Mission Overview
The CAnadian NIRISS Unbiased Cluster Survey (CANUCS)
Primary Investigator: Chris Willott, Adam Muzzin
HLSP Authors: Ghassan Sarrouh, Yoshi Asada, Nicholas Martis, Chris Willott, Kartheik Iyer, Gaël Noirot, Adam Muzzin, Marcin Sawicki, Gabriel Brammer, Guillaume Desprez, Gregor Rihtaršič
Released: 2025-06-25
Updated: 2025-06-25
Primary Reference(s): Asada et al. 2024, Desprez et al. 2024 , Sarrouh et al. 2024, Gledhill et al. 2024, Martis et al. 2024, Rihtaršič et al. 2025
DOI: https://doi.org/10.17909/18nv-np70
Citations: See ADS Statistics
Source Data:
- JWST GTO 1208 (DOI: 10.17909/ph4n-6n76)
- JWST GO 3362 (DOI: 10.17909/cyh7-mm53)
CANUCS fields
CANUCS Highlight Images
The upper-left panel shows the NIRCam image of the MACS J1423 cluster with the upper-right a zoom in on the gravitationally lensed redshift 8.3 Firefly Sparkle galaxy from Mowla et al. 2024. The lower-left panel shows the NIRCam image of the center of the MACS J0417 cluster showing the brightest cluster galaxy and the gravitationally lensed Question Mark pair from Estrada-Carpenter et al. 2024. The lower-right panel shows a vertical stack of 733 NIRSpec prism spectra ordered by redshift increasing from bottom to top.
CANUCS Field Layouts
Layouts for the CANUCS observations in the five cluster target fields including their flanking fields. Background grayscale images are from Hubble imaging. Shaded polygons show the NIRCam and NIRISS coverage. Open blue rectangles show the NIRSpec MSA follow-up locations.
Overview
The CAnadian NIRISS Unbiased Cluster Survey (CANUCS - http://canucs-jwst.com) is a JWST Cycle 1 GTO program targeting 5 lensing clusters and their adjacent flanking fields in parallel (Abell 370, MACS0416, MACS0417, MACS1149, MACS1423), using a unique combination of NIRCam imaging, NIRISS slitless spectroscopy, and NIRSpec prism multi-object spectroscopy. Fields centered on galaxy cluster cores include imaging in 7 wide and 1 medium band from 0.9–4.4 μm, alongside continuous NIRISS coverage from 1.15–2 μm, while the NIRCam flanking fields provide 5 wide and 9 medium band filters for exceptional spectral sampling, all to 29–30 mag.
JWST in Technicolor is a Cycle 2 follow-up GO program targeting 3 of the CANUCS clusters (Abell 370, MACS0416, MACS1149). The Technicolor program adds slitless spectroscopy with NIRISS in F090W to the cluster fields, while adding 8 wide, medium, and narrow band filters to the flanking fields. Taken together, and including the benefits of gravitational lensing, these programs enable both integrated and spatially-resolved science, probing low-mass galaxies well into the Epoch of Reionization.
In the first data release, the HLSP team provides NIRCam, NIRISS and HST imaging, including custom modeling and subtraction of bright cluster galaxies, PSFs and PSF-matched imaging, photometric catalogs, photometric and spectroscopic redshifts, lens models, and stellar population parameters derived using both the Bagpipes and Dense Basis codes.
Data Products
CANUCS observed 5 lensing clusters. This release contains data from the central Cluster field (CLU) and NIRCam Flanking field (NCF) of each lensing cluster.
Images and catalogs are provided separately for each of the 10 fields, whereas the lensing model products cover the full area of each lensing cluster, so there are only 5 fields for lensing products.
The CANUCS release includes imaging from JWST NIRCam and NIRISS along with pixel-matched imaging from HST ACS and WFC3.
All filters have images on a 40-milliarcsec (40mas) pixel scale. NIRCam short wavelength filters have an additional product on a 20-milliarcsec (20mas) pixel scale.
Data file naming convention:
hlsp_canucs_<telescope>_<instrument>_<target>_<filter>_v1_<prodType>.fits
where:
- <telescope> is "jwst", "hst" or "jwst-hst" when both telescopes contributed
- <instrument> is "nircam", "niriss", "acs", "wfc3", or "multi" when more than one instrument contributed
- <target>
- for images: the combination of cluster-field-pixelscale
- for catalogs: the combination of cluster-field
- for lensing models: the combination of cluster-lensingproducttype-bestorsamples-pixelscale where lensingproducttype is defined below and bestorsamples is best for the best fit and samples for the 100 Bayesian lens model samples to provide uncertainties.
- <filter> is the filter name or multi when more than one filter contributed. NIRISS filters have the letter 'N' appended.
- <prodType> is the product semantic type, see "Data file types" below.
Data file types:
| _sci.fits | Science image |
| _rms.fits | RMS uncertainty image |
|
_bgsub-sci.fits |
Background- and bright galaxy-subtracted science image |
|
_bcgmodel-sci.fits |
Image containing bright galaxy models |
| _bgsub-psfconv-sci.fits | Common-PSF-convolved background- and bright galaxy-subtracted science image |
| _psf.fits | Image of PSF |
| _convkernel.fits | Image of PSF convolution kernel used to make common-PSF-convolved image |
| _segmentation.fits | Segmentation image of detected sources |
| _photometry-cat.fits | Catalog of photometry, photometric redshifts, and lensing magnifications of detected sources |
| _bagpipes-cat.fits | Catalog of galaxy physical parameters determined with the Bagpipes code |
| _densebasis-cat.fits | Catalog of galaxy physical parameters determined with the Dense Basis code |
| _model.fits |
Lensing model maps |
Click "Expand All" to learn more about the catalogs:
photometry-cat: These catalogs contains positions, photometry, EAzY-py (Brammer et al. 2008) photometric redshifts and spectroscopic redshifts, when available, of all detected sources. Photometry is measured on the images that have been PSF-matched to the NIRCam F444W PSF and is provided in a range of apertures. Several quality flags are also included to indicate whether the source is covered by NIRCam or HST optical imaging, whether the source extends beyond the field-of-view or contains bad pixels, and whether photometry is contaminated. See the README for further details.
bagpipes-cat: BAGPIPES (Carnall et al. 2018) is run on all provided photometric catalogs including all available photometry. The HLSP team provides results from two separate BAGPIPES runs, using the "COLOR03" and "COLOR07" total fluxes. These are fluxes preserving the colors in 0.3" and 0.7" apertures scaled to total by the F277W flux. In order of preference, the redshift is to the spectroscopic redshift (ancillary or NIRSpec prism) or the photometric redshift from EAzY. In both cases, a gaussian prior of width 0.05 is applied to the redshift. In the case of failed EAzY fits, the redshift is left as a free parameter with 0.01 < z < 20. All physical parameters in these catalogs have not been corrected for lensing. Lensing magnifications can be found in the photometry catalogs. See the README for further details.
densebasis-cat: DENSE BASIS (Iyer & Gawiser 2017, Iyer et al. 2019) is run on all provided photometric catalogs including all available photometry. The team provides results from two separate DENSE BASIS runs, using the "COLOR03" and "COLOR07" total fluxes. These are fluxes preserving the colors in 0.3" and 0.7" apertures scaled to total by the F277W flux. In order of preference, the redshift is set to the spectroscopic redshift (ancillary or NIRSpec prism) or the photometric redshift from EAzY. In both cases, a gaussian prior of width 0.05 is applied to the redshift. In the case of failed EAzY fits, the redshift is left as a free parameter with 0.01 < z < 12. All physical parameters in these catalogs have not been corrected for lensing. Lensing magnifications can be found in the photometry catalogs. See the READMEfor further details.
Click "Expand All" to learn more about the lensing models:
The HLSP team provides the following lensing product types:
| kappa-dlsds1 | Convergence maps |
| gamma-dlsds1 | Shear maps |
| gamma1-dlsds1 | Shear component 1 maps |
| gamma2-dlsds1 | Shear component 2 maps |
| dpl1-dlsds1 | Deflection (x) maps |
| dpl2-dlsds1 | Deflection (y) maps |
The best fit maps are provided in these resolutions and sizes:
| best-50mas | 50 mas resolution, 200 arcsec size |
| best-100mas | 100 mas resouliton, 500 arcsec size |
| best-300mas | 300 mas resolution, 1200 arcsec size |
The 100 Bayesian lens model samples are provided in these resolutions and sizes:
| samples-200mas | 200 mas resolution, 500 arcsec size |
| samples-600mas | 600 mas resolution, 1200 arcsec size |
Text files are provided that are required for lens modeling reproducibility with names:
| hlsp_canucs_jwst-hst_multi_<cluster>-lenstool-readme_multi_v1_model.txt |
| hlsp_canucs_jwst-hst_multi_<cluster>-lenstool-clugal_multi_v1_model.txt |
| hlsp_canucs_jwst-hst_multi_<cluster>-lenstool-param_multi_v1_model.txt (Abell 370, MACS0416 only) |
| hlsp_canucs_jwst-hst_multi_<cluster>-lenstool-bestparam_multi_v1_model.txt (Abell 370, MACS0416 only) |
| hlsp_canucs_jwst-hst_multi_<cluster>-lenstool-multim_multi_v1_model.txt (Abell 370, MACS0416 only) |
| hlsp_canucs_jwst-hst_multi_<cluster>-lenstool-allmultim_multi_v1_model.txt (MACS0416 only) |
Text files for the remaining clusters will be added when their lens model papers are published.
Data Access
MAST Portal and Astroquery
The CANUCS data are available in the MAST Search Portal (web-based, cross-mission search interface) and Astroquery (Python package to search for and download files from Python scripts you write).
- A direct Portal link to CANUCS data is provided here.
- Alternatively, in the MAST Search Portal, set the Provenance Name filter to "canucs" in an Advanced Search to find these data. The user guide for how to search and download products using the MAST Portal is available here.
- For Astroquery, the following example code demonstrates how to search for and download these products. This code assumes that you want to download all products from this HLSP, so you may want to consider narrowing down your search for large HLSPs (> 10 GB) or those with many individual files (> 10k). You can find more astroquery.mast tutorials here.
from astroquery.mast import Observations
# Search for all CANUCS products
all_obs = Observations.query_criteria(provenance_name="canucs")
data_products = Observations.get_product_list(all_obs)
# Print the number of data products that would be downloaded
print(len(data_products))
# Filter the products to the science images and catalogs
filtered_products = Observations.filter_products(data_products, extension=["_cat.fits.gz", "_sci.fits.gz"])
# Download data
Observations.download_products(filtered_products)
-
A web-based interface for cross-mission searches of data at MAST or the Virtual Observatory.
-
Search for and download data products for this HLSP programmatically in Python.
