Oof, It’s the Oscars
We watch rich people get praised
Alas, go Parasite
This week has been a little slow so far. Getting the data downloaded was a long process, but alas it is done. So far no additional work has gone into the master bias function. It seems straight forward enough to put together. I understand the scale factor calculation, it was easy enough to do for one image. Right now my plan is to create 8 master biases, one for each night. In order to do this I will median combine them like last semester, the only caviat now is I have to consider the overscan. For each science at flat frame the master bias for the respective night that that data was taken on has to be scaled. This should be easy enough to do with some sort of loop. Once the code has been written I will make an account of if it was truly as simple as I say now.
The feedback I received on my proposal outline was really informative. I think it really drove the idea home for me how important it is to make sure that the scientist is aware of the context that their work falls into and is contributing to. The first place where this needs to be put across to someone outside of the research group is in the proposal. The scientist has to be so knowledgeable not only about the work that they are doing but also knowledgeable about the work that surroounds it and that is involved in, because when a work is published you are subjecting it to be considered and used by another research group.
I definitely need to flesh the proposal out a little more. I think after completing the annotated bibliography for Herbst will allow me to do this a little more completely. The section I am struggling most with is the technical justification portion. If the project was not technically feasible, then why would it have been passed in the first place? Is the tone more like: “we have all of this data, this is what we can do with it” ? I just don’t know what kind of contents should populate that part of the template.
I just finished my bias subtractions of the science frames of the first night. I continued to venture into flatfeilding, which was successful. This is alarming to me as I forgot to cut out the overscans of the flats to avoid forbidden math. Surprisingly, there we’re no errors while executing this. The counts of the flats I obtained are about 300-400. As far as I know the process thus far has worked as expected. Mean counts for the bias subtracted science frames, however, was 237.76 counts. This leads me to believe that the reduction as far as the bias subtraction of the science frames was successful. I have included some sample figures below. I will try and resolve this flatfeilding problem between now and tomorrow.
This process of bias correction is necessary because we see the median overscan value change from frame to frame. Overscans are like ‘mini biases’ for each image. This means that we have to correct for these fluctuations by scaling the master bias for each image before subtracting it. My pseudocode from class kind of went evolved as I expected. The overscan activity was enlightening. Dealing with this overscan was not as difficult as I thought it would be. I am still puzzled that despited my lack of trimming I was still able to flatfield, but that portion is not due until next week. The bias subtraction went well, I shall tackle the other issues…not right now.
I’ve just visited Sarah in her office hour and we have come to the conclusion that the images makes sense in terms of the counts/arithmetic despite e neglecting to trim the images before flatfielding. I would still like to know why it worked. I will now trim these images, however, so we don’t get strange artifacts or false data in the future. I will continue to do digging on this matter.
The paper I will bring to class tomorrow is the Nakajima 1995 paper describing the first brown dwarf discovered. I think this will be informative as it is a nice display of the background information that is now ‘taken for granted’ or more ubiquitously known. It will be nice to see where that all came from.
The moral of the Herbst 2007 paper seems to be “physics is not broken, but not in the way that we expect…and we do not know why.” YSOs are complicated and bizarre regions of our universe that are difficult to take data for. Furthermore, if we have data this it is tough to understand what is going on.
Owen H. ASTRO-341 Research Blog 