Category: Admin Test

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SEFD vs Antenna Amplitude vs Receiver information

We derive the Antenna Amplitude in this post:

Solving the antenna matrix from individual baseline

Then we compare the Antenna Amplitude and SEFD to see whether they correlate with each other.

(X-pol) Green is USB and Red is LSB. The dash line is the best-fit line via linear regression method.
(Ypol) Green is USB and Red is LSB. The dash line is the best-fit line via linear regression method.

We found the anti-correlation between Antenna amplitude and SEFD, which is making sense that the higher Antenna response (i.e. higher Antenna amplitude) because of the lower SEFD.

Then we are wondering whether the bad performance (i.e. lower Antenna amplitude and/or higher SEFD) may due to the internal receiver. We check the 15 parameters in receiver information:

Tch – Cold head temp

Tma – LNA Main arm temp

Tsa – LNA Side arm temp

Vd1, Vd2, Vd3, Vd4 – drain voltage for 4 LNAs

Id1, Id2, Id3, Id4 – drain current for 4LNAs

Vg1, Vg2, Vg3, Vg4 – gate voltage for 4 LNAs

The resulting plots will present in the google word document:
https://docs.google.com/document/d/1cy81rhwcNAiVZ6L8SRDlIvL8ksLxICxlU8wvCY2b0IY/edit

In summary, we did not find any direct correlation between bad performance and 15 parameters in receiver information.

 

-Ming-yi

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Jupiter bandpass slope from 2018-12 to 2019-08

Goal: In order to know whether we can create a universal Jupiter bandpass. If Jupiter cannot observe at night as calibrator, we may apply the universal bandpass to calibrate the data.

Methods & Results:
1. I am checking the Jupiter bandpass between 2018-12 and 2019-04. We want know whether the bandpass is stable across 5 months.
We have digital gain slope equalizer during this time. After the normalization, the slope is quite stable.

Bad data has flagged out, total sample size is 17, across 2018-12 to 2019-04

2. Then I am looking at the Jupiter bandpass between 2019-06 and 2019-08. They are using the non-equalized bitcode.
We exclude the 2019-05 because 1Y and 6Y have analog equalizer.
Because some baseline has lower SEFD, the bandpass is quite noise. I use following criterion to define the highRMS and lowRMS data.

I fit the 1-D polynomial function to 50-160 channels, divide the bandpass by the best-fit 1D polynomial function, and then I measure the residual standard deviation as RMS.

lowRMS: stddev < 0.02 (have higher amplitude with lower SEFD)

highRMS: stddev > 0.02 (have lower amplitude with higher SEFD)

To compare the bandpass with different RMS together, I smooth the highRMS bandpass with 40 channels. Colour lines indicate the smoothed highRMS bandpass, while black lines indicate the lowRMS bandpass.

Conclusion:
Although there is a slightly slope discrepancy in Ant1 & Ant2, other baseline bandpass slope look stable and nice. We may use the Jupiter bandpass between 2019-06 and 2019-08 (sample size:20) to generate the universal Jupiter bandpass (with non-equalized bitcode).

-Ming-yi

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a_boss problems

Hello,

we have encounter a large amount of errors/problems from a_boss in the last observations. These problems are likely to be related with the latest software upgrades (either bugs or simply because we don’t know how to use the upgraded versions … ja… sad).

We are working on the problem with the support of Michael, we shall post a summary once we sort it out.

Greetings,

Pablo