relativistic-reverberation-in-mcg-5-23-16-and-swift-j127.4-5654

import os
import sys
base_dir = '/home/abzoghbi/data/mcg5-23-16/nustar_re_analysis'
if not base_dir in sys.path: sys.path.insert(0, base_dir)

from helpers import *

%load_ext autoreload
%autoreload 2

os.chdir(base_dir)
wdir = 'data/timing'
os.system('mkdir -p %s/lag'%wdir)
os.chdir(wdir)

nu_obsids = np.array(['60001046002', '60001046004', '60001046006', '60001046008'])

#!rm lc_22l3_512.npz
loc_info, nen, dt = [base_dir, '22l3'], 22, 512
LC = read_lc(loc_info, nu_obsids, dt, nen, combine_ab=True)

#!rm lc_22l3_512_bgd.npz
loc_info, nen, dt = [base_dir, '22l3'], 22, 512
LCb = read_lc(loc_info, nu_obsids, dt, nen, combine_ab=True, bgd=True)

LC = remove_high_bgd(LC, LCb)

reading data from lc_22l3_512.npz ..
reading data from lc_22l3_512_bgd.npz ..

# average counts per bin #
txt = '\n'.join([' '.join(['%8.4g'%(l[1].mean()*dt) for l in lc]) for lc in LC])
print(txt)

7    89.82    101.6    104.7
  116.9    133.9    136.4
6      162    187.2    191.9
8    170.1    194.2    199.9
6    151.5    172.4    176.7
9    183.8    207.5    214.8
8    193.2    218.4    225.7
1    241.9    269.1    278.8
7    225.6    247.7    257.9
4    212.5    236.5    245.7
  171.7    191.7    200.5
  179.5    199.1    208.1
6    176.8    198.5      208
2    234.2    258.1    272.4
2    188.3    207.4    217.4
2    146.9    160.4      170
1    113.7    123.8    131.4
9    81.72    89.02    94.24
5    111.6    119.7    126.4
02    45.44    47.16    52.52
03    26.58    28.11    30.25
4    16.59    13.63    17.31

# plot light curves in the last bin
ie = 21
nlc = len(LC[ie])
fig, ax = plt.subplots(nlc, 1, figsize=(12, 8))
for ilc,lc in enumerate(LC[ie]):
    ax[ilc].errorbar((lc[0] - lc[0][0])/1e3, lc[1], lc[2], fmt='o', ms=3, alpha=0.5)
    ax[ilc].set_xlim([0, 450])
    

png

Prepare Segments

ebins, dt = ('3 3.3 3.6 4 4.4 4.8 5.2 5.7 6.3 6.9 7.6 8.3 9.1 10 '
             '11.7 13.8 16.2 19 22 31 42 58 79'), 512

tlen = 200
Lc, LcIdx = split_LC_to_segments(LC, tlen*1e3, plot=False)

`_f7`: Fig. 7 in Zoghbi+14: `6e-6 6e-4`

Note that because of the segment lengths, the effective low bin bounday is 1e-5, so we use it
a,b,c,d refer to the individual observations.

fqL = np.array([2e-6, 6e-6, 6e-4, 1.3e-3])
iEn = [[0,1], [2,3], [4,5], [6,7], [8,9], [10,11], [12,13], [14,15], [16,17], [18,19], [20,21]]
lag_f7 = calculate_lag(Lc, fqL, dt, ebins, 'lag/lag_22l3b_f7.npz', iEn=iEn,  mcmc=[-4, 2000], logmod=False)

cache file lag/lag_22l3b_f7.npz found. Reading ...!

lag_f7a = calculate_lag(Lc, fqL, dt, ebins, 'lag/lag_22l3b_f7a.npz', iEn=iEn, iLc=[0],  
                        mcmc=[-4, 2000], logmod=False)

cache file lag/lag_22l3b_f7a.npz found. Reading ...!

lag_f7b = calculate_lag(Lc, fqL, dt, ebins, 'lag/lag_22l3b_f7b.npz', iEn=iEn, iLc=[1],
                        mcmc=[-4, 2000], logmod=False)

cache file lag/lag_22l3b_f7b.npz found. Reading ...!

lag_f7c = calculate_lag(Lc, fqL, dt, ebins, 'lag/lag_22l3b_f7c.npz', iEn=iEn, iLc=[2],
                        mcmc=[-4, 2000], logmod=False)

cache file lag/lag_22l3b_f7c.npz found. Reading ...!

lag_f7d = calculate_lag(Lc, fqL, dt, ebins, 'lag/lag_22l3b_f7d.npz', iEn=iEn, iLc=[3], 
                        mcmc=[-4, 2000], logmod=False)

cache file lag/lag_22l3b_f7d.npz found. Reading ...!

lag_f7bc = calculate_lag(Lc, fqL, dt, ebins, 'lag/lag_22l3b_f7bc.npz', iEn=iEn, iLc=[1,2], 
                          mcmc=[-4, 2000], logmod=False)

cache file lag/lag_22l3b_f7bc.npz found. Reading ...!

# process mcmc from fqlag and plot histograms 
lagMC_f7   = proc_lag_mcmc('lag_22l3b_f7')
lagMC_f7a  = proc_lag_mcmc('lag_22l3b_f7a')
lagMC_f7b  = proc_lag_mcmc('lag_22l3b_f7b')
lagMC_f7c  = proc_lag_mcmc('lag_22l3b_f7c')
lagMC_f7d  = proc_lag_mcmc('lag_22l3b_f7d')
lagMC_f7bc = proc_lag_mcmc('lag_22l3b_f7bc')

png

for x in ['a', 'b', 'c', 'd', '', 'bc']:
    exec('plot_lag(lagMC_f7%s)'%x)
    exec('write_lag(lagMC_f7%s, "_22l3bMC_f7%s", pha=True)'%(x,x))

png

# Model the PHA data from fqlag mcmc with xspec
os.chdir('%s/%s/lag/pha'%(base_dir, wdir))
for x in ['a', 'b', 'c', 'bc', 'd', '']:
    fit_pha_with_loglin('22l3bMC_f7%s__1'%x, recalc=1)
os.chdir('%s/%s'%(base_dir, wdir))

chains for 22l3bMC_f7a__1
chains for 22l3bMC_f7b__1
chains for 22l3bMC_f7c__1
chains for 22l3bMC_f7bc__1
chains for 22l3bMC_f7d__1
chains for 22l3bMC_f7__1

png

Different Frequency binning

Lag vs freq

Compare iron line to Compton hump and highest energies

# '3 3.3 3.6 4 4.4 4.8 5.2 5.7 6.3 6.9 7.6 8.3 9.1 10 11.7 13.8 16.2 19 22 31 42 58 79'
fqL, fqd = get_fq_bins(Lc[0], dt, mode=1, Nfq=5)
iEn = [[6,7,8], [16,17,18], [19,20,21]]
lag_fq_1 = calculate_lag(Lc, fqL, dt, ebins, 'lag/lag_fq_1.npz', iEn=iEn, iref=[0,1,2,3,4,5], 
                        mcmc=[-4, 2000], logmod=False)

nfq:  7
fqL:  2.31413e-06 4.97082e-06 2.13549e-05 4.5871e-05 9.85324e-05 0.000211651 0.000454632 0.00195312
cache file lag/lag_fq_1.npz found. Reading ...!

# process mcmc from fqlag and plot histograms 
lag_fqMC_1 = proc_lag_mcmc('lag_fq_1')

png

plot_lag(lag_fqMC_1)
write_lag(lag_fqMC_1, '_fqMC_1', pha=False, pha_fq=True, null_tests=False)

png

Followup the `_fqMC__1` results

# 2.31413e-06 4.97082e-06 2.13549e-05 4.5871e-05 9.85324e-05 0.000211651 0.000454632 0.00195312
fqL = np.array([2e-6, 5e-6, 2e-5, 1e-4, 2e-4, 2e-3])
iEn = [[0,1], [2,3], [4,5], [6,7], [8,9], [10,11], [12,13], [14,15], [16,17], [18,19], [20,21]]
lag_e1 = calculate_lag(Lc, fqL, dt, ebins, 'lag/lag_22l3b_e1.npz', iEn=iEn, mcmc=[-4, 2000], logmod=False)

cache file lag/lag_22l3b_e1.npz found. Reading ...!

# process mcmc from fqlag and plot histograms 
lagMC_e1 = proc_lag_mcmc('lag_22l3b_e1')

png

plot_lag(lagMC_e1)
write_lag(lagMC_e1, '_22l3bMC_e1', pha=True)

png

# Model the PHA data from fqlag mcmc with xspec
os.chdir('%s/%s/lag/pha'%(base_dir, wdir))
fit_pha_with_loglin('22l3bMC_e1__1', recalc=1)
fit_pha_with_loglin('22l3bMC_e1__2', recalc=1)
fit_pha_with_loglin('22l3bMC_e1__3', recalc=1)
os.chdir('%s/%s'%(base_dir, wdir))

chains for 22l3bMC_e1__1
chains for 22l3bMC_e1__2
chains for 22l3bMC_e1__3

png

`_e1` for individual groups

lag_e1a = calculate_lag(Lc, fqL, dt, ebins, 'lag/lag_22l3b_e1a.npz', iEn=iEn, iLc=[0],  
                        mcmc=[-4, 2000], logmod=False)

cache file lag/lag_22l3b_e1a.npz found. Reading ...!

lag_e1d = calculate_lag(Lc, fqL, dt, ebins, 'lag/lag_22l3b_e1d.npz', iEn=iEn, iLc=[3], 
                        mcmc=[-4, 2000], logmod=False)

cache file lag/lag_22l3b_e1d.npz found. Reading ...!

lag_e1bc = calculate_lag(Lc, fqL, dt, ebins, 'lag/lag_22l3b_e1bc.npz', iEn=iEn, iLc=[1,2], 
                          mcmc=[-4, 2000], logmod=False)

cache file lag/lag_22l3b_e1bc.npz found. Reading ...!

# process mcmc from fqlag and plot histograms 
lagMC_e1   = proc_lag_mcmc('lag_22l3b_e1')
lagMC_e1a  = proc_lag_mcmc('lag_22l3b_e1a')
lagMC_e1b  = proc_lag_mcmc('lag_22l3b_e1b')
lagMC_e1c  = proc_lag_mcmc('lag_22l3b_e1c')
lagMC_e1d  = proc_lag_mcmc('lag_22l3b_e1d')
lagMC_e1bc = proc_lag_mcmc('lag_22l3b_e1bc')

png

# plot lag-energy data from the mcmc chains #
for x in ['a', 'b', 'c', 'd', 'bc', '']:
    exec('plot_lag(lagMC_e1%s)'%x)
    exec('write_lag(lagMC_e1%s, "_22l3bMC_e1%s", pha=True)'%(x,x))

png

relativistic-reverberation-in-mcg-5-23-16-and-swift-j127.4-5654

Codes and data associated with the paper: A Hard Look At Relativistic Reverberation in MCG--5-23-16 & SWIFT J2127.4+5654: Testing the Lamp-Post Model

Prepare Segments

_f7: Fig. 7 in Zoghbi+14: 6e-6 6e-4

Different Frequency binning

Lag vs freq

Followup the _fqMC__1 results

_e1 for individual groups

`_f7`: Fig. 7 in Zoghbi+14: `6e-6 6e-4`

Followup the `_fqMC__1` results

`_e1` for individual groups