Advanced Signal Processing for ICARUS@SBN: Achievements and Collaborations
In-depth analysis of intense activities at FNAL focusing on wire signal processing, SBN analysis, and tools development for ICARUS@SBN. Highlights include noise characterization, hit-finding performances, detector systematics, and preliminary measurements of ICARUS noise level and spectrum. Detailed collaboration outcomes and advancements in signal processing techniques shared.
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Presentation Transcript
Report on INTENSE (WP-2) activity at FNAL: September 9-October 7 2019 F. Varanini (INFN Padova) H2020, M. Sklodowska-Curie R&I No. 822185 INTENSE INTENSE meeting Pisa, November 6th, 2019
Work on wire signal processing A significant part of my work was focused on the characterization of the wire signal and noise for ICARUS@SBN: l Signal formation modeled with response of new ICARUS readout electronics l The default noise model (previously developed using input from ICARUS@LNGS and 50-liter TPC at CERN) was assumed l Summarized at ICARUS collaboration meeting (September 11-13) l Hit-finding performances (efficiency and fake hit rate) were studied for MIP signals in all the 3 wire planes l Collection and Induction-1 planes allow full efficiency with <1 fake hit per wire per drift length; Induction-2 has ~90% efficiency with ~4 fake hits 3mm MIP Collection plane fakes Collection plane efficiency Slide: 2
SBN analysis activity SBN Analysis workshop (September 16-20): joint work by ICARUS/SBND software communities on common analysis framework, comparison between detectors, development of shared tools I was convener of the detector systematics subgroup, coordinating activity of ~10 people. General discussion/planning on detector systematics and operative work on noise and space charge effects Work on variation of reconstruction performances as a function of noise level in both ICARUS and SBND l l l Example: Space charge distortions in SBND Effect on ICARUS will be negligible (detailed simulation coming soon!) Slide: 3
Tools development for ICARUS@SBN VST tests gave first in-situ measurements of ICARUS noise level and spectrum Very preliminary information, likely different from noise conditions in future data-taking However, real data are extremely useful to test signal processing, noise filtering, reconstruction chain My stay at FNAL within the INTENSE project allowed a close collaboration between software and hardware ICARUS work, and direct networking with colleagues from FNAL and elsewhere This resulted in many important contributions: Developing VST data reading/decoding within the larsoft framework (allowing standard display/reconstruction) Characterizing VST noise (RMS+spectrum) crate by crate Feeding VST noise to MC and overlap it to simulated data Preliminary work on possible use of machine learning techniques for signal/noise discrimination l l l l l Slide: 4
Tools development for ICARUS@SBN Example noise waveform read within Larsoft: Preliminary ML hit-finding example (~99% efficiency): Thank you! Slide: 5
