The Continuous Readout Stream of the MicroBooNE Liquid Argon Time Projection Chamber for Detection of Supernova Neutrinos José I. Crespo-Anadón Columbia University Nevis Laboratories 10/21/2019 DUNE DAQ Meeting Based on MICROBOONE-NOTE-1030-PUB from https://microboone.fnal.gov/public-notes/
The Short-Baseline Neutrino Program at Fermilab Booster Neutrino Beam (BNB) 8 Gev protons on Be target 〈 E ν ~ 700 MeV 〉 MicroBooNE ICARUS SBND L = 470 m L = 600 m L = 110 m 85 t 476 t 112 t ν data since Oct 2015 ν data in 2019 ν data in 2021 MicroBooNE physics goals: Investigate the excess of electron-like events observed in MiniBooNE. ● Perform high-precision measurements of cross-sections of ν µ and ν e on Ar. ● Develop further the LArTPC detector technology. ● Perform searches for astroparticles and exotic physics exploiting the LArTPC capabilities ● (on-beam & off-beam) José I. Crespo-Anadón 2
The Short-Baseline Neutrino Program at Fermilab 85 t Near surface 4 × 10 kt 1475 m underground MicroBooNE physics goals: Investigate the excess of electron-like events observed in MiniBooNE. ● Perform high-precision measurements of cross-sections of ν µ and ν e on Ar. ● Develop further the LArTPC detector technology. ● Perform searches for astroparticles and exotic physics exploiting the LArTPC capabilities ● (on-beam & off-beam) José I. Crespo-Anadón 3
MicroBooNE TPC & PMTs 85 t of liquid argon (active) . ● Drift time: 2.3 ms. ● Three wire planes to reconstruct 3D interaction. ● 3 mm wire pitch . 8256 channels. 2 induction planes with 2400 wires each at ± 60º from vertical. JINST 12 P02017 (2017) 1 collection plane with 3456 vertical wires. Beam Cold front-end electronics. 2 MHz digitization with warm electronics. 32 8” Hamamatsu R5912 Cryogenic PMTs ● mounted behind the wire planes. 2.3 m 64 MHz digitization readout electronics. Scintillation light in coincidence with beam gates 1 0 . 4 used for triggering. m 2.5 m (drift) José I. Crespo-Anadón 4
The MicroBooNE Continuous Readout Stream José I. Crespo-Anadón 5
MicroBooNE TPC continuous readout Prediction for DUNE [arXiv:1807.10334] Enable the acquisition of the neutrino ● burst from a nearby core-collapse SN. Expectation in MicroBooNE: ● ~ O(10) events for a SN at 10 kpc. – ν e CC: ν e + 40 Ar →e - + 40 K* (E th ~ 5 MeV) – MicroBooNE challenges: ● Near-surface detector: large cosmic- – ray rate: ~ 5.5 kHz Small number of events. – Prediction for DUNE (40 kt) [arXiv:1512.06148] Low energy. – → No self-trigger . Instead, read out detector continuously ● and rely on a delayed external trigger from the SNEWS alert. Also, demonstrate processing of TPC ● information in real time . Foundation for a TPC-based trigger for DUNE. Complementary to a PMT-based trigger. José I. Crespo-Anadón 6
The MicroBooNE Continuous Readout Stream Legend Triggered stream Continuous stream PMT stream not used for this work. ● Talk at May 2019 Collaboration Meeting with more details on electronics: ● https://indico.fnal.gov/event/18681/session/4/contribution/21/material/slides/1.pdf and MICROBOONE-NOTE-1030-PUB at https://microboone.fnal.gov/public-notes/ José I. Crespo-Anadón 7
Data challenge Data is stored temporarily on a 15 TB HDD at each DAQ server, awaiting an SNEWS alert to be ● transferred to permanent storage. Continuous readout of the TPC generates 33 GB/s ● → Distributed between 9 servers: ~ 3.7 GB/s/server Bottleneck: disk writing speed of the DAQ servers (conservatively 50 MB/s ). ● Need a compression factor ~ 80 . ● Lossless compression (Huffman) gives factor ~ 5: not enough. ● Requires additional lossy compression . Feasible since images are sparse . ● Also: writing at 50 MB/s gives us a window of > 48 h before data is deleted . ● José I. Crespo-Anadón 8
Data reduction algorithms Implemented in the Front End Module ● FPGA (Altera Stratix III). Zero suppression: only the waveform ● passing the amplitude threshold (configurable per channel) with respect to the channel baseline is saved plus presamples and postsamples (configurable per FEM) . The baseline can be ● dynamically computed using preceding samples (if within mean and RMS tolerances) or loaded as static value at the beginning of the run (both have been commissioned and tested). Additional compression provided by Huffman ● encoding of ADC differences using a fixed table. José I. Crespo-Anadón 9
Configuration of zero suppression parameters: Physics-driven plane-wide thresholds First approach ( now deprecated ). ● One threshold per plane. ● Physics driven: separate signal (cosmic- ● ray muons) from electronics noise using previous Trigger Stream data. ADC distributions of max and min values ● found after running a zero-suppression emulation with dynamic baseline and a loose threshold. U plane threshold: - 25 ADC V plane threshold: ± 15 ADC Y plane threshold: +30 ADC José I. Crespo-Anadón 10
Configuration of zero suppression parameters: Bandwidth-driven channel-wise thresholds Current method. ● One threshold per channel. ● Bandwidth driven: suppress 98.5% of ● ADC distribution using Trigger Stream data. Tested with dynamic and static baseline ● firmwares. Static baseline is given by the mode of the distribution. Average threshold is 3.6, 2.2 and 5.2 ● times smaller than previous method for planes U, V, Y, respectively → Higher acceptance for low-energy signals. José I. Crespo-Anadón 11
Data compression results José I. Crespo-Anadón 12
Data compression results Expected data rate Compression factor = ● Observed data rate SN Run Period 1: physics-driven plane-wide thresholds + dynamic baseline . ● Excessive compression. Except for SEB06: too many noisy channels, preventing dynamic baseline estimation. Feasible to lower thresholds. José I. Crespo-Anadón 13
Data compression results Expected data rate Compression factor = ● Observed data rate SN Run Period 2: bandwidth-driven channel-wise thresholds + dynamic baseline . ● Compression closer to target goal, but unstable. Noisy channels still preventing dynamic baseline estimation. José I. Crespo-Anadón 14
Data compression results Expected data rate Compression factor = ● Observed data rate SN Run Period 3: bandwidth-driven channel-wise thresholds + static baseline . ● Target compression factor achieved. Data rates stable. Current running mode José I. Crespo-Anadón 15
Analysis of Continuous Readout Stream data José I. Crespo-Anadón 16
Event building Unlike Trigger Stream, “events” are not built by the DAQ. The DAQ just writes the data to the ● local 15 TB disk at each server. Data lifetime is > 48 h. Continuous data needs to be first retrieved from the servers, and then built. ● “Event” needs to be defined (done at LArSoft level). For this analysis: ● 1 event =[last 0.8 ms (1600 samples) from frame N - 1] + [full frame N (1.6 ms, 3200 samples)] + [first 0.8 ms (1600 samples) from frame N + 1] so TPC objects can be reconstructed across frame boundaries. Only objects in the central frame are used for analysis. ● José I. Crespo-Anadón 17
Signal processing Two data streams: ● Continuous (SN) stream . Zero – suppressed. Trigger Stream . Not zero suppressed. – First stages of signal processing are different. No noise filter : not effective since most of the ● baseline is zero suppressed. No ROI Finder since it is done by the FPGA. A Zero-Suppression Emulation allows us to convert Trigger Stream data into SN Stream- like data. Challenge: flipping bits (FB) (bits in ADC ● word switching from 0 to 1, or vice versa) affecting 4% of samples→ ADC values shifted by a combination of powers of 2. Baseline subtraction : linear interpolation ● using presamples and postsamples. Flipping-bit correction : linear interpolation ● between neighboring samples. If difference is bigger than 32 ADC, replace ADC value with the interpolated one. José I. Crespo-Anadón 18
Signal processing Common stages: ● 1-D deconvolution using the same tool as ● in the Trigger Stream. Same Hit Finder and Pandora Cosmic ● clustering. Same Michel Reconstruction as in ● MicroBooNE publication. Extended to any of the 3 TPC planes. Michel electrons used as a proxy for low- energy electrons produced by SN neutrinos. JINST 12 P09014 (2017) José I. Crespo-Anadón 19
Michel electron reconstruction: total energy spectrum 53.31 min of SN stream DATA. ● 58.82 min of Trigger stream DATA , normalized to SN stream exposure. ● Also processed through Zero-Suppression (ZS) Emulation. – Plane Y Deficit at low energy. Excess at high energy. Flat ratio. José I. Crespo-Anadón 20
Michel electron reconstruction: total energy spectrum (II) Plane U SN stream Michel ● electrons reconstructed at lower energies. Plane V: large rate and ● shape disagreement with Plane V respect to Trigger Stream. Signals on V plane are smaller, and more likely to be zero suppressed. Effects well reproduced ● by zero-suppression emulation. José I. Crespo-Anadón 21
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