The observational data from The IRAS16293 Millimeter And Submillimeter Spectral Survey (TIMASSS: http://www-laog.obs.ujf-grenoble.fr/heberges/timasss/; Caux et al. (2011)) were used for this project. Briefly, the survey covers the 80-280 and 328-366 GHz frequency intervals and it was obtained at the IRAM-30m and JCMT-15m telescopes during the period from January 2004 to August 2006. Overall, the observations required a total of about 300 hours ( 200hr at IRAM and 100hr at JCMT) of observing time.
Details on the data reduction and calibration can be found in Caux et al. (2011). Observations are centered on IRAS16293B at (2000.0) = 16h 32m 22s.6, (2000.0)= -24o 280 33:00 The A and B components are both inside the beam of observations at all frequencies. Parameters of the observations are listed in Table (3.1).
The Large Program (ASAI:Astrochemical Surveys at IRAM ), carried out at the IRAM 30m telescope joins the efforts of several groups in astrochemistry in Spain and France, to address the question of our “chemical origins”. ASAI goal is to obtain a completecensus of the gas chemical composition, its evolution along the main stages of the star formation process, from prestellar cores and protostars to protoplanetary disks, in order to understand the processes which govern the emergence of molecular complexity, and the formation of pre-biotic molecules. This is achieved through highly sensitive and systematic spectral line surveys of a sample of sources illustrative of the various stages of protostellar evolution. Observations and data reduction procedures are presented in detail in López-Sepulcre et al. (2015). Briefly, the spectral data were obtained in several observing runs between 2011 and 2014 using the EMIR receivers at 3 mm (80–116 GHz), 2 mm (129–173 GHz), and 1.3 mm (200– 276 GHz), see Table 3.2.
CASSIS (Centre d’Analyse Scientifique de Spectres Instrumentaux et Synthétiques) software has been developed by IRAP-UPS/CNRS (http://cassis.irap.omp.eu) since 2005. I have used this software to explore the TIMASSS (Caux et al. 2011) and ASAI (López- Sepulcre et al. 2015) spectral survey files, in order to search for complex organic and cyanopolyynes molecules in IRAS16293 and OMC2-FIR4 respectively. This tool reads the spectra file header and displays in the graphical interface the portions of spectra corresponding to the lines of the selected species. In addition, to help to look for contamination by nearby lines, the lines of other species included in the database can be shown in the same spectral window. Figure 3.5 shows an example of such a plot. During my thesis, I used CASSIS for the following tasks:
1. Line identification.
2. Gaussian fit.
3. Derivation of the upper limit to the abundance for undetected species.
In this section I only describe the first point. The second & third steps will be described in the next section (§3.4)
General description of the package
GRAPES consists of two sub-packages: radiative-transfer fortran codes and IDL procedures to launch the codes and/or analyze the results.
1. FORTRAN codes: Line flux calculation. The first one is a fortran code which provides theoretical predictions of the integrated flux of the lines from a selected chemical species (notably molecules whose spectral parameters are available in the JPL database Pickett et al. (1998)). It computes the line fluxes from a gas and dust sphere for a given physical structure. Depending whether the collisional coefficients are available or not, two kinds of calculations are executed:
NON-LTE computations: If the collisional coefficients are available, the level populations are computed by solving the radiative transfer&statistical equilibrium equations simultaneously. The adopted formalism for treating the radiative transfer, following the escape probability method, is described in three articles (Ceccarelli et al. 1996, 2003a; Parise et al. 2005). Note that the level population computation take into account also the radiative pumping from the dust emission.
LTE computations: When the collisional coefficients are not available, the level population are assumed to follow the Boltzmann distribution. The opacity of the lines are, however, computed by integrating on the solid angle the optical depth, following the formalism of the above mentioned articles.
2. IDL procedures The second sub-package consists of several IDL procedures to run grids of models, to compare the predicted with observed line intensities and find the best fit, plus several procedures to facilitate the understanding of the model predictions.
We searched for lines of all the oxygen and nitrogen bearing COMs already detected in the ISM (as reported in the CDMS database: http://www.astro.uni-koeln.de/cdms/molecules), they are listed in Table 4.1. At this scope, we used the list of identified lines in Caux et al. (2011) and double-checked for possible blending and misidentifications. This was obtained via the publicly available package CASSIS (http://cassis.irap.omp.eu), and the CDMS (Müller et al. 2005) and JPL (Pickett et al. 1998) databases. References to the specific articles on the laboratory data of the detected species are Guarnieri & Huckauf (2003); Kleiner et al. (1996); Neustock et al. (1990); Maeda et al. (2008). In case of doubt on the line identification or in case of presence of important residual baseline effects, we did not consider the relevant line. Except for those few ( 10%) cases, we used the line parameters (flux, linewidth, rest velocity) in Caux et al. (2011). With these tight criteria, we secured the detection of six COMs: ketene (H2CCO: 13 lines), acetaldehyde (CH3CHO: 130 lines), formamide (NH2CHO: 17 lines), dimethyl ether (CH3OCH3: 65 lines), methyl formate (HCOOCH3: 121 lines) and methyl cyanide (CH3CN: 38 lines). For comparison, Cazaux et al. (2003) detected 5 CH3CHO lines, 7 CH3OCH3 lines, and 20 CH3CHO lines. We do not confirm the Cazaux et al. (2003) detection of acetic acid (CH3COOH) and formic acid (HCOOH), where these authors reported the possible detection of 1 and 2 lines respectively, none of them in the TIMASSS observed frequency range.
Our goal is to estimate the abundance of the detected COMs across the envelope of IRAS16293, with particular emphasis on the cold envelope (see Introduction). For that, we used the Spectral Line Energy Distribution (SLED) of the detected COMs, and the package GRAPES (GRenoble Analysis of Protostellar Envelope Spectra), based on the code described in Ceccarelli et al. (1996, 2003a). Briefly, GRAPES (i) computes the species SLED from a spherical infalling envelope with a given structure; (ii) it solves ocally the level population statistical equilibrium equations in the beta escape formalism, consistently computing the line optical depth by integrating it over the solid angle at each point of the envelope; (iii) the predicted line flux is then integrated over thewhole envelope after convolution with the telescope beam. The abundance X of the considered species is assumed to vary as a function of the radius with a power law in the cold part of the envelope and to jump to a new abundance in the warm part.
The transition between the two regions is set by the dust temperature, to simulate the sublimation of the ice mantles, and occurs at Tjump. It holds: X(r) = Xout r Rmax T Tjump X(r) = Xin > Tjump (4.1).
Table of contents :
1.2 Low-mass star formation
1.3 Chemical complexity evolution
1.4 Census of molecules in the interstellar medium
1.5 Aims and structure of this thesis
2 Description of IRAS16293-2422 and OMC-2 FIR 4
2.1.1 The physical structure
2.1.2 The outflow system in the IRAS16293
2.1.3 The chemical structure
2.1.4 Deuteration in IRAS16293
2.2 OMC-2 FIR4
3 Used Tools
3.2 Spectral surveys
3.3 Lines identification
3.3.1 Criteria for identification
3.3.2 Tool: CASSIS
3.4 Lines parameters
3.4.1 Gaussian fit
3.4.2 LTE Modeling for upper limits
3.5 SLED Modeling
3.5.2 General description of the package
3.5.3 Method of work
4 COMs in IRAS16293-2422
4.3 Source description
4.4 The data set
4.4.2 Species identification
4.5 Analysis and results
4.5.1 Model description
5 Cyanopolyynes in IRAS16293-2422
5.3 Source description
5.4 The data set
5.4.2 Species identification
5.5 Line modeling
5.5.1 Model description
Undetected species and conclusive remarks
5.6 The chemical origin of HC3N
5.6.1 Cold envelope
5.6.2 Hot corino
5.7.1 General remarks on cyanopolyynes in different environments .
5.7.2 The present and past history of IRAS16293
5.7.3 The HC3N deuteration
6 Formamide in Low- and Intermediate-Mass Objects
6.3 Source sample
6.4 Observations and data reduction
6.5.1 Line spectra
6.5.2 Derivation of physical properties
Rotational diagram analysis
Radiative transfer analysis taking into account the source structure
6.6.1 Formation routes of NH2CHO
6.6.2 Correlation between HNCO and NH2CHO
7 Conclusions and FutureWork
A Figures on the HC3N modeling for the Chapter 5
B Analyses comparison in Chapter 6
B.1 Comparison between GRAPES and rotational diagram analyses
C Chapter 6 Tables
D Chapter 6 Figures