Avalanche statistics from data with low time resolution
Extracting avalanche distributions from experimental microplasticity data can be hampered by limited time resolution. We compute the effects of low time resolution on avalanche size distributions and give quantitative criteria for diagnosing and circumventing problems associated with low time resolution. We show that traditional analysis of data obtained at low acquisition rates can lead to avalanche size distributions with incorrect powerlaw exponents or no powerlaw scaling at all. Furthermore, we demonstrate that it can lead to apparent data collapses with incorrect powerlaw and cutoff exponents. We propose new methods to analyze lowresolution stresstime series that can recover the size distribution of the underlying avalanches even when the resolution is so low that naive analysis methods give incorrect results. We test these methods on both downsampled simulation data from a simple model and downsampled bulk metallic glass compression data and find that the methods recover the correct critical exponents.
 Authors:

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 Univ. of Illinois, Urbana, IL (United States). Dept. of Physics. Inst. of Condensed Matter Theory
 Bucknell Univ., Lewisburg, PA (United States). Dept. of Mechanical Engineering. Dept. of Chemical Engineering
 Bucknell Univ., Lewisburg, PA (United States). Dept. of Mechanical Engineering
 Publication Date:
 Grant/Contract Number:
 FE0011194; CBET 1336634; DMS 1069224; DMR 1042734; PHY1125915
 Type:
 Accepted Manuscript
 Journal Name:
 Physical Review E
 Additional Journal Information:
 Journal Volume: 94; Journal Issue: 5; Journal ID: ISSN 24700045
 Publisher:
 American Physical Society (APS)
 Research Org:
 Univ. of Illinois at UrbanaChampaign, IL (United States); Bucknell Univ., Lewisburg, PA (United States)
 Sponsoring Org:
 USDOE Office of Fossil Energy (FE); National Science Foundation (NSF)
 Country of Publication:
 United States
 Language:
 English
 Subject:
 97 MATHEMATICS AND COMPUTING; avalanches; plasticity; time series analysis; statistical physics
 OSTI Identifier:
 1427978
 Alternate Identifier(s):
 OSTI ID: 1333321
LeBlanc, Michael, Nawano, Aya, Wright, Wendelin J., Gu, Xiaojun, Uhl, J. T., and Dahmen, Karin A.. Avalanche statistics from data with low time resolution. United States: N. p.,
Web. doi:10.1103/PhysRevE.94.052135.
LeBlanc, Michael, Nawano, Aya, Wright, Wendelin J., Gu, Xiaojun, Uhl, J. T., & Dahmen, Karin A.. Avalanche statistics from data with low time resolution. United States. doi:10.1103/PhysRevE.94.052135.
LeBlanc, Michael, Nawano, Aya, Wright, Wendelin J., Gu, Xiaojun, Uhl, J. T., and Dahmen, Karin A.. 2016.
"Avalanche statistics from data with low time resolution". United States.
doi:10.1103/PhysRevE.94.052135. https://www.osti.gov/servlets/purl/1427978.
@article{osti_1427978,
title = {Avalanche statistics from data with low time resolution},
author = {LeBlanc, Michael and Nawano, Aya and Wright, Wendelin J. and Gu, Xiaojun and Uhl, J. T. and Dahmen, Karin A.},
abstractNote = {Extracting avalanche distributions from experimental microplasticity data can be hampered by limited time resolution. We compute the effects of low time resolution on avalanche size distributions and give quantitative criteria for diagnosing and circumventing problems associated with low time resolution. We show that traditional analysis of data obtained at low acquisition rates can lead to avalanche size distributions with incorrect powerlaw exponents or no powerlaw scaling at all. Furthermore, we demonstrate that it can lead to apparent data collapses with incorrect powerlaw and cutoff exponents. We propose new methods to analyze lowresolution stresstime series that can recover the size distribution of the underlying avalanches even when the resolution is so low that naive analysis methods give incorrect results. We test these methods on both downsampled simulation data from a simple model and downsampled bulk metallic glass compression data and find that the methods recover the correct critical exponents.},
doi = {10.1103/PhysRevE.94.052135},
journal = {Physical Review E},
number = 5,
volume = 94,
place = {United States},
year = {2016},
month = {11}
}