Continuous and discrete spectra

In summary: Not sure I fully understand your question. Sampling the spectrum is equivalent to multiplying it with a Dirac Comb function and then the signal you get from transforming back to the time domain would be a convolution of the original signal and the FT of the comb function. If the original signal is only present for a limited time 0 - T , this would allow lossless reconstruction when the frequency samples are less than 1/(2T) apart
  • #1
Domenico94
130
6
Is there any way to convert a continuous, aperiodic spectrum, to a discrete spectrum, in a signal? If so, would part of he energy of this signal be lost, I am this process of conversion, or would it be " distributed" amomg the various frequencies?
 
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  • #2
Not sure I fully understand your question. Sampling the spectrum is equivalent to multiplying it with a Dirac Comb function and then the signal you get from transforming back to the time domain would be a convolution of the original signal and the FT of the comb function. If the original signal is only present for a limited time 0 - T , this would allow lossless reconstruction when the frequency samples are less than 1/(2T) apart
 
  • #3
BvU said:
Not sure I fully understand your question. Sampling the spectrum is equivalent to multiplying it with a Dirac Comb function and then the signal you get from transforming back to the time domain would be a convolution of the original signal and the FT of the comb function. If the original signal is only present for a limited time 0 - T , this would allow lossless reconstruction when the frequency samples are less than 1/(2T) apart
Yes. Thanks..It s exactly what I was asking for. No losses than? Of any kind?
 
  • #4
No losses. It is the equivalent of Shannon's theorem but now from the frequncy domain to the time domain instead of the other way around.
 
  • #5
BvU said:
Not sure I fully understand your question. Sampling the spectrum is equivalent to multiplying it with a Dirac Comb function and then the signal you get from transforming back to the time domain would be a convolution of the original signal and the FT of the comb function. If the original signal is only present for a limited time 0 - T , this would allow lossless reconstruction when the frequency samples are less than 1/(2T) apart
This is the equivalent of the Nyquist criterion for temporal sampling. I'm trying to get my head around how to specify the equivalent to a practical Nyquist LP filter in this process.
 

Related to Continuous and discrete spectra

What is the difference between continuous and discrete spectra?

Continuous spectra consist of an unbroken range of wavelengths or energies, while discrete spectra consist of specific, distinct wavelengths or energies.

What are some examples of continuous spectra?

The visible light spectrum is an example of a continuous spectrum, as it includes all the colors of the rainbow with no gaps. The spectrum of a blackbody radiator is also continuous.

What are some examples of discrete spectra?

The emission spectrum of a hydrogen atom is a discrete spectrum, as it only consists of specific wavelengths corresponding to the transitions of electrons between energy levels. The absorption spectrum of a substance is also a discrete spectrum, as it only shows the specific wavelengths that are absorbed by the substance.

How are continuous and discrete spectra used in scientific research?

Continuous and discrete spectra are used in a variety of fields, including astronomy, chemistry, and physics. They are used to identify the composition of substances, study the behavior of atoms and molecules, and understand the properties of light and other forms of electromagnetic radiation.

Can continuous and discrete spectra overlap?

Yes, they can overlap in certain cases. For example, in the case of a gas discharge tube, both a continuous spectrum and discrete spectral lines may be present. This is because the gas in the tube produces both a continuous spectrum due to its temperature and discrete spectral lines due to its composition.

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