Jove
Visualize
お問い合わせ
JoVE
x logofacebook logolinkedin logoyoutube logo
JoVEについて
概要リーダーシップブログJoVEヘルプセンター
著者向け
出版プロセス編集委員会範囲と方針査読よくある質問投稿
図書館員向け
推薦の声購読アクセスリソース図書館諮問委員会よくある質問
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experimentsアーカイブ
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教員リソースセンター教員サイト
利用規約
プライバシーポリシー
ポリシー

関連する概念動画

Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

7.1K
When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
7.1K
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

8.6K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
8.6K
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

4.2K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
4.2K
Mass Analyzers: Common Types01:19

Mass Analyzers: Common Types

1.7K
The quadrupole mass analyzer consists of four cylindrical metal rods arranged in a diamond carrying a DC voltage and a radio-frequency AC voltage. The motion of ions through the quadrupole depends on the field strength, causing only ions of a certain m/z to resonate successfully and strike the detector at a given field strength. Though the transmission rate for these analyzers is high, the exact elemental composition of the sample is not determined because of low resolution; however, they are...
1.7K
Mass Analyzers: Overview01:13

Mass Analyzers: Overview

1.9K
The mass analyzer is a crucial component of the mass spectrometer. In the ionization chamber, the vaporized sample is bombarded with a high-energy electron beam to generate a radical cation and further fragment into neutral molecules, radicals, and cations. A series of negatively charged accelerator plates accelerate the cations into the mass analyzer. The mass analyzer separates ions according to their mass-to-charge (m/z) ratios and then directs them to the detector. The common types of mass...
1.9K
Detection of Black Holes01:10

Detection of Black Holes

2.6K
Although black holes were theoretically postulated in the 1920s, they remained outside the domain of observational astronomy until the 1970s.
Their closest cousins are neutron stars, which are composed almost entirely of neutrons packed against each other, making them extremely dense. A neutron star has the same mass as the Sun but its diameter is only a few kilometers. Therefore, the escape velocity from their surface is close to the speed of light.
Not until the 1960s, when the first neutron...
2.6K

こちらも読む

関連記事

共著者、ジャーナル、引用グラフによってこの研究に関連する記事。

並び替え
Same author

A Family-Inclusive Taskforce for Improving NICU Quality of Care.

Pediatrics·2026
Same author

Language mapped to a high-resolution brain atlas for surgical evaluation of epilepsy patients.

Epilepsia·2026
Same author

Microplastics crossing the blood-testis barrier: A call to action for urological research.

Current urology·2026
Same author

Baicalein Alleviates Iron Overload-Induced Ferroptosis and Osteogenic Blockade in Osteoblasts by Activating the Nrf2/GPX4 Pathway.

BME frontiers·2026
Same author

Lipid Nanoparticle Database towards structure-function modeling and data-driven design for nucleic acid delivery.

Nature communications·2026
Same author

How pro-inflammatory diets influence perioperative outcomes.

Pain management·2025

関連する実験動画

Updated: Feb 24, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

43.7K

CMS検出器における自動データ品質監視のための異常検知

Andrew Brinkerhoff1,2, Chosila Sutantawibul1, Indara Suarez3

  • 1Baylor University, Waco, USA.

EPJ research infrastructures
|February 23, 2026
PubMed
まとめ

自動データ品質監視（AutoDQM）は、高エネルギー物理学実験であるコンパクトミューオニソレノイド（CMS）実験のデータ品質を迅速に評価するために機械学習を使用します。このシステムは、誤動作している検出器データを効果的に特定し、物理解析の信頼性を向上させます。

キーワード:
異常検知データ品質監視主成分分析素粒子物理学

関連する実験動画

Last Updated: Feb 24, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

43.7K

科学分野:

  • 高エネルギー物理学
  • データサイエンス
  • 検出器計装

背景:

  • CERNの大型ハドロン衝突型加速器にあるコンパクトミューオニソレノイド（CMS）のような大規模粒子検出器の効果的な運用には、迅速かつ徹底的なデータ品質評価が必要です。
  • 従来の С方法では時間がかかり、微妙な異常を捉えられない場合があります。

研究 の 目的:

  • 自動データ品質監視のためのAutoDQMシステムの導入と評価。
  • 高エネルギー物理学実験における異常データの特定効率と精度を向上させること。

主な方法:

  • 高度な統計技術と教師なし機械学習を利用したAutoDQMシステムの開発。
  • ベータ二項確率関数や主成分分析を含む異常検知アルゴリズムの実装。
  • CMSの2022年の陽子-陽子衝突データの完全なデータセットでのテスト。

主要な成果:

  • AutoDQMは、検出器の誤動作によって引き起こされた異常な「不良」データを正常に特定しました。
  • このシステムは、「不良」データに対する検出率が「良好」データと比較して4〜6倍高いことを示しました。
  • AutoDQMの一般的なデータ品質監視ツールとしての有効性が検証されました。

結論:

  • AutoDQMは、複雑な素粒子物理学実験におけるリアルタイムデータ品質監視のための堅牢で効率的なソリューションを提供します。
  • 異常を検出するシステムの能力は、科学的結果の整合性を確保する上で大きく役立ちます。
  • AutoDQMは、衝突型実験からの大規模データセットの管理と検証における重要な進歩を表します。