Takumi Aoki | Searcharxiv

arXiv2026

Irradiation Studies of TGC Electronics Components for the ATLAS Experiment at High-Luminosity LHC

This paper evaluates the radiation tolerance of commercial off-the-shelf (COTS) electronics components for use in the Thin Gap Chamber (TGC) frontend electronics of the ATLAS experiment at the High-Luminosity LHC (HL-LHC). The ATLAS experiment has accumulated more than 450 fb^-1 of data as of 2025. Its luminosity upgrade, the HL-LHC scheduled to begin operation in 2030, will deliver 3000-4000 fb^-1 over ten years and lead to substantially higher radiation levels in detector electronics. The radiation levels for the TGC frontend electronics are estimated to be 4.1-7.3 Gy in terms of Total Ionizing Dose (TID) and 1.1-2.2 x 10^11 n_1MeV cm^-2 in terms of Non-Ionizing Energy Loss (NIEL). To evaluate component suitability under these conditions, TID tests were conducted using Cobalt-60 gamma rays at Nagoya University, and NIEL tests were performed with the Tandem Accelerator at Kobe University. Various COTS components, including SFP+ optical transceivers, clock jitter cleaners, optical fibers, voltage references, operational amplifiers, analog-to-digital converters, digital-to-analog converters, SD cards, flash memories, and low-dropout regulators, were tested and evaluated against the required radiation levels. The results demonstrate that all evaluated components meet the TID and NIEL tolerance requirements for application in the TGC frontend electronics at the HL-LHC.

physics.ins-det↗

arXiv2020

Text Classification through Glyph-aware Disentangled Character Embedding and Semantic Sub-character Augmentation

We propose a new character-based text classification framework for non-alphabetic languages, such as Chinese and Japanese. Our framework consists of a variational character encoder (VCE) and character-level text classifier. The VCE is composed of a $β$-variational auto-encoder ($β$-VAE) that learns the proposed glyph-aware disentangled character embedding (GDCE). Since our GDCE provides zero-mean unit-variance character embeddings that are dimensionally independent, it is applicable for our interpretable data augmentation, namely, semantic sub-character augmentation (SSA). In this paper, we evaluated our framework using Japanese text classification tasks at the document- and sentence-level. We confirmed that our GDCE and SSA not only provided embedding interpretability but also improved the classification performance. Our proposal achieved a competitive result to the state-of-the-art model while also providing model interpretability. Our code is available on https://github.com/IyatomiLab/GDCE-SSA

cs.CL↗