Stellar Evolution is largely ascertained by emprical observations of stellar clusters:
From such observations an HR diagram is made:
Which is a plot of the brightness of a star (y-axis) versus its temperature or color. This diagram is what the entire theory of stellar evolution rests on.
However, hundreds of thousands of astronomy students per year are exposed to this diagram, try and memorize it, but have no understanding of the kinds of observations needed to make the diagram in the first place.
The reason we have been talking about detectors, measuring stellar brightnesses, taking about atoms and light and spectroscopy and now today, being to measure stellar temperature is that, in a few weeks, we will be making our own HR diagrams from simulations and real data, so that we gain an empirical understanding of how this process occurs.
Remember, this is not a class about what you can memorize. Its a class about measuring things and engaging in the scientific process.
Now when we are making a plot of surface temperature vs energy output
(stellar luminosity) we are plotting the two external attributes of
stars that we get from observation. We didn't have to go to the star
to get this information.
We can simulate this kind of correlation in external attributes as
follows. Using this table enter in your
approximate height, weight, and the color of your shirt. Use
the following numerical conversion of shirt color.
When you publish to global view you don't have to use your real name.
So what are the expections. A person's choice of shirt color is
quite unlikely to be correlated with their height or weight, but
we can check.
We do expect there to be a correlation between height and weight
(two external characteristics of humans). Let's check with the
data that you input.
So we can now think of the resulting height and weight sequence
as some kind of "main sequence" for humans.
But is this main sequence we made in this class
today for height vs. weight representative?
When we then make the plot of temperature vs. luminosity, the resulting
distribution of points is not random 
there
is correlation in the data. This is discovery. This is a result.
This is science.
If you want you can graduate your choices in terms of light or dark.
So, e.g. dark red would be 1.8, light red 1.2, etc.
Deriving stellar distances using the method of stellar parallax.
| Figure 1.3 Schematic Representation of stellar Parallax. Distant stars act as a fixed reference coordinate system. Nearby stars, when observed 6 months apart, will show a small movement with respect to the background of fixed stars. At position 1, the nearby star would be viewed against a background that contained star B while 6 months later, at position 2, the nearby star would be viewed against a background that contained star A. |
This angle would have a size of 1 arc second (1/3600 of a degree) for a star that had a distance of 1 parsec from the earth. 1 parsec is equal to 3.26 light years.
The nearest star to us has a distance of 4.1 light years so that all parallactic angles are less than 1 arc second for all stars.
The observational problem in measuring accurate stellar distances is then that atmospheric motions/smearing make positional measurements of stars, at levels of accuracy less than 1 arc second difficult.
Therefore, many measurements of the star are needed to record an accurate parallax. In practice, one usually requires 20 years of measurements of a single star.
If we measure the parallactic angle, then we can directly know the distance to the star. The distance in parsecs is simply
where p is the angle measured in arcseconds. Thus a star that has p = 0.1 would have distance of 1/p = 10 parseconds = 32.6 light years.
Now let's consider the following scenarios:
Simulation of stellar parallax with no error. This star has a distance of 2 parsecs so its parallax angle would be 0.5 arcseconds.
Distance = 2 parsecs; error per measurement is comparable to the actual parallax
Distance = 2 parsecs; error per measurement s 10 times smaller than the actual parallax