You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
NIRCam is capable of downlinking the first read separately even when the first group includes the first read in an average. For example, in DEEP8 mode, the first group consists of the first 8 reads, but the first read itself may be individually available in the uncalibrated data file. This ticket is to make use of that first read in ramp fitting and jump detection.
There are a few reasons why this is valuable:
For a source that saturates after the first group. With the first read separated out, there are now two groups that can be fit rather than just one.
If there are only four groups, then jump detection becomes very difficult. For example, if a cosmic ray hits in the middle of group 2 of a four group ramp, then the differences between group 2 and group 1 and between group 3 and group 2 are both bad; only the difference between group 4 and group 3 is valid. This is very difficult to handle in jump detection. If the first read can be separated out, we then have five groups and can almost fully recover the data.
The approach to separating out the first read is described in
Clarifying that this is for use in the likelihood-based ramp fitting algorithm. Ticket is ON HOLD until successful resolution of https://jira.stsci.edu/browse/JP-3765
Issue JP-3766 was created on JIRA by Timothy Brandt:
NIRCam is capable of downlinking the first read separately even when the first group includes the first read in an average. For example, in DEEP8 mode, the first group consists of the first 8 reads, but the first read itself may be individually available in the uncalibrated data file. This ticket is to make use of that first read in ramp fitting and jump detection.
There are a few reasons why this is valuable:
For a source that saturates after the first group. With the first read separated out, there are now two groups that can be fit rather than just one.
If there are only four groups, then jump detection becomes very difficult. For example, if a cosmic ray hits in the middle of group 2 of a four group ramp, then the differences between group 2 and group 1 and between group 3 and group 2 are both bad; only the difference between group 4 and group 3 is valid. This is very difficult to handle in jump detection. If the first read can be separated out, we then have five groups and can almost fully recover the data.
The approach to separating out the first read is described in
https://ui.adsabs.harvard.edu/abs/2024PASP..136d5005B/abstract
In short, if we have a list of lists for the read times that comprise each group, e.g.
readtimes = [[1, 2, 3, 4], [9, 10, 11, 12], [17, 18, 19, 20], [25, 26, 27, 28]]
then we would like to modify this with something like
readtimes = [readtimes[0][0]] + [readtimes[0][1:]] + readtimes[1:]
to make
readtimes = [[1], [2, 3, 4], [9, 10, 11, 12], [17, 18, 19, 20], [25, 26, 27, 28]]
We would also need to remove the first read from the first group. In this case, that means the first group becomes
group1_new = 1/3*(group1_old*4 - read1)
and the first read would get prepended to the groups.
The text was updated successfully, but these errors were encountered: