Related papers: Analysing the SEDs of protoplanetary disks with ma…
Pre-main sequence (PMS) models provide invaluable tools for the study of star forming regions as they allow to assign masses and ages to young stars. Thus it is of primary importance to test the models against observations of PMS stars with…
The new generation of galaxy surveys will provide unprecedented data allowing us to test gravity at cosmological scales. A robust cosmological analysis of the large-scale structure demands exploiting the nonlinear information encoded in the…
We present a new-generation tool to model and interpret spectral energy distributions (SEDs) of galaxies, which incorporates in a consistent way the production of radiation and its transfer through the interstellar and intergalactic media.…
Classical Be stars are fast rotating, near main sequence B-type stars. The rotation and the presence of circumstellar discs profoundly modify the observables of active Be stars. Our goal is to infer stellar and disc parameters, as well as…
We summarize the updated set of multiphase chemical evolution models performed with 44 theoretical radial mass initial distributions and 10 possible values of efficiencies to form molecular clouds and stars. We present the results about the…
The long wavelength emission of protostellar objects is commonly attributed to a disk of gas and dust around the central protostar. In the first stages of disk accretion or in the case of high mass protostars, the disk mass is likely to be…
Molecular line observations that could resolve protoplanetary disks of ~100 AU both spatially and kinematically would be a useful tool to unambiguously identify these disks and to determine their kinematical and physical characteristics. In…
We present spectral energy distribution (SED) modeling of 338 disks around T Tauri stars from eleven star-forming regions, ranging from $\sim$0.5 to 10 Myr old. The disk masses we infer from our SED models are typically greater than those…
Fitting the spectral energy distributions (SEDs) of galaxies is an almost universally used technique that has matured significantly in the last decade. Model predictions and fitting procedures have improved significantly over this time,…
We present a proof-of-concept analysis of photometric redshifts with Bayesian priors on physical properties of galaxies. This concept is particularly suited for upcoming/on-going large imaging surveys, in which only several broad-band…
We present a new 2D axisymmetric code, cuDisc, for studying protoplanetary discs, focusing on the self-consistent calculation of dust dynamics, grain size distribution and disc temperature. Self-consistently studying these physical…
This paper addresses the scalability problem of Bayesian deep neural networks. The performance of deep neural networks is undermined by the fact that these algorithms have poorly calibrated measures of uncertainty. This restricts their…
One of the major goals of next-generation space-borne and ground-based telescopes is to detect and characterize the first galaxies that were in place in the first few hundred million years after the big bang. We study the spectral energy…
Dust growth and settling considerably affect the spectral energy distributions (SEDs) of protoplanetary disks. We investigated dust growth and settling in protoplanetary disks through numerical simulations to examine time-evolution of the…
We present a detailed comparison of two approaches, the use of a pre-calculated database and simulated annealing (SA), for fitting the continuum spectral energy distribution (SED) of astrophysical objects whose appearance is dominated by…
HD 179821 is an evolved star of unknown progenitor mass range (either post-Asymptotic Giant Branch or post-Red Supergiant) exhibiting a double peaked spectral energy distribution (SED) with a sharp rise from $\sim8-20$ $\mu$m. Such features…
We investigate the observational signatures of a straightforward evolutionary scenario for protoplanetary disks in which the disk mass of small (50 micron) particles decreases homologously with time, but the disk structure and stellar…
Deep neural networks (NNs) are powerful black box predictors that have recently achieved impressive performance on a wide spectrum of tasks. Quantifying predictive uncertainty in NNs is a challenging and yet unsolved problem. Bayesian NNs,…
The total amount of dust grains in protoplanetary disks is one of the key properties that characterize the potential for planet formation. With (sub-)millimeter flux measurements, literature studies usually derive the dust mass using an…
Neutron stars (NSs) probe the high-density regime of the nuclear equation of state (EOS). However, inferring the EOS from observations of NSs is a computationally challenging task. In this work, we efficiently solve this inverse problem by…