transit2012: after the event writeup

For those that haven’t heard about it, transit2012 got people together to observe the transit of Venus. Observations of the transit from different locations on the Earth can be used to calculate the distance between the Earth and the Sun. People around the globe tweeted their observations to twitter, and some software took those observations and combined them into a result.

I promoted transit2012 via twitter, Facebook and by emailing well known astronomy bloggers and podcasters. A few twitter users got excited and started promoting the project heavily themselves, and I was really lucky and got a mention in the popular Jodcast astronomy podcast.

I was concerned that people would go to the site, think “that’s cool”, then close it and forget about it. I implemented a registration system in the hopes that once people had registered – and had their very own point on a map of everyone observing – they’d feel more committed and be more likely to observe.

In the end 34 people registered. Registrations came from Germany, the United States, Canada, Australia, India, Turkey, Italy, Pakistan, Japan, Estonia, and the United Kingdom.

On the day we were cursed with bad weather. We used the Delisle’s method to calculate the parallax and thus the size of the solar system; this method requires only one observation from each observer, but observations of Venus entering or leaving the Sun’s disc can only be paired with other observations of the same event.

Europe and Western Australia were both covered with heavy cloud cover; I received some beautiful photographs of overcast Germany. The United States and Canada were both overcast but managed to get a look at it so we had several observations from there, and we got one observation from Queensland.

My software picked each compatible pair observations it could “parse” – decode the required latitude, longitude, observation type and time – and applied Delisle’s method. It then discarded any pairs which produced a negative or undefined result, and averaged those results together.

As most of the observations were from North America (either the northern US states or Canada), they paired together really badly. To observe the North/South parallax, you need pairs that are far apart on latitude. So pairs between close northern observers didn’t produce good results, but unfortunately were averaged into the result equally.

On the day some users varied from the prescribed tweet formats; my software was quite tolerant, but required particular words to appear (such as ‘entered’ or ‘left’), and required times to be converted to UTC. It was also necessary for the observer to geotag their tweet so the software knew their location. Several observations were lost because of these problems.

I’ve been through and remedied this as much as possible, discarding more aggressively close observation sites; after doing that, the result we got was 1AU = 142807811km. The accepted number for 1AU is 149597870.700 ± 0.003 km; so our answer is about 5% out from the real value. Not bad, especially considering we only had one southern hemisphere observer!

I have the observation tweets on disk, so if anyone is interested I can dump them out and share them; just get in touch.

There likely won’t be another Venus transit within my lifetime, but I’m keen to try this again with the 2016 transit of Mercury. Hopefully we’ll get a better number, and my software will improve having learnt from the problems described above.

A huge thank you to everyone that observed, discussed, promoted or got excited about this project. It was very, very rewarding to have so many people get caught up in it. In the end it was never about getting a good number, it was about re-enacting an important moment in scientific history, and getting excited about science in general. Given that we were hampered by the weather even now, it’s amazing that our 18th century counterparts achieved what they did.

Thanks everybody!

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One Response to transit2012: after the event writeup

  1. dab says:

    When you averaged the results for this (and I know it was a long time ago) did you weight the average based on the length of the latitude pairings, thereby weighting the largest pair (the one with the SthHemis obs) to be more influential? I wonder what effect this would show in the dataset.

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