Radio astronomy - integration time vs. sample rate

In summary: I read too!In summary, the discussion revolves around the radiometer equation for a simple radio telescope project at a university. The main point of confusion is the concept of "integration time" and how it relates to the sampling and application of Fourier analysis. The use of a fast sample rate and an effective integration time is suggested, but it ultimately depends on the application and considerations such as bandwidth and dead-time. The discussion also mentions using a software defined radio approach and the use of a low pass filter. The importance of effective integration time and effective noise bandwidth is emphasized, especially in regards to avoiding aliasing phenomena.
  • #1
wheels1888
6
0
Sooo I wasn't sure if I should put this here or in the astrophysics section, but I figured electrical engineering was more relevant for my question.

I'm involved in a project at my university to build a (very) simple radio telescope, but I'm having a bit of trouble with the radiometer equation:[itex]\sigma_T \approx \dfrac{T_{sys}}{\sqrt{\Delta \nu_{RF} \cdot \tau}}[/itex]Where [itex]\sigma_T[/itex] is the "sensitivity" or the minimum detectable temperature, [itex]T_{sys}[/itex] is the system temperature, [itex]\Delta \nu_{RF}[/itex] is the bandwidth, and [itex]\tau[/itex] is the integration time.

I'm mostly having trouble with "integration time". This seems to imply that one has to use an integrating analog to digital converter (something like a dual-slope ADC I guess...), but this seems to prevent the application of Fourier analysis (am I wrong in thinking this?). However, I have seen some examples of radiometers that appear to take discrete samples, and apply some sort of "integration time".

Since the product [itex]\nu \cdot \tau[/itex] is effectively the number of samples that would have been taken over the integration time, I was wondering if this product could equated to some function of sample rate of a non-integrating ADC?Rereading my above questions, they aren't the most coherent sentences in the world... But I'm not quite sure how else to pose them. I have a feeling I'm overthinking this, or missing something simple. If anyone needs anything clarified, please let me know!

Thanks so much for the help!
 
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  • #2
I think the answer is that it depends a bit on the application. One way to get an "integration time" is to simply use a very fast sample rate and then integrate numerically.
However, one thing to keep in mind is that the circuit before the ADC can ALSO give you an "effective" integration time (just think of it as a low-pass filter), and as long as you sample fast enough the dead-time between sample will presumably not matter (since there isn't enough time for successive samples to change anyway). Most of the formulas of these types assume a negligible dead-time between samples, which is certainly not the the case of you use a fast ADC with a low(ish) rate.
Ideally, these two "methods" should of course agree.

So again, it depends. The main things to keep an eye on is the BW of your circuit and the dead-time of the ADC.
 
  • #3
Thanks for the response!

We are taking a software defined radio approach to radio astronomy, which is something that hasn't really been done, or at least documented to a great extent, so it's difficult to find information. As a result of using SDR, we aren't using any sort of frequency-to-voltage converter, and instead are just going to run the discrete Fourier transform on a sample of data. If our sample rate is fast enough (I read somewhere where the sample rate should be comparable to the bandwidth? I don't really see why this should be true though.) would it then be acceptable to use the total time over which we collected data as our "effective" integration time?

Thanks again for the help!
 
  • #4
As you know, any radiometer have the Low Pass Filter (LPF) at it's output. The term 'integration time' relates to the device, named an `ideal integrator`, used as LPF. This is not good LPF, since it power response looks like (sin f)^2 / f^2. However, the such responce corresponds to the 'moving average' filter, which can be performed programmatically (after the sampling) but not in continuous time, as physical device. Instead this kind of LPF you can use any you prefer, but take into account the `effective integration time`, which defined in the Kraus book, Radioastronomy, Chapter 7. This effective integration time defined via the Effective Noise Bandwidth (ENB) of your real LPF. No matter at this point, are you using the ADC or not.

If you use an ADC, the sampling frequency should be equal or greater the `Fcutoff*2`, othervise the `aliasing` phenomena (`sampling theorem`, Shannon, Nyquist, Kotelnikov). Fcutoff - the cutoff frequency your LPF (before the ADC).
 
  • #5
You don't want to know what I read instead of "Fcutoff" :D
 
  • #6
Yes! I'm do not
 

1. What is the difference between integration time and sample rate in radio astronomy?

In radio astronomy, integration time refers to the amount of time over which a signal is collected and combined to improve the signal-to-noise ratio. On the other hand, sample rate is the number of measurements taken per unit time. Essentially, integration time is how long the telescope is pointed at a particular source, while sample rate is how frequently data is collected during that time.

2. How does integration time affect the quality of radio astronomy data?

The longer the integration time, the better the signal-to-noise ratio of the data. This is because longer integration time allows for more measurements to be taken and combined, reducing the impact of random noise. This is especially important for faint or distant sources that produce weaker signals.

3. What is the relationship between integration time and sample rate?

Integration time and sample rate are inversely related. This means that increasing the integration time will result in a decrease in the sample rate, and vice versa. This relationship is important in finding a balance between collecting enough data for accurate results and processing the data in a timely manner.

4. How do researchers determine the optimal integration time and sample rate for a radio astronomy observation?

The optimal integration time and sample rate can vary depending on the specific research goals and the characteristics of the observed source. Generally, researchers will conduct tests with different integration times and sample rates to find the balance that produces the best signal-to-noise ratio while also allowing for efficient data processing.

5. Are there any limitations to increasing the integration time and sample rate in radio astronomy?

While longer integration times and higher sample rates can improve the quality of data, there are practical limitations to consider. Increasing integration time too much can result in diminishing returns, as the signal-to-noise ratio may not improve significantly beyond a certain point. Additionally, higher sample rates can also lead to longer data processing times and larger data storage requirements.

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