REVIEW 2 cited by
First Linearity and Stability Characterization for CZT Detection System in a e^+e^- Collider Environment
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
First Linearity and Stability Characterization for CZT Detection System in a e$^+$e$^-$ Collider Environment
abstract
The SIDDHARTA-2 collaboration built a new cadmium-zinc-telluride (CZT, CdZnTe)-based X-ray detection system, used for the first time in the DA$\Phi$NE electron-positron collider at INFN-LNF. The aim of this work is to show that these detectors present optimal long- and short-term linearity and stability to perform precise spectroscopic measurements in a collider environment. The spectra used as references for calibration are reported, and the results about the linearity and stability studies are presented. It is also discussed and showed what is the proper function to describe all the effects that alter the Gaussian shape in semiconductors, particularly evident in the CZT case. Good residuals and resolutions were obtained for all the calibrations. In a test run with the source and the collider beam on, it was demonstrated that the calibrations made with beam off are optimal also when the beam is on, and the actual systematics in a physics run were estimated. These promising results show the potentialities of this detector in the high rate environment of a particle collider, and pave the way for the use of CZT detectors in kaonic atoms researches and in accelerators, with applications for particle and nuclear physics.
Forward citations
Cited by 2 Pith papers
-
The Economics of AI Training Data: A Research Agenda
The paper organizes AI training data into five exchangeable units, documents 24 licensing deals, and argues data should be a separate factor in production functions.
-
The Economics of AI Training Data: A Research Agenda
The paper synthesizes fragmented research to frame data economics around data's nonrivalry and context dependence, catalogs 2020-2025 AI training data deals, and proposes a hierarchy of data units while listing four f...
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.