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41 records · Page 3

Infrared absorption spectroscopy of dynamically compressed water

Streaked visible spectroscopy is well established in dynamic compression research. Infrared measurements remain problematic, however, due to the diminishing sensitivity of streak camera photocathodes beyond 800 nm. Time-stretch techniques offer an alternative method for probing infrared features during single-event experiments. This paper discusses the development of a time-stretch spectroscopy diagnostic using dispersed supercontinuum laser pulses. The technique is applied to near-infrared measurements of liquid water during multiple shock compression.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Mu2e Muon Beam Optimization

Mu2e is a new experiment under construction at Fermilab, which will search for coherent neutri-noless conversion of muons to electrons. In order to reach its projected single-event sensitivityof 310

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Beam-Based Target Alignment for Mu2e

The Mu2e Experiment is a precision experiment at Fermi National Accelerator Laboratory, searching for charged lepton flavor violation (CLFV) in the conversion of a muon to an electron in the presence of an atomic nucleus. In order to achieve the expected single-event sensitivity of $3\times 10^{-17}$ , Mu2e will require an intense muon beam, generated via pion decay. These pions are the product of a proton beam striking a radiatively-cooled tungsten target. In order to maximize pion production and prevent target failure, the beam will have to be aligned with the target center to within 0.5 mm. The production target monitor (PTM) will ensure this alignment. The PTM consists of a series of proportional wire chambers (PWC’s) upstream and downstream of the production target. In this dissertation, I explain the requirements of this detector system and derive the decisions about which detector model to use and how they should be arranged from these requirements. After this, I describe my e xperience building the detectors and show an early use for them in the Mu2e proton beam. I also performed a study of the sensitivity and time resolution of this model of PWC and its electronics, and give the results here. I then describe a series of simulation campaigns which shed light on the interactions between the beam and the target, and how these interactions manifest on the downstream detectors. Finally, I give the target scanning procedure to be used to align the beam and target during experiment start up.

43 PARTICLE ACCELERATORS↗

The design and test results of A Giga-Bit Cable Receiver (GBCR) for the ATLAS Inner Tracker Pixel Detector

This paper presents the design and test results of a Gigabit Cable Receiver ASIC called GBCR for the HL-LHC upgrade of the ATLAS Inner Tracker (ITk) pixel detector. Three prototypes (GBCR1, GBCR2, and GBCR3) have been designed in the CERN-identified 65 nm CMOS technology. GBCR receives seven (GBCR2) or six (GBCR3) channels (RX) each at 1.28 Gbps from the front-end readout chip RD53B via flex cables up to 1 meter and Twinax cables up to 5 meters and sends the equalized and retimed signals to lpGBT. Both GBCR2 and GBCR3 ASICs have two transmitting channels (TX) that pre-emphasize the signals from lpGBT before sending them to RD53B through the same cables. No Single-Event Upset (SEU) is observed in any tested channels of GBCR2 in a 400 MeV proton beam. The extrapolated bit error rate for the future HL-LHC application is below 8*10^(-16), significantly below the specified BER criterion. GBCR3 is designed to improve the immunity to single-event-upset by applying the Triple Modular Redundancy (TMR) technology to all RX channels. The retimed signals from GBCR3 have less total jitter than those from GBCR2 (35 ps versus 79 ps). Each receiver channel of GBCR3 consumes 75% more power than that of GBCR2.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

A Reconfigurable Neural Network ASIC for Detector Front-End Data Compression at the HL-LHC

Despite advances in the programmable logic capabilities of modern trigger systems, a significant bottleneck remains in the amount of data to be transported from the detector to off-detector logic where trigger decisions are made. We demonstrate that a neural network (NN) autoencoder model can be implemented in a radiation-tolerant application-specific integrated circuit (ASIC) to perform lossy data compression alleviating the data transmission problem while preserving critical information of the detector energy profile. For our application, we consider the high-granularity calorimeter from the Compact Muon Solenoid (CMS) experiment at the CERN Large Hadron Collider. The advantage of the machine learning approach is in the flexibility and configurability of the algorithm. By changing the NN weights, a unique data compression algorithm can be deployed for each sensor in different detector regions and changing detector or collider conditions. To meet area, performance, and power constraints, we perform quantization-aware training to create an optimized NN hardware implementation. The design is achieved through the use of high-level synthesis tools and the hls4ml framework and was processed through synthesis and physical layout flows based on a low-power (LP)-CMOS 65-nm technology node. The flow anticipates 200 Mrad of ionizing radiation to select gates and reports a total area of 3.6 mm 2 and consumes 95 mW of power. The simulated energy consumption per inference is 2.4 nJ. Furthermore, this is the first radiation-tolerant on-detector ASIC implementation of an NN that has been designed for particle physics applications.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗