karllark / spitzer_mir_ext

Spitzer MIR extinction curves

Code for a Spitzer MIR Extinction Paper

Routines for Spitzer MIR dust extinction curve paper. This paper is Gordon, Misselt, et al. (in prep).

In Development!

Active development. Data still in changing. Use at your own risk.

Contributors

Karl Gordon

License

This code is licensed under a 3-clause BSD style license (see the LICENSE file).

Extinction Curves

External packages used include dust_extintion, measure_extinction, and extstar_data. The basic information on each star is given in extstar_data including (TBD) the IRS/IUE/STIS spectra. dust_extinction provides the fitting curves shapes (FM90, AxAvToExv). measure_extinction gives the routines to read the data and calculate extinction curves.

Curves calculated with run_all_ext.

NIR/MIR portion fit with powerlaw + plus 2 modified drudes using 'fit_plasymdrude_all_ext`.

UV portion fit with FM90 shape using fit_fm90_all_ext.

Average extinction curve made using Figs/plot_mir_mext.py --alav --ave data/all_ext_14oct20_diffuse.dat.

Fit average: python utils/fit_mir_ext_powerlaw.py data/all_ext_14oct20_diffuse_ave.fits --nsteps=10000 --burnfrac=0.4 --png python utils/fit_uv_ext_fm90.py data/all_ext_14oct20_diffuse_ave_POWLAW2DRUDE.fits --png --nsteps=10000 --burnfrac=0.4

Figures

1. MIR spectra comparison stars: Figs/plot_mir_spectra_standards.py
2. MIR spectra reddened stars: Figs/plot_mir_spectra_reddened.py
3. MIR spectra windy stars: Figs/plot_mir_spectra_reddened_windy.py
4. J-K versus K-MIPS24: Figs/plot_nir_mir_phot.py
5. Example fit: utils/fit_mir_ext_powerlaw.py fits/hd112272_hd204172_ext.fits --notitle --nsteps=10000 --burnfrac=0.4 --pdf
6. MIR E(lambda-V) curves: Figs/plot_mir_mext.py data/all_ext_14oct20_pldrude.dat
7. UV+MIR extinction: Figs/plot_uv_mir_mext.py data/all_ext_14oct20_pldrude.dat --models
8. Sample properties: Figs/plot_sampprob.py data/all_ext_14oct20_pldrude.dat
9. UV+MIR Average diffuse extinction curve: Figs/plot_ave_ext.py
10. MIR ext literature comparison: Figs/plot_ext_litcomp.py
11. MIR ext dust grain model comparison: Figs/plot_ext_modcomp.py
12. Silicate versus various: Figs/plot_silicate.py data/all_ext_14oct20_pldrude.dat
13. Dense versus diffuse ave: Figs/plot_ave_ext.py --dense
14. Fit residuals of average curve: Figs/plot_residuals.py
15. A(V) from E(K-V) or E(I3-V): Figs/plot_avekv.py data/all_ext_14oct20_pldrude.dat

Tables

1. Spiter Data: idl/make_ir_phot_table.pro
2. Opt/NIR Data: idl/make_ir_ophot_table.pro
3. Sightline properties: by hand
4. MIR extinction parameters: by hand
5. MIR general extinction parameters: Tables/gen_mir_ext_params_table.py data/all_ext_14oct20_pldrude.dat
6. MIR silicate extinction parameters: Tables/gen_mir_ext_params_table.py data/all_ext_14oct20_pldrude.dat --sil
7. UV extinction parameters: Tables/gen_uv_ext_params_table.py data/all_ext_14oct20_uv.dat
8. Generated as part of Figs/plot_ave_ext.py and saved as average_table.tex

spitzer_mir_ext's Issues

Fitting to get A(V) and other parameters

Document in paper how the P92 fitting goes.
Mention the dust_extinction repository.
Give a table of the P92+A(V) parameters.

Write conclusions/summary

Make spectra plots

Identify good and bad stars for extinction from these plots
Bad = not flat in lambda^4*F
Bad may also mean lost of emission lines.
Document this in the paper.

Create average extinction curve

In units of A(l)/A(V).
Both from the data and from the fits?
Or maybe from the data and then provide the P92 fit parameters.
Document in paper.

Complete the data section

Revise intro

Do this after most of the rest of the paper is fully drafted.

Measured extinction curve plot

Leave in units of E(l-V) given normalization challenges

Comparison to previous work

Observed MIR extinction curves
Dust grain model MIR extinction curves

Correlations in fit parameters

Especially UV to MIR features