Pole-weapons in the Sagas of Icelanders: a comparison of literary and archaeological sources

The Icelandic sagas are a major source of information on the Vikings and their fighting prowess. In these stories, several mysterious pole-weapons appear, which are often called “halberds”, for lack of a better word. In order to better identify what these weapons could have been, and to provide a better understanding of how the sagas relate to the Viking-age events they describe, we confront textual and archaeological evidence for several of these weapons (the höggspjót, the atgeirr, the kesja, the krókspjót, the bryntroll and the fleinn), keeping in mind the contextualisation of their appearances in sagas. The description of the use of each weapon allows to pick several candidates likely to correspond to the studied word. Without a perfect knowledge of what context the authors of the sagas wanted to describe, it appears to be impossible to give a final answer. However, we show that some specific types of spears are good candidates for some of the studied weapons

GAUSSPY+: A fully automated Gaussian decomposition package for emission line spectra

Our understanding of the dynamics of the interstellar medium is informed by the study of the detailed velocity structure of emission line observations. One approach to study the velocity structure is to decompose the spectra into individual velocity components; this leads to a description of the data set that is significantly reduced in complexity. However, this decomposition requires full automation lest it become prohibitive for large data sets, such as Galactic plane surveys. We developed GAUSSPY+, a fully automated Gaussian decomposition package that can be applied to emission line data sets, especially large surveys of HI and isotopologues of CO. We built our package upon the existing GAUSSPY algorithm and significantly improved its performance for noisy data. New functionalities of GAUSSPY+ include: (i) automated preparatory steps, such as an accurate noise estimation, which can also be used as stand-alone applications; (ii) an improved fitting routine; (iii) an automated spatial refitting routine that can add spatial coherence to the decomposition results by refitting spectra based on neighbouring fit solutions. We thoroughly tested the performance of GAUSSPY+ on synthetic spectra and a test field from the Galactic Ring Survey. We found that GAUSSPY+ can deal with cases of complex emission and even low to moderate signal-to-noise values

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

Turbulence and star formation efficiency in molecular clouds: solenoidal versus compressive motions in Orion B

Context. The nature of turbulence in molecular clouds is one of the key parameters that control star formation efficiency: compressive motions, as opposed to solenoidal motions, can trigger the collapse of cores, or mark the expansion of Hii regions. Aims. We try to observationally derive the fractions of momentum density (ρv) contained in the solenoidal and compressive modes of turbulence in the Orion B molecular cloud and relate these fractions to the star formation efficiency in the cloud. Methods. The implementation of a statistical method applied to a 13CO(J = 1−0) datacube obtained with the IRAM-30 m telescope, enables us to retrieve 3-dimensional quantities from the projected quantities provided by the observations, which yields an estimate of the compressive versus solenoidal ratio in various regions of the cloud. Results. Despite the Orion B molecular cloud being highly supersonic (mean Mach number ~ 6), the fractions of motion in each mode diverge significantly from equipartition. The cloud’s motions are, on average, mostly solenoidal (excess > 8% with respect to equipartition), which is consistent with its low star formation rate. On the other hand, the motions around the main star forming regions (NGC 2023 and NGC 2024) prove to be strongly compressive. Conclusions. We have successfully applied to observational data a method that has so far only been tested on simulations, and we have shown that there can be a strong intra-cloud variability of the compressive and solenoidal fractions, these fractions being in turn related to the star formation efficiency. This opens a new possibility for star formation diagnostics in galactic molecular clouds

Deep learning denoising by dimension reduction: Application to the ORION-B line cubes

Context. The availability of large bandwidth receivers for millimeter radio telescopes allows the acquisition of position-position-frequency data cubes over a wide field of view and a broad frequency coverage. These cubes contain much information on the physical, chemical, and kinematical properties of the emitting gas. However, their large size coupled with inhomogenous signal-to-noise ratio (SNR) are major challenges for consistent analysis and interpretation.Aims. We search for a denoising method of the low SNR regions of the studied data cubes that would allow to recover the low SNR emission without distorting the signals with high SNR.Methods. We perform an in-depth data analysis of the 13 CO and C 17 O (1 -- 0) data cubes obtained as part of the ORION-B large program performed at the IRAM 30m telescope. We analyse the statistical properties of the noise and the evolution of the correlation of the signal in a given frequency channel with that of the adjacent channels. This allows us to propose significant improvements of typical autoassociative neural networks, often used to denoise hyperspectral Earth remote sensing data. Applying this method to the 13 CO (1 -- 0) cube, we compare the denoised data with those derived with the multiple Gaussian fitting algorithm ROHSA, considered as the state of the art procedure for data line cubes.Results. The nature of astronomical spectral data cubes is distinct from that of the hyperspectral data usually studied in the Earth remote sensing literature because the observed intensities become statistically independent beyond a short channel separation. This lack of redundancy in data has led us to adapt the method, notably by taking into account the sparsity of the signal along the spectral axis. The application of the proposed algorithm leads to an increase of the SNR in voxels with weak signal, while preserving the spectral shape of the data in high SNR voxels.Conclusions. The proposed algorithm that combines a detailed analysis of the noise statistics with an innovative autoencoder architecture is a promising path to denoise radio-astronomy line data cubes. In the future, exploring whether a better use of the spatial correlations of the noise may further improve the denoising performances seems a promising avenue. In addition

Pole-weapons in the Sagas of Icelanders: a comparison of literary and archaeological sources

The Icelandic sagas are a major source of information on the Vikings and their fighting prowess. In these stories, several mysterious pole-weapons appear, which are often called “halberds”, for lack of a better word. In order to better identify what these weapons could have been, and to provide a better understanding of how the sagas relate to the Viking-age events they describe, we confront textual and archaeological evidence for several of these weapons (the höggspjót, the atgeirr, the kesja, the krókspjót, the bryntroll and the fleinn), keeping in mind the contextualisation of their appearances in sagas. The description of the use of each weapon allows to pick several candidates likely to correspond to the studied word. Without a perfect knowledge of what context the authors of the sagas wanted to describe, it appears to be impossible to give a final answer. However, we show that some specific types of spears are good candidates for some of the studied weapons

Autonomous Gaussian decomposition of the Galactic Ring Survey I. Global statistics and properties of the (CO)-C-13 emission data

The analysis of large molecular line surveys of the Galactic plane is essential for our understanding of the gas kinematics on Galactic scales and, in particular, its link with the formation and evolution of dense structures in the interstellar medium. An approximation of the emission peaks with Gaussian functions allows for an efficient and straightforward extraction of useful physical information contained in the shape and Doppler-shifted frequency of the emission lines contained in these enormous data sets. In this work, we present an overview and the first results of a Gaussian decomposition of the entire Galactic Ring Survey (GRS) (CO)-C-13 (1-0) data that consists of about 2.3 million spectra. We performed the decomposition with the fully automated GAUSSPY+ algorithm and fitted about 4.6 million Gaussian components to the GRS spectra. These decomposition results enable novel and unexplored ways to interpret and study the gas velocity structure. We discuss the statistics of the fit components and relations between the fitted intensities, velocity centroids, and velocity dispersions. We find that the magnitude of the velocity dispersion values increase towards the inner Galaxy and around the Galactic midplane, which we speculate is partly due to the influence of the Galactic bar and regions with higher non-thermal motions located in the midplane, respectively. We also used our decomposition results to infer global properties of the gas emission and find that the number of fit components used per spectrum is indicative of the amount of structure along the line of sight. We find that the emission lines from regions located on the far side of the Galaxy show increased velocity dispersion values, which are likely due to beam averaging effects. We demonstrate how this trend has the potential to aid in characterising Galactic structure by disentangling emission that belongs to the nearby Aquila Rift molecular cloud from emission that is more likely associated with the Perseus and Outer spiral arms. With this work, we also make our entire decomposition results available

VizieR Online Data Catalog: Clustering the Orion B giant molecular cloud (Bron+, 2018)

VizieR On-line Data Catalog: J/A+A/610/A12. Originally published in: 2018A&A...610A..12BIntegrated line intensities maps (in the 9-12km/s range) of the Orion B molecular cloud in $^{12}$CO(1-0), $^{13}$CO(1-0), C$^{18}$O(1-0), CN(1-0), HCO$^+$(1-0), used as basis for the clustering analysis presented in the paper. Maps of the cluster attributions of each pixel resulting from the two clustering analyses presented (based on CO isotopes only, and based on CO isotopes, HCO$^+$ and CN) are also included. Description of the fits files: - ClustCO.fit : Cluster assignation map from the clustering analysis based on CO isotopes only. - ClustFUV.fit : Cluster assignation map from the clustering analysis based on CO isotopes, HCO+ and CN. - map$^{12}$CO.fit : Integrated intensity (9-12km/s) map of the $^{12}$CO J=1-0 line. - map$^{13}$CO.fit : Integrated intensity (9-12km/s) map of the $^{13}$CO J=1-0 line. - mapC$^{18}$O.fit : Integrated intensity (9-12km/s) map of the C$^{18}$O J=1-0 line. - mapCN.fit : Integrated intensity (9-12km/s) map of the CN N=1-0 line. - mapHCOp.fit : Integrated intensity (9-12km/s) map of the HCO$^+$ J=1-0 line. (2 data files)

Clustering the Orion B giant molecular cloud based on its molecular emission

Previous attempts at segmenting molecular line maps of molecular clouds have focused on using position-position-velocity data cubes of a single line to separate the spatial components of the cloud. In contrast, wide field spectral imaging with large spectral bandwidth in the (sub)mm domain now allows to combine multiple molecular tracers to understand the different physical and chemical phases that constitute giant molecular clouds. We aim at using multiple tracers (sensitive to different physical processes) to segment a molecular cloud into physically/chemically similar regions (rather than spatially connected components). We use a machine learning clustering method (the Meanshift algorithm) to cluster pixels with similar molecular emission, ignoring spatial information. Simple radiative transfer models are used to interpret the astrophysical information uncovered by the clustering. A clustering analysis based only on the J=1-0 lines of 12CO, 13CO and C18O reveals distinct density/column density regimes (nH~100, 500, and >1000 cm-3), closely related to the usual definitions of diffuse, translucent and high-column-density regions. Adding two UV-sensitive tracers, the (1-0) lines of HCO+ and CN, allows us to distinguish two clearly distinct chemical regimes, characteristic of UV-illuminated and UV-shielded gas. The UV-illuminated regime shows overbright HCO+ and CN emission, which we relate to photochemical enrichment. We also find a tail of high CN/HCO+ intensity ratio in UV-illuminated regions. Finer distinctions in density classes (nH~7E3, and 4E4 cm-3) for the densest regions are also identified, likely related to the higher critical density of the CN and HCO+ (1-0) lines. The association of simultaneous multi-line, wide-field mapping and powerful machine learning methods such as the Meanshift algorithm reveals how to decode the complex information available in molecular tracers