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JAVELIN

What is JAVELIN

JAVELIN stands (reluctantly) for Just Another Vehicle for Estimating Lags In Nuclei. As a version of our SPEAR algorithm written in Python to provide more flexibility in both functionality and visualization. You can use JAVELIN to model quasar variability using different covariance functions (Zu et al. 2013), and measure emission line lags using either spectroscopic light cruves (Zu et al. 2011) or photometric light curves (Zu et al. 2016).

Changelog

Please send an email to yingzu AT sjtu.edu.cn if you have any questions.

Version 0.33 includes the new Disk_Model method from Mudd et al. 2017

Version 0.32alpha now has the capability to keep some parameters fixed during MCMC sampling

Version 0.3 features photometric capabilities

Install JAVELIN

Prerequisites

JAVELIN requires

  1. Fortran Compiler (>F90)
  2. Python (>2.5)
  3. Numpy (>1.4)
  4. Scipy (>0.1)
  5. Matplotlib (>1.0)

We strongly recommend that you have the Lapack and Atlas libraries installed on the system, although they are not necessary. It requires no extra effort to install them, as many systems either come with LAPACK and BLAS pre-installed (MAC), or have them conveniently in software repositories (Linux distributions). They provide native support for fast solving linear systems inside JAVELIN, otherwise JAVELIN will simply install a subset of the the Lapack source from scratch.

Installation

To download the package, you can either go to the Downloads tab for stable releases or directly pull the cutting-edge version using mercurial. We strongly suggest you clone the repo for the latest version, as we are updating the package on a regular basis.

You can install JAVELIN by the standard Python package installation procedure:

$ python setup.py config_fc --fcompiler=intel  install

or if you want to install the package to a specified directory JAVELINDIR:

$ python setup.py config_fc --fcompiler=intel install --prefix=JAVELINDIR

where config_fc --fcompiler=intel tells Python to use the intel fortran compiler to compile Fortran source codes. You can also specifies other fortran compilers that are available in your system, e.g.,:

$ python setup.py config_fc --fcompiler=gnu95 install --prefix=JAVELINDIR

uses GFortran as its Fortran compiler.

Caution!

Note that the short names for Fortran compilers may vary from system to system, you can check the list of available Fortran compilers in your system using:

$ python setup.py config_fc --help-fcompiler

and you can find them in the Fortran compilers found: section of the output.

Caution!

If you installed Python from Conda that comes with the Accelerate framework, multithreading may not work for you, the solution is to set the environmental variable VECLIB_MAXIMUM_THREADS to 1 in your terminal:

$ export VECLIB_MAXIMUM_THREADS=1

Test Installation

After installing JAVELIN, navigate into the examples directory:

$ cd javelin/examples/

you can try:

$ python demo.py test

to make sure the code works (i.e., no error's reported). Also, to test whether the graphics work, you can try:

$ python plotcov.py

which exactly reproduces Figure 1 in Zu et al. (2013).

http://bitbucket.org/nye17/javelin/raw/default/examples/figs/covdemo.png

Fig. 1 : Illustration of four continuum models available in JAVELIN.

Demonstration

Here we briefly explain how to use JAVELIN to caculate the spectroscopic and photometric line lags for the AGN hosted by an imaginary Loopdeloop galaxy, where two emission lines are observed, Ylem and Zing. If you are already familiar with the Zu et al. (2011) paper, feel free to skip to the next section. Every file and script referred to here can be found inside examples directory:

$ cd javelin/examples

To give you an idea of how things work in JAVELIN, let us first go through several figures illustrating the underlying methodology of JAVELIN. You can also show the figures below locally by running:

$ python demo.py show

on the command line.

In our RM models, we assume the quasar variability on scales longer than a few days can be well described by a Damped Random Walk (DRW) model, and the emission line light curves are simply the lagged, smoothed, and scaled versions of the continuum light curve. Fig. 1 shows the true light curves for the continuum, the Ylem, the Zing, and the broadband Yelm lines. In particular, the Ylem (Zing) light curve is lagged by 100 (250) days, scaled by a factor of 0.5 (0.25), and smoothed by a top hat of width 2 (4) days, from the continuum light curve. The continuum light curve is generated from the DRW model with a time scale 400 days, a variability amplitude of sigma=3, and a mean of 10.0 (arbitratry flux units) Thus, for spectroscopic RM we have two parameters for the continuum DRW model, sigma and tau, and three parameters for each emission line model --- the lag t, the width of the tophat smoothing function w, and the flux scaling factor s; for photometric RM we have an additional parameter alpha describing the ratio between the two continua, one off and one on the line flux.

http://bitbucket.org/nye17/javelin/raw/default/examples/figs/signal.png

Fig. 2: True light curves of loopdeeloop (from top to bottom: the Yelm band flux, the Zing emission line, the Ylem emission line, and the continuum).

In practice, what we could observe are down-sampled and noisy versions of the true light curves, sometimes with seasonal gaps because of the conflict with our Sun's schedule, as shown by Fig. 3.

http://bitbucket.org/nye17/javelin/raw/default/examples/figs/mocklc.png

Fig. 3: Same as Fig. 2, but observed versions.

To directly derive lags from those sparse light curves is hard with traditional cross-correlation based methods. JAVELIN makes it much less formidable, by incorporating the statistical properties of the continuum light curve into the lag determination, keeping track of all the correlations of the model, and self-consistently removes the light curve mean. The first step is to build a continuum model to determine the DRW parameters of the continuum light curve. Fig. 4 shows the posterior distribution of the two DRW parameters of the continuum variability as calculated from JAVELIN using MCMC chains,

http://bitbucket.org/nye17/javelin/raw/default/examples/figs/mcmc0.png

Fig. 4: Posterior distributions of the DRW parameters based on fits to the continuum light curve.

Once we derive the posteriors of the DRW parameters, we then have a pretty good idea of how much the continuum light curves in unobserved epochs should vary relative to observed epochs, i.e., we know how to statistically interpolate the continuum light curve. To measure the lag between the continuum and the Ylem light curve, JAVELIN then tries to interpolate the continuum light curve based on the posteriors derived in Fig. 4, and then shifts, smooths, and scales each continuum light curve to compare to the observed Ylem light curve. After doing this many many times in a MCMC run, JAVELIN finally derives the posterior distribution of the lag t, the tophat width w, and the scale factor s of the emission line, along with updated posteriors for the timescale tau and the amplitude sigma of the continuum, as shown in Fig. 5.

http://bitbucket.org/nye17/javelin/raw/default/examples/figs/mcmc1.png

Fig. 5: Posterior distributions of the emission line lag t, tophat width w, and the scale factor s for the Ylem light curve (bottom). The top two panels show the updated posteriors for tau and sigma.

However, we can see two peaks for the lag distribution in Fig. 5, which is caused by the 180-day seasonal gaps in the two light curves - JAVELIN found that it is much easier to shift the continuum by 180 days to compare to the line light curve - there is no overlap between the two, therefore no objection from the data!

Fortunately, we also have observations of the Zing light curve. Although equally sparsely sampled and having the same gaps, the mere existence of the Zing light curve makes it impossible for JAVELIN to shift the continuum by 180 days TWICE to compare to the two line light curves! After another MCMC run, JAVELIN is able to eliminate the second peak at 180 days and solve the lags for both emission lines simultaneously, as shown in Fig. 6.

http://bitbucket.org/nye17/javelin/raw/default/examples/figs/mcmc2.png

Fig. 6: As in Fig. 5, but after running JAVELIN for both two line light curves plus the continuum simultaneously.

Finally, we want to know what the best--fit parameters from the last MCMC run look like. It is generally very hard to visualize the fit for the traditional cross-correlation methods, but JAVELIN is exceptionally good at this - after all what it has been doing is to interpolate and align light curves, so why not for the best-fit parameters? Fig. 7 compares the best-fit light curves and the observed ones shown earlier in Fig. 3. Apparently JAVELIN does a great job of recovering the true light curves (compare to Fig. 2). Remember, however, that these show the weighted mean of light curves consistent with the data and the dispersion of those light curves --- they are not a particular realizations of a single light curve.

http://bitbucket.org/nye17/javelin/raw/default/examples/figs/prediction.png

Fig. 7: Comparison between the simulated light curves as computed from the best-fit parameters, and the observed light curves.

For the photometric line light curve, just to demonstrate the photometric RM function of JAVELIN, we place a hard limit on range of lags during MCMC searching, so that the 180 ambiguity won't happen. Fig. 8 shows the posterior probability disgtribution of parameters in the photometric RM model

http://bitbucket.org/nye17/javelin/raw/default/examples/figs/mcmc3.png

Fig. 8: As in Fig. 5, but after running JAVELIN for the Yelm band light curves plus the continuum using the photometric RM model.

Usage

To use JAVELIN, it is useful to have some a priori knowledge of Python, but not necessary. Here we will walk you through the actual procedures outlined in the last section. In this section, we will manipulate the files in two different terminals, one is the usual Unix command line marked by "$" in the beginning, one is the Python terminal started with ">>>".

Running JAVELIN is Easy

Lag determination can usually be done by JAVELIN within a few lines of codes. The following is a quick example of inferring lag using spectroscopic light curves.

Starting from the data files in the examples/dat directly:

$ cd javelin/examples/dat

Fire up a Python terminal (iPython is strongly recommened!),:

$ python
Python 2.7.2+ (default, Jan 20 2012, 23:05:38)
[GCC 4.6.2] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>>

and do

>>>from javelin.zylc import get_data
>>>from javelin.lcmodel import Cont_Model, Rmap_Model, Pmap_Model

to load the necessary modules, then:

>>>c = get_data(["con.dat"])
>>>cmod = Cont_Model(c)
>>>cmod.do_mcmc()

to fit the continuum data, then:

>>>cy = get_data(["con.dat", "yelm.dat"])
>>>cymod = Rmap_Model(cy)
>>>cymod.do_mcmc(conthpd=cmod.hpd)

to fit the continuum+line data, where Rmap_Model is the the spectroscopic reverberation mapping~(RM) model. The results can be shown by:

>>>cymod.show_hist()

as the 1D posterior distributions of model parameters, including the lag t.

And finally, to do photometric RM using the continuum+line-band data, do:

>>>cyb = get_data(["con.dat", "yelmband.dat"])
>>>cybmod = Pmap_Model(cyb)
>>>cybmod.do_mcmc(conthpd=cmod.hpd)

where Pmap_Model is the two-band photometric RM model (there is also a one-band photometric RM model SPmap_Model available). Again, the results can be shown by:

>>>cybmod.show_hist()

For the more patient users, now I will go through each step in detail, starting from the supported data files.

Reading Light Curves

JAVELIN can work on two types of light curve files, the first one is the typical 3-column file like con.dat, yelm.dat, zing.dat, and yelmband.dat in the current directory. If you do:

$ head -n 3 con.dat

to show the first 3 rows of the continuum light curve file con.dat:

0.00000    6.75749    0.06846
8.00400    6.37599    0.06364
16.00800    5.74500    0.05907

where the 1st, 2nd, and 3rd columns are the observing epoch, the light curve value, and the measurement uncertainty, respectively. Since the basic data unit in JAVELIN is a LightCurve object, you need to read the data files through a function into the LightCurve object. Open a Python terminal in the dat directory and then do:

>>>from javelin.zylc import get_data
>>>javdata1 = get_data(["con.dat", "yelm.dat"], names=["Continuum", "Yelm"])

to load the continuum light curve con.dat and the Yelm light curve yelm.dat into a LightCurve object called javdata1, with names as "Continuum" and "Yelm". The brackets [] tell JAVELIN that the two light curves should be analyzed in one set, and if you want to look at the light curves in figures just run:

>>>javdata1.plot()

Note that in Python you have to keep the parentheses even when no arguments are needed.

The second type of file JAVELIN uses is a slight variant of the 3-column format, like loopdeloop_con.dat, loopdeloop_con_y.dat, loopdeloop_con_y_z.dat, and loopdeloop_con_yb.dat in the current directory. As suggested by the names of these files, since JAVELIN usually works on several light curves simultaneously, it is useful (at least to me) to keep different set of data files separated (similar to the brackets used in the reading of 3-column files).

Imagine you want to fit two light curves, the first one should always be the continuum light curves and the second one the line light curve. If the continuum light curve has 5 data points while the line light curve has 4, the data file should be like (text after # are comments, not part of the file)

2                       # number of light curves, continuum first
5                       # number of data points in the continuum light curve
461.5  22.48    0.36    # each light curve entry consists of "epoch", "value", and  "uncertainty"
490.6  20.30    0.30
520.3  19.59    0.56
545.8  20.11    0.15
769.6  21.12    1.20
4                       # number of data points in the first line light curve
545.8   9.82    0.23
890.4  11.86    0.58
949.4  10.55    0.87
988.6  11.06    0.27

To read the second type of file, simply do:

>>>javdata2 = get_data("loopdeloop_con_y.dat", names=["Continuum", "Yelm"])

Note right now there are only brackets from the names, but a single string for the input file. Given loopdeloop_con_y.dat is just another version of packing con.dat and yelm.dat together, javdata and javedata2 are equivalent to each other. You can varify this by doing javdata2.plot().

Constraining Continuum Variability

We can use JAVELIN to model the continuum variability, or as shown in the last section, for RM we need to fit the continuum light curve alone first to derive set priors on the DRW parameters for the second step of lag fitting. Since for now we only work on the continuum model, we can load the continuum light curve either by:

>>>javdata3 = get_data(["con.dat",], names=["Continuum",])

or by:

>>>javdata3 = get_data("loopdeloop_con.dat", names=["Continuum"])

Note that the brakets are still needed even for loading a single light curve.

After loading the data, we need to set up a continuum model. In JAVELIN, the light curve models are described in the javelin.lcmodel module, for now we need to initiate the Cont_Model class:

>>>from javelin.lcmodel import Cont_Model
>>>cont = Cont_Model(javdata3)

By default, Cont_Model will model the light curve as a DRW process, but you are also specify models like matern, pow_exp, kepler_exp, etc (see details in Zu et al. 2011). However, currently the spectroscopic and photometric RM models Rmap_Model and (S)PMap_Model do not yet support continuum covariance models other than DRW.

Without exploring any further options, you can simply run:

>>>cont.do_mcmc(fchain="mychain0.dat")

to start a MCMC analysis and the chain will be saved into "mychain0.dat" file. By default, the chain will go through 5000 iterations for a burn-in period, and then another 5000 iterations for the actual chain. JAVELIN uses the kick-ass MCMC sampler named emcee introduced by Dan Foreman-Mackey et al (2012). emcee works by randomly releasing numerous walkers at every possible corner of the parameter space, which then collaboratively sample the posterior probability distributions, so you do not need to tell the sampler where to start. The number of walkers, the number of burn-in iterations, and the number of sampling iterations for each walker are specified by nwalker (default: 100), nchain (default: 50), and nburn (default: 50), respectively. For examples, if you want to double the chain length of both burn-in and sampling periods (well, you do not want to do it right now):

>>>cont.do_mcmc(nwalkers=100, nburn=100, nchain=100, fchain="mychain0_long.dat")

The default values of nwalker, nchain, and nburn would usually be enough for fitting continuum or fitting continuum+one line, but the required values would rise quickly with the number of lines if you are doing fitting with muliple lines. So, whenever you find the MCMC chain does not converge well --- JAVELIN fail to find a unique combination of solutions but a broad lag distribution, try to increase these three parameters.

After sampling, you can check the 1D posterior distributions of tau and sigma:

>>>cont.show_hist(bins=100)

which looks like Fig. 4.

The output fchain is simply a two-column txt file with the first column log(sigma) and the second one log(tau), both natural logs. You can also store the log likelihoods as a separate chain in flogp.

Older chains can be reloaded for analysis by:

>>>cont.load_chain("mychain0.dat")

and the highest posterior density (HPD) intervals can be retrieved by:

>>>cont.get_hpd()
>>>conthpd = cont.hpd
>>>print(conthpd)
[[ 0.363  3.923]
 [ 0.518  4.29 ]
 [ 0.737  4.743]]

which is a 3x2 array with the three elements of the first (second) column being the 18%, 50%, and 84% values for log sigma (log tau). cont.hpd here is exactly what we are after in this subsection, as will become apparently below, to provide useful constraints on the DRW parameters to help determining lags,

Spectroscopic RM: Fitting the Continuum and one line (Yelm)

First, we need to load the necessary light curves files, in this case, both the continuum and the Ylem light curves, into a LightCurve object, which is simply the javdata1 or the javdata2 we created earlier. Also, we need to construct a model, this time a Continuum+Line model, which is called a Rmap_Model in JAVELIN:

>>>from javelin.lcmodel import Rmap_Model
>>>rmap1 = Rmap_Model(javdata1)

Remember that we need the results from fitting the continuum as priors on the DRW parameters in finding lags,

>>>rmap1.do_mcmc(conthpd=conthpd, fchain="mychain1.dat")

where conthpd is the HPD interval array we obtained from last subsection and fchain is again the file name for the output chain.

There are several interesting options that you can use to to tweak the MCMC sampler (you can always check the source for the full argument list):

>>>rmap1.do_mcmc(conthpd=conthpd, lagtobaseline=0.3, laglimit='baseline', nwalkers=100, nburn=100, nchain=100, threads=1, fchain="mychain1.dat")

In particular, lagtobaseline indicates that a logarithmic prior is applied to logarithmically penalize lag values larger than lagtobaseline times the baseline of the continuum light curve (default: 0.3). laglimit gives the boundaries beyond which lag values are forbidden. The default is baseline, meaning no lags larger than the observation baseline (total span of the light curves), and its non-default value could only be a list of 2-element lists, indicating the range of the possible lag values for each emission line. In particular, after a first run with laglimit=baseline, you can use the results to narrow the boundaries for the new run with a higher convergence MCMC search. For example, you can narrow down the boundaries to between 100 and 200 days and rerun a finer MCMC search:

>>>rmap1.do_mcmc(conthpd=conthpd, fchain="mychain1_fine.dat", laglimit=[[100, 200]])

where laglimit is a list that is comprised of a single 2-element list because we have only one emission line here.

The emcee sampler does multi-threading, so if your system has multiple cores, you should run the above command with threads set to the number of cores to speed things up:

>>>rmap1.do_mcmc(conthpd=conthpd, fchain="mychain1_fine.dat", laglimit=[[100, 200],], threads=4)

The other chain length related parameters are the same as in the continuum case.

After running the MCMC analysis, the 1D posterior distributions can be shown with:

>>>rmap1.show_hist()

which then looks like Fig. 5.

The output fchain file is comprised of 2+3*n columns, where n is the number of emission lines. Thus here we have 5 columns, with each column as, from left to right:

log(sigma), log(tau), lag, width, scale

and the number of columns augments by 3 for every additional emission line. Again, you can also store the log likelihoods as a separate chain using flogp. You can play with the fchain file in any way you like, but JAVELIN provides several tools to start with, for example,:

>>>rmap1.load_chain("mychain1.dat")

for reloading the chain file,:

>>>rmap1.break_chain([[100, 200],])

for abandoning the chain segments where the lag value is outside of [100, 200], and:

>>>rmap1.restore_chain()

to restore to the original untrimmed chain.

Usually the lag finding ends here if the 1D posterior distribution of lag shows a single peak, but sometimes you may want to fit two emission lines simultaneously to improve the results, as in our example of how fitting multiple lines eliminates seasonal aliasing problems.

Spectroscopic RM: Fitting the Continuum and two lines (Yelm and Zing)

The extrapolation from using one emission line to using two is rather trivial. Read the light curves by:

>>>javdata4 = get_data(["con.dat", "yelm.dat", "zing.dat"], names=["Continuum", "Yelm", "Zing"])

set the model by:

>>>rmap2 = Rmap_Model(javdata4)

and lastly, run the models using MCMC:

>>>rmap2.do_mcmc(conthpd=conthpd, fchain="mychain2.dat", threads=2)

if you have two cpus available.

In the loopdeeloop example here, the false peak seen in the last subsection should be largely eliminated, as shown by the 1D posteriors:

>>>rmap2.show_hist()

which looks like Fig. 6.

To isolate the peaks in the chain, you can do (assuming both peaks land between 100 and 300 days):

>>>rmap2.break_chain([[100, 300],[100, 300]])

Now you can retrieve and print out the HPD intervals for the double emission-line model fit:

>>>rmap2.get_hpd()
>>>rmap2hpd = rmap2.hpd

and the medians can be obtained by:

>>>par_best = rmap2hpd[1,:]
>>>print(par_best)
array([ 0.592, 4.262, 127.169, 0.525, 1.024, 254.262, 0.564, 0.498])

which shows the median values for log(sigma), log(tau), lag_yelm, width_yelm, scale_yelm, lag_zing, width_zing, and scale_zing, respectively.

To make the story more completely, you can draw the best-fit light curves on top of the observed ones as shown in Fig. 7.:

>>>javdata_best =  rmap2.do_pred(par_best)
>>>javdata_best.plot(set_pred=True, obs=javdata4)

Spectroscopic RM in JAVELIN is Highly Extensible

If you have more than three light curves for the same objects at the same period, you also plug the additional lines in JAVELIN in the same way, simply by feeding a longer list of light curves to get_data and constructing a new Rmap_Model. The estimation will improve a lot if the additional emission lines have drastically different lags. However, the estimation may also become worse if the additional light curves are intrinsically noisy or the uncertainties are overly underestimated.

Another important issue in fitting more than two line is, as mentioned earlier in the manual, the default values for nwalkers, nchain, and nburn may not be adequate because you have a rapid increase in the dimensionality of the problem. Therefore, try to increase the values of these parameters whenever you find the MCMC does not converge well.

Photometric RM: Two-Band or One-Band

The Photometric RM module is as easy to use as the Spectroscopic one:

>>>javdata5 = get_data("loopdeloop_con_yb.dat", names=["Continuum", "YelmBand"])
>>>pmap = Pmap_Model(javdata5)

for loading the Two-Band Photometric RM model, and:

>>>javdata6 = get_data("yelmband.dat", names=["YelmBand"])
>>>spmap = SPmap_Model(javdata6)

For the Two-Band method, the procedure is similar to the Spectroscopic RM, where we constrain the continuum variability first and use that as prior information for the second step of calling Pmap_Model. However, for the One-Band method, since we do not have independent continuum information, we directly fit the single broad band light curve without using conthpd. You can either look into the demo.py code under example dir, or check the source code lcmodel.py under javelin dir for details.

Disk RM Model of Mudd et al. 2017

The Disk_Model object is developed recently by Mudd et al. 2017 , and what it does is take in a series of continuum light curves at known wavelengths/effective wavelengths, and find the best-fitting thin disk model for the data. The thin disk assumes that the size of the accretion disk scales as

\begin{equation*} R_{\lambda} = R_{0}\left(\frac{\lambda}{\lambda_{0}}\right)^{\beta}, \end{equation*}

where \(R_{\lambda}\) is the disk size at wavelength \(\lambda\) and \(R_{0}\) is the disk size at a reference wavelength \(\lambda_{0}\). We can instead write this in terms of a time delay \(t\) between the emission at two wavelengths as

\begin{equation*} t = \frac{R_{0}}{c}\left[\left(\frac{\lambda}{\lambda_{0}}\right)^{\beta} - 1\right], \end{equation*}

where again \(R_{0}\) is the disk size at a reference wavelength \(\lambda_{0}\) and \(t\) is the time delay between a feature at wavelength \(\lambda_{0}\) and \(\lambda\). More information on the model and further references can be found in Mudd et al. 2017.

For a usage example, you can find an example script at

javelin/examples/thindisk/test_thindisk.py

Let's say you have a driving light curve "driver.dat", and then three other continuum light curves "wave2.dat", "wave3.dat", and "wave4.dat", measured at wavelengths of 2000A, 4000A, 5000A, and 8000A. You can use the "get_data()" method to read your light curves right into your Disk_Model object, the only difference here compared to the other models is that you also need to specify the wavelengths of the light curves in addition to reading in the light curves with "get_data()".:

>>>disk1 = Disk_Model(get_data(["driver.dat", "wave2.dat", "wave3.dat", "wave4.dat"], names=["Driver", "Wave 2", "Wave 3", "Wave 4"]), effwave=[2000., 4000., 5000., 8000.])

Note that the "get_data()" method takes in a list of file names as before (or a singular file with multiple light curves formatted as directed in a previous example) and a list of names, but it is the Disk_Model object instance that requires the "effwave" parameter as well, which is a list or array of wavelengths for the light curve. Note that the Disk Model will always treat the first light curve in the list as the driver. You can then run your model exactly as in other RM models:

>>>disk1.do_mcmc(nwalkers=100, nburn=100, nchain=500, threads=1, fchain="thin_disk_chain.dat", flogp="thin_disk_flogp.dat", fburn="thin_disk_burn.dat")

In this example, the "thin_disk_chain.dat" will wind up being a 50000 line text file with 10 columns and fburn will be a 10000 line text file with 10 columns. These will correspond to the DRW model amplitude (sigma) and timescale (tau), same as for all of the previous examples on this page. Rather than providing a lag, tophat width, and tophat scale for each light curve, however, the thin disk model fits for the \(R_{0}\) and \(\beta\) parameters. These can then be used to predict the time lags based on the effective wavelengths of the other light curves based on the earlier equations in this section. Each new light curve still needs a tophat width and scale, however. This means that each new light curve in the model adds 2 parameters rather than 3, and starts with 4 parameters (sigma, tau, \(R_{0}\), and \(\beta\)). Thus, with three light curves being fit to our driver, we expect 10 parameters- sigma, tau, \(R_{0}\), \(\beta\), width_wave2, scale_wave2, width_wave3, scale_wave3, width_wave4, and scale_wave4. The \(R_{0}\) parameter in the chain will correspond to the effective accretion disk size at a reference wavelength corresponding to that of "driver.dat".

Additional useful tools that might be helpful are the fact that, like with previous Model objects in JAVELIN, you may choose to supply DRW parameters at the outset with the "conthpd" parameter in "do_mcmc()", the ability to fix model parameters using a "fixed" array indicating which parameters are allowed to change and a supply of initial walker values in this case as "p_fix". Even though the lags are not fit for directly, you may still place boundaries on parameter spaces for each light curve using the "lagtobaseline" or "laglimit" parameters as with the "RMap_Model" before. Options unique to the Thin Disk object are the ability to set a top hat minimum width (in the same units as your light curve), as well as place limits on the \(R_{0}\) and \(\beta\) parameters when initializing the "Disk_Model" object. Using our previous example,:

>>>disk1 = Disk_Model(get_data(["driver.dat", "wave2.dat", "wave3.dat", "wave4.dat"], names = ["Driver", "Wave 2", "Wave 3", "Wave 4"]), effwave = [2000., 4000., 5000., 8000.], tophatminwidth = 0.1, alpha_lims = [-5., 5.], beta_lims = [-2., 2.])

would not allow top hat widths less than 0.1, \(R_{0}\) outside the range of [-5, 5], or \(\beta\) outside the range of [-2, 2]. Again, note that the units on the top hat minimum width and \(R_{0}\) are the same as those in your light curves.

Additional Information

Please refer to the JAVELIN source code for all the modules and their arguments (the code is in my humble opinion semi-well-documented).

Citation

You are welcome to use and modify JAVELIN, however please acknowledge its use either as is or with modifications with a citation to

Zu, Y., Kochanek, C.S., Kozlowski, S., & Udalski, A. 2013, ApJ, 765, 106

for quasar optical variability studies and general JAVELIN usage,

Zu, Y., Kochanek, C.S., & Peterson, B.M. 2011, ApJ, 735, 80

for spectroscopic reverberation mapping and general JAVELIN usage,

Zu, Y., Kochanek, C.S., Kozlowski, S., & Peterson, B.M. 2016, ApJ, 819, 122

for photometric reverberation mapping, and

Mudd et al. 2017, arxiv:1711.11588

for thin disk modelling.