Its important that you spend the
majority of class time today on the second part of this tutorial,
because a lot of new IDL commands and functions will be introduced.
In this tutorial, I ask you to generate two figures to send me by
Thursday Sept 25.

To students who electronically requested H205 accounts but have been
unable to log in: Please email Joe Cammisa (jcammisa@haverford) and
ask him to please create an account for you. For this to work, you
must have already also clicked on the online account generator.

**Making your first Hess diagram: Part 2**

This tutorial exercise is intended to pick up where you left off last
Thursday, and to guide you through the IDL you need to complete
Project Set 2, Section 2, Question 2.

1. Go back and finish the Hess diagram tutorial for last Thursday, if
you haven't already done so. This Hess diagram is scaled so that
black = a relatively small number of stars and white = a relatively
large number of stars. Reverse this scaling by simply reversing
the sign on the 2d histogram that contains your Hess diagram.
(i.e. maghist = -maghist). Once you have successfully debugged and
created a good looking Hess diagram for a single Milky Way field,
do the same for all four of the Milky Way fields.

2. Play around with the pixel sizes you are using for these CMD
images. Iterate a bit until you find pixels sizes that look
appealing - i.e. each pixel is large enough to have enough stars so
that the pixel values are statistically significant, and is small
enough to see features in the CMD.

3. Save these Hess diagrams to a .ps file and email it to me by class
on Thursday Sept 25 (along with the figure you will create below).
Its important that you spend a substantial amount of Tuesday class
on the second part of this tutorial, so feel free to move on to
that once you are comfortable making these basic Hess diagrams.

**Calculating the measurement
uncertainty on apparent magnitude in SDSS as f(m_r)**

This tutorial exercise will substantially help you with Project Set 2,
Section 1, Question 4. The new functions and IDL commands that you
learn will also substantially help you with all programming-based
assignments. We want to empirically determine the typical
measurement error on stars in the SDSS data that we are using, as a
function of apparent magnitude. We will then make a plot with IDL
that shows sigma_r vs. r magnitude.

1. Start this tutorial in a new IDL program. Read in one of the SDSS
data files you've been working with. It doesn't matter which one,
because the data all come from the same survey and thus have the
same measurement errors. Thus far, we've only used the ra, dec,
and apparent magnitudes contained with these files. We will now
also use the magnitude measurement errors, which we have been
reading in as "uerr, gerr, ierr, rerr, zerr".

2. The function "AVG" returns the average value of an array and
"MEDIAN" returns the median value of an array. Find the average
and median r-magnitude measurement error of all stars in the data
you've read in. Use the IDL Searchable Help to find the correct
syntax for average. Now find the minimum and maximum r-band
apparent magnitudes of stars in your dataset. An easy way to do
this is with the "MINMAX" function.

3. Take an eyeball look at what the distribution of measurement errors
looks like. Try doing this in a couple of ways. If you want to
see these figures on the screen, you may need to now tell IDL
*set_plot, 'x'* so that your figures pop up on the screen rather
than in a .ps file. You can plot a quick histogram of the data
with:

plothist, rerr, bin = 0.025

bin = 0.025 just sets the histogram bin size to 0.025 magnitudes. For this histogram to look sensible, you will need to play around with the xrange and perhaps also the bin size. (plothist takes the same xrange = [a,b] format as plot does). Now take a look at the magnitude measurement error as a function of apparent magnitude:

plot, r, rerr, psym = 4, xtitle = 'sigma_r', ytitle = 'r'

You will also need to play around with
the xrange and yrange on this figure for it to look reasonable.
You will likely need to play around with the symbol size as well,
using the "symsize" keyword in your plot statement.

We next want to
collapse the information contained in this 2 dimensional figure
into a one dimensional numerical function - sigma_r = f(r).

4. Define an array containing values of r for which we want to
evaluate this function. You could do this in a simple brute force
approach by typing out an array of values. For example:

rvals = [15.0,15.5,16.0,16.5,17.0,17.5,18.0,18.5,19.0,19.5,20.0,20.5,21.0,21.5]

But also try something more elegant and versatile:

rval_max = MAX(r) ;the maximum value of r magnitude

rval_min = MIN(r) ;the minimum value of r magnitude

rval_bin = 0.25 ;in magnitudes

num_vals = 1.+(rval_max-rval_min)/rval_bin

rvals = findgen(num_vals)*rval_bin + rval_min

print, rvals

The findgen function generates an array of
floating point numbers. Each element contains the number of its array
position (hence "F"loating-point "IN"teger "GEN"erator... or something
like that). In the above example, you created an array with
*num_vals* elements. Play with the findgen function:

a = findgen(5)

print, a

Now define an empty array to contain the MEDIAN magnitude measurement errors at all of these values of r-magnitude.

avg_magerr = fltarr(num_vals)

For each element of this array (for each
value of rvals), calculate the median magnitude measurement error.
The error needs to be calculated within a range of r-magnitude. The
range we will use is 0.25 mag, the value of rval_bin. *There is a
small bug in the below lines of code. This bug is something you can
figure out yourself, so I'm not going to fix it... from here on out you
won't have so much code written for you anyway :)*:

FOR j = 0, n_elements(rvals) - 1 do begin

inbin = where (r ge rvals[j] and r lt rvals[j] + rval_bin

avg_magerr = MEDIAN(rerr(inbin))

ENDFOR

5. Visually check your results by
overplotting them on top of the scatter plot of rerr vs. r that you
generated earlier in this tutorial. Keep in mind that we've defined
our *rvals* vector to be the bright magnitude limit of each
r-magnitude bin for which we've found the median measurement
uncertainty. Email me this figure (along with the set of 4 Hess
diagrams) by class on Thursday Sept 25.

6. If you have successfully completed all of the above, then
start to work on Project Set 2, Section 1, Questions 3 and 4.

Last modified: Thu Sep 25 09:09:30 EDT 2008