19 research outputs found
Probing Convolutional Neural Networks for Event Reconstruction in {\gamma}-Ray Astronomy with Cherenkov Telescopes
A dramatic progress in the field of computer vision has been made in recent
years by applying deep learning techniques. State-of-the-art performance in
image recognition is thereby reached with Convolutional Neural Networks (CNNs).
CNNs are a powerful class of artificial neural networks, characterized by
requiring fewer connections and free parameters than traditional neural
networks and exploiting spatial symmetries in the input data. Moreover, CNNs
have the ability to automatically extract general characteristic features from
data sets and create abstract data representations which can perform very
robust predictions. This suggests that experiments using Cherenkov telescopes
could harness these powerful machine learning algorithms to improve the
analysis of particle-induced air-showers, where the properties of primary
shower particles are reconstructed from shower images recorded by the
telescopes. In this work, we present initial results of a CNN-based analysis
for background rejection and shower reconstruction, utilizing simulation data
from the H.E.S.S. experiment. We concentrate on supervised training methods and
outline the influence of image sampling on the performance of the CNN-model
predictions.Comment: 8 pages, 4 figures, Proceedings of the 35th International Cosmic Ray
Conference (ICRC 2017), Busan, Kore
Climate change in the Baltic Sea region : a summary
Based on the Baltic Earth Assessment Reports of this thematic issue in Earth System Dynamics and recent peer-reviewed literature, current knowledge of the effects of global warming on past and future changes in climate of the Baltic Sea region is summarised and assessed. The study is an update of the Second Assessment of Climate Change (BACC II) published in 2015 and focuses on the atmosphere, land, cryosphere, ocean, sediments, and the terrestrial and marine biosphere. Based on the summaries of the recent knowledge gained in palaeo-, historical, and future regional climate research, we find that the main conclusions from earlier assessments still remain valid. However, new long-term, homogenous observational records, for example, for Scandinavian glacier inventories, sea-level-driven saltwater inflows, so-called Major Baltic Inflows, and phytoplankton species distribution, and new scenario simulations with improved models, for example, for glaciers, lake ice, and marine food web, have become available. In many cases, uncertainties can now be better estimated than before because more models were included in the ensembles, especially for the Baltic Sea. With the help of coupled models, feedbacks between several components of the Earth system have been studied, and multiple driver studies were performed, e.g. projections of the food web that include fisheries, eutrophication, and climate change. New datasets and projections have led to a revised understanding of changes in some variables such as salinity. Furthermore, it has become evident that natural variability, in particular for the ocean on multidecadal timescales, is greater than previously estimated, challenging our ability to detect observed and projected changes in climate. In this context, the first palaeoclimate simulations regionalised for the Baltic Sea region are instructive. Hence, estimated uncertainties for the projections of many variables increased. In addition to the well-known influence of the North Atlantic Oscillation, it was found that also other low-frequency modes of internal variability, such as the Atlantic Multidecadal Variability, have profound effects on the climate of the Baltic Sea region. Challenges were also identified, such as the systematic discrepancy between future cloudiness trends in global and regional models and the difficulty of confidently attributing large observed changes in marine ecosystems to climate change. Finally, we compare our results with other coastal sea assessments, such as the North Sea Region Climate Change Assessment (NOSCCA), and find that the effects of climate change on the Baltic Sea differ from those on the North Sea, since Baltic Sea oceanography and ecosystems are very different from other coastal seas such as the North Sea. While the North Sea dynamics are dominated by tides, the Baltic Sea is characterised by brackish water, a perennial vertical stratification in the southern subbasins, and a seasonal sea ice cover in the northern subbasins.Peer reviewe
Natural variability is a large source of uncertainty in future projections of hypoxia in the Baltic Sea
Coastal seas worldwide suffer from increasing human impact. One of the most severe environmental threats is excessive nutrient pollution from land, which causes oxygen depletion and harmful algal blooms. In 2018, the semi-enclosed Baltic Sea was determined to contain the largest hypoxic area among the world's coastal seas, with a size equal to the Republic of Ireland. In this study, ensemble modelling was used to investigate whether climate change will intensify hypoxia in the Baltic Sea and whether nutrient load abatement strategies would counteract this scenario. We analysed the largest ensemble of scenario simulations for the Baltic Sea currently available (including different boundary conditions) and estimated the magnitude of various sources of uncertainty. The results showed that natural variability was a larger source of uncertainty than previously considered. The earliest time and appropriate location to detect a trend above the background noise were estimated. A significant decrease in hypoxia can be achieved by further reductions in nutrient loads implemented in combination with existing measures. Natural variability makes future projections of hypoxia in the Baltic Sea uncertain, but nutrient load reductions can reduce its severity, shows a large range of future scenarios downscaled from an ensemble of Earth System models