David V. Stark

As a member of the Hubble Advanced Camera for Survey (ACS) team, much of my work is devoted to studying the performance of the instrument, and helping observers make the best of their data. Below are some major projects I've been involved in.

Satellite Trail Identification and Masking

ACS has had its data occasionally photobombed by satellites since its installation in 2022. As satellites pass through the field of view during an exposure, they leave narrow streaks in the data due to the light they reflect (the figure on the right shows such a trail in a single exposure). To create the best final data products, satellite trails should be identified and masked.

One of my first tasks as a new staff scientist at STScI was to work on a new algorithm to accomplish the goal of automatically identifying satellite trails. The workhorse method for our approach is technique called the "Median Radon Transform" (MRT) which is a slight modification to the standard Radon Transform. The MRT calculates the median of the data along every possible straight path through an image. Those paths mostly covering empty sky return a median of zero (or the sky level), even if there is the occasional star/galaxy along the path. Those paths that align with a persistent linear feature (like a satellite trail!) will return a median value greater than zero. Some additional checks are done on sources identified in the MRT to make sure they are indeed satellite trails. We found that our method was 5-10 times more sensitive than the previously developed satellite identification software, so definitely represented a substantial improvement. It is even able to find things that are not easily seen by eye. The code was added into the acstools Python package, and in principle it should be applicable to most types of astronomical imaging data.

With our new algorithm, and the database of satellite trails it found over 20 years of ACS/WFC data, we examined how the rate of satellite trails and their brightness evolved over time. The typical trail brightness as stayed pretty much constant, but the rate of trails has roughly doubled (see the figure below), from a trail every 3-4 hours in 2002, to a trail every 1-2 hours in 2022. About 5% of images were affected in 2002, while 10% were affected in 2022. So satellite trails are (unsurprisingly) getting more common, but it's worth remembering that it is highly unlikely they will completely ruin a science program. They are narrow, likely affecting less than half a percent of the pixels in an image (meanwhile cosmic rays affect 3-6 times that in a 1000s exposure). Furthermore, we can identify them, mask them, and since we take multiple images per field, the trail can be effectively removed from our final data products.

For more info, check out ACS ISR 2022-08. To use the new Python module, be sure to update to acstools v3.6.0 or greater. This work was also the subject of a press release!