Earthshine blog

We have often come across good ideas for student projects. Here is a start of a collection of projects – just links, but text can be added to explain more.

How do meteorological conditions determine seeing at the telescope?
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http://iloapp.thejll.com/blog/earthshine?Home&post=343

Was the bias pattern constant?
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http://iloapp.thejll.com/blog/nextelescope?Home&post=3

Understanding the PSF:
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http://iloapp.thejll.com/blog/earthshine?Home&post=313

Albedo maps and their use in modelling observations:
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http://iloapp.thejll.com/blog/earthshine?Home&post=304

Atmospheric turbulence studied via Moon images:
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http://iloapp.thejll.com/blog/earthshine?Home&post=295

Colour of earthshine – Danjons work:
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http://iloapp.thejll.com/blog/earthshine?Home&post=280

Image analysis methods – Laplacian method:
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http://iloapp.thejll.com/blog/earthshine?Home&post=273
http://iloapp.thejll.com/blog/earthshine?Home&post=272

Modelling Earth:
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http://iloapp.thejll.com/blog/earthshine?Home&post=258

In this post we saw that the difference between B and V (magnitude) images could have the shape of a linear slope on the DS and plateau on the BS. We are trying to recreate that using synthetic models. It is surprisingly difficult!

Using V and V images we saw that differences typically had the shape of level offsets – not slopes. In the B-V images we saw linear slopes on the BS. I thought the linear slopes originated in different PSFs in two filters – different alfa-parameters, for instance.

Well, taking a synthetic image and convolving it twice with two slightly different PSFs and converting to magnitudes and subtracting gives this:

Upper panel shows the ideal image we are using – BS to the right and the rest is DS. Bottom panel shows the difference between the image convolved with alfa=1.73 and alfa=1.72*1.02. DS is columns left of 360 – there is no linear slope. There are plenty of features on the DS above, but none ‘slope away linearly from the BS’.

A straight line in a lin-log plot corresponds to an exponential term. The difference between two Gaussians of different width is probably another Gaussian. Are we learning that the real PSF has a Gaussian term in it that varies between filters? Since V-V images did not show this behaviour the Gaussian is not manifested by the inevitable slight image alignment problems. Our model PSF is an empirical core with power-law extensions – and the above experiments show that such PSFs do not yield linear-slope differences.

Perhaps we could study the real PSF by studying difference images in a thorough way? Student project!