movphot 0.0.1

moving object photometry

Moving Object Photometry (movphot)

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Overview

Photometry of moving objects could be done in this repository. Though optimized for Seimei/TriCCS data, you can apply it for imaging data taken with other telescopes (MITSuME, MuSaSHI, MuSCUT, etc.)

Successive analyses below are done using other scripts. See README.md in each directory for detail.

• Periodic analysis Use package4peri(in preparation).
• Visualization (Plot object light curve, comparison light curve, etc.) and Spectral type estimation Use package4vis(in preparation) to plot lightcurve, color-color diagrams and reflectance on Bus-DeMeo templates.

Procedure

1. Standard reduction dark(bias) subtraction, flat fielding, split fits from 3d to 2d Use TriCCS Data Reduction (TDR) (bitbucket).
2. Create a comparison star catalog
3. Create standard location text (x, y, nframe)
4. Photometry

Installing

# Install from PyPI
pip install movphot

or

# Install with `setup.py`
git clone https://[e-mail]/jin_beniyama/movphot.git
python setup.py install

Usage

Here 3-bands observation by Seimei/TriCCS is assumed. So psdb.py is used to handling the database for comparison stars. If you need other catalogs for data taken by other instruments, you can also use gaiadb.py or other scripts.

0. Standard reduction

Input fits data should be 2-dimensional even taken with TriCCS. Standard reduction and splitting fits could be done using scripts in TriCCS Data Reduction (TDR) (bitbucket).

1. Create a comparison star catalog

The first step before photometry is to construct a homemade catalog database. When your fits file has almost correct WCS coordinates, type commands below.

At first, create a catalog database you are interested in. When you used Seimei/TriCCS(Pan-STARRS system), create Pan-STARRS catalog database as ps.db in ~/db4movphot/ (hereinafter db). Please specify a directory to change the path of the database in psdb.py and in phot_color as optional arguments. The database should be created manually until the automated function will be implemented in near future.

[usage]
# Create database (once)
psdb.py dbstart --first
# Create new tables
psdb.py create --table (table name)
# Insert stars to database
psdb.py insert --tablename (table name) --ra (ra in degree) --dec (dec in degree)
--radius (fov radius in degree) --magmin (minimum magnitude) --magmax (maximum magnitude)
# Check stars in database
psdb.py extract --tablename (table name) --ra (ra in degree) --dec (dec in degree)
--radius (fov radius in degree) --magmin (minimum magnitude) --magmax (maximum magnitude)

[example]
# Create 2021DX1 table.
# Observed locations are below.
psdb.py create --table _2021DX1
psdb.py insert --table _2021DX1 --ra 208.87 --dec 44.68 --radius 0.2 --magmin 12 --magmax 21

2. Create standard location text

The next step is registering target locations. Ephemerides of newly discovered fast-moving (v > a few arcsec/s) asteroids are sometimes not perfect. In this script, use some observed points of the target in the 2nd band and interpolate and extrapolate them to determine photometry circle locations. If the target is close to any stars, it is better to remove it as an outlier. But eflag is useful to remove contaminations after photometry.

All information needed to create location text is x, y (pixel number) and nframe (number of frames). The format example when moving object is at (100,200) and (150, 230) in 3rd and 13th frames.

[location_band2.txt]
x y nframe
100 200 3
150 230 13

3. Photometry

Photometry is done using ps.db, location.txt and some input parameters. Typical FWHM is needed (by IRAF projection etc.) and should be converted to pixel scale by the observer. There are 2 photometry types:

1. app (circle aperture photometry)
2. iso (isophotal photometry, in prep.)

Before photometry, create a fits list, for example, by

cat *(band unique character)*.fits > fitslist_band.txt

and save as fitslist_band1.txt, fitslist_band2.txt and fitslist_band3.txt.

The photometry command examples are below. If the database is not in the current directory, please specify the db path. 2 parameters, refmagmax and refmagmin, are used to

1. create a mask to calculate eflag values (both target and comparison stars)
2. do comparison star photometry .

[usage]
phot_color (obj) (instrumental) (fitslist_band1.txt) (fitslist_band2.txt) (fitslist_band3.txt)
(fitsdir) (location_band2.txt) --bands (band1) (band2) (band3) --catalog (catalogname)
--table (tablename) --radius_ref (photometry radius of comparison stars)
--radius_obj (photometry radius of object) --refphot (photometry type of comparison stars)
--objphot (photmetry type of comparison stars)
--refmagmax (maximum comparison magnitude) --refmagmin (minimum comparison magnitude)

[example]
photcolor 2021DX1 TriCCS glist9.txt rlist9.txt zlist9.txt wcs
standardr9.txt --bands g r z --catalog ps --table _2021DX1 --radius_ref 25
--radius_obj 25 --refphot app --objphot app --refmagmax 20 --refmagmin 12

The result is saved as photometry_result.txt. Visualization and detail analyses can be done in other repositories in the overview. Unfavorable objects (such as too redder or low Quality) should be removed at that time.

Visualization

1. Plot Color Term (CT), Color Transformation Gradient (CTG), Color Transformation Intercept (CTI) and object light curve by plot_colorterm.py

These values are important for absolute calibrations if standard stars are different in each frames (due to fast-moving etc.)

Command,

[usage]
plot_colorterm.py (objname) photometric_result.csv
--bands (band1) (bands2) (band3)

[example]
plot_colorterm.py 2021EX1 photometric_result.csv
--bands g r i

generates

• figure: mag_inst - mag_catalog vs color_catalog
• figure: color_instrument vs color_catalog
• figure: magnitude light curve
• figure: color light curve
• csv: time series result including colors .

Dependencies

This library is depending on NumPy, SciPy and SEP. Scripts are developed on Python 3.7.10, NumPy 1.19.2, SciPy 1.6.1 and SEP 1.0.3.

LICENCE

This software is released under the MIT License.