############################## ### CEERS Data Release 0.5 ### ############################## Date Released: 2 November, 2022 Here we provide our team's reductions of the first set of CEERS observations, MIRI primary imaging of four pointings with NIRCam imaging in parallel. These CEERS Epoch 1 observations are of pointings 1, 2, 3 and 6, and were obtained on 21 June and 28 June 2022. All our data products are available at MAST as a High Level Science Product via DOI 10.17909/z7p0-8481. ############ ### Summary Instrument: NIRCam Mode: Imaging Calibration Pipeline Used: jwst v1.7.2 CRDS Context pmap: 0989 Target: NIRCam pointings 1, 2, 3, 6 (in parallel with prime MIRI Imaging) Filters: SW F115W, F150W, F200W; LW F277W, F356W, F410M, F444W Readout: MEDIUM8 Observation specification: 5 or 9 groups, 1 integration Dithers: 3 point dither in each filter, defined by the MIRI primary observations Contact: Micaela Bagley (mbagley@utexas.edu) Steven Finkelstein (stevenf@astro.as.utexas.edu) ########################################## ### Epoch 1 NIRCam Imaging and Reduction This 0.5 data release includes CEERS NIRCam pointings 1, 2, 3 and 6. For each pointing we provide fully reduced mosaics in all 7 filters. The mosaics are on a pixel scale of 0.03"/pix, and so the zeropoint is 28.08652 AB mag. The mosaics are pixel-aligned across all filters. We also provide pixel-aligned images in all available HST filters for each pointing. Each mosaic is a gzipped FITS file with 13 extensions, described below. The gzipped files are each ~1.4-1.6 GB and unzip to ~2.3 GB in NIRCam1, 3, 6 and ~3.2 GB in NIRCam2. For each pointing, we also provide a gzipped tarball (tar.gz) containing mosaics for all filters. These tarballs are ~15-17 GB. There are two sets of observations for F200W and F444W in NIRCam pointing 2, obtained 1 week apart. The second set (which we call "nircam2b") was affected by significant persistence. We provide mosaics of each observation separately (i.e., "nircam2" and "nircam2b") as well as combined together ("nircam2all"). The nircam2.tar.gz file includes all of these mosaics, and so is larger at ~23 GB. We have reduced the raw images through the JWST Calibration Pipeline (v1.7.2) with custom modifications and reduction steps designed to address additional features and challenges with the data such as snowballs, wisps, 1/f noise, and astrometric alignment. We will provide all scripts necessary to reproduce our reduction on GitHub, and will advertise that link soon. The data reduction is described in detail in Bagley et al. (2023, ApJL, 946, 12). (https://ui.adsabs.harvard.edu/abs/2023ApJ...946L..12B/abstract) Please cite this paper if you use the reduced images or reduction scripts for your project. We note that we have not made any corrections for correlated noise. As a result, there will be a difference between the RMS measured on the science images and that from the error arrays, and the ERR maps will require some additional scaling to account for this. ####################### ### Directory Contents Mosaics for each pointing are stored in separate directories. The NIRCam mosaic filenames are of the form: [pointing]/hlsp_ceers_jwst_nircam_[pointing]_[filter]_dr0.5_i2d.fits where: - [pointing] is one of 'nircam1', 'nircam2', 'nircam3' or 'nircam6' - [filter] is one of 'f115w', 'f150w', 'f200w', 'f277w', 'f356w', 'f410m', or 'f444w' The HST mosaic filenames are of the form: egs_all_[instrument]_[filter]_030mas_v1.9_[pointing]_mef.fits where: - [instrument] is either 'acs_wfc' or 'wfc3_ir', - [filter] is one of 'f606w', 'f814w', 'f105w', 'f125w', 'f140w', or 'f160w' - [pointing] is one of 'nircam1', 'nircam2', 'nircam3' or 'nircam6' The gzipped tarballs [pointing].tar.gz include both the NIRCam and HST mosaics. #################### ### File structure The NIRCam images are multi-extension fits files with 13 extensions: 0. PRIMARY header 1. SCI_BKSUB - 2D background-subtracted science image 2. SCI - 2D science image (not background subtracted) 3. ERR - 2D array of uncertainties, given as standard deviation and constructed as the sum in quadrature of the resampled variance maps 4. CON - 2D context image, encoding info about which input images contribution to each output pixel 5. WHT - 2D weight image giving the relative weight of the output pixels, constructed from the VAR_RNOISE map during resampling 6. VAR_POISSON - 2D variance array based on Poisson noise only 7. VAR_RNOISE - 2D variance array based on read noise only 8. VAR_FLAT - 2D variance array based on uncertainty in the flat-field 9. BKGD - 2D background model subtracted from the science image 10. BKGMASK - the tiered source mask used to create the background 11. HDRTAB - table containing metadata (FITS keyword values) for all the input images 12. ASDF - metadata for the JWST data model For more information on JWST file names and extension explanations, see: jwst-pipeline.readthedocs.io/en/latest/jwst/data_products/science_products.html JWST images can be read in several ways. For example, with astropy: >>> from astropy.io import fits >>> with fits.open('hlsp_ceers_jwst_nircam_nircam1_f115w_dr0.5_i2d.fits') as hdu: ... hdu.info() Or using the jwst datamodels: >>> from jwst.datamodels import ImageModel >>> with ImageModel('hlsp_ceers_jwst_nircam_nircam1_f115w_dr0.5_i2d.fits.gz') as im: ... im.info() For more information on JWST Data Models, see: jwst-pipeline.readthedocs.io/en/latest/jwst/datamodels/index.html The HST drizzled science images (*mef.fits) are multi-extension fits files with 6 extensions: 0. PRIMARY header 1. SCI_BKSUB - 2D background-subtracted science image 2. SCI - 2D science image (not background subtracted) 3. RMS - 2D RMS map 4. BKGD - 2D background model subtracted from the science image 5. BKGMASK - the tiered source mask used to create the background Note that the RMS maps in the WFC3/IR filters are a newer version (v1.9.1), which correct a problem in the v1.9 WFC3/IR RMS maps that were not properly scaled by the exposure time.