Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userAMOEBA: Automated Molecular Excitation Bayesian Line-Fitting Algorithm
91
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
The hyperfine transitions of the ground-rotational state of the hydroxyl radical (OH) have emerged as a versatile tracer of the diffuse molecular interstellar medium. We present a novel automated Gaussian decomposition algorithm designed specifically for the analysis of the paired on-source and off-source optical depth and emission spectra of these transitions. In contrast to existing automated Gaussian decomposition algorithms, AMOEBA (Automated MOlecular Excitation Bayesian line-fitting Algorithm) employs a Bayesian approach to model selection, fitting all 4 optical depth and 4 emission spectra simultaneously. AMOEBA assumes that a given spectral feature can be described by a single centroid velocity and full width at half-maximum, with peak values in the individual optical depth and emission spectra then described uniquely by the column density in each of the four levels of the ground-rotational state, thus naturally including the real physical constraints on these parameters. Additionally, the Bayesian approach includes informed priors on individual parameters which the user can modify to suit different data sets. Here we describe AMOEBA and evaluate its validity and reliability in identifying and fitting synthetic spectra with known parameters.
rate research
Read More
The vast quantity of strong galaxy-galaxy gravitational lenses expected by future large-scale surveys necessitates the development of automated methods to efficiently model their mass profiles. For this purpose, we train an approximate Bayesian convolutional neural network (CNN) to predict mass profile parameters and associated uncertainties, and compare its accuracy to that of conventional parametric modelling for a range of increasingly complex lensing systems. These include standard smooth parametric density profiles, hydrodynamical EAGLE galaxies and the inclusion of foreground mass structures, combined with parametric sources and sources extracted from the Hubble Ultra Deep Field. In addition, we also present a method for combining the CNN with traditional parametric density profile fitting in an automated fashion, where the CNN provides initial priors on the latters parameters. On average, the CNN achieved errors 19 $pm$ 22 per cent lower than the traditional methods blind modelling. The combination method instead achieved 27 $pm$ 11 per cent lower errors over the blind modelling, reduced further to 37 $pm$ 11 per cent when the priors also incorporated the CNN-predicted uncertainties, with errors also 17 $pm$ 21 per cent lower than the CNN by itself. While the CNN is undoubtedly the fastest modelling method, the combination of the two increases the speed of conventional fitting alone by factors of 1.73 and 1.19 with and without CNN-predicted uncertainties, respectively. This, combined with greatly improved accuracy, highlights the benefits one can obtain through combining neural networks with conventional techniques in order to achieve an efficient automated modelling approach.
We present ProFit, a new code for Bayesian two-dimensional photometric galaxy profile modelling. ProFit consists of a low-level C++ library (libprofit), accessible via a command-line interface and documented API, along with high-level R (ProFit) and Python (PyProFit) interfaces (available at github.com/ICRAR/ libprofit, github.com/ICRAR/ProFit, and github.com/ICRAR/pyprofit respectively). R ProFit is also available pre-built from CRAN, however this version will be slightly behind the latest GitHub version. libprofit offers fast and accurate two- dimensional integration for a useful number of profiles, including Sersic, Core-Sersic, broken-exponential, Ferrer, Moffat, empirical King, point-source and sky, with a simple mechanism for adding new profiles. We show detailed comparisons between libprofit and GALFIT. libprofit is both faster and more accurate than GALFIT at integrating the ubiquitous Serrsic profile for the most common values of the Serrsic index n (0.5 < n < 8). The high-level fitting code ProFit is tested on a sample of galaxies with both SDSS and deeper KiDS imaging. We find good agreement in the fit parameters, with larger scatter in best-fit parameters from fitting images from different sources (SDSS vs KiDS) than from using different codes (ProFit vs GALFIT). A large suite of Monte Carlo-simulated images are used to assess prospects for automated bulge-disc decomposition with ProFit on SDSS, KiDS and future LSST imaging. We find that the biggest increases in fit quality come from moving from SDSS- to KiDS-quality data, with less significant gains moving from KiDS to LSST.
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 dataset that is significantly reduced in complexity. However, this decomposition requires full automation lest it becomes prohibitive for large datasets, such as Galactic plane surveys. We developed GaussPy+, a fully automated Gaussian decomposition package that can be applied to emission line datasets, 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 standalone 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.
A new Bayesian method for performing an image domain search for line-emitting galaxies is presented. The method uses both spatial and spectral information to robustly determine the source properties, employing either simple Gaussian, or other physically motivated models whilst using the evidence to determine the probability that the source is real. In this paper, we describe the method, and its application to both a simulated data set, and a blind survey for cold molecular gas using observations of the Hubble Deep Field North taken with the Plateau de Bure Interferometer. We make a total of 6 robust detections in the survey, 5 of which have counterparts in other observing bands. We identify the most secure detections found in a previous investigation, while finding one new probable line source with an optical ID not seen in the previous analysis. This study acts as a pilot application of Bayesian statistics to future searches to be carried out both for low-$J$ CO transitions of high redshift galaxies using the JVLA, and at millimeter wavelengths with ALMA, enabling the inference of robust scientific conclusions about the history of the molecular gas properties of star-forming galaxies in the Universe through cosmic time.
The optically thin critical densities and the effective excitation densities to produce a 1 K km/s (or 0.818 Jy km/s $(frac{ u_{jk}}{100 rm{GHz}})^2 , (frac{theta_{beam}}{10^{primeprime}})^2$) spectral line are tabulated for 12 commonly observed dense gas molecular tracers. The dependence of the critical density and effective excitation density on physical assumptions (i.e. gas kinetic temperature and molecular column density) is analyzed. Critical densities for commonly observed dense gas transitions in molecular clouds (i.e. HCN $1-0$, HCO$^+$ $1-0$, N$_2$H$^+$ $1-0$) are typically $1 - 2$ orders of magnitude larger than effective excitation densities because the standard definitions of critical density do not account for radiative trapping and 1 K km/s lines are typically produced when radiative rates out of the upper energy level of the transition are faster than collisional depopulation. The use of effective excitation density has a distinct advantage over the use of critical density in characterizing the differences in density traced by species such as NH$_3$, HCO$^+$, N$_2$H$^+$, and HCN as well as their isotpologues; but, the effective excitation density has the disadvantage that it is undefined for transitions when $E_u/k gg T_k$, for low molecular column densities, and for heavy molecules with complex spectra (i.e. CH$_3$CHO).
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
