Extract Outburst, model outbursts, and select good candidatdes

Related Scripts

cand_all.py

light_curve_fitting.py

Preparation

Install packages

python3 -mpip install pandas scipy matplotlib numpy light_curve

Data: OGLE dwarf novae data in ./phot

Results

outburst extracted

outburst extracted plots

fitting plots, which is by default saved under ./pictures directory.

Run Code

Extract outburst

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.

Fitting model

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

Provided results

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.

Retrieve Distance and extinction

Related scripts

get_distance.py

get_extinction.py

Preperation

Packages: cand_all, numpy, matplotlib, os, light_curve

Data: ./phot

Run Code

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

Results:

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.

Provided Results:

Objects name, coordinates(l, b), distances, extinctions for all selected objects are given in ./analysis/extinction_1arces.csv

Find Accretion rate and luminosities in other passbands

Related Codes

Find_Mdot_DerivedLuminosity.py

Find_Mdot_with_error.py

Preparation:

Packages: spicy, satrapy, functors, numpy, (matplotlib), os, light_curve, cand_all

Data:./phot,./analysis/fitting_info.csv, ./analysis/extinction_1arcese.csv

Run code

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.

Results

--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)

Provided Results

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

Generating realsitic objects and write LCLIB file

Related Code

Generator.py

Preparation

packages: lumpy, astropy, matplotlib, pandas, dustmaps, ch_vars

Data: ./analysis_Mdot, dustmaps

Run Code

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]

Results

The code will generate a txt file containing the header for the file and 10k outburst instances.

Provided results

The LCLIB txt file result is provided and is compressed as ./LCLIB_dwarf_nova_sim.txt.zip