Is the disagreement between 1-D surface brightness profiles of the data and the 2-D model fit really a problem with 2-D fitting, or with the 1-D profile extraction?
While the answer might seem very obvious (for, how can the data be “wrong”?), a closer look of 1-D profiles of Figure 7 will reveal why the answer is rather subtle: the blue line is the galaxy surface brightness profile when neighboring objects in the image are not properly masked out, whereas the square, discrete, data points are the profile with some amount of masking. After masking, it is clear that the excess wing drops considerably, but apparently not completely. So it is not hard to believe that at least part of the discrepancy between the data and the model may be due to incomplete masking. Still, it begs the questions: why shouldn’t GALFIT fit that excess flux by raising the sky further to achieve a better fit? The answer is that a “better fit” in 1-D can actually be a worse fit in 2-D. Sounds crazy, but read on. In fact, GALFIT does attempt to raise the sky, but the neighbors are discrete and localized, so their profiles can’t be modeled if they are not fitted simultaneousl
Related Questions
- Is the disagreement between 1-D surface brightness profiles of the data and the 2-D model fit really a problem with 2-D fitting, or with the 1-D profile extraction?
- How do the problem model, needs assessment and strategic plan fit together in the proposal?
- Can AGPM quickly overlay model vs field data (profiles, time-series, etc)?
