Sign in to use this feature.

Years

Between: -

Subjects

Journals

Article Types

Countries / Regions

Search Results (2)

Search Parameters:
Keywords = detector response unfolding

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
12 pages, 6565 KB  
Communication
Charged Particle Pseudorapidity Distributions Measured with the STAR EPD
by Mátyás Molnár
Universe 2023, 9(7), 335; https://doi.org/10.3390/universe9070335 - 15 Jul 2023
Cited by 1 | Viewed by 2118
Abstract
In 2018, in preparation for the Beam Energy Scan II, the STAR detector was upgraded with the Event Plane Detector (EPD). The instrument enhanced STAR’s capabilities in centrality determination for fluctuation measurements, event plane resolution for flow measurements, and in triggering overall. Due [...] Read more.
In 2018, in preparation for the Beam Energy Scan II, the STAR detector was upgraded with the Event Plane Detector (EPD). The instrument enhanced STAR’s capabilities in centrality determination for fluctuation measurements, event plane resolution for flow measurements, and in triggering overall. Due to its fine radial granularity, it can also be utilized to measure pseudorapidity distributions of the produced charged primary particles, in EPD’s pseudorapidity coverage of 2.15<|η|<5.09. As such a measurement cannot be done directly, the response of the detector to the primary particles has to be understood well. The detector response matrix was determined via Monte Carlo simulations, and corrected charged particle pseudorapidity distributions were obtained in Au + Au collisions at the center of mass collision energies sNN = 19.6 and 27.0 GeV using an iterative unfolding procedure. Several systematic checks of the method were also done. Full article
(This article belongs to the Special Issue Zimányi School – Heavy Ion Physics)
Show Figures

Figure 1

19 pages, 4489 KB  
Article
Data Augmentation for Neutron Spectrum Unfolding with Neural Networks
by James McGreivy, Juan J. Manfredi and Daniel Siefman
J. Nucl. Eng. 2023, 4(1), 77-95; https://doi.org/10.3390/jne4010006 - 3 Jan 2023
Cited by 2 | Viewed by 4134
Abstract
Neural networks require a large quantity of training spectra and detector responses in order to learn to solve the inverse problem of neutron spectrum unfolding. In addition, due to the under-determined nature of unfolding, non-physical spectra which would not be encountered in usage [...] Read more.
Neural networks require a large quantity of training spectra and detector responses in order to learn to solve the inverse problem of neutron spectrum unfolding. In addition, due to the under-determined nature of unfolding, non-physical spectra which would not be encountered in usage should not be included in the training set. While physically realistic training spectra are commonly determined experimentally or generated through Monte Carlo simulation, this can become prohibitively expensive when considering the quantity of spectra needed to effectively train an unfolding network. In this paper, we present three algorithms for the generation of large quantities of realistic and physically motivated neutron energy spectra. Using an IAEA compendium of 251 spectra, we compare the unfolding performance of neural networks trained on spectra from these algorithms, when unfolding real-world spectra, to two baselines. We also investigate general methods for evaluating the performance of and optimizing feature engineering algorithms. Full article
(This article belongs to the Special Issue Nuclear Security and Nonproliferation Research and Development)
Show Figures

Figure 1

Back to TopTop