The Swift DeepSky (SDS) project has three goals:

To accomplish those goals a mixture of technologies and client/server agents were assembled to deliver a completely automated mechanism to handle all the complexities of astronomical x-ray data processing and the production of high-level products.

A deep view of the X-ray Sky

The SDS pipeline is capable of combining Swift observations ever since the satellite started operating – in 2004. The goal is to provide the most sensitive view of the X-ray sky to the date of the processing. The pipeline will automatically download and combine as much as possible Swift/XRT observations covering the region of the sky requested by the user will automatically detect the objects in the combined, deep image and then measure average energy emitted by the objects in it throughout history.

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If you like photography, you probably know about the concept of exposure time and its relevance to take pictures of dark scenes – if the scene we are trying to take the picture is low in light, our camera will have to set a long exposure time to be able to capture the objects in the photo.

When we observe the sky with telescopes the same concept applies: to capture the light from faint objects (galaxies, stars) the telescope has to integrate the light from such an object for a long period of time. An example of such a process is the famous and gorgeous Hubble Deep Field observation.

Sometimes, though, it is not possible to continuously observe a given region of the sky for long periods of time. For many reasons. One reason, for example, in the case of satellites like Swift, is our own planet, the Earth, that will block the satellite’s field of view at every orbit (~90 minutes).

But if a particular region of the sky has been observed many times we can combine those images to “simulate” long exposure photography. Sure many technical details have to be considered – the perfect alignment of the pixels across the images is the first of them – but once we handle them we effectively have a deep*, sensitive view (i.e., “observation”) of that region of the sky.

*: the term deep is used in observational astronomy as a synonym to distant, which has a direct relation to the light intensity of an object. The more distant an object (e.g., a galaxy) is from us, the fainter it is from our point of view.

The live catalog

A particularly interesting feature of the Swift-DeepSky pipeline is to push the final results of each processing to a central server for global publication through the network of Virtual Observatories.

Docker packaging

Software should be simple to use – especially if we want to deliver it to non-specialists.

The Swift-DeepSky pipeline uses a lot of scientific software to do its job. One of the software packages used is NASA’s HEASoft, some are written by us, some use compiled languages (e.g., Fortran) while others use interpreted ones (Python, Perl). Bottom line is: the install procedure of scientific software may get nasty sometimes.

After considering the profile of our potential users and the resources we should provide Docker containers provided the right solution either to the first step – install – and also to another aspect: portability. Our users are most likely to work on either MacOS-X or GNU/Linux operating systems, and Docker containers will run transparently in either one.

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