Hey everyone!
I’m working on 2D classification of gravitational wave signals (using spectrograms), but the detector noise is giving me a hard time.

Has anyone here dealt with noisy GW data before?
What kind of preprocessing or denoising methods worked best for you before training your model — like CNNs, autoencoders, or something else?

Would love to hear your experiences or tips! 🚀

I haven’t worked with gravitational wave data, but in communications we often use classic signal processing methods to deal with noise — like filtering, correlation analysis, adaptive filters, and spectral techniques. For example, matched filtering or adaptive noise suppression methods are very effective for detecting weak signals. I think similar approaches could be useful for gravitational wave analysis too.

That’s a great project — working with gravitational wave spectrograms is super exciting but yeah, detector noise can be a nightmare 😅

In my experience, a few things can help a lot:

• Band-pass filtering or wavelet thresholding to remove low-frequency seismic noise and high-frequency artifacts.

• Adaptive noise cancellation using reference channels from the detector environment (like seismic or magnetic sensors).

• For ML approaches — denoising autoencoders or U-Net architectures work surprisingly well at separating real GW patterns from background noise.

• Some teams also use transfer learning with pretrained CNNs on clean synthetic data, then fine-tune on noisy detector data.

If you haven’t tried it yet, combining traditional filtering with a learned denoising stage often gives the best of both worlds. Definitely a challenging task, but it’s where the frontier of GW data analysis is moving! 🌌💡

10 hours ago, Alisher said:

Hey everyone!
I’m working on 2D classification of gravitational wave signals (using spectrograms), but the detector noise is giving me a hard time.

Has anyone here dealt with noisy GW data before?
What kind of preprocessing or denoising methods worked best for you before training your model — like CNNs, autoencoders, or something else?

Would love to hear your experiences or tips! 🚀

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! 🚀✨

11 hours ago, Alisher said:

Hey everyone!
I’m working on 2D classification of gravitational wave signals (using spectrograms), but the detector noise is giving me a hard time.

Has anyone here dealt with noisy GW data before?
What kind of preprocessing or denoising methods worked best for you before training your model — like CNNs, autoencoders, or something else?

Would love to hear your experiences or tips! 🚀

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! 🚀

11 hours ago, Alisher said:

Hey everyone!
I’m working on 2D classification of gravitational wave signals (using spectrograms), but the detector noise is giving me a hard time.

Has anyone here dealt with noisy GW data before?
What kind of preprocessing or denoising methods worked best for you before training your model — like CNNs, autoencoders, or something else?

Would love to hear your experiences or tips! 🚀

Hi! 👋I’ve faced similar issues — detector noise can be really tricky. Denoising autoencoders or wavelet filtering might help reduce it before training.🤔