Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userRooStats for Searches
88
0
0.0
(
0
)
Authors
Gregory Schott
Ask ChatGPT about the research
No Arabic abstract
The RooStats toolkit, which is distributed with the ROOT software package, provides a large collection of software tools that implement statistical methods commonly used by the High Energy Physics community. The toolkit is based on RooFit, a high-level data analysis modeling package that implements various methods of statistical data analysis. RooStats enforces a clear mapping of statistical concepts to C++ classes and methods and emphasizes the ability to easily combine analyses within and across experiments. We present an overview of the RooStats toolkit, describe some of the methods used for hypothesis testing and estimation of confidence intervals and finally discuss some of the latest developments.
rate research
Read More
RooStats is a project to create advanced statistical tools required for the analysis of LHC data, with emphasis on discoveries, confidence intervals, and combined measurements. The idea is to provide the major statistical techniques as a set of C++ classes with coherent interfaces, so that can be used on arbitrary model and datasets in a common way. The classes are built on top of the RooFit package, which provides functionality for easily creating probability models, for analysis combinations and for digital publications of the results. We will present in detail the design and the implementation of the different statistical methods of RooStats. We will describe the various classes for interval estimation and for hypothesis test depending on different statistical techniques such as those based on the likelihood function, or on frequentists or bayesian statistics. These methods can be applied in complex problems, including cases with multiple parameters of interest and various nuisance parameters.
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.
The CMS experiment at the LHC accelerator at CERN relies on its computing infrastructure to stay at the frontier of High Energy Physics, searching for new phenomena and making discoveries. Even though computing plays a significant role in physics analysis we rarely use its data to predict the system behavior itself. A basic information about computing resources, user activities and site utilization can be really useful for improving the throughput of the system and its management. In this paper, we discuss a first CMS analysis of dataset popularity based on CMS meta-data which can be used as a model for dynamic data placement and provide the foundation of data-driven approach for the CMS computing infrastructure.
We present an introduction to some concepts of Bayesian data analysis in the context of atomic physics. Starting from basic rules of probability, we present the Bayes theorem and its applications. In particular we discuss about how to calculate simple and joint probability distributions and the Bayesian evidence, a model dependent quantity that allows to assign probabilities to different hypotheses from the analysis of a same data set. To give some practical examples, these methods are applied to two concrete cases. In the first example, the presence or not of a satellite line in an atomic spectrum is investigated. In the second example, we determine the most probable model among a set of possible profiles from the analysis of a statistically poor spectrum. We show also how to calculate the probability distribution of the main spectral component without having to determine uniquely the spectrum modeling. For these two studies, we implement the program Nested fit to calculate the different probability distributions and other related quantities. Nested fit is a Fortran90/Python code developed during the last years for analysis of atomic spectra. As indicated by the name, it is based on the nested algorithm, which is presented in details together with the program itself.
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 blinding is a traceable and thus trustworthy method to circumvent the bias and to convince a public audience that there is not even an accidental psychological bias. Data blinding is nowadays a standard practice in particle physics, but it is particularly difficult for experiments searching for the neutron electric dipole moment, as several cross measurements, in particular of the magnetic field, create a self-consistent network into which it is hard to inject a fake signal. We present an algorithm that modifies the data without influencing the experiment. Results of an automated analysis of the data are used to change the recorded spin state of a few neutrons of each measurement cycle. The flexible algorithm is applied twice to the data, to provide different data to various analysis teams. This gives us the option to sequentially apply various blinding offsets for separate analysis steps with independent teams. The subtle modification of the data allows us to modify the algorithm and to produce a re-blinded data set without revealing the blinding secret. The method was designed for the 2015/2016 measurement campaign of the nEDM experiment at the Paul Scherrer Institute. However, it can be re-used with minor modification for the follow-up experiment n2EDM, and may be suitable for comparable efforts.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
