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The development of large-area homogeneous photo-detectors with sub-millimeter path lengths for direct Cherenkov light and for secondary-electrons opens the possibility of large time-of-flight systems for relativistic particles with resolutions in the pico-second range. Modern ASIC techniques allow fast multi-channel front-end electronics capable of sub-pico-second resolution directly integrated with the photo-detectors. However, achieving resolution in the pico-second range requires a precise knowledge of the signal generation process in order to understand the pulse waveform, the signal dynamics, and the noise induced by the detector itself, as well as the noise added by the processing electronics. Using the parameters measured for fast photo-detectors such as micro-channel plates photo-multipliers, we have simulated and compared the time-resolutions for four signal processing techniques: leading edge discriminators, constant fraction discriminators, multiple-threshold discriminators and pulse waveform sampling.
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In this paper we illustrate techniques for digitized waveform signal processing of fast timing detectors. In the example discussed here, timing analysis of SiPM signals in the presence of high Dark Count Rates, a large data set of digitized waveforms is used to develop an optimal strategy relevant to the electronics front end design.
This paper presents results that take a critical step toward proving 10 ps timing resolutions feasibility for particle identification in the TOPSiDE detector concept for the Electron-Ion Collider. Measurements of LGADs with a thickness of 35 micro-m and 50 micro-m are evaluated with a 120 GeV proton beam. The performance of the gain and timing response is assessed, including the dependence on the reverse bias voltage and operating temperature. The best timing resolution of UFSDs in a test beam to date is achieved using three combined planes of 35 micro-m thick LGADs at -30 degree celsius with a precision of 14.3 ps (uncertainty 1.5 ps).
An optimized digital shaping filter has been developed for the GERDA experiment which searches for neutrinoless double beta decay in 76Ge. The GERDA Phase I energy calibration data have been reprocessed and an average improvement of 0.3 keV in energy resolution (FWHM) at the 76Ge Q value for 0 ubetabeta decay is obtained. This is possible thanks to the enhanced low-frequency noise rejection of this Zero Area Cusp (ZAC) signal shaping fillter.
This work presents an analysis of monoenergetic electronic recoil peaks in the dark-matter-search and calibration data from the first underground science run of the Large Underground Xenon (LUX) detector. Liquid xenon charge and light yields for electronic recoil energies between 5.2 and 661.7 keV are measured, as well as the energy resolution for the LUX detector at those same energies. Additionally, there is an interpretation of existing measurements and descriptions of electron-ion recombination fluctuations in liquid xenon as limiting cases of a more general liquid xenon re- combination fluctuation model. Measurements of the standard deviation of these fluctuations at monoenergetic electronic recoil peaks exhibit a linear dependence on the number of ions for energy deposits up to 661.7 keV, consistent with previous LUX measurements between 2-16 keV with $^3$H. We highlight similarities in liquid xenon recombination for electronic and nuclear recoils with a comparison of recombination fluctuations measured with low-energy calibration data.
The Liquid Argon Time Projection Chamber (LArTPC) is an advanced neutrino detector technology widely used in recent and upcoming accelerator neutrino experiments. It features a low energy threshold and high spatial resolution that allow for comprehensive reconstruction of event topologies. In current-generation LArTPCs, the recorded data consist of digitized waveforms on wires produced by induced signal on wires of drifting ionization electrons, which can also be viewed as two-dimensional (2D) (time versus wire) projection images of charged-particle trajectories. For such an imaging detector, one critical step is the signal processing that reconstructs the original charge projections from the recorded 2D images. For the first time, we introduce a deep neural network in LArTPC signal processing to improve the signal region of interest detection. By combining domain knowledge (e.g., matching information from multiple wire planes) and deep learning, this method shows significant improvements over traditional methods. This work details the method, software tools, and performance evaluated with realistic detector simulations.
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