Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 1001.3050

Microchannel avalanche photodiode with wide linearity range

Z. Sadygov·A. Olshevski·N. Anphimov·T. Bokova·V. Chalyshev·I. Chirikov-Zorin·A. Dovlatov·Z. Krumshtein·R. Mekhtieva·R. Mukhtarov·V. Shukurova·M. Troitskaya·V. Zhezher
View original source ↗Read source document ↗Export metadata ↓

Abstract

Design and physical operation principles of new microchannel avalanche photodiode (MC APD) with gain up to 10^5 and linearity range improved an order of magnitude compared to known similar devices. A distinctive feature of the new device is a directly biased p-n junction under each pixel which plays role of an individual quenching resistor. This allows increasing pixel density up to 40000 per mm^2 and making entire device area sensitive.

Explore related subjects

physics.ins-det

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Z. Sadygov, A. Olshevski, N. Anphimov, T. Bokova, V. Chalyshev, I. Chirikov-Zorin, A. Dovlatov, Z. Krumshtein, R. Mekhtieva, R. Mukhtarov, V. Shukurova, M. Troitskaya, V. Zhezher. 2010-01-18. Microchannel avalanche photodiode with wide linearity range. https://doi.org/10.1134/s106378501006012x

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

ARGUS: an experiment-agnostic framework for detector-health monitoring in particle physics

Every particle-physics experiment builds detector-health monitoring, and many end up rebuilding it, because a first system written under commissioning pressure often proves too rigid to maintain. ARGUS (Automated Anomaly-detection, Run-quality and General Unified Surveillance) is a configuration-driven framework that separates what is generic in this problem (the health monitors, their validation, and the reporting) from what is not (the data schema and the thresholds). Standing it up for a new detector requires a data adapter and a configuration file, not a rewrite. Detector health is judged by four independent monitors: expert cuts, which are authoritative; a machine-learning second opinion that is additive and off by default; a trend forecast that gives early warning before a threshold is crossed; and a reference-histogram comparison with pluggable statistical tests. The monitors are compared and never merged, and an alerting layer turns their flags and warnings into deduplicated notifications. We demonstrate the full chain on a synthetic reference detector with planted pathologies and a fault-injection campaign that manufactures ground truth: the cuts recover every planted pathology with no false positives, the machine-learning monitor reaches a ROC AUC of 0.896 on injected faults and extends coverage to fault classes no cut was written for, the forecast flags a drifting channel before its cut fires, and the histogram comparison catches every planted shape distortion while holding its configured false-positive rate on clean pairs. A second demonstration shows the framework adapting to a reorganized file layout with no change to code or configuration. Every result and figure in this paper regenerates from the code alone. The framework has been implemented for two particle-physics experiments, where it is under evaluation; adoption decisions rest with the collaborations.

physics.ins-det↗
arXiv2026

Leveraging Industrial Foundation Models at the Edge of Particle Physics Detectors via Distillation Learning and Hardware Co-design

Data acquisition (DAQ) systems at future particle physics experiments stand to benefit from the extremes of AI/ML development: large-scale foundation models can enhance the performance of feature extraction algorithms, and small-scale on-detector deployments can enable real-time intelligent data handling. This work provides the first fine-tuning of an industrial foundation model for particle physics DAQ. Starting from the backbone of Google Research's TimesFM (Time Series Foundation Model), we demonstrate fine-tuning on real-time regression tasks for drift chamber trackers and dual-readout calorimeters. Furthermore, the fine-tuned TimesFM model is distilled into a student and co-designed with FPGA implementation to enable these models to run in real-time at future colliders. The fine-tuned distillations meet or exceed the performance of previously published AI/ML solutions for each task. Further, the pipeline of distillation and model compression from TimesFM is generic and can be easily adapted to a variety of 1D waveform tasks across domains.

physics.ins-det↗
arXiv2026

Proposing, never assigning: fair allocation of control-room shifts in experimental particle physics collaborations from ranked wishes under institutional quotas

Experimental particle physics collaborations staff their detectors around the clock with members drawn from dozens of institutions. The established practice is a credit system with self-sign-up: institutions owe a quota of credit, and people take the seats they want when booking opens. This paper describes a method in which seats are instead allocated from ranked wishes under institutional quotas, the result is issued as offers that each person accepts or declines, and an independent checker recomputes every rule before anything is published: the platform proposes, never assigns, and proves that it kept the rules. The credit and quota model is inherited from current practice. Around that core the paper adds a quota rule that keeps indivisible blocks fair to institutions whose share of a period is smaller than one block, and four rules that make fairness visible beyond the count: shares of owl, weekend and holiday blocks, seats shown in the laboratory's clock and the institution's own, reachable pools for institutions that cannot travel, and booking order by balance. On a synthetic collaboration of 48 institutions and 256 eligible members, the method allocates 70 to 72 of 72 seats per period in two rounds, with a mean rank satisfaction of 84 to 95% and no rule broken; after a year of running every institution of four members or more is within 13% of its cumulative share, and the smallest institutions, which one block overshoots by arithmetic, are brought within the same range by pooling in pairs. The method is stated completely enough to be reimplemented; a reference implementation exists and is available to collaborations under license. It applies as it stands to any scientific collaboration that staffs a facility in blocks of shifts under institutional quotas.

physics.ins-det↗