<commit_before>Spectral
===============================
*<NAME> :: Bois d'Arc Observatory, Kansas  ::  June 2014*

This repository supports my June 14, 2014 presentation *Toward Millimagnitude Photometric Calibration* before the SAS (Society for Astronomical Science) 2014 conference in Ontario, California, USA.

Contents
--------------------
This repository provides:

- simulation code (in C#, file extension .cs) for multiplying photon flux spectra and then producing for each combination of light source, atmospheric conditions, airmass, telescope, filter, and detector: (1) a computed Instrumental Magnitude, and (2) an expected exo-atmospheric magnitude within a bandpass. [directory "Simulation"]
- statistical code (in R, file extension .R) for comparing the Instrumental Magnitudes and bandpass magnitude, using a specific model formula. This is the calibration phase, and the intent is to simulate the extraction of photometric values for: Zero-point, extinction, and system transform. [directory "Statistics"]
- a few input and output data files as examples [directory "Data"]
- the slides shown during the June 2014 presentation [file: SAS Presentation 2014.pdf]

The accompanying paper is copyrighted and will be published in the *Proceedings for the 33rd Annual Conference of the Society for Astronomical Sciences*, probably available August 2014 via http://www.socastrosci.org/publications.html .

Disclosure of my own attitudes/biases:

- Nothing beats experimental data properly planned, skillfully taken, honestly analyzed. I'll count observation as experimentation, especially astronomical observation including photometry.
- For some systems, experiments are possible but too hard, expensive, or disruptive, as in social sciences, economics, and space travel planning. Computational simulation can get you a shortcut to learning something anyway.
- Other systems are just too inaccessible for experiments, for example, how a supernova starts. Or how cosmological structure developed over 15 billion years. Simulations can help here, if only to decide what observations to make.
- Finally, there's this project's case: one can and should do experiments to verify a model's description of reality, but alas current technology just can't give data that's good enough. In deciding on photometric calibration models, we want to know how best to collect and model photometric data, but real data has too much noise to make these decisions generally. What's needed is a way to compute how systems would work with noise levels below what's possible experimentally, then to add the noise back in--in this case noise of various kinds--to determine accurately the effects of model choices under realistic conditions. It's also true that computation is much quicker than experiment--well, if you don't count years of software development.

If you plan to examine the code...
--------------------------------------<commit_msg>Date of SAS Presentation corrected<commit_after>Spectral
===============================
*<NAME> :: Bois d'Arc Observatory, Kansas  ::  June 2014*

This repository supports my June 13, 2014 presentation *Toward Millimagnitude Photometric Calibration* before the SAS (Society for Astronomical Science) 2014 conference in Ontario, California, USA.

Contents
--------------------
This repository provides:

- simulation code (in C#, file extension .cs) for multiplying photon flux spectra and then producing for each combination of light source, atmospheric conditions, airmass, telescope, filter, and detector: (1) a computed Instrumental Magnitude, and (2) an expected exo-atmospheric magnitude within a bandpass. [directory "Simulation"]
- statistical code (in R, file extension .R) for comparing the Instrumental Magnitudes and bandpass magnitude, using a specific model formula. This is the calibration phase, and the intent is to simulate the extraction of photometric values for: Zero-point, extinction, and system transform. [directory "Statistics"]
- a few input and output data files as examples [directory "Data"]
- the slides shown during the June 2014 presentation [file: SAS Presentation 2014.pdf]

The accompanying paper is copyrighted and will be published in the *Proceedings for the 33rd Annual Conference of the Society for Astronomical Sciences*, probably available August 2014 via http://www.socastrosci.org/publications.html .

Disclosure of my own attitudes/biases:

- Nothing beats experimental data properly planned, skillfully taken, honestly analyzed. I'll count observation as experimentation, especially astronomical observation including photometry.
- For some systems, experiments are possible but too hard, expensive, or disruptive, as in social sciences, economics, and space travel planning. Computational simulation can get you a shortcut to learning something anyway.
- Other systems are just too inaccessible for experiments, for example, how a supernova starts. Or how cosmological structure developed over 15 billion years. Simulations can help here, if only to decide what observations to make.
- Finally, there's this project's case: one can and should do experiments to verify a model's description of reality, but alas current technology just can't give data that's good enough. In deciding on photometric calibration models, we want to know how best to collect and model photometric data, but real data has too much noise to make these decisions generally. What's needed is a way to compute how systems would work with noise levels below what's possible experimentally, then to add the noise back in--in this case noise of various kinds--to determine accurately the effects of model choices under realistic conditions. It's also true that computation is much quicker than experiment--well, if you don't count years of software development.

If you plan to examine the code...
--------------------------------------
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