Imrm tricky things
Posted: Fri Sep 22, 2023 5:12 pm
hello,
I recently found some tricky things about the rm and pa0 fitting.
I used L, S, C, and X band data to fit the rm and pa0 for a source. I used the qcut=0.001, while the fitting often looks as poor as the attached image.
That looks like some serious nπ ambiguity problems; while the final rm and pa0 images look pretty smooth, most pixels in the region I selected showed up, and no significant pa0 or RM value jumps seen as expected. More interestingly, I saw some negative slope in the rm pa0 linear fitting but never found a negative RM value in the final rm image. so, may I inquire what are the logic behind these tricky things?
btw, is it less proper to use C and X bands together for this fit? the imrm fitting algorithm may rely a lot on the first two frequency data (c and x bands). if the X band image looks noisy and as the λ^2 of c and x band are close to each other, would this introduce any troubles?
Many thanks for helping in advance!
Best
Yihan
I recently found some tricky things about the rm and pa0 fitting.
I used L, S, C, and X band data to fit the rm and pa0 for a source. I used the qcut=0.001, while the fitting often looks as poor as the attached image.
That looks like some serious nπ ambiguity problems; while the final rm and pa0 images look pretty smooth, most pixels in the region I selected showed up, and no significant pa0 or RM value jumps seen as expected. More interestingly, I saw some negative slope in the rm pa0 linear fitting but never found a negative RM value in the final rm image. so, may I inquire what are the logic behind these tricky things?
btw, is it less proper to use C and X bands together for this fit? the imrm fitting algorithm may rely a lot on the first two frequency data (c and x bands). if the X band image looks noisy and as the λ^2 of c and x band are close to each other, would this introduce any troubles?
Many thanks for helping in advance!
Best
Yihan