13
Infall rates from observations Joseph Mottram 1

Infall rates from observations

  • Upload
    riona

  • View
    22

  • Download
    0

Embed Size (px)

DESCRIPTION

Infall rates from observations. Joseph Mottram. Why is infall relevant?. Infall must happen for star formation to proceed The rate of infall on envelope scales is important for the t imescale for envelope depletion - PowerPoint PPT Presentation

Citation preview

Page 1: Infall  rates from observations

Infall rates from observations

Joseph Mottram

1

Page 2: Infall  rates from observations

Why is infall relevant?

• Infall must happen for star formation to proceed• The rate of infall on envelope scales is important for

the timescale for envelope depletion• Balance of infall and outflow relates to local star

formation efficiency• Effect disk properties including: – Stability– Heating– Chemical composition– When it forms?

2

Page 3: Infall  rates from observations

Observing Infall

• Identified through asymmetric line profiles

• Commonly used: HCO+, CS, H2CO, N2H+

3Myers et al., 2000

Page 4: Infall  rates from observations

Extracting the mass infall rate

4

1. Skewness/asymmetry (e.g. Gregersen+ `97, Mardonnes+ `97) – doesn’t measure infall rate but can identify infall candidates. No model required.

2. Slab model (e.g. Myers+ `96, Di Francesco+ `01) – calculate mass infall rate at characteristic radius. No RT needed but no information on velocity variation.

Page 5: Infall  rates from observations

Extracting the mass infall rate

3. PV diagram (e.g. Tobin+ `12, Wang+ `12) – use analytical/RT model and observed PV & moment 1 maps to constrain balance of infall and other motions

4. 1-D RT model to fit line profiles from multiple lines (e.g. Hogerheijde & Sandell `00, Mottram+ `13) – self-consistently constrain fit to 1-D model using line shape and intensity.

5

Page 6: Infall  rates from observations

Water line profiles• Line profiles dominated by

outflow – remove with Gaussian fits

• IPC observed towards 7 WISH LM sources

• Mostly only in the ground-state lines– Also 202-111 line in IRAS4A

6

Mottram et al., 2013

Page 7: Infall  rates from observations

1-D line modelling• Tdust and n power-law profiles from a grid of 1-D continuum model fits to the

SED and SCUBA images (Kristensen+ ‘12)

• Non-LTE line radiative transfer modelling using RATRAN (Hogerheijde & van der Tak ‘00)

• Assume:– v = v0 (r/r0)-0.5

– Tgas = Tdust

– o/p for H2 is thermal, for H2O = 3

• Water abundance profile from simple chemical network (Schmalzl+ in prep.)

7

Page 8: Infall  rates from observations

8

Limit of Infall radius

• Infall continues to at least ~1000AU

Mottram et al., 2013

Page 9: Infall  rates from observations

9

Limit of Infall radius

• Infall must be to outer edge of model for all sources

• Similar result also found by Beloche et al., ‘06

Mottram et al., 2013

Page 10: Infall  rates from observations

10

Extent and rate of infall

• Infall to ≤ 3000 AU in four other sources – mass infall rate of order few x 10-5 Myr-1

• Outer edge of the model must be infalling for all sources

• tff ~ 10 tinf , tinf 0.4 – 2.5 x 104 yrs

• Mass infall rate in IRAS4A (2×10-4 Myr-1 at 1000 AU) is an order of magnitude larger than the mass outflow and accretion rate

• Profile in two source not due to envelope infall

Page 11: Infall  rates from observations

Future requirements & prospects

• To date, almost all analysis methods have had to impose assumptions about the velocity field, usually 1D

• Few have been directly sensitive to infall on envelope-> disk scales

• ALMA will be able to change this, but it requires:– Multiple transitions and/or molecules which are sensitive

to different spatial scales– Observations sensitive to emission from few 1000 AU to

~100AU– Consider infall, turbulence and rotation– Self-consistent analysis which fits both line profiles and

spatial variation11

Page 12: Infall  rates from observations

12

Page 13: Infall  rates from observations

13

Water abundance profile

• Simple chemistry required to reproduce all line profiles

Mottram et al., 2013

Cloud/ISM