The rm_type Argument

By default, the JAVELIN module will ssume that the input light curves are spectroscopic light curves, and perform spectrooscopic reverberation mapping (RM) analysis. However, pyPetal can also analyze light curves using photometric RM analysis.

This can be changed with the rm_type argument, which can be either “spec” or “phot”.

Warning

JAVELIN’s photometric RM analysis can only be done with two light curves, so together must be set to False if rm_type=phot.

We can run the JAVELIN module assuming photometric light curves:

%matplotlib inline
import pypetal_jav.pipeline as pl

output_dir = 'javelin_output4/'
line_names = ['Continuum', 'H-alpha', 'H-beta']

params = {
    'rm_type': "phot",
    'together': False
}

res = pl.run_pipeline( output_dir, line_names,
                 javelin_params=params,
                 verbose=True,
                 plot=True,
                 file_fmt='ascii',
                 lc_unit=['', 'Jy', 'mJy'],
                 time_unit='d',
                 threads=40)

Running JAVELIN
--------------------
rm_type: phot
lagtobaseline: 0.3
laglimit: [[-1976.98849, 1976.98849], [-1976.98849, 1976.98849]]
fixed: True
p_fix: True
subtract_mean: True
nwalker: 100
nburn: 100
nchain: 100
output_chains: True
output_burn: True
output_logp: True
nbin: 50
metric: med
together: False
--------------------

start burn-in
nburn: 100 nwalkers: 100 --> number of burn-in iterations: 10000
burn-in finished
save burn-in chains to /home/stone28/pypetal/javelin_output4/H-alpha/javelin/burn_cont.txt
start sampling
sampling finished
acceptance fractions for all walkers are
0.66 0.62 0.68 0.72 0.68 0.68 0.80 0.69 0.75 0.72 0.68 0.62 0.78 0.78 0.61 0.72 0.61 0.74 0.75 0.76 0.70 0.67 0.70 0.75 0.66 0.74 0.79 0.67 0.69 0.75 0.72 0.73 0.70 0.73 0.74 0.70 0.67 0.64 0.71 0.74 0.69 0.66 0.71 0.70 0.65 0.60 0.71 0.76 0.68 0.69 0.69 0.67 0.71 0.67 0.78 0.55 0.64 0.62 0.70 0.64 0.72 0.72 0.67 0.76 0.75 0.68 0.70 0.64 0.75 0.80 0.74 0.62 0.70 0.73 0.73 0.78 0.73 0.66 0.61 0.76 0.75 0.69 0.76 0.69 0.73 0.74 0.63 0.67 0.69 0.73 0.73 0.67 0.80 0.66 0.75 0.76 0.72 0.76 0.76 0.69
save MCMC chains to /home/stone28/pypetal/javelin_output4/H-alpha/javelin/chain_cont.txt
save logp of MCMC chains to /home/stone28/pypetal/javelin_output4/H-alpha/javelin/logp_cont.txt
HPD of sigma
low:    1.776 med    2.180 hig    2.920
HPD of tau
low:  140.397 med  214.958 hig  392.480

run parallel chains of number 40
start burn-in
using priors on sigma and tau from continuum fitting
[[  1.776 140.397]
 [  2.18  214.958]
 [  2.92  392.48 ]]
penalize lags longer than 0.30 of the baseline
nburn: 100 nwalkers: 100 --> number of burn-in iterations: 10000
burn-in finished
save burn-in chains to /home/stone28/pypetal/javelin_output4/H-alpha/javelin/burn_rmap.txt
start sampling
sampling finished
acceptance fractions are
0.06 0.06 0.04 0.05 0.06 0.01 0.03 0.13 0.04 0.09 0.06 0.03 0.06 0.05 0.05 0.05 0.02 0.04 0.13 0.06 0.10 0.02 0.08 0.02 0.03 0.07 0.09 0.12 0.12 0.14 0.07 0.07 0.04 0.05 0.13 0.06 0.13 0.08 0.13 0.01 0.07 0.02 0.03 0.01 0.03 0.09 0.03 0.05 0.08 0.08 0.03 0.05 0.06 0.04 0.04 0.10 0.03 0.01 0.07 0.02 0.05 0.05 0.14 0.07 0.05 0.03 0.02 0.06 0.01 0.07 0.09 0.07 0.10 0.08 0.09 0.10 0.07 0.08 0.02 0.03 0.13 0.06 0.16 0.14 0.00 0.07 0.02 0.02 0.08 0.03 0.05 0.10 0.08 0.00 0.04 0.02 0.08 0.06 0.11 0.08
save MCMC chains to /home/stone28/pypetal/javelin_output4/H-alpha/javelin/chain_rmap.txt
save logp of MCMC chains to /home/stone28/pypetal/javelin_output4/H-alpha/javelin/logp_rmap.txt
HPD of sigma
low:    2.205 med    2.446 hig    2.742
HPD of tau
low:  202.760 med  254.754 hig  317.105
HPD of lag_line
low: -1932.874 med  182.854 hig 1943.414
HPD of wid_line
low:    3.068 med    5.826 hig    9.226
HPD of scale_line
low:    0.636 med    0.748 hig    0.884
HPD of alpha
low:    0.063 med    0.244 hig    0.507

covariance matrix calculated
covariance matrix decomposed and updated by U

start burn-in
nburn: 100 nwalkers: 100 --> number of burn-in iterations: 10000
burn-in finished
save burn-in chains to /home/stone28/pypetal/javelin_output4/H-beta/javelin/burn_cont.txt
start sampling
sampling finished
acceptance fractions for all walkers are
0.67 0.70 0.77 0.67 0.60 0.73 0.73 0.70 0.67 0.64 0.69 0.64 0.76 0.71 0.66 0.68 0.85 0.80 0.72 0.65 0.72 0.64 0.63 0.71 0.72 0.65 0.64 0.77 0.77 0.69 0.63 0.70 0.76 0.81 0.74 0.87 0.64 0.72 0.72 0.63 0.77 0.71 0.78 0.67 0.66 0.75 0.80 0.71 0.75 0.78 0.74 0.72 0.72 0.77 0.79 0.77 0.79 0.77 0.67 0.67 0.78 0.70 0.70 0.82 0.69 0.68 0.77 0.69 0.73 0.72 0.65 0.66 0.75 0.71 0.70 0.68 0.69 0.62 0.63 0.69 0.72 0.76 0.69 0.66 0.68 0.73 0.69 0.73 0.78 0.57 0.80 0.63 0.73 0.59 0.72 0.76 0.71 0.66 0.73 0.65
save MCMC chains to /home/stone28/pypetal/javelin_output4/H-beta/javelin/chain_cont.txt
save logp of MCMC chains to /home/stone28/pypetal/javelin_output4/H-beta/javelin/logp_cont.txt
HPD of sigma
low:    1.753 med    2.145 hig    2.806
HPD of tau
low:  140.488 med  211.810 hig  366.568

run parallel chains of number 40
start burn-in
using priors on sigma and tau from continuum fitting
[[  1.753 140.488]
 [  2.145 211.81 ]
 [  2.806 366.568]]
penalize lags longer than 0.30 of the baseline
nburn: 100 nwalkers: 100 --> number of burn-in iterations: 10000
burn-in finished
save burn-in chains to /home/stone28/pypetal/javelin_output4/H-beta/javelin/burn_rmap.txt
start sampling
sampling finished
acceptance fractions are
0.06 0.05 0.04 0.06 0.09 0.09 0.10 0.08 0.01 0.08 0.13 0.12 0.16 0.05 0.10 0.05 0.05 0.15 0.02 0.11 0.03 0.11 0.05 0.12 0.20 0.03 0.12 0.08 0.08 0.01 0.04 0.06 0.07 0.04 0.06 0.00 0.00 0.06 0.09 0.10 0.11 0.11 0.14 0.08 0.14 0.10 0.06 0.03 0.15 0.12 0.05 0.07 0.08 0.01 0.05 0.02 0.09 0.03 0.11 0.08 0.13 0.04 0.07 0.08 0.06 0.12 0.05 0.14 0.17 0.02 0.12 0.13 0.07 0.09 0.11 0.10 0.10 0.05 0.05 0.01 0.06 0.12 0.10 0.07 0.14 0.11 0.06 0.10 0.05 0.05 0.06 0.00 0.01 0.16 0.06 0.09 0.08 0.07 0.02 0.00
save MCMC chains to /home/stone28/pypetal/javelin_output4/H-beta/javelin/chain_rmap.txt
save logp of MCMC chains to /home/stone28/pypetal/javelin_output4/H-beta/javelin/logp_rmap.txt
HPD of sigma
low:    2.146 med    2.303 hig    2.542
HPD of tau
low:  214.844 med  247.880 hig  319.664
HPD of lag_line
low: -1902.894 med  899.578 hig 1926.539
HPD of wid_line
low:    4.874 med    7.818 hig   10.254
HPD of scale_line
low:    0.360 med    0.489 hig    0.621
HPD of alpha
low:    0.074 med    0.190 hig    0.389

covariance matrix calculated
covariance matrix decomposed and updated by U

There are two main differences in the output dictionary (and data) for this case:

• rmap_model will return a javelin.lcmodel.Pmap_Model object instead of a javelin.lcmodel.Rmap_Model object.

• There are 4 tophat parameters (t, w, s, \(\alpha\)) for each line instead of 3.

All other output data and files are the same as in the basic case.