Entanglement characterization by single-photon counting with random noise

In this article, we investigate the problem of entanglement characterization by polarization measurements combined with maximum likelihood estimation (MLE). A realistic scenario is considered with measurement results distorted by random experimental errors. In particular, by imposing unitary rotations acting on the measurement operators, we can test the performance of the tomographic technique versus the amount of noise. Then, dark counts are introduced to explore the efficiency of the framework in a multi-dimensional noise scenario. The concurrence is used as a figure of merit to quantify how well entanglement is preserved through noisy measurements. Quantum fidelity is computed to quantify the accuracy of state reconstruction. The results of numerical simulations are depicted on graphs and discussed.

Backscattering of MeV Electrons. An Analysis of Tabata’s Experiment with Geant4.

A series of simulations were conducted with Geant4 in order to verify the electron backscattering experiments performed by Tabata in the low Z elements of Be, C, and Al. In general, quite good agreement was obtained by carefully choosing the physics lists employed. These results invalidate the claim made before about the presence of experimental errors in the above work.

Probabilistic Random Forest improves bioactivity predictions close to the classification threshold by taking into account experimental uncertainty

Measurements of protein–ligand interactions have reproducibility limits due to experimental errors. Any model based on such assays will consequentially have such unavoidable errors influencing their performance which should ideally be factored into modelling and output predictions, such as the actual standard deviation of experimental measurements (σ) or the associated comparability of activity values between the aggregated heterogenous activity units (i.e., Ki versus IC50 values) during dataset assimilation. However, experimental errors are usually a neglected aspect of model generation. In order to improve upon the current state-of-the-art, we herein present a novel approach toward predicting protein–ligand interactions using a Probabilistic Random Forest (PRF) classifier. The PRF algorithm was applied toward in silico protein target prediction across ~ 550 tasks from ChEMBL and PubChem. Predictions were evaluated by taking into account various scenarios of experimental standard deviations in both training and test sets and performance was assessed using fivefold stratified shuffled splits for validation. The largest benefit in incorporating the experimental deviation in PRF was observed for data points close to the binary threshold boundary, when such information was not considered in any way in the original RF algorithm. For example, in cases when σ ranged between 0.4–0.6 log units and when ideal probability estimates between 0.4–0.6, the PRF outperformed RF with a median absolute error margin of ~ 17%. In comparison, the baseline RF outperformed PRF for cases with high confidence to belong to the active class (far from the binary decision threshold), although the RF models gave errors smaller than the experimental uncertainty, which could indicate that they were overtrained and/or over-confident. Finally, the PRF models trained with putative inactives decreased the performance compared to PRF models without putative inactives and this could be because putative inactives were not assigned an experimental pXC50 value, and therefore they were considered inactives with a low uncertainty (which in practice might not be true). In conclusion, PRF can be useful for target prediction models in particular for data where class boundaries overlap with the measurement uncertainty, and where a substantial part of the training data is located close to the classification threshold.

Experimental noise in small-angle scattering can be assessed using the Bayesian indirect Fourier transformation

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 data sets used to refine the same model. Therefore, it is problematic when experimental errors are over- or underestimated. A method is presented, using Bayesian indirect Fourier transformation for small-angle scattering data, to assess whether or not a given small-angle scattering data set has over- or underestimated experimental errors. The method is effective on both simulated and experimental data, and can be used to 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 χ2 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 small-angle scattering data or ab initio modelling. Using the outlined method, it is shown that one can determine what reduced χ2 to aim for when fitting a model against small-angle scattering data. The method is easily accessible via the web interface BayesApp.

New Sources of Leptonic CP Violation at the DUNE Neutrino Experiment

We check the capability of the DUNE neutrino experiment to detect new sources of leptonic CP violation beside the single phase expected in the Standard Model. We illustrate our strategy based on the measurement of CP asymmetries in the case that new physics will show up as nonstandard neutrino interactions and sterile neutrino states and show that the most promising one, once the experimental errors are taken into account in both scenarios, is the one related to the νμ→νe transition.

The pp → W(→ lν) + γ process at next-to-next-to-leading order

We present details of the calculation of the pp → W(→ lν)γ process at next-to-next-to-leading order in QCD, calculated using the jettiness slicing method. The calculation is based entirely on analytic amplitudes. Because of the radiation zero, the NLO QCD contribution from the gq channel is as important as the contribution from the Born $$ q\overline{q} $$ q q ¯ process, disrupting the normal counting of leading and sub-leading contributions. We also assess the importance of electroweak (EW) corrections, including the EW corrections to both the six-parton channel 0 →$$ \overline{u} d\nu {e}^{+}\gamma g $$ u ¯ dν e + γg and the five-parton channel 0 →$$ \overline{u} d\nu {e}^{+}\gamma $$ u ¯ dν e + γ . Previous experimental results have been shown to agree with theoretical predictions, taking into account the large experimental errors. With the advent of run II data from the LHC, the statistical errors on the data will decrease, and will be competitive with the error on theoretical predictions for the first time. We present numerical results for $$ \sqrt{s} $$ s = 7 and 13 TeV. Analytic results for the one-loop six-parton QCD amplitude and the tree-level seven-parton QCD amplitude are presented in appendices.

An experimental study on the effects of compression on the acoustic performance of porous fibrous materials

The current investigation experimentally studied the effects of compression on the acoustic performance of porous fibrous material. Two inch and four inch thick samples of fiberglass and three varying densities of mineral wool were tested using two different impedance tube sizes at compression rates of 1, 1.3 and 2. The absorption coefficient was measured using Chung and Blaser’s method. The flow resistivity was measured using Tao et al.’s method. Overall, the 4” samples resulted in steadier results than the 2” samples. Compression generally led to a decrease in absorption coefficient and an increase in flow resistivity. These effects were most evident in the lower frequency range. Although there were some experimental errors in sample preparation, sample variation, compression technique, testing order and other initial errors, the current study demonstrated that the effects of compression on insulation should be not be overlooked.

An experimental study on the effects of compression on the acoustic performance of porous fibrous materials

The current investigation experimentally studied the effects of compression on the acoustic performance of porous fibrous material. Two inch and four inch thick samples of fiberglass and three varying densities of mineral wool were tested using two different impedance tube sizes at compression rates of 1, 1.3 and 2. The absorption coefficient was measured using Chung and Blaser’s method. The flow resistivity was measured using Tao et al.’s method. Overall, the 4” samples resulted in steadier results than the 2” samples. Compression generally led to a decrease in absorption coefficient and an increase in flow resistivity. These effects were most evident in the lower frequency range. Although there were some experimental errors in sample preparation, sample variation, compression technique, testing order and other initial errors, the current study demonstrated that the effects of compression on insulation should be not be overlooked.