Tomoe Note 020: Analysis of 20191203 = Dec 03, 2019

Michael Richmond
Jun 06, 2020

Executive Summary

I have analyzed a dataset involving all 84 chips and lasting for 62 minutes. The run suffered from poor seeing, with FWHM ranging from 5-8 arcseconds. No promising candidates were found.

The dataset for 20191203

The dataset "20191203" is based on images acquired during UT 2019 Dec 03. The images cover the span of time



    2458821.10659    ≤   JD  ≤ 2458821.14962

which is about 62 minutes.

The FITS headers of one of these images states, in part:

OBJECT  = 'J0433+1939_dith1'   / object name                                    
EXPTIME =           119.988480 / [s] total exposure time                        
TELAPSE =           120.500020 / [s] elapsed time                               
EXPTIME1=             0.999904 / [s] exposure time per frame                    
TFRAME  =             1.000000 / [s] frame interval in seconds                  
DATA-FPS=             1.000000 / [Hz] frame per second                          
DATA-TYP= 'OBJECT'             / data type (OBJECT,FLAT,DARK)                   
OBS-MOD = 'Imaging'            / observation mode                               
FILTER  = 'BLANK'              / filter name                                    
PROJECT = 'Earth Shadow Survey (1Hz)' / project name                            
OBSERVER= 'Noriaki Arima'      / observer name                                  
PIPELINE= 'wcs,stack,raw'      / reduction pipeline templete

The images were reduced and cleaned by others; I started with clean versions of the images. Each set of 120 images was packed into a single FITS file, covering a span of (120 * 1.0 sec) = 120 seconds. These "chunk" files were located on shinohara in the directory

   /gwkiso/tomoesn/raw/20191203

with names like

   rTMQ1201912030019167511.fits

These names can be decoded as follows:


       r             stands for    "reduced" ??
       TMQ1          means         "Tomoe data, part of quadrant 1"
       20191203      means         year 2019, month 12, day 03
       00191675      means         chunk index 00191675 (increases with time)
       11            means         chip 11
       .fits         means         a FITS file

I'll refer to each of these "composite" files as a "chunk".

There are typically 30 chunks for each chip, and a total of 2492 chunks in the entire dataset. Each chunk file was 1083 MByte, so the total volume of the chunk files was about 2699 GByte = 2.7 TByte.

Brief description of the pipeline analysis

I ran a slightly modified version of the Tomoe pipeline on the images; it was not the same as that used to analyze the 2016 images discussed in the transient paper for two reasons:

minor modifications to handle the slightly different format of the data (image size and overscan regions)
small improvements in the astrometric procedures to make the routines more robust

The main stages in the pipeline were:

split a chunk into individual images (120 images per chunk)
detect and measure stars in each image
add the Julian Date to each stellar measurement
perform ensemble photometry of all images in the chunk
perform astrometric and photometric calibration of the stars included in the ensemble (some stars, detected in only a small number of images, will not be included in the ensemble)
perform astrometric and photometric calibration of all stars detected in each image individually

The output of the pipeline includes a copy of each FITS image, plus a set of ASCII text files which include both the raw, uncalibrated star lists, and the calibrated versions of those lists, as well as the ensemble output.

"Weather" plots for the 4 quadrants

After running the pipeline to reduce the data, clean the images, find and measure stars, calibrate them astrometrically and photometrically, I used a script to look at properties of the data over the course of the night. You can read more about the "weather" in another note.

Below are links to the graphs produced for each of the 4 quadrants.

Quadrant 1:

Quadrant 2:

Quadrant 3:

Quadrant 4:

What do we learn from the "weather" graphs?

zp mag: the "zero-point" shown here is related to the difference between the instrumental magnitudes of stars in each image, and the calibrated magnitudes of those stars:
```
               zp  = (instrumental_mag) - (calibrated_mag)

     
```
Large positive values indicate extinction due to the atmosphere or clouds.
It seems that clouds appeared near the end of the run.

FWHM: the typical Full-Width at Half-Maximum for stars in each image, converted from pixels to arcseconds. We see similar patterns in all quadrants: a "ripple" in the FWHM values with a period of about 10-15 minutes.
It is clear that the FWHM for quadrants 3 and especially 4 is (as usual) considerably larger than that of quadrants 1 and 2. Moreover, the FWHM is larger than usual overall -- I guess the atmosphere was turbulent this evening.

depth mag: this is not the same thing as the "limiting magnitude" assigned to each chunk; it is a slightly different quantity which does give the same sense of "how faint are the stars typically detected in this chunk?" The method by which it is calculated is pretty simple:
- place all detected stars into bins by magnitude
- pick the bin with the largest number of stars
- compute the median magnitude in that bin
The images show that the "depth" is between mag 17 and 18 for the most part.

Transients and control time

After all the data had been calibrated, I ran the "transient_a.pl" script, which applies the rules described in the Tomo-e transient search paper to look for sources with only a brief existence. The code also computes a "control time" for the dataset.

The software found 84, 109, 67, and 75 candidates in quadrants 1, 2, 3, and 4, respectively. I created a web page showing the properties of these candidates in each quadrant:

The entry for each candidate includes some information about the chunk in which it appears, its position in (x,y) pixel coordinates and (RA, Dec) coordinates, and its magnitude. The "variability score" describes the ratio of the standard deviation of its magnitudes away from the mean to the standard deviation from the mean of stars of similar brightness; so, a high score means the object is varying from frame to frame more than most objects of similar brightness.

The entries for quadrants 2, 3, and 4, contain columns listing the magnitudes of any objects at this position (to within 5 arcsec) in the USNO B1.0 (avergage of R-band magnitudes) and in the 2MASS catalog (K-band magnitude). A value of "99.0" indicates that no source appears in the catalog as this position. You can see that the overwhelming majority of candidates do correspond to objects which were detected in one or both of these catalogs -- meaning that they are not true transients.

After these columns of text, the documents contain thumbnails of the images around the candidate. The thumbnails are oriented with North up, East left, and are 110 pixels (= 130 arcsec) on a side.

Interesting candidates are:

quad 1, 00191702_14. This is a ghost or glint of some kind; it shift position by 30-40 arcsec when the telescope shifted for a dither.

The table below shows the control times for each quadrant in this dataset:


quadrant         control time (square degrees * sec)
                   V=13     V=14     V=15     V=16    V=17  
-------------------------------------------------------------
  1               17953    17953    17817     7105       0
  2               18109    18109    17425     8255      28
  3               17247    17247    17028     1294      28
  4               18001    18001    17584      978       0

 total            71310    71310    69854    17632      56
-------------------------------------------------------------

These control times are comparable to to for each of the nights listed in the transient paper; It might be reasonable not to include this dataset in analysis, due to the poor weather.