Purpose
Three previously published datasets of high-mountain soil variation in proglacial valleys in the Swiss Alps (80 soils) and a new dataset of high-mountain soil variation in a formerly ...glaciated valley in the Colorado Rocky Mountains (9 soils) are used to test the validity of the chronosequence approach and to study divergence and convergence of soil properties.
Materials and methods
Standard field-based soil observations were done, complemented with simple laboratory measurements of pH and soil organic matter.
Results and discussion
The mean values of soil properties change over time, as well as their standard deviations and coefficients of variation. Variation in soil properties between soils of the same age is significant. Although sampling was performed at locations that are assumed to be geomorphically stable, the observed variation in properties casts doubt on this assumption. Depending on the valley and the soil property, standard deviations and coefficients of variation increase over time whereas in other cases, they decrease. This indicates divergence and convergence of soil properties over centennial and Holocene timescales, respectively. Both dynamics are explored quantitatively.
Conclusions
Divergence is observed in settings that are unaffected by outside (hillslope) influences and presumably caused by vegetation differences and small-scale (diffusive) redistribution of the fine earth fraction. Convergence is observed in settings where soil formation is disturbed by outside influences. In the Swiss Alps, this influence is the provision of material from surrounding hillslopes. Chronosequence studies should sample and average multiple soils per age group, to characterize soil variation and minimize the uncertainty in the estimation of soil properties.
Machine learning has become a popular instrument for the search of undiscovered particles and mechanisms at particle collider experiments. It enables the investigation of large datasets and is ...therefore suitable to operate directly on minimally-processed data coming from the detector instead of reconstructed objects. Here, we study patterns of raw pixel hits recorded by the Belle II pixel detector, that is operational since 2019 and presently features 4 M pixels and trigger rates up to 5 kHz. In particular, we focus on unsupervised techniques that operate without the need for a theoretical model. These model-agnostic approaches allow for an unbiased exploration of data while filtering out anomalous detector signatures that could hint at new physics scenarios. We present the identification of hypothetical magnetic monopoles against Belle II beam background using self-organizing kohonen maps and autoencoders. These two unsupervised algorithms are compared to a Multilayer Perceptron and a superior signal efficiency of the Autoencoder is found at high background-rejection levels. Our results strengthen the case for using unsupervised machine learning techniques to complement traditional search strategies at particle colliders and pave the way to potential online applications of the algorithms in the near future.
Machine learning has become a popular instrument for the identification of dark matter candidates at particle collider experiments. They enable the processing of large datasets and are therefore ...suitable to operate directly on raw data coming from the detector, instead of reconstructed objects. Here, we investigate patterns of raw pixel hits recorded by the Belle II pixel detector, that is operational since 2019 and presently features 4 M pixels and trigger rates up to 5 kHz. In particular, we focus on unsupervised techniques that operate without the need for a theoretical model. These model-agnostic approaches allow for an unbiased exploration of data, while filtering out anomalous detector signatures that could hint at new physics scenarios. We present the identification of hypothetical magnetic monopoles against Belle II beam background using Self-Organizing Kohonen Maps and Autoencoders. The two unsupervised algorithms are compared to a convolutional Multilayer Perceptron and a superior signal efficiency is found at high background rejection levels. Our results strengthen the case for using unsupervised machine learning techniques to complement traditional search strategies at particle colliders and pave the way to potential online applications of the algorithms in the near future.
