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A recipe on how to build size-reduced calibrated exposures, coadds and image differences using pre-made master calibration files and trimmed reference catalogs with Vera C. Rubin Science Pipelines.

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dirac-institute/kbmod_imdiff_recipe

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Summary

This repository serves as a recipe describing how to process DECam raw data and create calibrated exposures, coadds and image differences using Vera C. Rubin Science Pipelines.

Raw science data, master calibration and reference catalog files are provided with the repository for ease of use. The provided data has been modified, trimmed, in order to reduce the data volume required. To see full details of this procedure refer to the content section. Briefly, it contains only the raw, calibration and reference catalog data necessary to process the N4 detector of DECam i filter data taken on the night of 18.03.2021 for the DDDF survey.

The repository uses Git LFS to provide the data.

To run the processing execute

git clone https://github.com/dirac-institute/kbmod_imdiff_recipe.git
cd kbmod_imdiff_recipe
scripts/create_imdiffs.sh

The processing will produce the calibrated exposures, calexp, good seeing coadds, and good seeing image differenced exposures of sufficient quality to test the KBMOD functionality.

Content

Science data

The data was taken for the cosmos 1, 2, and 3 targets of the DECam Deep Drilling Field (DDF) survey on the night of 18.03.2021 for DECam i filter only. The provided science data has been trimmed, i.e. modified to reduce the total data volume required to store it. Trimming the dataset involves setting all the image data to 0, except for the detector of interest. The trimmed data zeroes out all the images except for detector N4, near the center of the focal plane. The detector ID is 35 and its AstroPy HDUList index is 37. To create the trimmed data, download the raw data, and execute:

To identify and download the original untrimmed raw science data run:

scripts/download_data.py \
    --download-science rawData/210318/science \
    --filters i

To then trim and reproduce the data provided with the repository run:

cp -r rawData trimmedRawData
scripts/trim_ccds.py trimmedRawData/210318/science N4 --verbose --overwrite

For convenience the scripts/download_and_trim_data.sh should preform the same action. The directories should contain the following data:

SCIENCE RAW

i md5sum ifilter proposal caldat archive_filename
0 020c85f7e80ca26821aed1f6c0902127 i DECam SDSS c0003 7835.0 1470.0 2021A-0113 2021-03-18 /net/archive/mtn/20210318/ct4m/2021A-0113/c4d_210319_024912_ori.fits.fz
1 35330f682b64d4b6ccd8bc402b408888 i DECam SDSS c0003 7835.0 1470.0 2021A-0113 2021-03-18 /net/archive/mtn/20210318/ct4m/2021A-0113/c4d_210319_024310_ori.fits.fz
2 e126aab419d1b5a7503b36d0b31d3e05 i DECam SDSS c0003 7835.0 1470.0 2021A-0113 2021-03-18 /net/archive/mtn/20210318/ct4m/2021A-0113/c4d_210319_023704_ori.fits.fz
3 f8290547a6124d65a63f75897087af04 i DECam SDSS c0003 7835.0 1470.0 2021A-0113 2021-03-18 /net/archive/mtn/20210318/ct4m/2021A-0113/c4d_210319_023059_ori.fits.fz
4 e33ae2af4f5f52d147dfd878dd958b2e i DECam SDSS c0003 7835.0 1470.0 2021A-0113 2021-03-18 /net/archive/mtn/20210318/ct4m/2021A-0113/c4d_210319_022455_ori.fits.fz
5 fa80fd7530560622e450a30174d9a2b7 i DECam SDSS c0003 7835.0 1470.0 2021A-0113 2021-03-18 /net/archive/mtn/20210318/ct4m/2021A-0113/c4d_210319_005134_ori.fits.fz
6 56d81414ab12a448b8ac5fce2181b339 i DECam SDSS c0003 7835.0 1470.0 2021A-0113 2021-03-18 /net/archive/mtn/20210318/ct4m/2021A-0113/c4d_210319_004532_ori.fits.fz
7 fea13cc71be5302de7e9f63e4109f26f i DECam SDSS c0003 7835.0 1470.0 2021A-0113 2021-03-18 /net/archive/mtn/20210318/ct4m/2021A-0113/c4d_210319_003928_ori.fits.fz
8 c74f675c59185dfee2cd8fd661806e7f i DECam SDSS c0003 7835.0 1470.0 2021A-0113 2021-03-18 /net/archive/mtn/20210318/ct4m/2021A-0113/c4d_210319_003322_ori.fits.fz
9 202f4137d1f7571054e9f0539ff9bc3d i DECam SDSS c0003 7835.0 1470.0 2021A-0113 2021-03-18 /net/archive/mtn/20210318/ct4m/2021A-0113/c4d_210319_002721_ori.fits.fz

The provided science exposures target three different pointings on the sky in the same night named cosmos 1, 2 and 3. The following table lays out which FITS files belong to which.

Filename Target
c4d_210319_003928_ori.fits.fz cosmos_1
c4d_210319_024310_ori.fits.fz cosmos_1
c4d_210319_022455_ori.fits.fz cosmos_1
c4d_210319_024912_ori.fits.fz cosmos_2
c4d_210319_023059_ori.fits.fz cosmos_2
c4d_210319_004532_ori.fits.fz cosmos_2
c4d_210319_002721_ori.fits.fz cosmos_2
c4d_210319_023704_ori.fits.fz cosmos_3
c4d_210319_005134_ori.fits.fz cosmos_3
c4d_210319_003322_ori.fits.fz cosmos_3

The script

Reference Catalogs

Included are the reference catalogs, as formatted and sharded into HTM shards for use with the Vera C. Rubin Science Pipelines. Included are the Gaia DR2 and Processing Version 3 of the Pan-STARRS1 3pi survey catalog shards, used for astrometry and photometry respectively, relevant for processing the included science data only.

To find out more about how the survey catalogs are formatted and sharded refer to the example of a trimmed Rubin refcat found in the ci_hsc_gen3 repository here and here.

To re-create the provided reference catalogs, access to full catalogs, already formatted and sharded for use with Rubin Science Pipelines, will be required. The scripts/refcat_shard_resolver.py contains the functionality required to identify and copy the appropriate catalog shards that overlap given exposures and to trim down the catalog YAML file intended to be used with the Rubin Data Butler to only contain those copied catalog shards. This reduces the catalog size and the time required to ingest it into a new Rubin Data Butler Data Repository.

To see the shard IDs, shard filenames and shard file locations of reference catalog shards that overlap a given set of exposures on your system invoke the script with the name of the catalog and the location of the catalog shard files:

scripts/refcat_shard_resolver.py trimmedRawData/210318/science/ \
    --refcat ps1_pv3_3pi_20170110 \
    --refcat-path <path_to_lsst_refcats>/gen3/refcats/gen2/ps1_pv3_3pi_20170110/

Refer to scripts/trim_refcats.sh script to see how more full usage of the script, including copying the shards and trimming of the Gen 3 Rubin Data Butler exported data YAML file. The file assumes paths appropriate for use with DiRAC computing resources, namely mox.hyak.

Following are the identified shard IDs and shard names as retrieved from the reference catalog made for Gen 2 Rubin Data Butler, and then exported for Gen 3 Rubin Data Butler use. Because the reference catalogs in question use the same HTM order the shard IDS for the catalogs are identical.

Shard IDs that overlap the exposures

ID
231812
231813
231815
231816
231818
231819
231820
231825
231826
231827
231828
231829
231830
231831
231832
231833
231834
231835
231836
231837
231838
231839
232344
232345
232347
231844
231845
231846
231847
231848
231849
231850
231851
231852
231853
231854
231855
231856
231857
231858
231859
231860
231861
231862
231863
231864
231865
231866
231867
231868
231869
231870
231871
231890
231896
231897
231899
231901

Master calibration files

The certified master, flat and bias, calibration files are the crosstalk and ISR corrected flat and bias files combined into a master calibration product appropriate for calibration of the included science data.

To see how these were created refer to the kbmod_mastercals_recipe and the instructions therein.

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A recipe on how to build size-reduced calibrated exposures, coadds and image differences using pre-made master calibration files and trimmed reference catalogs with Vera C. Rubin Science Pipelines.

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