相关论文: Blind Analysis in Particle Physics
The many ways in which machine and deep learning are transforming the analysis and simulation of data in particle physics are reviewed. The main methods based on boosted decision trees and various types of neural networks are introduced,…
The massive data sets from today's particle physics experiments present a variety of challenges amenable to the tools developed by the statistics community. From the real-time decision of what subset of data to record on permanent storage,…
One of the most severe limitations in particle accelerators and beam transport are non-linear effects. Techniques to study and possibly suppress some of these detrimental effects exist, the most popular are based on particle tracking and…
Making meaning with math in physics requires blending physical conceptual knowledge with mathematical symbology. Students in introductory physics classes often struggle with this, but it is an essential component of learning how to think…
The status and prospects of the experimental efforts in the detection of Particle Dark Matter is reviewed. Emphasis is put in the direct searches for WIMPs (Weakly Interacting Massive Particles), outlining the various strategies and…
Recently, much attention has been focused on the replicability of scientific results, causing scientists, statisticians, and journal editors to examine closely their methodologies and publishing criteria. Experimental particle physicists…
The a priori analysis (APA) is discussed as a tool to assess the reliability of grades in standard curricular courses. This unusual, but striking application is presented when teaching the section on data treatment of a Laboratory Course to…
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…
These lectures are intended to provide a brief pedagogical review of dark matter for the newcomer to the subject. We begin with a discussion of the astrophysical evidence for dark matter. The standard weakly-interacting massive particle…
Psychological bias towards, or away from, a prior measurement or a theory prediction is an intrinsic threat to any data analysis. While various methods can be used to avoid the bias, e.g. actively not looking at the result, only data…
After a very brief review of twentieth century elementary particle physics, prospects for the next century are discussed. First and most important are technological limits of opportunities; next, the future experimental program, and finally…
Current physics models used to interpret experimental measurements of particle beams require either simplifying assumptions to be made in order to ensure analytical tractability, or black box optimization methods to perform model based…
Experiments on solid-state materials and atomic quantum gases are increasingly investigating similar concepts in many-body quantum physics. Yet, the flavor of experiments on the gaseous atomic materials is different from that of…
Despite several indirect confirmations of the existence of dark matter, the properties of a new dark matter particle are still largely unknown. Several experiments are currently searching for this particle underground in direct detection,…
This report summarizes a series of three lectures aimed at giving an overview of basic particle detection principles, the interaction of particles with matter, the application of these principles in modern detector systems, as well…
It is argued that the nature of probability is essentially informational rather than physical and that quantum mechanical predictions should be viewed as logical inferences made on the basis of the information content of a given…
The asymmetrical path interference test of light is put forward in the paper. In the test, two different results would arise under the same experimental conditions if light is regarded as wave or particle. Therefore, the test can help us to…
A technique for background prediction using data, but maintaining a closed signal box is described. The result is extended to two background sources. Conditions on the applicability under correlated cuts are described. This technique is…
The Bayesian approach to the prediction of particle type given measurements of particle location is explored, using a parametric model whose prior is based on the transformation group. Two types of particle are considered, and locations are…
We describe two different approaches for incorporating systematics into analyses for parameter determination in the physical sciences. We refer to these as the Pragmatic and the Full methods, with the latter coming in two variants: Full…