Experimental noise in small-angle scattering can be assessed and corrected using the Bayesian Indirect Fourier Transformation Article Swipe
Small-angle X-ray and neutron scattering are widely used to investigate soft matter and biophysical systems. The experimental errors are essential when assessing how well a hypothesized model fits the data. Likewise, they are important when weights are assigned to multiple datasets used to refine the same model. Therefore, it is problematic when experimental errors are over- or underestimated. We present a method, using Bayesian Indirect Fourier Transformation for small-angle scattering data, to assess whether or not a given small-angle scattering dataset has over- or underestimated experimental errors. The method is effective on both simulated and experimental data, and can be used assess and rescale the errors accordingly. Even if the estimated experimental errors are appropriate, it is ambiguous whether or not a model fits sufficiently well, as the true reduced $\chi^2$ of the data is not necessarily unity. This is particularly relevant for approaches where overfitting is an inherent challenge, such as reweighting of a simulated molecular dynamics trajectory against a small-angle scattering data or ab initio modelling. Using the outlined method, we show that one can determine what reduced $\chi^2$ to aim for when fitting a model against small-angle scattering data. The method is easily accessible via a web interface.
Related Topics
Concepts
Overfitting
Small-angle scattering
Scattering
Experimental data
Fourier transform
Noise (video)
Transformation (genetics)
Computer science
Bayesian probability
Statistical physics
Physics
Statistics
Mathematics
Artificial intelligence
Optics
Chemistry
Mathematical analysis
Artificial neural network
Image (mathematics)
Gene
Biochemistry
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://arxiv.org/pdf/2012.04247
- OA Status
- green
- Cited By
- 3
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W3112347655
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3112347655Canonical identifier for this work in OpenAlex
- Title
-
Experimental noise in small-angle scattering can be assessed and corrected using the Bayesian Indirect Fourier TransformationWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-12-08Full publication date if available
- Authors
-
Andreas Haahr Larsen, Martin Cramer PedersenList of authors in order
- Landing page
-
https://arxiv.org/pdf/2012.04247Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2012.04247Direct OA link when available
- Concepts
-
Overfitting, Small-angle scattering, Scattering, Experimental data, Fourier transform, Noise (video), Transformation (genetics), Computer science, Bayesian probability, Statistical physics, Physics, Statistics, Mathematics, Artificial intelligence, Optics, Chemistry, Mathematical analysis, Artificial neural network, Image (mathematics), Gene, BiochemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
-
2022: 1, 2021: 2Per-year citation counts (last 5 years)
- Related works (count)
-
20Other works algorithmically related by OpenAlex
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| abstract_inverted_index.important | 33 |
| abstract_inverted_index.molecular | 156 |
| abstract_inverted_index.simulated | 93, 155 |
| abstract_inverted_index.Therefore, | 47 |
| abstract_inverted_index.accessible | 196 |
| abstract_inverted_index.approaches | 143 |
| abstract_inverted_index.challenge, | 149 |
| abstract_inverted_index.interface. | 200 |
| abstract_inverted_index.modelling. | 167 |
| abstract_inverted_index.scattering | 4, 69, 79, 162, 190 |
| abstract_inverted_index.trajectory | 158 |
| abstract_inverted_index.Small-angle | 0 |
| abstract_inverted_index.biophysical | 13 |
| abstract_inverted_index.investigate | 9 |
| abstract_inverted_index.necessarily | 136 |
| abstract_inverted_index.overfitting | 145 |
| abstract_inverted_index.problematic | 50 |
| abstract_inverted_index.reweighting | 152 |
| abstract_inverted_index.small-angle | 68, 78, 161, 189 |
| abstract_inverted_index.accordingly. | 106 |
| abstract_inverted_index.appropriate, | 114 |
| abstract_inverted_index.experimental | 16, 52, 85, 95, 111 |
| abstract_inverted_index.hypothesized | 25 |
| abstract_inverted_index.particularly | 140 |
| abstract_inverted_index.sufficiently | 124 |
| abstract_inverted_index.Transformation | 66 |
| abstract_inverted_index.underestimated | 84 |
| abstract_inverted_index.underestimated. | 57 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile |