Search

Context. The ionization fraction in the neutral interstellar medium (ISM) plays a key role in the physics and chemistry of the ISM, from controlling the coupling of the gas to the magnetic field to allowing fast ion-neutral reactions that drive interstellar chemistry. Most estimations of the ionization fraction have relied on deuterated species such as DCO+, whose detection is limited to dense cores representing an extremely small fraction of the volume of the giant molecular clouds that they are part of. As large field-of-view hyperspectral maps become available, new tracers may be found. The growth of observational datasets is paralleled by the growth of massive modeling datasets and new methods need to be devised to exploit the wealth of information they contain. Aims. We search for the best observable tracers of the ionization fraction based on a grid of astrochemical models, with the broader aim of finding a general automated method applicable to searching for tracers of any unobservable quantity based on grids of models. Methods. We built grids of models that randomly sample a large range of physical conditions (unobservable quantities such as gas density, temperature, elemental abundances, etc.) and computed the corresponding observables (line intensities, column densities) and the ionization fraction. We estimated the predictive power of each potential tracer by training a random forest model to predict the ionization fraction from that tracer, based on these model grids. Results. In both translucent medium and cold dense medium conditions, we found several observable tracers with very good predictive power for the ionization fraction. Many tracers in cold dense medium conditions are found to be better and more widely applicable than the traditional DCO+/HCO+ ratio. We also provide simpler analytical fits for estimating the ionization fraction from the best tracers, and for estimating the associated uncertainties. We discuss the limitations of the present st

Molecular hydrogen being unobservable in cold molecular clouds, the column density measurements of molecular gas currently rely either on dust emission observation in the far-IR or on star counting. (Sub-)millimeter observations of numerous trace molecules are effective from ground based telescopes, but the relationships between the emission of one molecular line and the H2 column density (NH2) is non-linear and sensitive to excitation conditions, optical depths, abundance variations due to the underlying physico-chemistry. We aim to use multi-molecule line emission to infer NH2 from radio observations. We propose a data-driven approach to determine NH2 from radio molecular line observations. We use supervised machine learning methods (Random Forests) on wide-field hyperspectral IRAM-30m observations of the Orion B molecular cloud to train a predictor of NH2, using a limited set of molecular lines as input, and the Herschel-based dust-derived NH2 as ground truth output. For conditions similar to the Orion B molecular cloud, we obtain predictions of NH2 within a typical factor of 1.2 from the Herschel-based estimates. An analysis of the contributions of the different lines to the predictions show that the most important lines are $^{13}$CO(1-0), $^{12}$CO(1-0), C$^{18}$O(1-0), and HCO$^+$(1-0). A detailed analysis distinguishing between diffuse, translucent, filamentary, and dense core conditions show that the importance of these four lines depends on the regime, and that it is recommended to add the N$_2$H$^+$(1-0) and CH$_3$OH(20-10) lines for the prediction of NH2 in dense core conditions. This article opens a promising avenue to directly infer important physical parameters from the molecular line emission in the millimeter domain. The next step will be to try to infer several parameters simultaneously (e.g., NH2 and far-UV illumination field) to further test the method. [Abridged]

New stars in galaxies form in dense, molecular clouds of the interstellar medium. Measuring how the mass is distributed in these clouds is of crucial importance for the current theories of star formation. This is because several open issues in them, such as the strength of different mechanism regulating star formation and the origin of stellar masses, can be addressed using detailed information on the cloud structure. Unfortunately, quantifying the mass distribution in molecular clouds accurately over a wide spatial and dynamical range is a fundamental problem in the modern astrophysics. This thesis presents studies examining the structure of dense molecular clouds and the distribution of mass in them, with the emphasis on nearby clouds that are sites of low-mass star formation. In particular, this thesis concentrates on investigating the mass distributions using the near infrared dust extinction mapping technique. In this technique, the gas column densities towards molecular clouds are determined by examining radiation from the stars that shine through the clouds. In addition, the thesis examines the feasibility of using a similar technique to derive the masses of molecular clouds in nearby external galaxies. The papers presented in this thesis demonstrate how the near infrared dust extinction mapping technique can be used to extract detailed information on the mass distribution in nearby molecular clouds. Furthermore, such information is used to examine characteristics crucial for the star formation in the clouds. Regarding the use of extinction mapping technique in nearby galaxies, the papers of this thesis show that deriving the masses of molecular clouds using the technique suffers from strong biases. However, it is shown that some structural properties can still be examined with the technique., Galaksien uudet tähdet syntyvät tähtienvälisen avaruuden tiheissä kaasupilvissä joita kutsutaan molekyylipilviksi. Näiden molekyylipilvien rakenteen määrittäminen on tähtien syntyteorioiden kannalta erittäin tärkeää, koska monet teorioihin liittyvät fysikaaliset ilmiöt heijastuvat suoraan pilvien rakenteeseen. Molekyylipilvien rakenteen tarkka määritys on kuitenkin ongelmallista, sillä kaasua josta pilvet koostuvat on verrattaen vaikea havaita. Tutkin väitöskirjatyössäni tähtien syntyprosessin alkuhetkiä tarkastelemalla kaasun muodostamia rakenteita molekyylipilvissä. Työssä keskitytään soveltamaan uutta ns. lähi-infrapuna-alueen värieksessi-menetelmää, jossa pilven rakenne määritetään tarkastelemalla sen läpi loistavien tähtien säteilyä. Menetelmän soveltamisen lisäksi työssä tutkitaan menetelmän tarkkuutta ja mahdollisia sovellusalueita. Väitöskirjatyöni tutkimukset havainnollistavat kuinka väriksessi-menetelmällä voidaan saavuttaa verrattaen tarkka ja näin ollen erittäin hyödyllinen näkymä tähtien syntyrakenteisiin molekyylipilvissä. Lisäksi tutkimuksissa tarkastellaan erilaisten rakenteiden merkitystä nykyisille tähtien syntyteorioille. Tutkimuksissa selvitetään myös millä tarkkuudella menetelmää voidaan soveltaa Linnunradan ulkopuolisten galaksien molekyylipilvien tutkimukseen.