Detection of a Super Star Cluster as the Ionizing Source in the Low Luminosity AGN NGC 4303

HST UV STIS imaging and spectroscopy of the low luminosity AGN (LLAGN) NGC 4303 have identified the previously detected UV-bright nucleus of this galaxy, as a compact, massive and luminous stellar cluster. The cluster with a size (FWHM) of 3.1 pc, and an ultraviolet luminosity log L (1500 A}(erg/s/A)= 38.33 is identified as a nuclear super star cluster (SSC) like those detected in the circumnuclear regions of spirals and starburst galaxies. The UV spectrum showing the characteristic broad P Cygni lines produced by the winds of massive young stars, is best fitted by the spectral energy distribution of a massive cluster of 1e5 Msol generated in an instantaneous burst 4 Myr ago. No evidence for an additional non-thermal ionizing source associated with an accreting black hole is detected in the ultraviolet. We hypothesize that at least some LLAGNs in spirals could be understood as the result of the combined ionizing radiation emitted by an evolving SSC and a black hole (BH) accreting with low radiative efficiency, coexisting in the inner few parsecs region.Comment: 4 figure

Dispatcher3 D4.2 - Prototype package (first release) - User manual

This deliverable along with deliverable D4.1. Technical documentation first release consists of the release of the first prototype of Dispatcher3. The release consists of the binaries and Docker version of the prototype (sent to the Topic Manager). The first release prototype package consists of a set on individual machine learning models which can be executed using Jupyter notebooks. It also includes the integration of the outcome of some of these individual models into a visualisation which would be part of the advice generator to provide high-level information to the end users. All models described in the Deliverable D4.1 will be available and executable in this release. Data required to run the models (with some examples) are also provided. If data are public raw sample values are provided, otherwise pre-computed features are delivered so that the models can be run on individual flight examples. The prototypes can be run using local data (provided in the release) or with data stored in cloud storage (Amazon Web Services (AWS)). This deliverable serves as a manual for the execution of the first release prototype software

Bedform variability and flow regime in a barrier-inlet system. The mesotidal Piedras mouth (Huelva, SW Spain)

Bedform fields from the Piedras River mouth (Huelva, SW Spain) have been studied using side-scan sonar techniques, combined with visual scuba-dives, and direct geometric measuring. The dominant flow regime has been determined from the results in these tidal environments, where erosive processes dominate during ebb, transporting sand as a bedload towards the mouth and central sector of the tidal channel. The process is reversed during tidal floods. During neap tides, larger bedforms maintain their geometry and position, whereas small ripples are re-oriented under different tidal conditions. Sand patches, dunes and ripples are interpreted as sediment bypassing zones. Large forms indicate high energy flow, which can only migrate when flow velocity reaches threshold values for the movement, with net sand transport towards open areas. Depositional features indicate low, moderate, and high-energy conditions. Here, a depositional regime dominated by sediment accommodation is dominant, where sandy sediments are continuously remobilized, transported and re-deposited, even closer to the estuarine mouth. In inner zones finer particles, such as clay and silt, are transported by tides as suspended matter and deposited in protected inner areas. The final results are long narrow tidal flats, which alternate with sandy areas dominated by erosion

Pre-Tactical Prediction of Atfm Delay for Individual Flights

The day prior operations, the operation plan is drawn generating a first set of flight plans with the objective of identifying potential network issues and preparing pre-tactical preventing measures. During the day of operation flight plans will be updated and pre-tactical actions implemented if needed by the duty manager, in order to minimise the propagation of disruption in the network. This paper focuses on the estimation of ATFM delay for individual flights during the pre-tactical phase. The main objective is to anticipate which flights might be affected by ATFM regulations and the amount of delay will be assigned to them

Dispatcher3 – Machine learning to support flight planning processes

This poster will present the final results of the Clean Sky 2 project Dispatcher3. Dispatcher3 focuses on the use of machine learning techniques to support flight operations prior departure with holding predictions, runway at arrival estimation and fuel deviations pre-departure to support the flight crew, and ATFM and reactionary delays on D-1 to support the duty manage

Dispatcher3 – Machine learning for efficient flight planning - Approach and challenges for data-driven prototypes in air transport

Machine learning techniques to support decision making processes are in trend. These are particularly relevant in the context of flight management where large datasets of planned and realised operations are available. Current operations experience discrepancies between planned and executed flight plan, these might be due to external factors (e.g. weather, congestion) and might lead to sub-optimal decisions (e.g. recovering delay (burning extra fuel) when no holding is expected at arrival and therefore it was no needed). Dispatcher3 produces a set of machine learning models to support flight crew pre-departure, with estimations on expected holding at arrival, runway in use and fuel usage, and the airline’s duty manager on pre-tactical actions, with models trained with a larger look ahead time for ATFM and reactionary delay estimations. This paper describes the prototype architecture and approach of Dispatcher3 with particular focus on the challenges faced by this type of data-driven machine learning models in the field of air transport ranging: from technical aspects such as data leakage to operational requirements such as the consideration and estimation of uncertainty. These considerations should be relevant for projects which try to use machine learning in the field of aviation in general

Evolutionary synthesis models of starbursts IV. Soft X-ray emission

In this work we investigate the evolution of the X-ray emission of a cluster of single young massive stars with different metallicities. We have considered the X-ray contribution originated by the diffuse gas heated by the mechanical energy released by stellar winds and SN explosions as well as the X-ray contribution from SN remnants. The resulting ionizing spectrum (i.e. lambda < 912 A) has been used to compute the expected intensity of the nebular He II 4686 A. The observational ratio He II/Hbeta could be reproduced by the models assuming that a fraction of the mechanical energy produced by the star-formation episode is reprocessed by interaction with the ISM as soft X-ray radiation, contributing to the He ionization. However, the discreteness of the stellar populations affects the ionizing flux and may be responsible for the observed dispersion of the ratio. We have finally used the synthesis models to estimate the contribution of circumnuclear star-forming regions to the multiwavelength energy distribution in Active Galactic Nuclei, finding that the UV to soft X-ray continuum in many Seyfert 2 galaxies seems to be dominated by star-formation processes.Comment: 14 pages, 10 fig, A&A accepte

An agonist–antagonist cerebellar nuclear system controlling eyelid kinematics during motor learning

The presence of two antagonistic groups of deep cerebellar nuclei neurons has been reported as necessary for a proper dynamic control of learned motor responses. Most models of cerebellar function seem to ignore the biomechanical need for a double activation–deactivation system controlling eyelid kinematics, since most of them accept that, for closing the eyelid, only the activation of the orbicularis oculi (OO) muscle (via the red nucleus to the facial motor nucleus) is necessary, without a simultaneous deactivation of levator palpebrae motoneurons (via unknown pathways projecting to the perioculomotor area). We have analyzed the kinetic neural commands of two antagonistic types of cerebellar posterior interpositus neuron (IPn) (types A and B), the electromyographic (EMG) activity of the OO muscle, and eyelid kinematic variables in alert behaving cats during classical eyeblink conditioning, using a delay paradigm. We addressed the hypothesis that the interpositus nucleus can be considered an agonist–antagonist system controlling eyelid kinematics during motor learning. To carry out a comparative study of the kinetic–kinematic relationships, we applied timing and dispersion pattern analyses. We concluded that, in accordance with a dominant role of cerebellar circuits for the facilitation of flexor responses, type A neurons fire during active eyelid downward displacements—i.e., during the active contraction of the OO muscle. In contrast, type B neurons present a high tonic rate when the eyelids are wide open, and stop firing during any active downward displacement of the upper eyelid. From a functional point of view, it could be suggested that type B neurons play a facilitative role for the antagonistic action of the levator palpebrae muscle. From an anatomical point of view, the possibility that cerebellar nuclear type B neurons project to the perioculomotor area—i.e., more or less directly onto levator palpebrae motoneurons—is highly appealing

Dispatcher3 – Machine learning for efficient flight planning: approach and challenges for data-driven prototypes in air transport

Machine learning techniques to support decisionmaking processes are in trend. These are particularly relevant in the context of flight management where large datasets of planned and realised operations are available. Current operations experience discrepancies between planned and executed flight plan, these might be due to external factors (e.g. weather, congestion) and might lead to sub-optimal decisions (e.g. recovering delay (burning extra fuel) when no holding is expected at arrival and therefore it was no needed). Dispatcher3 produces a set of machine learning models to support flight crew pre-departure, with estimations on expected holding at arrival, runway in use and fuel usage, and the airline’s duty manager on pre-tactical actions, with models trained with a larger look ahead time for ATFM and reactionary delay estimations. This paper describes the prototype architecture and approach of Dispatcher3 with particular focus on the challenges faced by this type of data-driven machine learning models in the field of air transport ranging: from technical aspects such as data leakage to operational requirements such as the consideration and estimation of uncertainty. These considerations should be relevant for projects which try to use machine learning in the field of aviation in general.This work is performed as part of Dispatcher3 innovation action which has received funding from the Clean Sky 2 Joint Undertaking (JU) under grant agreements No 886461. The Topic Manager is Thales AVS France SAS. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and the Clean Sky 2 JU members other than the Union. The opinions expressed herein reflect the authors’ views only. Under no circumstances shall the Clean Sky 2 Joint Undertaking be responsible for any use that may be made of the information contained herein.Postprint (published version