cand_all.py
light_curve_fitting.py
Install packages
python3 -mpip install pandas scipy matplotlib numpy light_curveData: OGLE dwarf novae data in ./phot
outburst extracted
outburst extracted plots
fitting plots, which is by default saved under ./pictures directory.
cand_all.py extracts candidate outburst and return the time arrays and magnitudes arrays. It also plots the extracted data against the original OGLE data.
After manually check each selected outburst, couple of good candidates have been selected and their information is stored in a csv file.
light_curve_fitting.py fit the extracted outbursts with peicewise function and used scipy.optimize to fit the data. It also plots the model against data per light curve and is by default saved under ./pictures directory.
Light_curve_bazin.py used bazin function to fit the data and plot it.
Further steps used fitting model from light_curve package
Selected objects:
selected_obj = [
'OGLE BLG-DN-0001', 'OGLE BLG-DN-0002', 'OGLE BLG-DN-0036',
'OGLE BLG-DN-0087', 'OGLE BLG-DN-0168', 'OGLE BLG-DN-0174',
'OGLE BLG-DN-0233', 'OGLE BLG-DN-0275', 'OGLE BLG-DN-0286',
'OGLE BLG-DN-0305', 'OGLE BLG-DN-0373', 'OGLE BLG-DN-0376',
'OGLE BLG-DN-0421', 'OGLE BLG-DN-0444', 'OGLE BLG-DN-0458',
'OGLE BLG-DN-0531', 'OGLE BLG-DN-0588', 'OGLE BLG-DN-0595',
'OGLE BLG-DN-0690', 'OGLE BLG-DN-0783', 'OGLE BLG-DN-0826',
'OGLE BLG-DN-0899']
Model Fitting information: saved as a csv files analysis/fitting_info.csv, with OGLE ID, outburst index, starting time, ending time, and model parameters.
get_distance.py
get_extinction.py
Packages: cand_all, numpy, matplotlib, os, light_curve
Data: ./phot
Searching scope for Gaia is 1 arcsec. If no objects is found in Gaia, then there will be no distane data for this OGLE object.
If distance is not provided from Gaia, extinction is given by SFD
If distance is provided from Gaia, extinction is given by Bayestar
get_distance.py provides distance information for all extracted objects (not the manually selected objects) and is by default saved as analysis/distance_1arcsec.csv. Objects without data from Gaia will be skipped.
get_extinction.py provides extinction information for all selected objects. Columns: OGLE ID (name), galactic coordinates (l,b), extinction from Bayestar, SFD, and Marshall.
Objects name, coordinates(l, b), distances, extinctions for all selected objects are given in ./analysis/extinction_1arces.csv
Find_Mdot_DerivedLuminosity.py
Find_Mdot_with_error.py
Packages: spicy, satrapy, functors, numpy, (matplotlib), os, light_curve, cand_all
Data:./phot,./analysis/fitting_info.csv, ./analysis/extinction_1arcese.csv
The code will extract outburst from original OGLE data, use Basin function to model the extracted outbursts, use standard disk model to derive accretion rate M_dot, and derive luminosity at LSST passbands.
The difference between Find_Mdot_DerivedLuminosity.py and Find_Mdot_with_error.py is that the latter one incorporate light curve magnitude error, which is what this work used.
--Two sets of results are provided:
In analysis_Mdot directory, there are individual csv files for each outburst with columns of time (t), accretion rate (Mdot), and luminosites in each LSST passband (L_u, L_g, etc.). The name for each file has a format of OGLE ID + outburst index
In analysis_luminosities directory, there are individual csv fiels fore ach outburst with columns of lumimosities and magnitudes. The name for each file has a format of OGLE ID + outburst index + luminosity.
--When running the code, basic information will be printed: the method used to find Mdot (method 2: no distance provided and use 1kpc), whether the mode involves normalization, Mdot before normalization(Mdot_old), Mdot after normalization(Mdot_norm)
The code has already been run and results have already been saved under analysis_Mdot and analysis_Luminosity, which can be used directly for generator.py
Generator.py
packages: lumpy, astropy, matplotlib, pandas, dustmaps, ch_vars
Data: ./analysis_Mdot, dustmaps
Running generator.py will generate 10k realistic outbursts in all LSST passbands and output them into LCLIB file.
It used numpy random generator with starting index of 42
Outburst instances are the selected outbursts. Coordinates instances follow milkyway density model.
It filters out instances with any luminosity number outside the range [5, 99]
The code will generate a txt file containing the header for the file and 10k outburst instances.
The LCLIB txt file result is provided and is compressed as ./LCLIB_dwarf_nova_sim.txt.zip