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I am currently working on pulsar signal periodicity detection. Most of the algorithm or paper which i read they use FFT (Fast Fourier Transform) and FFA (Fast Folding Algorithm ). Do we have any other algorithm that we can use to find the periodicity of pulsar signal because pulsar signal is highly noisy so as per my knowledge the LSTM can't be used to find the periodicity of pulsar signal? So if we have any alternative method please suggest me.

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    $\begingroup$ Hi, irfan. In this context, what do you mean by LSTM (is that referring to the neural network) - and is there any reason that just using normal implementations of the standard methods of frequency-domain and time-domain searches (DFT and FFA, like you mentioned) are inadequate? $\endgroup$ – HDE 226868 Feb 20 at 19:31
  • $\begingroup$ yes LSTM (Long short time memory) is a neural network, as per my knowledge the pulsar signal is highly noisy so its hard to find the periodicity of pulsar signal so mostly researcher use DFT or FFA techniques, what I want to know is that can we have any alternative method for this problem. $\endgroup$ – irfan tariq Feb 20 at 19:48
  • $\begingroup$ I'm not sure if there is anything helpful here, but have a look at Technique for folding sparse event data in order to detect an underlying periodicity? $\endgroup$ – uhoh Feb 21 at 6:25
  • $\begingroup$ I'm not sure if there is anything helpful here, but have a look at Technique for folding sparse event data in order to detect an underlying periodicity? and possibly How to find period of a pulsar from a given time sampled voltage data? $\endgroup$ – uhoh Feb 21 at 6:35
  • $\begingroup$ my question is related to finding the periodicity of the pulsar, the standard way is to do fft and do folding, or FFA but here what makes me confuse that can we apply FFT directly on pulsar data for periodicity search, or first we need to do dedispersion on accurate DM (Dispersion measure ), so any research who face this issue or deal with this issue can guide me. $\endgroup$ – irfan tariq Feb 21 at 18:08

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