ZTF i-band images are contaminated with atmospheric fringes that significantly
effect the photometric solution of sources within the image. fringez
uses
principal component analysis to generate a fringe model from these i-band
images. Each ZTF readout channel generates similar atmospheric fringe patterns
and therefore requires its own fringe model. That fringe model can then be
used to generate a fringe bias image for each contaminated i-band image which,
when subtraction from the i-band image, removes the atmospheric fringes.
This results in a clean i-band image.
fringez
installs three executables.
fringez-download
: Downloads pre-generated fringe models from the NERSC web portalfringez-clean
: Cleans contaminated i-band images using fringe modelsfringez-generate
: Generates new fringe models using contaminated i-band images
Preferred method is through pip:
pip install fringez
Latest version can also be installed from github:
git clone https://github.com/MichaelMedford/fringez.git
cd fringez
python setup.py install
Users must have fringe models on disk in order to clean i-band images with atmospheric fringes. These fringe models are what generate a fringe bias image for each contaminated i-band image which, when subtraction from the i-band image, removes the atmospheric fringes. This results in a clean i-band image.
Pre-generated fringe models can be downloaded from the NERSC web portal
with the fringez-download
executable. Select a -fringe-model-date
from the below list to download a version of the pre-generated fringe models.
The script will also download the model lists, containing the list of images
used to create the fringe model.
Current model versions:
- 20200723 (32 GB)
Fringe models can either be downloaded for all 64 readout channels with the
-all
argument, or only a single fringe model can be downloaded with
the -single
argument. If the -single
argument is selected, then
only the model with the -fringe-model-id
argument is downloaded. The
-fringe-model-id
is the cid and the qid of the fringe model combined
in the following syntax: c01_q1
By default the fringe models and fringe model lists will be downloaded to the
current directoy, but can also be sent a specific directory with the
-fringe-model-folder
argument.
Contaminated images can be cleaned with the fringez-clean
executable.
Contaminated images can either be cleaned one at a time with the
-single-image
argument, or all images in a folder can be cleaned with the
-all-images-in-folder
argument. All clean images will have the same
filename as the science images, but with the extension sciimg.clean.fits
.
To save the fringe bias images to disk for debugging, select the -debug
argument.
Cleaning a folder of contaminated images requires specifying the folder where
fringe models are located. fringez-clean
will automatically pair the
correct fringe model with each contaminated image.
From within the directory where all of the science images are located,
execute fringez-clean -all-images-in-folder -fringe-model-folder={FRINGE_MODEL_FOLDER}
. The -fringe-model-folder
argument must be set when -all-images-in-folder
is selected.
When cleaning all images, fringez-clean
can either clean one image at a
time with the -serial
argument, or clean images in parallel with the
-parallel
argument. Images are cleaned in -serial
by default.
Parallelization is executed with the mpi4py package and simply splits the list
of images in the folder across the processes used to launch the
fringez-clean
executable.
Cleaning a single contaminated image requires specifying a fringe model. This
fringe model should match the readout channel ID of the contaminated image.
The image and the model will both be labelled with the same quadrant ID and
ccd ID. For example, if ztf_20190703184838_000481_zi_c02_o_q1_sciimg.fits
is the contaminated image, then
fringe_PCArandom_comp06.c02_q1.20190618.model
would be the correct model
because of the matching c02
and q1
parameters.
From within the directory where the contaminated science image is located,
execute fringez-clean -single-image -image-name={IMAGE_NAME} -fringe-model-name={FRINGE_MODEL_NAME}
. The -image-name
and
-fringe-model-name
arguments must be set when -single-image
is
selected.
The presence of correlated background noise can be determined by measuring the Uniform Background Indicator, or UBI, or an image. The measurement is made by performing aperture photometry on random background locations and measuring how well the scatter in background flux measurements is captured by locally determined estimates of the flux error.
An ideal UBI = 1
indicates no correlated background noise, with larger values
indicating more correlated noise. Here are the results of calculating UBI on
an image of random noise for various aperture sizes, as well as three example
images to qualitatively show how UBI scales with the presence of
atmospheric fringes.
To measure the UBI on an image:
from fringez.metric import calculate_UBI
UBI, UBI_err = calculate_UBI(sciimg_fname, mskimg_fname, updateHeader=True)
The science image header will be updated with the value of the image's UBI.
Note: Downloading pre-generated models using the fringez-download executable is recommended. Most users will not need to generate new fringe models. New models must be generated from images that have not already been cleaned.
Fringe models are generated with the fringez-generate
executable.
The models which will be generated are listed in
$PATH_TO_FRINGEZ_DIR/model.py:return_estimators
.
The number of components in each model fit are set with the
-n-components
argument. Plots of the eigenimages can be generated for
debugging with the -plots
argument.
To generate fringe models:
- Place images containing fringes into a directory. All images within the
directory must have the same RCID. Models are made separately for each ZTF
RCID. Image names are expected to begin with ztf and end with
sciimg.fits
. - From within this folder, execute
fringez-generate
. By default the script will choose six components and will not generate debugging plots. - The current directory will now contain a model or models of the fringes,
named fringe_{MODEL_NAME}_comp{N_COMPONENTS}.c{CID}_q{QID}.{DATE}.model.
By default the script will only generate PCArandom models, but more models
can be tested by editing the
$PATH_TO_FRINGEZ_DIR/model.py:return_estimators
function. The current directory will also contain a*.model_list
file for each model listing the images that went into the creation of the model.
- Python 3.6
- Michael Medford MichaelMedford@berkeley.edu
- Peter Nugent penugent@lbl.gov