Evidence for a two-peak structure in the A2 meson

Chikovani, G.E.; Focacci, M.N.; Kienzle, W.; Lechanoine, C.; Levrat, B.; Maglić, B.; Martin, M. Aldaya; Schübelin, P.; Dubal, L.; Fischer, M.; Grieder, P. K. F.; Neal, H. A.; Nef, C.

doi:10.1016/0370-2693(67)90072-x

Cited by 101 publications

(7 citation statements)

References 6 publications

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“…Our experiment has approximately the same statistical sensitivity to structure within the K N (1420) as the combined experiments of Ref. 1 have to structure within the A 2 ; in both experiments the uncertainty per typical 5-MeV bin is about 17% of the amount of resonance. In our experiment no statistically significant structure is seen for either if*.…”

Section: Physical Review Letterssupporting

confidence: 55%

“…2(a), where it is seen that our calculated resolution function fits the K° data extremely well (confidence level = 71%). Figure 2(b) shows the computed halfwidth of the resolution function as a function of the Q value of the K + TT~ system in Reaction (1), with the results of the K° analysis included to illustrate how well the K° reaction simulates Reaction (1).…”

Section: 4(m#-m) 2 (M P -M)* + [R(m)/2]mentioning

confidence: 99%

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UnsplitK*'S

et al. 1969

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Section: Physical Review Letterssupporting

confidence: 55%

Section: 4(m#-m) 2 (M P -M)* + [R(m)/2]mentioning

confidence: 99%

UnsplitK*'S

et al. 1969

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“…Indeed, because of several high-profile false discoveries, high-energy physics has already gone through a replicability crisis, and has had to rearrange its methods to deal with the consequences. A classic case, which was a big wake-up call, was the so-called split-A2 from Chikovani et al (1967). Had this effect been genuine, it would have engendered a theoretical revolution, but when more data became available, the effect disappeared; see Harrison (2002) for a recent view.…”

Section: Introductionmentioning

confidence: 99%

I Tried a Bunch of Things: The Dangers of Unexpected Overfitting in Classification

Skocik¹,

Collins

Callahan-Flintoft

et al. 2016

Preprint

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Machine learning is a powerful set of techniques that has enhanced the abilities of neuroscientists to interpret information collected through EEG, fMRI, and MEG data. With these powerful techniques comes the danger of overfitting of hyper-parameters which can render results invalid, and cause a failure to generalize beyond the data set. We refer to this problem as 'over-hyping' and show that it is pernicious despite commonly used precautions. In particular, over-hyping occurs when an analysis is run repeatedly with slightly different analysis parameters and one set of results is selected based on the analysis. When this is done, the resulting method is unlikely to generalize to a new dataset, rendering it a partially, or perhaps even completely spurious result that will not be valid outside of the data used in the original analysis. While it is commonly assumed that cross-validation is an effective protection against such spurious results generated through overfitting or overhyping, this is not actually true. In this article, we show that both one-shot and iterative optimization of an analysis are prone to over-hyping, despite the use of cross-validation. We demonstrate that non-generalizable results can be obtained even on non-informative (i.e. random) data by modifying hyper-parameters in seemingly innocuous ways. We recommend a number of techniques for limiting over-hyping, such as lock-boxes, blind analyses, preregistrations, and nested cross-validation. These techniques, are common in other fields that use machine learning, including computer science and physics. Adopting similar safeguards is critical for ensuring the robustness of machine-learning techniques in the neurosciences.

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“…In each of these experiments structure in the A^ mass spectra is observed, although some variation of the structure occurs MeV, respectively (7,8).…”

Section: (16)mentioning

confidence: 98%

Model analyses of elementary particle scattering amplitudes

Coleman

1971

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Evidence for a two-peak structure in the A2 meson

Cited by 101 publications

References 6 publications

UnsplitK*'S

UnsplitK*'S

I Tried a Bunch of Things: The Dangers of Unexpected Overfitting in Classification

Model analyses of elementary particle scattering amplitudes

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