Neural network-based emulation of interstellar medium models

The interpretation of observations of atomic and molecular tracers in the galactic and extragalactic interstellar medium (ISM) requires comparisons with state-of-the-art astrophysical models to infer some physical conditions. Usually, ISM models are too time-consuming for such inference procedures, as they call for numerous model evaluations. As a result, they are often replaced by an interpolation of a grid of precomputed models. We propose a new general method to derive faster, lighter, and more accurate approximations of the model from a grid of precomputed models. These emulators are defined with artificial neural networks (ANNs) designed and trained to address the specificities inherent in ISM models. Indeed, such models often predict many observables (e.g., line intensities) from just a few input physical parameters and can yield outliers due to numerical instabilities or physical bistabilities. We propose applying five strategies to address these characteristics: 1) an outlier removal procedure; 2) a clustering method that yields homogeneous subsets of lines that are simpler to predict with different ANNs; 3) a dimension reduction technique that enables to adequately size the network architecture; 4) the physical inputs are augmented with a polynomial transform to ease the learning of nonlinearities; and 5) a dense architecture to ease the learning of simple relations. We compare the proposed ANNs with standard classes of interpolation methods to emulate the Meudon PDR code, a representative ISM numerical model. Combinations of the proposed strategies outperform all interpolation methods by a factor of 2 on the average error, reaching 4.5% on the Meudon PDR code. These networks are also 1000 times faster than accurate interpolation methods and require ten to forty times less memory. This work will enable efficient inferences on wide-field multiline observations of the ISM

Multistatic estimation of high-frequency radar surface currents in the region of Toulon

International audienceThe High-Frequency Radar coastal network in Toulon operates in multistatic mode for the monitoring of the ocean circulation in the NorthWestern Mediterranean Sea. With 2 transmitters and 2 receivers on 3 distant sites it measures 4 different elliptical components of the surface velocity. We provide a methodology for improved current mapping using this augmented number of available projections and we show some typical results obtained during the year 2019. The validity and the quality of the reconstruction are assessed through comparisons with two types of in situ measurements, namely drifters velocities from a dedicated campaign and ADCP data from an opportunity oceanographic campaign. The results of these assessments confirm the accuracy of these HFR measurements and their ability to capture the meso-to submeso-scale variability of the near shelf circulation

High-Frequency radar measurements with CODARs in the region of Nice: improved calibration and performances

11 pages, 9 figures, 3 tablesWe report on the installation and first results of one compact oceanographic radar in the region of Nice for a long-term observation of the coastal surface currents in the North-West Mediterranean Sea. We describe the specific processing and calibration techniques which were developed at the laboratory to produce high-quality radial surface current maps. In particular, we propose an original self-calibration technique of the antenna patterns, which is based on the sole analysis of the databasis and does not require any shipborne transponder or other external transmitters. The relevance of the self-calibration technique and the accuracy of inverted surface currents have been assessed with the launch of 40 drifters that remained under the radar coverage for about 10 days

Performance evaluation of a combined ADCP- scientific echosounder system.

International audienceEchosounders are widely used to quantify fish behavior, fish stocks, and zooplankton biomass. Acoustic Doppler Current Profilers have also been used to accurately measure currents in all of the world’s major water bodies over the last 30 years. The present work evaluates the performance of a combined echosounder/ADCP system, the Nortek Signature100, for simultaneous biomass assessment and current profile data analysis. Due to its combined current profiling and scientific echosounding capabilities, the system is seeing increased usage in biomass flux applications, particularly in Antarctic krill research. However, capabilities of the system are still being studied and the present work aims to expand characterization of its performance. To that effect, a four month deployment was carried out by the French National Center for Scientific Research (CNRS) in the Mediterranean Sea with an up-looking Signature100 mounted atop the ALBATROSS mooring line. The line was at a total water depth of 2420 m and its top was approximately 370 m below the surface. Data show significant variations in scattering conditions between daytime and nighttime due to diel vertical migration (DVM), often unrelated to horizontal velocity fluctuations, highlighting not only the multiple frequency band capabilities of the system (up to 7 bands), but also the strength of the combined echosounder and current profiling functions. Echoview, a commercial software package for hydroacoustic data processing, was used to further explore the spatial and temporal patterns of the organisms observed in the echosounder data. A semi-automated technique was implemented to efficiently and objectively clean (e.g. remove interference generated by passing ship traffic), classify (e.g. based on relative frequency response or morphology), and characterize the narrow bandwidth and pulse compressed echosounder data by generating outputs that can contribute to the management and monitoring of aquatic resources

HF radar in French Mediterranean Sea: an element of MOOSE Mediterranean Ocean Observing System on Environment

International audienceIn the framework of the French MOOSE project (Mediterranean Ocean Observing System on Environment), the Mediterranean Institute of Oceanography is operating HF radars on the North Western Mediterranean coast. The surface circulation in this region is characterized by a large-scale flow (Northern Current) and by a broad range of other scales of variability induced by meteorological and tidal forcing. The ability of HF radars is to provide synoptic observation as sea surface current map every hour and over long distances. One site is already operational nearby Toulon for more than two years and a second one is in deployment around Nice. This paper gives an overview of the radars network, of the surface current mapping facility offered by the system, and of recent observation results and applications

Réduction d’un modèle astrophysique par réseaux de neurones

National audienceNumerical models requiring significant resources in time, memory and computing power are present in many scientific disciplines. We consider an astrophysical simulation that computes many outputs from few input parameters, and that may occasionally produce anomalies. We propose a neural network based regression model reduction method and an anomaly robust learning method. We inform the choice of an architecture with a statistical analysis of the code predictions. We demonstrate the interest of the proposed approach by comparing it with model reduction methods commonly used in radio astronomy.Les modèles numériques nécessitant des ressources importantes en temps, mémoire et puissance de calcul sont présents dans de nombreuses disciplines scientifiques. Nous considérons une simulation astrophysique qui calcule de nombreuses sorties à partir de peu de paramètres d’entrée, et qui peut ponctuellement produire des anomalies. Nous proposons une méthode de réduction de modèle par régression basée sur des réseaux de neurones et une méthode d’apprentissage robuste aux anomalies. Nous informons le choix d’une architecture avec une analyse statistique des prédictions du code. Nous démontrons l’intérêt de l’approche proposée en la comparant avec les méthodes de réduction de modèle couramment utilisées en radioastronomie

Réduction d’un modèle astrophysique par réseaux de neurones

Numerical models requiring significant resources in time, memory and computing power are present in many scientific disciplines. We consider an astrophysical simulation that computes many outputs from few input parameters, and that may occasionally produce anomalies. We propose a neural network based regression model reduction method and an anomaly robust learning method. We inform the choice of an architecture with a statistical analysis of the code predictions. We demonstrate the interest of the proposed approach by comparing it with model reduction methods commonly used in radio astronomy.Les modèles numériques nécessitant des ressources importantes en temps, mémoire et puissance de calcul sont présents dans de nombreuses disciplines scientifiques. Nous considérons une simulation astrophysique qui calcule de nombreuses sorties à partir de peu de paramètres d’entrée, et qui peut ponctuellement produire des anomalies. Nous proposons une méthode de réduction de modèle par régression basée sur des réseaux de neurones et une méthode d’apprentissage robuste aux anomalies. Nous informons le choix d’une architecture avec une analyse statistique des prédictions du code. Nous démontrons l’intérêt de l’approche proposée en la comparant avec les méthodes de réduction de modèle couramment utilisées en radioastronomie

Réduction d’un modèle astrophysique par réseaux de neurones

Numerical models requiring significant resources in time, memory and computing power are present in many scientific disciplines. We consider an astrophysical simulation that computes many outputs from few input parameters, and that may occasionally produce anomalies. We propose a neural network based regression model reduction method and an anomaly robust learning method. We inform the choice of an architecture with a statistical analysis of the code predictions. We demonstrate the interest of the proposed approach by comparing it with model reduction methods commonly used in radio astronomy.Les modèles numériques nécessitant des ressources importantes en temps, mémoire et puissance de calcul sont présents dans de nombreuses disciplines scientifiques. Nous considérons une simulation astrophysique qui calcule de nombreuses sorties à partir de peu de paramètres d’entrée, et qui peut ponctuellement produire des anomalies. Nous proposons une méthode de réduction de modèle par régression basée sur des réseaux de neurones et une méthode d’apprentissage robuste aux anomalies. Nous informons le choix d’une architecture avec une analyse statistique des prédictions du code. Nous démontrons l’intérêt de l’approche proposée en la comparant avec les méthodes de réduction de modèle couramment utilisées en radioastronomie

Réduction d’un modèle astrophysique par réseaux de neurones

National audienceNumerical models requiring significant resources in time, memory and computing power are present in many scientific disciplines. We consider an astrophysical simulation that computes many outputs from few input parameters, and that may occasionally produce anomalies. We propose a neural network based regression model reduction method and an anomaly robust learning method. We inform the choice of an architecture with a statistical analysis of the code predictions. We demonstrate the interest of the proposed approach by comparing it with model reduction methods commonly used in radio astronomy.Les modèles numériques nécessitant des ressources importantes en temps, mémoire et puissance de calcul sont présents dans de nombreuses disciplines scientifiques. Nous considérons une simulation astrophysique qui calcule de nombreuses sorties à partir de peu de paramètres d’entrée, et qui peut ponctuellement produire des anomalies. Nous proposons une méthode de réduction de modèle par régression basée sur des réseaux de neurones et une méthode d’apprentissage robuste aux anomalies. Nous informons le choix d’une architecture avec une analyse statistique des prédictions du code. Nous démontrons l’intérêt de l’approche proposée en la comparant avec les méthodes de réduction de modèle couramment utilisées en radioastronomie

Bias versus variance when fitting multi-species molecular lines with a non-LTE radiative transfer model: Application to the estimation of the gas temperature and volume density

International audienceRobust radiative transfer techniques are requisite for efficiently extracting the physical and chemical information from molecular rotational lines.We study several hypotheses that enable robust estimations of the column densities and physical conditions when fitting one or two transitions per molecular species. We study the extent to which simplifying assumptions aimed at reducing the complexity of the problem introduce estimation biases and how to detect them.We focus on the CO and HCO+ isotopologues and analyze maps of a 50 square arcminutes field. We used the RADEX escape probability model to solve the statistical equilibrium equations and compute the emerging line profiles, assuming that all species coexist. Depending on the considered set of species, we also fixed the abundance ratio between some species and explored different values. We proposed a maximum likelihood estimator to infer the physical conditions and considered the effect of both the thermal noise and calibration uncertainty. We analyzed any potential biases induced by model misspecifications by comparing the results on the actual data for several sets of species and confirmed with Monte Carlo simulations. The variance of the estimations and the efficiency of the estimator were studied based on the Cramér-Rao lower bound.Column densities can be estimated with 30% accuracy, while the best estimations of the volume density are found to be within a factor of two. Under the chosen model framework, the peak 12CO(1−0) is useful for constraining the kinetic temperature. The thermal pressure is better and more robustly estimated than the volume density and kinetic temperature separately. Analyzing CO and HCO+ isotopologues and fitting the full line profile are recommended practices with respect to detecting possible biases.Combining a non-local thermodynamic equilibrium model with a rigorous analysis of the accuracy allows us to obtain an efficient estimator and identify where the model is misspecified. We note that other combinations of molecular lines could be studied in the future