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Proton Energy Dependence of Radiation Induced Low Gain Avalanche Detector Degradation
Veronika Kraus, Marcos Fernandez Garcia, Luca Menzio, Michael Moll
https://arxiv.org/abs/2602.01800 https://arxiv.org/pdf/2602.01800 https://arxiv.org/html/2602.01800
arXiv:2602.01800v1 Announce Type: new
Abstract: Low Gain Avalanche Detectors (LGADs) are key components for precise timing measurements in high-energy physics experiments, including the High Luminosity upgrades of the current LHC detectors. Their performance is, however, limited by radiation induced degradation of the gain layer, primarily driven by acceptor removal. This study presents a systematic comparison of how the degradation evolves with different incident proton energies, using LGADs from Hamamatsu Photonics (HPK) and The Institute of Microelectronics of Barcelona (IMB-CNM) irradiated with 18 MeV, 24 MeV, 400 MeV and 23 GeV protons and fluences up to 2.5x10^15 p/cm2. Electrical characterization is used to extract the acceptor removal coefficients for different proton energies, whereas IR TCT measurements offer complementary insight into the gain evolution in LGADs after irradiation. Across all devices, lower energy protons induce stronger gain layer degradation, confirming expectations. However, 400 MeV protons consistently appear less damaging than both lower and higher energy protons, an unexpected deviation from a monotonic energy trend. Conversion of proton fluences to 1 MeV neutron-equivalent fluences reduces but does not eliminate these differences, indicating that the standard Non-Ionizing Energy Loss (NIEL) scaling does not fully account for the underlying defect formation mechanisms at different energies and requires revision when considering irradiation fields that contain a broader spectrum of particle types and energies.
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Im using case_when() quite a lot, case_match() is new to me: #rstats

A general vectorised switch() — case_match
This function allows you to vectorise multiple switch() statements. Each case is evaluated sequentially and the first match for each element determines the corresponding value in the output vector. If no cases match, the .default is used. case_match() is an R equivalent of the SQL "simple" CASE WHEN statement. Connection to case_when() While case_when() uses logical expressions on the left-hand side of the formula, case_match() uses values to match against .x with. The following two statement…

In 2014, NASA awarded a Commercial Crew Transportation Capability contract to Boeing to fly astronauts to and from the International Space Station with its #Starliner spacecraft; as part of its contract, Boeing was awarded up to six crewed flights to the orbital complex - after a thorough evaluation, the definitive order has been adjusted to four missions, with the remaining two available as options: https://www.nasa.gov/blogs/spacestation/2025/11/24/nasa-boeing-modify-commercial-crew-contract/. The next Starliner flight, known as Starliner-1, will be used by NASA to deliver necessary cargo to the orbital laboratory and allow in-flight validation of the system upgrades implemented following the Crew Flight Test mission last year - NASA and Boeing are targeting no earlier than April 2026 to fly the uncrewed Starliner-1 pending completion of rigorous test, certification, and mission readiness activities.

Our new ASL Consulting page is now live:
#asl #deaf

Head Coach Jim Harbaugh on Building Momentum After the Cowboys Win https://www.chargers.com/video/jim-harbaugh-press-conference-monday-week-17-2025

Estimating Spatially Resolved Radiation Fields Using Neural Networks
Felix Lehner, Pasquale Lombardo, Susana Castillo, Oliver Hupe, Marcus Magnor
https://arxiv.org/abs/2512.17654 https://arxiv.org/pdf/2512.17654 https://arxiv.org/html/2512.17654
arXiv:2512.17654v1 Announce Type: new
Abstract: We present an in-depth analysis on how to build and train neural networks to estimate the spatial distribution of scattered radiation fields for radiation protection dosimetry in medical radiation fields, such as those found in Interventional Radiology and Cardiology. Therefore, we present three different synthetically generated datasets with increasing complexity for training, using a Monte-Carlo Simulation application based on Geant4. On those datasets, we evaluate convolutional and fully connected architectures of neural networks to demonstrate which design decisions work well for reconstructing the fluence and spectra distributions over the spatial domain of such radiation fields. All used datasets as well as our training pipeline are published as open source in separate repositories.
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