Advanced Meteor Analysis

To perform multistation analysis you will need the WMPL libraries and RMS installed on your Desktop machine. Installing RMS and WMPL is explained in the github repos for each of the products (here and here).

Manual Reduction of An Individual Detection

Sometimes the RMS software will miss a meteor, or it will be too bright / complex to analyse. However you can manually reduce the data using tools built into RMS.

NB : Although you can do fireball data reduction on the Pi, it is very slow. I therefore strongly recommend you install the RMS libraries on your PC or Mac instead.

The following is condensed from Denis Vida’s excellent video tutorial here.

  • Create a new folder on your PC for the analysis – lets call it c:\meteors
  • If you’re working with a zip file obtained from me, then it already contains the right structure so just make sure the “use folder names” checkbox is ticked when unzipping it with winzip.
  • Otherwise, create a subfolder using the date of the event you want to process, eg 20230811_2232. Then within that folder create a subfolder named for the camera eg UK0006.
  • You’ll end up with something like this.
  • This structure might seem odd, but its useful later on if you want to do multistation analysis. So stick with it!
  • Copy the FF file, platepar file and .config file from your Pi to the camera folder. If there’s a fireball file (FR file) copy this too.
  • enable the RMS python environment.
  • In the RMS directory run the following all on one line (replacing UK0006 with the correct camera ID of course!)
python -m Utils.SkyFit2 c:\meteors\20230811_2232\UK0006 -c c:\meteors\20230811_2232\UK0006\.config
  • This will open the manual meteor analysis window.

  • First, you should check the platepar.
  • The initial screen shows you where the platepar thinks you are pointing. If the red markers are exactly on top of the real stars, all is fine. Otherwise you should recalibrate the platepar.
  • Personally i always do this anyway – it only takes a few minutes if the original plate was pretty close.
  • Use the mouse scroll wheel to zoom in and check. If the patepar is inaccurate follow this process:
    • If necessary, use the keys a/d/w/s to move the map around. Other keys rotate and stretch the view. Press F1 to reveal and hide the instructions. Press left and right arrow to move between FF files.
    • When pretty close, press Ctrl-R to enter star-picking mode, then zoom in on a star using the mouse scroll wheel.
    • Click on a star. You should see two crosses appear, a yellow one on the real star and a blue one on the red circle that the software thinks is the corresponding star in the map.
    • If the right reference star was selected, press enter.
    • If the wrong star was selected, press escape and try again. Avoid double stars where the software might get confused.
    • Repeat this for at least 20 stars, covering all parts of the sky.
    • After you have about 20 stars matched, press Ctrl-Z to recalculate the plate. Press Alt-tab to switch back to the window you started SkyFit2 from and you should see the results of the fit. Aim for a accuracy of < 1 pixel and a few arcminutes.
    • If the plate looks good. Press Ctrl-S to save it. Otherwise you can right click to deselect stars, or left-click to add more.
    • Once you’ve finished doing the platepar, press Ctrl-R to exit star-picking mode.
    • Exit SkyFit2 at this point.

  • Now you can do manual reduction.
  • Using the FR files:
    • The FR file contains uncompressed video and can be used to do more accurate data reduction, whereas the FF file video has been compressed.
    • To use the FR file, rename the FF file to “.unused” (otherwise skyfit will automatically load it).
    • Now, restart SkyFit2. This time you will be prompted to select an FR file to process
  • Using only the FF files:
    • If you only have the FF files, don’t worry, you can still proceed though its less accurate. Just restart SkyFit2 again.
  • Select the platepar you made in step 1 and then select the Manual Reduction tab.
  • We are going to identify each frame that contains the meteor and mark the location of the “centroid”. The centroid is the middle point of the visible meteor as best we can estimate it. We will also mark the area covered by the trail so we can estimate magnitude.
    • You will notice that when you move the mouse around the message bar at the bottom shows the RA and Dec, Alt and Az etc. This indicates that the platepar has been loaded.
    • Use the cursor keys to find the first frame which shows the meteor. Up and Down arrows move 25 frames at a time. Left and right move one frame. You can zoom in with the mouse scroll wheel.
    • Press Ctrl-R to enter point picking mode. Now zoom in on the area containing the meteor.
    • Left click where you think the centroid is. The software will try to guess. If the guess is clearly wrong, hold the Ctrl key and click again and the marker will move.
    • Hold down shift and left-click to shade in the meteor track that’s visible in this frame. This is used to measure the meteor’s brightness. Shift-right-click will rub out and Ctrl-mousewheel will make the circle smaller or bigger.
    • Go to the next frame and repeat. Do this for each frame containing the meteor.
    • When done, press Control-S. This will save a new file containing the meteor position and brightness.
    • You can now quit SkyFit2

Output of Manual Reduction

  • Manual reduction creates the following files:
    • A new FTPdetectinfo file ending in “_manual”, and a revised platepar, containing the revised RMS data and alignment data.
    • A file ending in “.ecsv”. This is a common data format that can be used to share data with other networks. The ECSV file can be used with other ECSV files in the Multistation analysis mentioned below.

Multistation Analysis

This is the manual process for calculating orbits and trajectories. To do this analysis you need data from at least two cameras in different geographic locations more than 50km but less than 700km apart, and which saw the same event.

We’re using WMPL – Western Meteor Python Library – which is written by the Meteor group at the University of Western Ontario (UWO) who also set up and run the Global Meteor Network. Download WMPL from github here and carefully follow the instructions to install it. It is not trivial.

To solve an orbit using WMPL:

Collect the Data

  • if not already done, create a folder on your C drive to do the analysis in – for example c:\meteors
  • create a subfolder using the date of the event you want to process, eg 20230811_2232.
  • There are three sources of data I’m going to discuss
    • If you’re working with a zip file obtained from me, then it contains the right structure so just unzip it into the analysis folder. (in Winzip remember to tick “use folder names”).
    • You can also use UKMON’s detections API to obtain ECSV files of individual detections. Copy all the ECSV files into the dated subfolder then jump to Identify the Best Data below.
    • And lastly, you might have obtained raw data directly from cameras. In that case
      • Within the dated subfolder create a sub-subfolder named for each camera eg UK005V, UK0086.
      • Collect the data from the cameras and put it into the relevant folder. You will need the following:
        • FF and FR file or files containing the event if available. Events may traverse two FF or FR files so watch out for that.
        • and .config
      • You need to use separate folders per camera to avoid overwriting the config and plate files.
    • Make sure your operating system hasn’t secretly renamed any files. Windows loves to add .txt to the end of files!
    • You should end up with a structure something like this:

Identify the best data.

  • This will generally be images where the start and especially the end of the meteor are in-shot, the meteor’s track is long, and no part of it is completely saturated. Pay particular attention to images with the end point of the meteor, as we want as much data as possible for this. Avoid images where the meteor track is very short as these head-on or very distant views offer poor spatial and velocity resolution and will negatively impact the solution.
  • You can use SkyFit2 to view the FF files if you have them, or you can look on UKMON’s live stream if you’re working with UK data.
  • At first, you’ll be tempted to include as much data as possible. This is usually counterproductive, but do experiment to see why. A good choice is three or four cameras which had side-on views of the event.

Manually Reduce Each Camera

  • This is required if you’re working with the raw data either direct from cameras or from a zip file.
  • Once you’ve identified the images you want to include, go through the Manual Reduction process described above for each useful image in turn.

Finally, Solve It

  • Once you’ve reduced data from at least two cameras and have ECSV files, open a command prompt, change folder to the location of WMPL, and activate its python virtual environment.
  • Then run the correlation process as follows:
    python -m wmpl.Formats.ECSV -l -x -r 20 -w c:\meteors\20230811_2232
  • (or wherever your data is)
  • By default, ECSV will try to find 100 good simulations but this may take a long time – many hours – so i usually restrict it to 20 using the -r parameter eg “-r 20”. I generally find 20 is sufficient except for very low quality data.
  • If the solution fails, revisit the data.
    • If one camera was consistently marked as ignore, try excluding it.
    • If you have more than two cameras, you can try systematically excluding them one by one.
    • To exclude a camera, rename its ecsv file from .ecsv to .bad. Don’t delete the file!
  • At the end of this process a new folder will appear containing the results of the analysis. The report.txt file contains the text output that is also displayed onscreen during processing.
    • Ideally, the residuals graphs should all be under 200-300 metres and the state vector should be a smooth curve.
    • If any camera is wildly off the state vector, or has really big residuals, try excluding it.
    • A camera whose data is at an angle to the state vector is a different meteor.

Leave a Reply

Your email address will not be published. Required fields are marked *