying the thermodynamic properties of black holes and how quantities like temperature, entropy, and pressure behave near critical points. Does anybody know what models or approaches are best for analyzing phase transitions and stability in black hole thermodynamics? I’ve read about the analogy with standard thermodynamic systems, but I’m still not sure how to interpret the results in an extended phase space. Any advice or recommended papers would be greatly appreciated. Thanks in advance! 🙏

Hi there! 👋 Yes, noise in gravitational wave (GW) data can be really challenging — especially the non-stationary, transient glitches from LIGO/Virgo detectors. A good starting point is using wavelet-based denoising or Wiener filtering before transforming the signal into spectrograms. If you’re training CNNs, applying whitening + band-pass filtering (usually 20–500 Hz) helps a lot. Many groups also use autoencoders or denoising diffusion models to clean spectrograms before classification — check out George & Huerta (2018, Physics Letters B) and Miller et al. (2019, Phys. Rev. D) for examples. You might also want to explore PyCBC or GWpy libraries — they have built-in functions for noise removal and spectrogram generation. Good luck with your models — sounds like a great project! 🚀