I’m glad I look at
the stars for school or else I’d
spend less time outside
This week has been fairly productive. I did a dry run of some photometry in python and got some results that match up with my findings from AIJ last week. My results are shown in the gallery below. sThus far I have only done lightcurves for I band-Brown dwarf data but it is looking promising. There is a gap in our data which is unfortunate because it definitely appears to be some ramping up in the flux that we miss in the gap. I feel the peridograms would be cleaner if there was more information about the period. Alas, weather is weather. That being said my one thing I did find strange was the detrend step in the python photometry. It is looking like my data is not detrended at all.
I think the best match was for the most central target in our frame (from the BD list). The AIJ curve is the bottom right of the first gallery, and the python curve is the first from the next gallery. That being said all of them look similar within the errors. The strange thing to me is the second curve in the python gallery. It looks like it went up almost a whole magnitude, while there is no other AIJ curve to match. I think this could be a mishap because there really isn’t any variation of that kind from the AIJ curves. The good thing though is that there is variation across the board. We are indeed looking at young, volatile objects and not something a little less interesting. In terms of how variable they actually I are I think the LS analysis will start to shed a light on the legitimacy of these periodicities. The errorbars on the AIJ curves are larger across the board so it’s hard to make any sort of conclusions/comparison about that data.
I have not yet had a chance to look at Python curves of R-Band data to compare with the AIJ curves that I reported last week, but I would guess that the variances between those AIJ I/R band data could be due to the sensitivity in those bands to procesees that require or produce energy at peaking in those wavebands. For instance the lower mass, cooler objects will appear brighter in I-band than R-Band. That being said these are both ‘red’ or long wavelength bands (relatively; to the other data being examined by my colleagues in other groups), so I think it’s more a matter of the magnitude and less of probing different activity if that makes sense.
It is my understanding that in the figure directly above the data should be continuous. I could be wrong but that is what I thought the detrending step did: get rid of the implicit sampling bias in the data. Since my first dry run at photometry in python I have complied a ‘super’ region file with all of the targets, not just low-mass or vice versa. I ran the targets through the program but now I am confused as to how to plot my data. .npz files are strange so I’m not so sure how to parse it but as far as I know I have taken data for all of my targets in the I-Band. Scratch that, I have now learned how to plot individual lightcurves but now I am having trouble reading them into the periodogram analysis routine. What we have done in class is reading in a csv file, but how do we read in a npz file. When trying to open it from finder, it is incomprehensible. So I assume there is some function to read it.
I’ve created a property table for the brown dwarf targets cross checked with the TESS input catalog v8.0: https://vizier.u-strasbg.fr/viz-bin/VizieR?-source=IV/38. It is posted below:
I’ve since discovered that you read in .npz files with the function np.load. However, I am still trying to wrap my head around the contents of the file, and furthermore my results from photometry. The map of my sources and comparison stars is below:
There are a total of 41 target stars, but the length of the data read in from the npz file is only 7. Why this is I do not know.
Starting to make more sense of it now. I’m thinking if I we’re to put it into an array it would be a 5 x 41 rectangle with the positions in ra/dec or x/y on the top and the data following is flux +/- error for each of the 5 days of observation (in my case this is TESS I band data so there is only 5 nights, for other bands/fields it would be 6 x 41) . I am now digging into the periodogram analysis for the 3rd star in the list. I am doing it for this target right now because it seems most periodic.
Here are some results, a little more promising than I had anticipated [I think (hope?)]:
Owen H. ASTRO-341 Research Blog 