2017, September 8 - A new web portal for CAMS data visualization has come online. It is now possible to see last night's meteor detections by the LOCAMS and Florida networks and the most recently reduced data from the BeNeLux network by Martin Breukers and Carl Johannink. Just go to http://cams.seti.org/FDL/ and choose the date from the "Pick a date" window. Data from the other CAMS networks will gradually come online in the coming months.
This new web tool is part of an effort by Frontier Development Lab 2017, a NASA research accelerator program at the SETI Institute, led by James Parr and Bill Diamond and supported by NVidia and IBM amongst others, that set out to use artificial intelligence techniques to automate the CAMS data reduction pipeline.
Over the summer, CAMS software tools have been updated by Pete Gural to run in batch mode. Python scripts designed by Jack Collison, Peter Jenniskens, and Siddha Ganju collect the submitted files, run the CAMS software, and compare the calculated orbit with a meteor shower template file based on showers assigned by Jenniskens. The new web tool designed by Jenniskens, Ganju and Leo Silverberg displays each shower radiant in sun-centered ecliptic coordinates, with a color assigned proportional to the entry speed. By hovering the cursor over a colored meteor radiant, one can see the IAU shower number. Clicking brings up a new window that shows the 2010-2016 CAMS data for that shower displayed in the planetarium program by Ian Webster. That makes the new web tool a portal to the minor showers in Webster's visualization program.
At the moment, the reduction of CAMS data from California and New Zealand is still very slow due to numerous false detections (clouds, birds, planes...). During the summer, Susana Zoghbi, Antonio Ordonez, Marcelo de Cicco, and Pete Gural developed machine learning and deep-learning tools to discriminate meteors from other detections. Gural will use this to improve future versions of the Confirmation and Coincidence programs. Further improvements are expected now Jim Albers, Dave Samuels, Steve Rau and Peter Jenniskens are upgrading the hardware and software environment at the existing CAMS networks.
Finally, Andres Plata Stapper used deep-learning tools to discover additional structure in the 2010-2016 shower data pre-assigned by Jenniskens, which in the near future is expected to improve and enrich the meteor shower assignment template. Also, David Holman is working to created a new stream finder tool to help improve the shower isolation from the sporadic background.
The CAMS data reduction automation will make it possible to expand and create new low-light video camera networks to facilitate a continuous global monitoring of night-time meteor shower activity. [More here]
2017, August 13 - Carl Johannink and Martin Breukers of CAMS BeNeLux report that some 1070 meteors were captured during the peak of the Perseid meteor shower, with the results from some cameras still expected to come in after the observers have returned from vacation.
2017, August 8 - Peter Jenniskens finished the shower assignments, enabling Ian Webster to update the CAMS shower vizualisation tool to include all CAMS data up to the end of 2016. Many of the minor meteor showers are now well depicted.
2017, July 30 - Tim Cooper reports that the new CAMS South Africa network operated successfully. Data analysis shows that 167 meteors were captured simultaneously by the four cameras at Bredell and the four at Victory Park. Aside from many Southern delta Aquariids, there was a diffuse activity around R.A. = 20, Dec. = -15 degrees, and a compact radiant around R.A. = 40, Dec. = -32. No meteors from the Borisov trail were captured. Visual observations suggested a hint of activity later in the night. Peter Jenniskens, Mike Koop and Marcelo de Cicco observed the moon for possible impacts from California.
2017, July 29 - Peter Zimnikoval writes that the dust trail encounter with comet C/2015 D4 (Borisov) may also cause a noticeable uptick of lunar impact flashes if there are enough large meteoroids in the trail. He writes: "Of course, no one knowns if particles in the stream will reach sizes to be detected this way." If it does, the Moon happens to be in a favorable geometry to see this, with best viewing at the longitude of the central USA. The trail will hit the Moon a little later than Earth. Train your telescope at the dark side of the Moon in the area dotted in the attached graph. Update: The moon will cross the Borisov trail on July 29, 01h16m UT, 0.9 hours after the Earth, according to calculations by Pete Gural.
2017, July 29 (00:22 UT) - Earth is about to travel through the 1-revolution dust trail of long-period (700 year) comet C/2015 D4 (Borisov).
Only about once every 25 years is such an intermediate long-period comet discovered that passes close enough to Earth's orbit to have dust trail encounters. This one passed perihelion in 2014. On July 29 at 0h22m UT, we are crossing the trail just behind the comet and near the center on the inside [Read more: CBET], both favorable conditions for meteor activity. However, we do not know if the comet was active in the previous return. No annual shower is known from this comet. If meteoroids were released, the shower may be visible from South Africa, radiating from the constellation Columba near the horizon in the south east.
[Picture of the comet, courtesy of G. Borisov]
2017, July 23 - First light tonight for the CAMS South Africa network. 87 meteoroid orbits!! Network coordinator is Tim Cooper, former director of the Meteor Section of ASSA, who operates four Watec Wat902H cameras from Bredell, a suburb of Johannesburg. Four more cameras are operated by Oleg Toumilovich from Victoria Park, at a distance of 31 km. Setup support was provided by Steve Rau. Stations are ready for the upcoming encounter with the dust trail of comet C/2015 D4 (Borisov) on July 29.
2017, June 26 - The Frontier Development Lab has started its activities. The FDL Comet Team that will work to automate the CAMS data processing consists of (from left to right): Susana Zoghbi, Antonio Ordonez, Andres Plata Stapper, and Marcelo de Cicco. They will work on deep-learning tools to filter and process the data. In addition, Jack Collison will provide support with the data processing automation. Pete Gural (CAMS software) and Siddha Ganju (Deep Learning techniques) are the technical mentors.