Seeking for the best conditions for fish fossil preservation in Las Hoyas Konservat-Lagerstätte using microbial mats

Actuotaphonomic experiments demonstrate how microbial mats prevent or delay destructive processes. The rate at which carcasses are covered is a key to their preservation. Because of the growth rate of microbial mats depends on environmental conditions, a set of experiments have been carried out emulating the Barremian environmental conditions, analysed for temperatures at 14°C and 26°C (cooler and warmer seasons respectively) and atmospheric pCO2 (1000 ppm). For this purpose, the microbial mats were grown in mesocosms within an environmental chamber. Variations in primary production were quantified by measuring changes in dissolved O2 concentration in the water. Zebrafish carcasses were laid on the mats, and their coverage rates were calculated from the daily surface area covered by the mat. The results showed that the fish was covered twice as fast at 26°C, in coincidence with the highest values for the gross primary production and community respiration of the microbial mats. Therefore, for these Barremian conditions, the early stages of carcasses preservation would take place most effectively during the warmer seasons as decomposing activity would release nutrients that would enhance, together with temperature, the growth of matsThis study was funded by the Spanish Ministry of Science, Innovation and Universities [project PID2019-105546GB-I00

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino de tector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower-or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches