102 research outputs found
Zwischen Lexik und Hermenutik. Die Realien als Orientierungspunkte
No full textIl passaggio da un'attenta valutazione della traducibilità in potenza e in atto degli aspetti lessicali, legati all'affascinante mondo dei Realia - non di rado identificati con gli intraducibili tout court - alla complessa e di fatto inesauribile geografia delle isotopie che strutturano i testi permette di recuperare ampliandolo il concetto di ermeneutica applicato alla traduzione letteraria. La dimensione ermeneutica si impone di prepotenza innnestata su un tessuto di parole che al di là della dimensione referenziale sono caricate di una connotatività pervasiva e non esclusivamente autoriale
An advanced pulse-avalanche stochastic model of long gamma-ray burst light curves
No full textContext. A unified explanation of the variety of long-duration gamma-ray burst (GRB) light curves (LCs) is essential for identifying the dissipation mechanism and possibly the nature of their central engines. In the past, a model was proposed to describe GRB LCs as the outcome of a stochastic pulse avalanche process, possibly originating from a turbulent regime, and it was tested by comparing average temporal properties of simulated and real LCs. Recently, we revived this model and optimised its parameters using a genetic algorithm (GA), a machine-learning-based approach. Our findings suggested that GRB inner engines may operate near a critical regime.
Aims. Here we present an advanced version of the model, which allows us to constrain the peak flux distribution of individual pulses, and evaluate its performance on a new dataset of GRBs observed by the Fermi Gamma-ray Burst Monitor (GBM).
Methods. After introducing new model parameters and a further comparison metric, that is the observed signal-to-noise (S/N) distribution, we test the new model on three complementary datasets: CGRO/BATSE, Swift/BAT, and Fermi/GBM. As in our previous work, the model parameters are optimised using a GA.
Results. The updated sets of parameters achieve a further reduction in loss compared to both the original model and our earlier optimisation. The different values of the parameters across the datasets are shown to originate from the different energy passbands, effective areas, trigger algorithms, and, ultimately, different GRB populations of the three experiments.
Conclusions. Our results further underpin the stochastic and avalanche character of the dissipation process behind long GRB prompt emission, with an emphasis on the near-critical behaviour, and establish this new model as a reliable tool for generating realistic GRB LCs as they would be seen with future experiments
Distributions of energy, luminosity, duration, and waiting times of gamma-ray burst pulses with known redshift detected by Fermi/GBM
No full textDiscovered more than 50 years ago, gamma-ray burst (GRB) prompt emission remains the most puzzling aspect of GRB physics. Its complex and irregular nature should reveal how newborn GRB engines release their energy. In this respect, the possibility that GRB engines could operate as self-organized critical (SOC) systems has been put forward. Here, we present the energy, luminosity, waiting time, and duration distributions of individual pulses of GRBs with known redshift detected by the Fermi Gamma-ray Burst Monitor (GBM). This is the first study of this kind in which selection effects are accounted for. The compatibility of our results with the framework of SOC theory is discussed. We found evidence for an intrinsic break in the power-law models that describe the energy and the luminosity distributions
A Lattice Boltzmann Method for relativistic rarefied flows in (2+1) dimensions
No full textWe propose an extension to recently developed Relativistic Lattice Boltzmann solvers (RLBM), which allows the simulation of flows close to the free streaming limit. Following previous works Ambruş and Blaga (2018), we use product quadrature rules and select weights and nodes by separately discretizing the radial and the angular components. This procedure facilitates the development of quadrature-based RLBM with increased isotropy levels, thus improving the accuracy of the method for the simulation of flows beyond the hydrodynamic regime. In order to quantify the improvement of this discretization procedure over existing methods, we perform numerical tests of shock waves in one and two spatial dimensions in various kinetic regimes across the hydrodynamic and the free-streaming limits
Distribution of the number of peaks within a long gamma-ray burst: The full
No full textContext. The dissipation process responsible for the long gamma-ray burst (GRB) prompt emission and the kind of dynamics that drives the release of energy as a function of time are still key open issues. We recently found that the distribution of the number of peaks per GRB is described by a mixture of two exponentials, suggesting the existence of two behaviours that turn up as peak-rich and peak-poor time profiles.
Aims. Our aims are to study the distribution of the number of peaks per GRB of the entire catalogue of about 3000 GRBs observed by the Fermi Gamma-ray Burst Monitor (GBM) and to make a comparison with previous results obtained from other catalogues.
Methods. We identified GRB peaks using the MEPS
Geiparvarin analogs. 2. Synthesis and cytostatic activity of 5-(4-arylbutadienyl)-3(2H)-furanones and of N-substituted 3-(4-oxo-2-furanyl)-2-buten-2-yl carbamates.
No full textIn an attempt to determine some of the structural features of geiparvarin (1) that account for ita cytostatic activity
in vitro, a series of geiparvarin analogues (loa-i, 1, 12, and 14-16) which contain novel modifications in the region
of the olefinic double bond and of the coumarin moiety have been designed and synthesized.' Among the derivatives
containing a carbamate moiety, only the analogues containing a carbamate group linked to an alkyl moiety lob-i
were endowed with potent cytostatic activity, whereas the corresponding benzene derivative 10a was devoid of any
antiproliferative activity. 6-Methoxygeiparvarin 101 proved equally effective as geiparvin (l), while compounds
containing an additional double bond at the side chain (12 and 14-16) were invariably 5-1Wfold less effective than
geiparvarin. Diene derivative 15, bearing a coumarin moiety, was essentially inactive against murine (L1210, FM3A)
tumor cells but exhibited good activity against human (Molt/4F, MT-4) tumor cell
Pyrazole related nucleosides. Synthesis and antiviral / antitumor activity of some substituted pyrazole and pyrazolo [4,3-d]1,2,3-triazin-4-one nucleosides
No full textSeveral pyrazole and pyrazolo[4,3-d]-1,2,3-triazin-4-one ribonucleosides were prepared and tested for antiviral/antitumor activities. Appropriate heterocyclic bases were prepared by standard methodologies. Glycosylation of pyrazoles 6a-e,g,i and of pyrazolo[4,3-d]-1,2,3-triazin-4-ones 12f-1 mediated by silylation with hexamethyldisilazane, with 1-beta-O-acetyl-2,3,5-tri-O-benzoyl-D-ribofuranose, gave in good yields the corresponding glycosides 7a-e,g, 8g,i, 13f,h,k, and 14f, but could not be applied to compounds 12g,i,j,l. To overcome this occurrence, a different strategy involving the preparation, diazotization, and in situ cyclization of opportune pyrazole glycosides 9 and 10 was required. Moreover derivatives having the general formula 5 were considered not only as synthetic intermediates in the synthesis of 3 but also as carbon bioisosteres of ribavirin 4. All compounds were evaluated in vitro for cytostatic and antiviral activity. The pyrazolo[4,3-d]-1,2,3-triazin-4-one nucleosides that resulted were substantially devoid of any activity; only 15h,k showed a moderate cytostatic activity against T-cells. However, pyrazole nucleosides 9b,c,e were potent and selective cytotoxic agents against T-lymphocytes, whereas 9e showed a selective, although not very potent, activity against coxsackie B1
Searching for strong galaxy-scale lenses in galaxy clusters with deep networks: I. Methodology and network performance
No full textStrong galaxy-scale lenses in galaxy clusters provide a unique tool with which to investigate the inner mass distribution of these clusters and the subhalo density profiles in the low-mass regime, which can be compared with predictions from λ CDM cosmological simulations. We search for galaxy-galaxy strong-lensing systems in the Hubble Space Telescope (HST) multi-band imaging of galaxy cluster cores by exploring the classification capabilities of deep learning techniques. Convolutional neural networks (CNNs) are trained utilising highly realistic simulations of galaxy-scale strong lenses injected into the HST cluster fields around cluster members (CLMs). To this aim, we take advantage of extensive spectroscopic information available in 16 clusters and accurate knowledge of the deflection fields in half of these from high-precision strong-lensing models. Using observationally based distributions, we sample the magnitudes (down to F814W = 29 AB), redshifts, and sizes of the background galaxy population. By placing these sources within the secondary caustics associated with the cluster galaxies, we build a sample of approximately 3000 strong galaxy-galaxy lenses, which preserve the full complexity of real multi-colour data and produce a wide diversity of strong-lensing configurations. We study two deep learning networks, processing a large sample of image cutouts, in three bands, acquired by HST Advanced Camera for Survey (ACS), and we quantify their classification performance using several standard metrics. We find that both networks achieve a very good trade-off between purity and completeness (85%-95%), as well as a good stability, with fluctuations within 2%-4%. We characterise the limited number of false negatives (FNs) and false positives (FPs) in terms of the physical properties of the background sources (magnitudes, colours, redshifts, and effective radii) and CLMs (Einstein radii and morphology). We also demonstrate the high degree of generalisation of the neural networks by applying our method to HST observations of 12 clusters with previously known galaxy-scale lensing systems
Distribution of the number of peaks within a long gamma-ray burst
No full textContext. The variety and complexity of long duration gamma-ray burst (LGRB) light curves (LCs) encode a wealth of information about the way LGRB engines release their energy following the collapse of the progenitor massive star. Thus far, attempts to characterise GRB LCs have focused on a number of properties, such as the minimum variability timescale and power density spectra (both ensemble average and individual), or considering different definitions of variability. In parallel, a characterisation as a stochastic process has been pursued by studying the distributions of waiting times, peak flux, and fluence of individual peaks that can be identified within GRB time profiles. However, an important question remains as to whether the diversity of GRB profiles can be described in terms of a common stochastic process.
Aims. Here, we address this issue by extracting and modelling, for the first time, the distribution of the number of peaks within a GRB profile.
Methods. We analysed four different GRB catalogues: CGRO/BATSE, Swift/BAT, BeppoSAX/GRBM, and Insight-HXMT. The statistically significant peaks were identified by means of well tested and calibrated algorithm MEPS
Searching for galaxy-scale strong-lenses in galaxy clusters with deep networks -- I: methodology and network performance
No full textGalaxy-scale strong lenses in galaxy clusters provide a unique tool to
investigate their inner mass distribution and the sub-halo density profiles in
the low-mass regime, which can be compared with the predictions from
cosmological simulations. We search for galaxy-galaxy strong-lensing systems in
HST multi-band imaging of galaxy cluster cores from the CLASH and HFF programs
by exploring the classification capabilities of deep learning techniques.
Convolutional neural networks are trained utilising highly-realistic
simulations of galaxy-scale strong lenses injected into the HST cluster fields
around cluster members. To this aim, we take advantage of extensive
spectroscopic information on member galaxies in 16 clusters and the accurate
knowledge of the deflection fields in half of these from high-precision strong
lensing models. Using observationally-based distributions, we sample
magnitudes, redshifts and sizes of the background galaxy population. By placing
these sources within the secondary caustics associated with cluster galaxies,
we build a sample of ~3000 galaxy-galaxy strong lenses which preserve the full
complexity of real multi-colour data and produce a wide diversity of strong
lensing configurations. We study two deep learning networks processing a large
sample of image cutouts in three HST/ACS bands, and we quantify their
classification performance using several standard metrics. We find that both
networks achieve a very good trade-off between purity and completeness
(85%-95%), as well as good stability with fluctuations within 2%-4%. We
characterise the limited number of false negatives and false positives in terms
of the physical properties of the background sources and cluster members. We
also demonstrate the neural networks' high degree of generalisation by applying
our method to HST observations of 12 clusters with previously known
galaxy-scale lensing systems.Comment: 17 pages, 13 figures, to be published on A&
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