CERN Muon Team
Introduction to CERN
The European Council for Nuclear Research, known by its French acronym CERN, is the world's preeminent nuclear research organization. Scientists at CERN are able to examine the behavior of matter under extreme conditions via particle collisions in the Large Hadron Collider, which grants privileged insight to the fundamental nature of our universe. With this tool, CERN affiliated scientists have made enormous strides in the field of theoretical physics.
Pictured on the right is the ATLAS detector, which measures the outputs from these collisions. Note the people in the diagram, standing in front of and on the detector, for a sense of scale. The remainder of this page largely concerns the light blue segments, which contain instruments known as the muon drift tubes.
Muon Drift Tubes
The LHC is able to collide particles at high speeds, but for that to mean anything, scientists need to be able to measure and interpret the resulting data. This means tracing the paths of particles as they exit the point of collision, to gain insights from their direction and momentum.
For muons, a particular type of particle, this can be tricky. Muons penetrate materials easily, and interact with matter mainly through the electromagnetic force. To track and identify them, then, we use what are called muon drift tubes.
These tubes are designed with a charged wire suspended in the center of a hollow tube. When a muon passes through the tube and excites the gas molecules contained within, the excited particles become drawn to the wire, and by measuring how long it takes for them to arrive at the center we can tell how near the muon got to the wire.
This allows us to construct a circle around the wire, such that the muon's path was tangent to it. By stacking multiple layers of these tubes atop one another, as depicted on the right, we can get a series of these measurements and estimate a muon trajectory that is roughly tangent to them all, yielding valuable data.
Autocalibration Algorithm
A muon drift tube's success relies on an accurate r(t) function, which estimates the radius of closest distance based on the time at which the wire receives the signal. Minor variations from chamber to chamber - perhaps in the charge of the wire, distance from the collision point, or composition and temperature of the tubes' gas - mean that this r(t) function is not perfectly consistent across the many hundreds of chambers located in the ATLAS detector. Nevertheless, the same approximate function is used for each one. This skews the data.
To account for this, I helped to implement an algorithm which would calibrate a unique r(t) function for each chamber when given a set of collected data. Relying on the statistical expectation for a uniform distribution of radii, this algorithm was designed to ensure greater granularity and precision, increasing the value of the collected data.
What I've Learned
This project taught me enormous amounts about the demands of real-world software design. My initial approach to this project was akin to a house of cards, where making a change to the underlying fundamentals would cause the rest of the structure to collapse. Later approaches increased abstraction, so that development would flow more smoothly. It also involved learning how to use Root, a data analysis software used by CERN which is compatible with both C++ and Python. I'm glad to have worked on this project, and the understanding it's given me of how to be a better software developer is incredibly valuable.