Why can’t everyone enjoy the Large Hadron Collider as much as I do?
What do particles sound like? Can we make music out of LHC collisions? Will it teach us anything? I regularly talk to non-physicists about the LHC. The general consensus among the people I speak to seems to be that it is really exciting and interesting, but that the details are incomprehensible. One of my favourite feelings in the world is getting to the end of some really difficult calculations and realising that I have gained some meaningful knowledge about the universe. But not everyone is quite so keen on the idea of spending 7 or 8 years doing maths in order to get that feeling! How to share the love without sharing the pain?
Sonification means taking data and turning it into sounds while retaining the information in the data. A simple example of sonification is the car parking sensor that informs you of the space behind you via a beeping sound. The distance between you and the car behind you is mapped to the period of the sound, so that small distances produce a series of beeps that are very close together in time.
There are many more complex examples of using for sonification for some form of analysis; helping blind people to see, predicting earthquakes and identifying micrometeoroids impacting the Voyager II spacecraft.
Can we sonify the ATLAS detector data in such a way as to make them appreciable to non-physicists? It seems that this is the ideal candidate for sonification- it ticks all the boxes. Collisions data is associated with spatial postion and direction, changing in time and multi-dimensional. Because there is so much going on in the data, physicists often use artifical neural networks (computers programmed to behave a bit like very simple brains). In simple terms, if we were classifying birds we would do so based on their colour, wingspan, beak shape, diet, song, and so on. We can do this fairly easily using our eyes and ears. But what if we were to try and classify something more abstract? We turn to complex ‘black-box’ computer programmes because we have not found another way to deal with large amounts of multi-dimensional information.
Sound seems the perfect tool with which to represent the complexity of the data; our ears are superb at locating the source and location of sounds relative to one another, we can hear a vast range of frequencies and distinguish timbres (different instruments) before they have even played a full cycle. We also have an incredible ability to notice slight changes is pitch or tempo over time and to recognise patterns in sound after hearing them just once. Perhaps using our ears could allow us to make full use of the neural networks between them.
LHCsound, the project to sonify ATLAS detector data, is taking shape.
We have several sounds up on the website now, including a Higgs jet composition (the energy deposits in a fat jet are sonified in terms of their energy, distance from interaction point and angular distance from jet axis), an event monitor (the number of charged particles in events picked out by the minimum bias trigger determines pitch and the timing is time-stretched real time difference between triggers), and various whole event sonifications.
There is a lot of work to be done. Our hope is that other physicists, composers, and real-world people will get in touch with their own ideas.
Lily has finally worked up the nerves for running on the goliath of processing power known as the grid, meaning that we will shortly be able to sonify real 7 TeV collisions data. There is a growing list of physics processes and we would like to sonify, from event shape variables (Lily’s favourite) to Feynman diagrams themselves (Richard’s bold idea).
The website is still a bit rusty and amateurish, as one might expect from a physicist, but be assured we have grand designs for the future!