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.

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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.

It is Superbowl

Sunday. Doing homework. Now

I shall begin it

The trip to Arizona was so enlightening in a lot of ways for me. We hit the ground running and immediately started cool stuff. The mirror lab tour was out of this world. Just being in the room with those things was special. Knowing that they would be shipped off to Chile along with the others that had been manufactured there, that will be doing some really cool science in the near future. I think also throughout the trip I was starting to get a grasp of the wealth of career trajectories that can be followed after I graduate. It being the second semester of my senior year I find that to be on my mind more and more. After the tour of the national park which was absolutely gorgeous (and educational), going up to the mountain was really special. After arriving, getting settled, and starting our first night observing I started to really enjoy it. It was like summer camp for astronomers up there. I’ve always enjoyed the hands on experience when it comes to science, and this just further cemented that for me. It was probably the most stars I have ever seen in a night sky. It was really something. More than anything I realize I would absolutely be open to being an observer after meeting all the different kinds of scientists and engineers on the mountain. Our reading and discussion of the giant telescope opened my eyes a little to be all the bureaucracy that goes into the work we do as astronomers as well as providing valuable context about the place we would be spending the next four nights.

During the actually observing run, the role of me and my cohorts we’re to essentially operate the telescope and all of the mechanisms and procedures that go into making the WIYN 0.9 meter operate. Fortunately we did not have any large hiccups, besides the fact that the guiding was failing throughout the entire run. But thankfully, nothing malfunctioned, nor did anybody get hurt. My colleagues and I rotated doing two major tasks: logging and driving. Those tasks we’re divided into personal sub-tasks that each of us would handle personally. I admit that driving was more fun than logging, but all of it was experience I value.

HDI is a really cool instrument. Thankfully it was repaired in time for our run. We we’re able to use it. It had arrived back from The University of Hawaii in October/November (?). We we’re sure to be very careful with it. The most interesting part of the instrument to me was the fact that we didn’t have to take darks. The instrument provides to little dark current to the signal it would have been redundant to subtract the darks from the science frames. From the proposal I knew we we’re going to be looking at YSOs. What I am learning and thinking on more now is the science questions that can be tackled with the data we have taken. I had known about the variability of the targets on the scale of the length of our entire observing run (8 nights), but what could be determined with these light curves and other information that we will retrieve from the data. For our project in particular, we are looking at longer wavelength bands in the Presepe and TESS feilds in order to get rotation curves in order to get an idea of an age of a population of stars in the Presepe, and do an exploratory search for other YSOs in the TESS field outside of the six already determined.

The immediate steps i should take once the data is in hand is try and organize it with my scripts I already have as much as I can, make edits as needed and hopefully calibrate and align all of the images. After this the process for further reduction I do not have an idea of how to do it since we are not going to be following the same roadmap when it comes to the aperture photometry that we did last semester. It appears we are going to be taking rotation curves of our targets. So there will be a time in the roadmoap for learning how to do that. Currently I think the main goal is to get the data in hand and start to organize it. By next Wednesday if we can have all of the master calibrations I’d call that a win.

In terms of the data that we will need it should boil down to R and I band images of the Praesepe and TESS fields. The images should be as follows:

Science Frames:

Night 1 Praesepe Images:

• 140-144 (R)
• 145-149 (I)
• 161-165 (R)
• 166-170 (I)
• 182-186 (R)
• 187-191 (I)
• 203 – 207 (R)
• 208-212 (I)

Night 1 TESS images:

• 219-222 (R)
• 223-225 (I)

Night 2 Praesepe images:

• 155-159 (R)
• 160-164 (I)
• 177-181 (R)
• 182-186 (I)
• 198-202 (R)
• 203-207 (I)

Night 2 TESS images:

• 218-220 (R)
• No I band data on night 2

NO DATA ON NIGHTS 3 OR 4

Night 5 Preasepe images:

• 140-144 (R)
• 145-149 (I)
• 179-183 (R)
• 184-188 (I)
• 213-217 (R)
• 218-222 (I)

Night 5 TESS images:

• 160-162 (R)
• 163 – 165 (I)
• 196-198 (R)
• 199-201 (I)
• 230 – 232 (R)
• 233 – 235 (I)

Night 6 Praesepe images:

• 174-178 (R)
• 179-183 (I)
• 207-211 (R)
• 212-211 (I)
• 242-246 (R)
• 247 (R)
• 248 – 252 (I)

Night 6 TESS images:

• 190-192 (R)
• 193-195 (I)
• 223-225 (R)
• 226-228 (I)
• 259 – 261 (R; log comment says the object label is wrong)
• 262-264 (I; comment now says label is correct
• 271-273 (R)
• 274-276 (I)

Night 7 Praesepe images:

• 137-141 (R)
• 142-146 (I)
• 170-174 (R)
• 165-179 (I)
• 203-207 (R)
• 208-212 (I)

Night 7 TESS images:

• 153-155 (R)
• 156-158 (I)
• 186-188 (R)
• 189-191 (I)
• 219-221 (R)
• 222-224 (I)
• 231-233 (R)
• 234-236 (I)

Night 8 Praesepe Images:

• 181-185 (R)
• 186-190 (I)
• 214-218 (R)
• 219-223 (I)
• 247-251 (R)
• 252-256 (I)

Night 8 TESS images:

• 197-198 (R)
• 199-201 (I)
• 230-232 (R)
• 233-235 (I)
• 263-265 (R; comment reads 242 looks crazy)
• 266-268 (I)

Calibrations:

Night 1:

• 1-13 (Bias frames)
• 20-24 (Dome flat R)
• 25-30 (Dome flat I)

Night 2:

• 236-245 (Bias frames)
• 252-255 (Dome flat R)
• 2 – 5 (Dome flat I)
• 23-15 (Twi flat I)
  

Night 3

• 221-231 (Bias frames)
• 9-16 (Dome flat R)
• 17-22 (Dome flat I)
• 34-38 (Dome flat I)

NO DATA TAKEN ON NIGHT 4

Night 5

• 1-6 (Dome flat V)
• 7-17 (Bias frames)
• 2-6 (Dome flat R)
• 7-11 (Dome flat I)

Night 6

• 1-11 (Bias frames)
• 282-286 (Dome flat R)
• 287-291 (Dome flat I)

Night 7

• 309-313 (Dome flat R)
• 314-318 (Dome flat I)
• 319-329 (Bias frames)

Night 8

• 248-252 (Dome flat R)
• 253-257 (Dome flat I)
• 261-275 (Bias frames)

Be yourself; Everyone else is already taken.

— Oscar Wilde.

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