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Register a new userCalculating particle correlators with the account of detector efficiency
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The formulae for correlators K2 and K3 of a given particle observable (e.g. energy or transverse momentum) accounting for the track reconstruction efficiency are presented. Similar to the case of an ideal detector, the correlators can be expressed through the event-by-event fluctuation of the observable single event mean and the observable pseudo-central moments. However, on the contrary to the ideal case, this splitting does not allow for a substantial reduction of the computation time.
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These three lectures provide an introduction to the main concepts of statistical data analysis useful for precision measurements and searches for new signals in High Energy Physics. The frequentist and Bayesian approaches to probability theory are introduced and, for both approaches, inference methods are presented. Hypothesis tests will be discussed, then significance and upper limit evaluation will be presented with an overview of the modern and most advanced techniques adopted for data analysis at the Large Hadron Collider.
We present a procedure for reconstructing particle cascades from event data measured in a high energy physics experiment. For evaluating the hypothesis of a specific physics process causing the observed data, all possible reconstructi
I would like to thank Junk and Lyons (arXiv:2009.06864) for beginning a discussion about replication in high-energy physics (HEP). Junk and Lyons ultimately argue that HEP learned its lessons the hard way through past failures and that other fields could learn from our procedures. They emphasize that experimental collaborations would risk their legacies were they to make a type-1 error in a search for new physics and outline the vigilance taken to avoid one, such as data blinding and a strict $5sigma$ threshold. The discussion, however, ignores an elephant in the room: there are regularly anomalies in searches for new physics that result in substantial scientific activity but dont replicate with more data.
Muons are the most abundant charged particles arriving at sea level originating from the decay of secondary charged pions and kaons. These secondary particles are created when high-energy cosmic rays hit the atmosphere interacting with air nuclei initiating cascades of secondary particles which led to the formation of extensive air showers (EAS). They carry essential information about the extra-terrestrial events and are characterized by large flux and varying angular distribution. To account for open questions and the origin of cosmic rays, one needs to study various components of cosmic rays with energy and arriving direction. Because of the close relation between muon and neutrino production, it is the most important particle to keep track of. We propose a novel tracking algorithm based on the Geometric Deep Learning approach using graphical structure to incorporate domain knowledge to track cosmic ray muons in our 3-D scintillator detector. The detector is modeled using the GEANT4 simulation package and EAS is simulated using CORSIKA (COsmic Ray SImulations for KAscade) with a focus on muons originating from EAS. We shed some light on the performance, robustness towards noise and double hits, limitations, and application of the proposed algorithm in tracking applications with the possibility to generalize to other detectors for astrophysical and collider experiments.
We derive formulas for the efficiency correction of cumulants with many efficiency bins. The derivation of the formulas is simpler than the previously suggested method, but the numerical cost is drastically reduced from the naive method. From analytical and numerical analyses in simple toy models, we show that the use of the averaged efficiency in the efficiency correction can lead to wrong corrected values, which have larger deviation for higher order cumulants. These analyses show the importance of carrying out the efficiency correction without taking the average.
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