揭示复杂性:复杂实验数据分析中的奇异值分解

IF 4.6 2区 物理与天体物理 Q1 PHYSICS, MULTIDISCIPLINARY SciPost Physics Pub Date : 2024-09-11 DOI:10.21468/scipostphyscore.7.3.061
Judith F. Stein, Aviad Frydman, Richard Berkovits
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引用次数: 0

摘要

分析具有多个参数的复杂实验数据具有挑战性。我们建议使用奇异值分解(SVD)作为有效的解决方案。通过实际实验数据分析证明,这种方法在理解复杂物理数据方面超越了传统方法。奇异值和矢量能区分并突出各种物理机制和尺度,揭示以前难以理解的元素。SVD 是浏览复杂实验景观的有力工具,为各种实验测量带来了希望。
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Unraveling complexity: Singular value decomposition in complex experimental data analysis
Analyzing complex experimental data with multiple parameters is challenging. We propose using Singular Value Decomposition (SVD) as an effective solution. This method, demonstrated through real experimental data analysis, surpasses conventional approaches in understanding complex physics data. Singular values and vectors distinguish and highlight various physical mechanisms and scales, revealing previously challenging elements. SVD emerges as a powerful tool for navigating complex experimental landscapes, showing promise for diverse experimental measurements.
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来源期刊
SciPost Physics
SciPost Physics Physics and Astronomy-Physics and Astronomy (all)
CiteScore
8.20
自引率
12.70%
发文量
315
审稿时长
10 weeks
期刊介绍: SciPost Physics publishes breakthrough research articles in the whole field of Physics, covering Experimental, Theoretical and Computational approaches. Specialties covered by this Journal: - Atomic, Molecular and Optical Physics - Experiment - Atomic, Molecular and Optical Physics - Theory - Biophysics - Condensed Matter Physics - Experiment - Condensed Matter Physics - Theory - Condensed Matter Physics - Computational - Fluid Dynamics - Gravitation, Cosmology and Astroparticle Physics - High-Energy Physics - Experiment - High-Energy Physics - Theory - High-Energy Physics - Phenomenology - Mathematical Physics - Nuclear Physics - Experiment - Nuclear Physics - Theory - Quantum Physics - Statistical and Soft Matter Physics.
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