I am a graduate student in experimental particle physics, working on the CMS experiment at the LHC. Right now, my research work mainly involves simulations of the calorimeters (detectors which measure the energy deposited by particles as they traverse the material and create “showers” of secondary particles). The main simulation tool I use is software called GEANT, which stands for GEometry ANd Tracking. (Particle physicists have a special talent for tortured acronyms.) This is a Monte Carlo simulation, i.e. one that uses random numbers. The current version of the software is Geant4, which is how I will refer to it.
The simulation environment does have an explicit description of the detector. Geant4 has a geometry system which allows the user to define objects with specific material properties, size, and position in the overall simulated “world”. A lot of work is done to ensure the accuracy of the detector setup (with respect to the actual, physical detector) in the main CMS simulation software. Right now, I am working on a simplified model with a less complicated geometry, necessary for testing upgrades to the calorimeters. The simplified geometry makes it easier to swap in new materials and designs.
Geant4 also has various physics lists which describe the various scattering and interaction processes that particles will undergo when they traverse a material. Different models are used for different energy ranges. The choice of physics list can make a significant difference in the results of the simulation. Like the geometry setup, the physics lists can be modified and tuned for better agreement with experimental data or to introduce new models. The user can specify how long the program should keep track of particles, as well as a minimum energy cutoff for secondary particles (generated in showers).
An often frustrating part of Geant4 simulations is that the computing time scales roughly linearly with the number of particles and the energy of the particles. One can mitigate this problem to some extent by running in parallel, e.g. submitting 10 jobs with 1000 events each, instead of one job with 10000 events. (Rolf talks about parallelization here.) However, as we keep getting more events with higher energies at the LHC, computing time becomes more of an issue.
Because of this, there is an ongoing effort in “fast simulation.” To do a faster simulation than Geant4, we can come up with parameterizations that reproduce some essential characteristics of particle showers. Specifically, we parameterize the distribution of energy deposited in the material in both the longitudinal and transverse directions. (For example, the longitudinal distribution is often parameterized as a gamma distribution.) The development of these parameterizations can be complicated, but once we have an algorithm, the simulation just requires evaluating the functions at each step. Fast simulation essentially occurs above the particle level, which is what makes it faster. A caveat: this is much easier for electromagnetic showers (which involve only electrons and photons, and only a few main processes for high energies) than for hadronic showers (which involve numerous hadrons and processes, because the strong force plays a crucial role, and therefore the energy distributions fluctuate quite a bit).
What I have given here is an overview of the simulation study of detectors; in all of this, we send single particles through the detector material. We do the same thing in real life, with a “test beam”, so that we can compare to data. The actual collisions at the LHC, however, produce events far more complex than a single particle test beam. We simulate those events, too (Rolf discusses some of that below), and there are even more complications involved. I am not as knowledgeable there (yet), and this post is long enough as it is, so I will hold off on elaborating. I hope this has given you some insight into modern particle simulations!
I am a graduate student in experimental particle physics, working on the CMS experiment at the LHC. Right now, my research work mainly involves simulations of the calorimeters (detectors which measure the energy deposited by particles as they traverse the material and create “showers” of secondary particles). The main simulation tool I use is software called GEANT, which stands for GEometry ANd Tracking. (Particle physicists have a special talent for tortured acronyms.) This is a Monte Carlo simulation, i.e. one that uses random numbers. The current version of the software is Geant4, which is how I will refer to it.
The simulation environment does have an explicit description of the detector. Geant4 has a geometry system which allows the user to define objects with specific material properties, size, and position in the overall simulated “world”. A lot of work is done to ensure the accuracy of the detector setup (with respect to the actual, physical detector) in the main CMS simulation software. Right now, I am working on a simplified model with a less complicated geometry, necessary for testing upgrades to the calorimeters. The simplified geometry makes it easier to swap in new materials and designs.
Geant4 also has various physics lists which describe the various scattering and interaction processes that particles will undergo when they traverse a material. Different models are used for different energy ranges. The choice of physics list can make a significant difference in the results of the simulation. Like the geometry setup, the physics lists can be modified and tuned for better agreement with experimental data or to introduce new models. The user can specify how long the program should keep track of particles, as well as a minimum energy cutoff for secondary particles (generated in showers).
An often frustrating part of Geant4 simulations is that the computing time scales roughly linearly with the number of particles and the energy of the particles. One can mitigate this problem to some extent by running in parallel, e.g. submitting 10 jobs with 1000 events each, instead of one job with 10000 events. (Rolf talks about parallelization here.) However, as we keep getting more events with higher energies at the LHC, computing time becomes more of an issue.
Because of this, there is an ongoing effort in “fast simulation.” To do a faster simulation than Geant4, we can come up with parameterizations that reproduce some essential characteristics of particle showers. Specifically, we parameterize the distribution of energy deposited in the material in both the longitudinal and transverse directions. (For example, the longitudinal distribution is often parameterized as a gamma distribution.) The development of these parameterizations can be complicated, but once we have an algorithm, the simulation just requires evaluating the functions at each step. Fast simulation essentially occurs above the particle level, which is what makes it faster. A caveat: this is much easier for electromagnetic showers (which involve only electrons and photons, and only a few main processes for high energies) than for hadronic showers (which involve numerous hadrons and processes, because the strong force plays a crucial role, and therefore the energy distributions fluctuate quite a bit).
What I have given here is an overview of the simulation study of detectors; in all of this, we send single particles through the detector material. We do the same thing in real life, with a “test beam”, so that we can compare to data. The actual collisions at the LHC, however, produce events far more complex than a single particle test beam. We simulate those events, too (Rolf discusses some of that below), and there are even more complications involved. I am not as knowledgeable there (yet), and this post is long enough as it is, so I will hold off on elaborating. I hope this has given you some insight into modern particle simulations!