REVIEW 3 cited by
The Data Acquisition System of the LZ Dark Matter Detector: FADR
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
The Data Acquisition System of the LZ Dark Matter Detector: FADR
read the original abstract
The Data Acquisition System (DAQ) for the LUX-ZEPLIN (LZ) dark matter detector is described. The signals from 745 PMTs, distributed across three subsystems, are sampled with 100-MHz 32-channel digitizers (DDC-32s). A basic waveform analysis is carried out on the on-board Field Programmable Gate Arrays (FPGAs) to extract information about the observed scintillation and electroluminescence signals. This information is used to determine if the digitized waveforms should be preserved for offline analysis. The system is designed around the Kintex-7 FPGA. In addition to digitizing the PMT signals and providing basic event selection in real time, the flexibility provided by the use of FPGAs allows us to monitor the performance of the detector and the DAQ in parallel to normal data acquisition. The hardware and software/firmware of this FPGA-based Architecture for Data acquisition and Realtime monitoring (FADR) are discussed and performance measurements are described.
Forward citations
Cited by 3 Pith papers
-
Searches for Light Dark Matter and Evidence of Coherent Elastic Neutrino-Nucleus Scattering of Solar Neutrinos with the LUX-ZEPLIN (LZ) Experiment
LZ's new 5.7-tonne-year search reports a 4.5σ hint of solar 8B neutrino CEνNS and world-leading dark matter limits down to 5 GeV/c².
-
Dark Matter Search Results from 4.2 Tonne-Years of Exposure of the LUX-ZEPLIN (LZ) Experiment
LZ reports a null result from 4.2 tonne-year exposure and sets new world-leading upper limits on spin-independent WIMP-nucleon cross sections reaching 2.2e-48 cm2 at 40 GeV.
-
Cohort Organized Learning: Clustering Through Agreement
CoOL clusters data by training neural networks via expectation-maximization gradients to assign groups based on agreement.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.