Minimum bias vertex finder in high pile-up environment (minBiasVertexing).
Reference: F. Siklér: Study of clustering methods to improve primary vertex finding for collider detectors [http://dx.doi.org/10.1016/j.nima.2010.04.058 Nucl. Instrum. Meth. A 621 (2010) 526-533] [http://arxiv.org/abs/0911.2767 arXiv:0911.2767 (physics)]
Primary vertex finding for collider experiments is studied with the aim to detect all primary interactions. The efficiency and precision of finding interaction vertices can be improved by advanced clustering and classification methods, such as agglomerative clustering with fast pairwise nearest neighbor search, followed by Gaussian mixture model or k-means clustering. The results have been obtained with simplified simulation and Gaussian smearing, but insights on sensitivity to backgrounds are also given.
Gain calibration and energy loss estimation for silicon detectors (siEnergyLoss)
Reference: F. Siklér: A parametrisation of the energy loss distributions of charged particles and its applications for silicon detectors [http://dx.doi.org/10.1016/j.nima.2012.06.064 Nucl. Instrum. Meth. A 691 (2012) 16-29] [http://arxiv.org/abs/1111.3213 arXiv:1111.3213 (physics)]
The energy loss distribution of charged particles in silicon is approximated by
a simple analytical parametrisation. Its use is demonstrated through several
examples. With the help of energy deposits in sensing elements of the detector,
the position of track segments and the corresponding deposited energy are
estimated with improved accuracy and less bias. The parametrisation is
successfully used to estimate the energy loss rate of charged particles, and it
is applied to detector gain calibration tasks.
Event simulator + reconstructor and viewer (eventSimulator).
Track and cluster simulation + reconstruction module, general steering, control plots. Light event viewer in a web browser.
Works on these packages were supported by AIDA (Advanced European Infrastructures for Detectors at Accelerators).