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This code creates a calibrated mosaic from the individual UVOT snapshots downloaded from the archive.

The code and documentation are in active development. Please consult @lea-hagen before using this.

How to use

Required packages: astropy

Python 3 is required.

This is built around the UVOT processing tools that are part of HEASOFT/FTOOLS. Instructions to download and install HEASOFT are here: https://heasarc.gsfc.nasa.gov/lheasoft/install.html

You will also need the latest CALDB files. Download/installation information is here: https://heasarc.gsfc.nasa.gov/docs/heasarc/caldb/install.html

Installation of uvot-mosaic: Either download or clone the repository. You can keep the code wherever you like as long as it's in your python path.

Running uvot_deep.py to combine and stack images

Download the desired images from HEASARC (https://heasarc.gsfc.nasa.gov/cgi-bin/W3Browse/swift.pl) and ensure that you've chosen to download both the UVOT and Swift Auxiliary data. The downloads will be organized in folders named with the Observation ID (e.g., 00037723002), which is a combination of the target ID (00037723) and segment (002). All of the fits files will need to be unzipped (gunzip */*.gz, gunzip */*/*.gz, etc.). For all of the observations you wish to stack, put their folders in the same directory, and run uvot_deep.py from that directory.

Example: Download two observations of the edge of the M31 disk, with Obs IDs 00037723001 and 00037723002. You will have a directory structure something like

~/example/00037723001
~/example/00037723001/auxil
~/example/00037723001/uvot
~/example/00037723001/uvot/hk
~/example/00037723001/uvot/image
~/example/00037723001/uvot/products
~/example/00037723002
~/example/00037723002/auxil
~/example/00037723002/uvot
~/example/00037723002/uvot/hk
~/example/00037723002/uvot/image
~/example/00037723002/uvot/products

From ~/example, run

> import uvot_deep
> uvot_deep.uvot_deep(['00037723001','00037723002'], 'test_', ['w2','m2','w1'])

After a lot of verbosity from the UVOT tools, this will create several files in ~/example for each filter (replace ff with the filter name).

  • test_ff_sk_all.fits: each extension is a counts ("sky") image, in units of counts per pixel
  • test_ff_sk.fits: all extensions from test_ff_sk.fits added together
  • test_ff_ex_all.fits: each extension is an exposure map, in units of seconds
  • test_ff_ex.fits: all extensions from test_ff_ex.fits added together
  • test_ff_cr.fits: count rate image (test_ff_sk.fits divided by test_ff_ex.fits), in counts per second per pixel

It will also create files in ~/example/[obsid]/uvot/image for each exposure, which you most likely won't need to look at. But if you're curious, this is the list, where [obsid] is the Observation ID and [ff] is the filter.

  • sw[obsid]u[ff].badpix: bad pixel map
  • sw[obsid]u[ff]_ex_mask.img: masked exposure map
  • sw[obsid]u[ff].lss: large scale sensitivity (LSS) map
  • sw[obsid]u[ff]_mask.img: mask image
  • sw[obsid]u[ff]_sk_corr.img: sky (counts) image, corrected for LSS and masked
  • sw[obsid]u[ff].sl: scattered light image (assuming this option is enabled)

Running offset_mosaic.py to adjust background for individual snapshots

The background values in UVOT images are known to change, likely due to scattered light from the Earth/sun/moon, but sometimes also from UV-bright sources in or near the field of view. offset_mosaic.py is being written to do offsets between snapshots to better account for this.