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diminished or not noticed at all. Any deviations from the maximum temperature the noise source
is supposed to produce can impact data analysis negatively.
Because the noise temperature reading can differ in different parts of the sky, we used the
south hill, a constant noise source, to calibrate Smiley. PARI is located in the mountains, and
pointing at the hill is within the physical capabilities of the telescope, pointed at 180 degrees in
azimuth and 10 degrees in altitude. The ground emits a noise reading of approximately 300K at
our wavelength. The 4.6m telescope cannot automatically detect what temperature the sources it
points at are, so the equation
(1)
Y = {Tg+Tr}/{To+Tr}
is needed to determine what the temperature (K) and brightness (Jy) are of the objects we
observe. In Equation 1, Y is the ratio between the noise source being on and off, Tr is the
temperature of the receiver, To is the temperature of the background where we are pointing the
4.6m telescope when calibrating (300K), and Tg is the temperature the noise source produces
(200K) plus To. The relationship between the temperature of an object (K) and the brightness
(Jy) can be represented as 1 K =166 Jy, which is then used to convert temperature to brightness
and vice versa.
Because the temperature of the receiver is subject to change due to the conditions of the
surrounding environment, the value for Tr must be calculated before every observation in order
to ensure better precision in data collection. To find the temperature of the receiver, we use
(2)
Tr = {(I1*Tg)-(I2*To)}/{I2-I1}
Where I1 is power level of the sky and receiver, and I2 is the power level of the sky, receiver, and
the noise source. The other variables are the same as in Equation 1. These two equations are then
utilized to determine the temperature of the observed source.
I initially used Scilab, a free program similar to Matlab, to use the equations and develop
a program where the user can input the frequency of the data point, I1, I2, and the relative power
of the source itself, and obtain as output the temperature of the object at whatever frequency it is
situated at. Primarily, Scilab was utilized in checking that the data had been collected properly,
and that values were either close to accepted values or consistently a certain amount higher or
lower than expected.
To fit the needs of a person of any academic level, the final version available for the
public's use is an Open Office Spreadsheet. The spreadsheet uses three 'sheets' in the file, and the
only information that needs to be inserted is on the first sheet. The inputs are, in order:
Frequency (Khz), Intensity Of Target, Intensity Noise Source Off, and Intensity Noise Source
On.
There were discrepancies on what method should be used to calculate from the values we
obtain from Equations 1 and 2 the actual temperature of the targeted source, so two spreadsheet
files were created – one utilizing ratios and the other assuming that intensity of zero corresponds
to a value of 0K. Neither spreadsheet is completely accurate, but as of right now the latter
procedure is more accurate. I will describe how the two files differ from each other as I explain
how the spreadsheet works.
On the first 'sheet' of the file, after the four inputs, there is Temperature of the Receiver
and either Ratio between Source On and Source Off Intensity or Temperature per Intensity
Division, depending on whether the ratio or non-ratio file is used. The second sheet displays all
columns from the first sheet save for the frequency values, and includes more columns to the
right which then calculate the Temperature (K) and Brightness (Jy) of the radio source being
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