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Ad-hoc Pulse Shape Simulation using Cyclic Positional U-Net
Instrumentation and Detectors · Submitted 8 Dec 2022
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Taking the transfer learning approach, CPU-Net translates simulated pulses to detector pulses such that they are indistinguishable.
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The abstract, sentence by sentence
High-Purity Germanium~(HPGe) detectors have been a key technology for rare-event searches, such as neutrinoless double-beta decay and dark matter searches, for many decades. Pulse shape simulation is pivotal to improving the physics reach of these experiments. In this work, we propose a Cyclic Positional U-Net to achieve ad-hoc pulse shape simulations with high precision and low latency. Taking the transfer learning approach, CPU-Net translates simulated pulses to detector pulses such that they are indistinguishable. We demonstrate CPU-Net's performance on data taken from a local HPGe detector.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | High-Purity Germanium~(HPGe) detectors have been a key technology for rare-event searches, such as neutrinoless double-beta decay and dark matter searches, for many decades. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s1 | Pulse shape simulation is pivotal to improving the physics reach of these experiments. | background 0.99 | 0.05 | 0.04 | asserted 1.00 |
| s2 | In this work, we propose a Cyclic Positional U-Net to achieve ad-hoc pulse shape simulations with high precision and low latency. | contribution 0.93 | 0.82 | 0.28 | asserted 0.89 |
| s3 | Taking the transfer learning approach, CPU-Net translates simulated pulses to detector pulses such that they are indistinguishable. | method 0.97 | 0.75 | 0.20 | asserted 1.00 |
| s4 | We demonstrate CPU-Net's performance on data taken from a local HPGe detector. | method 0.59 | 0.95 | 0.10 | asserted 1.00 |
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Original abstract
High-Purity Germanium~(HPGe) detectors have been a key technology for rare-event searches, such as neutrinoless double-beta decay and dark matter searches, for many decades. Pulse shape simulation is pivotal to improving the physics reach of these experiments. In this work, we propose a Cyclic Positional U-Net to achieve ad-hoc pulse shape simulations with high precision and low latency. Taking the transfer learning approach, CPU-Net translates simulated pulses to detector pulses such that they are indistinguishable. We demonstrate CPU-Net's performance on data taken from a local HPGe detector.
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We choose attention-coupled recurrent neural network (RNN) [ref] as the discriminator model because position is intrinsically enforced by RNN.
Neural Machine Translation by Jointly Learning to Align and Translate · Cyclic Positional U-Net · 0.99
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