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Inside a typical astrochemical model

Inside a typical astrochemical model. Serena Viti IFSI-CNR (Rome). Preview. Why bother with astrochemical models A tour inside one Its ‘beauties’ and ‘horrors’ How to use it Improvements. Why astrochemistry?. The universe is chemically rich

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Inside a typical astrochemical model

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  1. Inside a typical astrochemical model Serena Viti IFSI-CNR (Rome)

  2. Preview • Why bother with astrochemical models • A tour inside one • Its ‘beauties’ and ‘horrors’ • How to use it • Improvements

  3. Why astrochemistry? • The universe is chemically rich • There is a large degree of diversity in a variety of interstellar and circumstellar regions • There is a large degree of diversity in the same region as a function of time

  4. Why do we need astrochemical models? • To represent the chemical and physical behaviour of astronomical objects • To interpret astronomical data • To test astronomical and chemical ideas • To estimate chemical abundances of the ISM, taking into account most of the chemical and dynamical processes affecting the gas and dust.

  5. Needs of an astrochemical model • Flexibility: • Time dependent/Time independent • Depth dependent/Single Point • Gas-grain/Gas chemistry • Static/Dynamic • Modularity • Easy to implement new experimental and theoretical input data

  6. Inside a chemical model: examples of input parameters • Initial elemental abundances • Initial temperature • Initial density • Size • Radiation field • Cosmic ray ionization rate • Dynamical ‘switches’ • Chemical species and rate file • Degree of depletion

  7. What does a model do? • Calculates density profiles as a fn of time and space • Calculates temperature structure as a fn of time and space • Calculates chemical rates for all relevant reactions • Calculates fractional abudances (gas-phase and solid by taking into account feeze out and desorption)

  8. Inside a chemical model: examples of output parameters • Chemical abundances as a fn of time and space • How the density varies with time and space • Thermal balance • Visual Extinction • Radiation field as a fn of time and space

  9. s p a c e S p a c e Time

  10. What needs to be done • Form the dense clumps (gradient of densities) • This involves: depletion, surface reactions • Account for the UV field from the source (time and space dependent) • Account for X-ray emission

  11. Example of output HCO+ abundance as a fn of time at Av ~ 3 mags

  12. Its ‘beauties’ • Very flexible because “ab initio” • Free to include as many species and reactions as we want/need • Time-dependent • Fast

  13. Its ‘horrors’ (too many to list all) • Degree of freedom may be as many as the no of reactions • Many rate coefficients are not experimental and/or have a large error bar • Rates for surface reactions missing • Surface reactions missing! • … • …

  14. What we still need: • Experimentally: • Branching ratios • Sticking probabilites • Mobilities of adsorbed species • Hydrogenation on grain surfaces • Desorption

  15. Laboratory Data  Time and depth dependentChemical Models Observations (high spectral and spatial resolution)

  16. Conclusions • Close interaction between models, observations and chemical data • Full coupling of chemistry with hydrodynamics and radiative transfer Investigate a larger parameter space

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