Modelling of gas dynamical properties of the KATRIN tritium source and implications for the neutrino mass measurement

The KATRIN experiment aims to measure the effective mass of the electron antineutrino from the analysis of electron spectra stemming from the beta-decay of molecular tritium with a sensitivity of 200 meV. Therefore, a daily throughput of about 40 g of gaseous tritium is circulated in a windowless source section. An accurate description of the gas flow through this section is of fundamental importance for the neutrino mass measurement as it significantly influences the generation and transport of beta-decay electrons through the experimental setup. In this paper we present a comprehensive model consisting of calculations of rarefied gas flow through the different components of the source section ranging from viscous to free molecular flow. By connecting these simulations with a number of experimentally determined operational parameters the gas model can be refreshed regularly according to the measured operating conditions. In this work, measurement and modelling uncertainties are quantified with regard to their implications for the neutrino mass measurement. We find that the systematic uncertainties related to the description of gas flow are represented by $\Delta m_{\nu}^2=(-3.06\pm 0.24)\cdot10^{-3}$ eV$^2$, and that the gas model is ready to be used in the analysis of upcoming KATRIN data.Comment: 28 pages, 13 figure

Breaking with Fixed Set Pathology Recognition through Report-Guided Contrastive Training

When reading images, radiologists generate text reports describing the findings therein. Current state-of-the-art computer-aided diagnosis tools utilize a fixed set of predefined categories automatically extracted from these medical reports for training. This form of supervision limits the potential usage of models as they are unable to pick up on anomalies outside of their predefined set, thus, making it a necessity to retrain the classifier with additional data when faced with novel classes. In contrast, we investigate direct text supervision to break away from this closed set assumption. By doing so, we avoid noisy label extraction via text classifiers and incorporate more contextual information. We employ a contrastive global-local dual-encoder architecture to learn concepts directly from unstructured medical reports while maintaining its ability to perform free form classification. We investigate relevant properties of open set recognition for radiological data and propose a method to employ currently weakly annotated data into training. We evaluate our approach on the large-scale chest X-Ray datasets MIMIC-CXR, CheXpert, and ChestX-Ray14 for disease classification. We show that despite using unstructured medical report supervision, we perform on par with direct label supervision through a sophisticated inference setting

Breaking with Fixed Set Pathology Recognition through Report-Guided Contrastive Training

When reading images, radiologists generate text reports describing the findings therein. Current state-of-the-art computer-aided diagnosis tools utilize a fixed set of predefined categories automatically extracted from these medical reports for training. This form of supervision limits the potential usage of models as they are unable to pick up on anomalies outside of their predefined set, thus, making it a necessity to retrain the classifier with additional data when faced with novel classes. In contrast, we investigate direct text supervision to break away from this closed set assumption. By doing so, we avoid noisy label extraction via text classifiers and incorporate more contextual information. We employ a contrastive global-local dual-encoder architecture to learn concepts directly from unstructured medical reports while maintaining its ability to perform free form classification. We investigate relevant properties of open set recognition for radiological data and propose a method to employ currently weakly annotated data into training. We evaluate our approach on the large-scale chest X-Ray datasets MIMIC-CXR, CheXpert, and ChestX-Ray14 for disease classification. We show that despite using unstructured medical report supervision, we perform on par with direct label supervision through a sophisticated inference setting.Comment: Provisionally Accepted at MICCAI202

β\beta-Decay Spectrum, Response Function and Statistical Model for Neutrino Mass Measurements with the KATRIN Experiment

The objective of the Karlsruhe Tritium Neutrino (KATRIN) experiment is to determine the effective electron neutrino mass $m(\nu_\text{e})$ with an unprecedented sensitivity of $0.2\,\text{eV}$ (90\% C.L.) by precision electron spectroscopy close to the endpoint of the $\beta$ decay of tritium. We present a consistent theoretical description of the $\beta$ electron energy spectrum in the endpoint region, an accurate model of the apparatus response function, and the statistical approaches suited to interpret and analyze tritium $\beta$ decay data observed with KATRIN with the envisaged precision. In addition to providing detailed analytical expressions for all formulae used in the presented model framework with the necessary detail of derivation, we discuss and quantify the impact of theoretical and experimental corrections on the measured $m(\nu_\text{e})$. Finally, we outline the statistical methods for parameter inference and the construction of confidence intervals that are appropriate for a neutrino mass measurement with KATRIN. In this context, we briefly discuss the choice of the $\beta$ energy analysis interval and the distribution of measuring time within that range.Comment: 27 pages, 22 figures, 2 table

Электропривод питателя сушильного барабана

Разработка и исследование систем векторного управления асинхронным электроприводом питателя сушильного барабана. Исследование электропривода будет произведено с учетом ШИМ преобразователя и квантованием сигналов управления и регуляторов во времени.Development and research of vector control systems of asynchronous electric drive of the dryer drum feeder. The study of the electric drive will be made taking into account the PWM Converter and quantization of control signals and controllers in time

On the Impact of Cross-Domain Data on German Language Models

Traditionally, large language models have been either trained on general web crawls or domain-specific data. However, recent successes of generative large language models, have shed light on the benefits of cross-domain datasets. To examine the significance of prioritizing data diversity over quality, we present a German dataset comprising texts from five domains, along with another dataset aimed at containing high-quality data. Through training a series of models ranging between 122M and 750M parameters on both datasets, we conduct a comprehensive benchmark on multiple downstream tasks. Our findings demonstrate that the models trained on the cross-domain dataset outperform those trained on quality data alone, leading to improvements up to $4.45\%$ over the previous state-of-the-art. The models are available at https://huggingface.co/ikim-uk-essenComment: 13 pages, 1 figure, accepted at Findings of the Association for Computational Linguistics: EMNLP 202

Improved Upper Limit on the Neutrino Mass from a Direct Kinematic Method by KATRIN.

We report on the neutrino mass measurement result from the first four-week science run of the Karlsruhe Tritium Neutrino experiment KATRIN in spring 2019. Beta-decay electrons from a high-purity gaseous molecular tritium source are energy analyzed by a high-resolution MAC-E filter. A fit of the integrated electron spectrum over a narrow interval around the kinematic end point at 18.57 keV gives an effective neutrino mass square value of (-1.0_{-1.1}^{+0.9})  eV^{2}. From this, we derive an upper limit of 1.1 eV (90% confidence level) on the absolute mass scale of neutrinos. This value coincides with the KATRIN sensitivity. It improves upon previous mass limits from kinematic measurements by almost a factor of 2 and provides model-independent input to cosmological studies of structure formation