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F. Resta

Publications and source records attributed to F. Resta.

2 recordsLinked to original sources

The ASD2 Chip for the Upgrade of the ATLAS MDT Chamber Readout at HL-LHC

Upgrading the ATLAS detector for operation at the High-Luminosity LHC requires a new, more se-lective trigger scheme to control the readout of MDT drift-tube chambers by incorporating RPC trigger information. This necessitates replacing the MDT readout electronics, including the front-end boards that house the ASD amplifiers and the custom-designed TDC. The latter buffers timing measurements for transmission to a chamber-mounted data concentrator (CSM), which, in turn, communicates with the MDT Data Processor, where MDT tracking data are combined with RPC timing information, to specifically identify high-energy muon tracks. In this article, we report on the new ASD2 preamplifier, discussing its architecture, design details, functionality and measured performance. We also present test results for chips from MPW runs, the engineering run and the volume production of 80,000 chips. Unlike the ASD1 preamplifier, currently used for MDT readout, which relies on 500 nm chip tech-nology, the ASD2 is manufactured using the more advanced 130 nm technology. This offers a range of technical improvements that enhance critical performance parameters such as signal rise time, noise levels, and the reproducibility of threshold settings across a chip's channels. Comparative measurements are presented to verify this improved performance. Finally, we discuss the robust-ness of ASD2 against environmental conditions like radiation exposure and potential high-voltage discharges within the MDT drift tubes.

physics.ins-det

State-dependent brain responsiveness, from local circuits to the whole brain

The objective of this paper is to review physiological and computational aspects of the responsiveness of the cerebral cortex to stimulation, and how responsiveness depends on the state of the system. This correspondence between brain state and brain responsiveness (state-dependent responses) is outlined at different scales from the cellular and circuit level, to the mesoscale and macroscale level. At each scale, we review how quantitative methods can be used to characterize network states based on brain responses, such as the Perturbational Complexity Index (PCI). This description will compare data and models, systematically and at multiple scales, with a focus on the mechanisms that explain how brain responses depend on brain states.

q-bio.NC