CSMAnalysis (xsnap.CSMAnalysis)#
Methods |
Attributes |
|---|---|
- class CSMAnalysis(manager=None)#
Bases:
objectSupernova circumstellar medium (CSM) analysis, including fitting \(L(t) \propto t^x\) to deriving unshocked CSM densities and mass-loss rates of the supernova progenitor.
Note
As our emission measure (EM) comes from the normalization of the thermal-bremsstrahlung, the
CSMAnalysisclass can only be used if theSpectrumManagerclass contains exactly one bremss model.- manager#
Input manager spectra analysis.
- Type:
SpectrumManager or None
- r_shock#
Shock radius.
- Type:
pandas.DataFrame or None
- times#
Times since explosion.
- Type:
pandas.DataFrame or None
- fit_lumin_params#
- Best-fit luminosity parameters wrapped in DataFrame table with
columns
['model', 'norm','lo_norm_err','hi_norm_err', 'exp','lo_exp_err','hi_exp_err','ndata'].
- Type:
pandas.DataFrame or None
- fit_temp_params#
- Best-fit temperature parameters wrapped in DataFrame table with
columns
['model', 'norm','lo_norm_err','hi_norm_err', 'exp','lo_exp_err','hi_exp_err','ndata'].
- Type:
pandas.DataFrame or None
- fit_density_params#
Best-fit density parameters, i.e. the fitted mass-loss rates in \(\rm g \ s^{-1}\), wrapped in DataFrame table with columns
['mdot', 'lo_mdot_err', 'hi_mdot_err']- Type:
pandas.DataFrame or None
- densities#
Computed unshocked CSM density in \(\rm g \ {cm}^{-3}\) wrapped in DataFrame table with columns
['time_since_explosion', 'rho', 'lo_rho_err', 'hi_rho_err']- Type:
pandas.DataFrame or None
- mass_loss_rate#
Computed mass-loss rates of the supernova progenitor in \(\rm M_{\bigodot} \ {yr}^{-1}\), wrapped in a DataFrame with columns
['m_dot', 'lo_m_dot_err', 'hi_m_dot_err']- Type:
- __init__(manager=None)#
Initialization of the
CSMAnalysisclass- Parameters:
manager (SpectrumManager, optional) – Pre-populated
SpectrumManagerinstance.
- calc_density(distance=None, lo_dist_err=None, hi_dist_err=None, z=None, lo_z_err=None, hi_z_err=None, mu_e=1.14, mu_ion=1.24, radius_ratio=1.2, f=1, v_wind=20.0, H0=70, nwalkers=500, nsteps=10000, nburn=2000, show_plots=True)#
Calculate unshocked CSM density \(\rho_{\mathrm{CSM}}(r)\) and fit using Markov chain Monte-Carlo (MCMC) based on
'bremss_norm'inparams.In calculating the density, we use the model of D. Brethauer et al. (2022), where the unshocked CSM density is expressed by:
\[\rho_{\mathrm{CSM}}(r) = \frac{m_p}{4} \left(\frac{2 \times \mathrm{EM}(r)\mu_e \mu_I}{V_{\mathrm{FS}}(r)}\right)^{1/2}\]where we parameterized:
\[ \begin{align}\begin{aligned}\mathrm{{bremss} \ {norm}} = \frac{3.02 \cdot 10^{-15}}{4 \pi d^2} \times \mathrm{EM}\\\mathrm{V}_{\mathrm{FS}} = \frac{4 \pi}{3} f \left(R_{\mathrm{out}}^3 - R_{\mathrm{in}}^3\right)\end{aligned}\end{align} \]From there, we fit \(\rho_{\mathrm{CSM}}(r)\) and \(\dot{M}\) with:
\[\rho_{\mathrm{CSM}} = \frac{\dot{M}}{4 \pi r_{\mathrm{shock}}^2 v_{\mathrm{wind}}}\]- Parameters:
distance (float, optional) – distance to source in Mpc.
z (float, optional) – redshift to source.
mu_e (float, optional) – mean molecular weight per electron. Defaults to
1.14(solar value).mu_ion (float, optional) – mean molecular weight per ion. Defaults to
1.24(solar value).radius_ratio (float, optional) – shock Rout/Rin ratio. Defaults to
1.2.f (int, optional) – filling factor. Defaults to
1.v_wind (float, optional) – wind velocity in km/s. Defaults to
20.H0 (float, optional) – Hubble constant in km/s/Mpc. Defaults to
70.nwalkers (int, optional) – Number of MCMC walkers. Defaults to
500.nsteps (int, optional) – Number of steps per walker. Defaults to
10000.nburn (int, optional) – Number of burn-in steps. Defaults to
2000.show_plots (bool, optional) – If
True, display diagnostic plots. Defaults toTrue.fit (bool, optional) – If
True, will fit density and get mass-loss rate in \(\rm g \ s^{-1}\). Defaults toTrue
- Returns:
- Densitypandas.DataFrame
Computed unshocked CSM density in \(\rm g \ {cm}^{-3}\) wrapped in DataFrame table with columns
['time_since_explosion', 'rho', 'lo_rho_err', 'hi_rho_err']
- Raises:
RuntimeError – When
paramsdo not have these columns:['bremss_norm', 'lo_bremss_norm_err', 'hi_bremss_norm_err', 'time_since_explosion']ValueError – When distance or redshift is not entered
Note
If
distanceis not supplied, it is inferred fromredshiftvia the Doppler relation:\[\begin{split}v = \begin{cases} c\,z, & z \ll 1, \\ c\,\frac{(1 + z)^2 - 1}{(1 + z)^2 + 1}, & \text{otherwise.} \end{cases}\end{split}\]and then:
\[d = \frac{v}{H_0} \ (\mathrm{Mpc}).\]- References:
Hogg, D. W. (1999). Distance measures in cosmology. arXiv:astro-ph/9905116
- clear()#
Reset all loaded data and computed results.
- fit_lumin(nwalkers=500, nsteps=10000, nburn=2000, show_plots=True)#
Fit power-law luminosity \(L(t) \propto t^x\) using Markov chain Monte-Carlo (MCMC).
- Parameters:
- Returns:
- Best-fit luminosity parameterspandas.DataFrame
Best-fit luminosity parameters wrapped in DataFrame table with columns
['model', 'norm','lo_norm_err','hi_norm_err', 'exp','lo_exp_err','hi_exp_err','ndata'].
- fit_temp(nwalkers=500, nsteps=10000, nburn=2000, show_plots=True)#
Fit power-law temperature \(T(t) \propto t^x\) using Markov chain Monte-Carlo (MCMC).
- Parameters:
- Returns:
- Best-fit temperature parameterspandas.DataFrame
Best-fit temperature parameters wrapped in DataFrame table with columns
['model', 'norm','lo_norm_err','hi_norm_err', 'exp','lo_exp_err','hi_exp_err','ndata'].
- get_mdot()#
Get mass loss rate in \(\rm M_{\bigodot} \ {yr}^{-1}\)
- Returns:
Mass-loss rate and its errors in \(\rm M_{\bigodot} \ {yr}^{-1}\), wrapped in a DataFrame with columns
['m_dot', 'lo_m_dot_err', 'hi_m_dot_err']
- load(manager, distance=None, lo_dist_err=None, hi_dist_err=None, z=None, lo_z_err=None, hi_z_err=None, v_shock=10000.0, H0=70)#
Load manager luminosity & parameter tables and compute shock radius.
- Parameters:
manager (SpectrumManager) – Must contain one
'bremss'model inlumin¶ms.distance (float, optional) – Distance in Mpc. Required if
zisNone.z (float, optional) – Redshift to compute distance. Required if distance is
None. Defaults toNone.lo_dist_err (float, optional) – Lower uncertainty of distance in Mpc. Defaults to
None.hi_dist_err (float, optional) – Upper uncertainty of distance in Mpc. Defaults to
None.lo_z_err (float, optional) – Lower uncertainty of redshift. Defaults to
None.hi_z_err (float, optional) – Upper uncertainty of redshift. Defaults to
None.H0 (float, optional) – Hubble constant in km/s/Mpc. Defaults to
70.0.v_shock (float, optional) – Shock velocity in km/s. Defaults to
10000.
- Returns:
- selfCSMAnalysis
The same
CSMAnalysiswithtimesandr_shockpopulated.
- Raises:
RuntimeError – If more than one bremss model is present.
ValueError – If required tables/columns are missing.
Note
If
distanceis not supplied, it is inferred fromredshiftvia the Doppler relation:\[\begin{split}v = \begin{cases} c\,z, & z \ll 1, \\ c\,\frac{(1 + z)^2 - 1}{(1 + z)^2 + 1}, & \text{otherwise.} \end{cases}\end{split}\]and then:
\[d = \frac{v}{H_0} \ (\mathrm{Mpc}).\]- References:
Hogg, D. W. (1999). Distance measures in cosmology. arXiv:astro-ph/9905116
- plot_density(model_color='red', v_wind=20)#
Plot the unshocked CSM density profile with its best-fit mass-loss rate.
- plot_lumin(model_color='red')#
Plot luminosity light curve with best-fit power-law.
- Parameters:
model_color (str, optional) – Color for the fit line. Defaults to
'red'.- Returns:
- Plotmatplotlib.figure.Figure
The fitted luminosity light curve plot with data and fit.
Data are grouped and labeled by instruments.
- plot_temp(model_color='red')#
Plot temperature evolution with best-fit power-law.
- Parameters:
model_color (str, optional) – Color for the fit line. Defaults to
'red'.- Returns:
- Plotmatplotlib.figure.Figure
The fitted temperature evolution plot with data and fit.
Data are grouped and labeled by instruments.