Niharika Sravan

QZO.csv
The QZO catalog, which includes 4,849,574 objects and columns as described below, excluding the duplicate objects flag. The classifications are based on XGB models trained on ZTF g-band median magnitude and light curves classification with transformer model, as well as WISE W[1-4] magnitudes and colors. The photo-zs are based on ZTF g-band magnitude and WISE magnitudes and colors. We remove duplicated ZTF light curves by removing objects which within the full ZTF catalog have at least one neighbour within 1 arcsec with more ZTF observation epochs. The final number of quasars was achieved with magnitude, number of observation epochs, and minimum quasar classification probability cuts, such that g < n_obs / 80 + 20.375, where n_obs is the number of ZTF observational epochs per light curve, and p_(QSO) > 0.9, where p_(QSO) is XGB classification probability for the QSO class. The photo-zs are available for 35% of these objects, depending on the availability of WISE observations.
ZTF_all_QSO.csv
This file provides all the columns for 78,078,450 objects classified as QSOs by at least one of the two XGB models with and without the WISE features. There are no cuts applied, and there are no duplicates removed. 26% of objects are marked with the duplicates flag.
train.csv
The train data predictions. This file contains 2,588,221 records, with ZTF ID and duplicates flag missing. Selecting the longest ZTF light curve for each non duplicated SDSS object removed ZTF duplicates.
Catalog columns
ID                                                ZTF identifier
ra                                                right ascension
dec                                             declination
n_obs                                         number of ZTF observation epochs
is_duplicate                               flag indicating duplicated light curves
mag_median                             ZTF g-band median magnitude
p_[galaxy, QSO, star]               classification probabilities
p_WISE_[galaxy, QSO, star]    classifications with added WISE data
redshift                                      redshift estimate
ANN_clf.[data-00000-of-00001, index]
ANN model for classification of ZTF g-band light curves. ANN model is trained on ZTF g-band data with at least 20 observation epochs per light curve. It does not require scaling of input light curves, which is done separately for each light curve as part of the transformer model. An example on how to load and use the ANN can be found in the script “run_inference.py” in the GitHub repository.
XGB_clf__ZTF_[PS, WISE, GAIA, PS_WISE, PS_GAIA, WISE_GAIA, PS_WISE_GAIA].pickle
XGB_z__ZTF_WISE.pickle
XGB classification and redshift models for different combinations of input surveys. XGB classification models are trained on all ZTF data with available ANN classification, learning to classify missing features. The XGB redshift model does not include ANN classification as features. An example on how to load and use XGB models can be found in the script “run_inference_XGB.py” in the GitHub repository.
Features order
ZTF         g_mag_median, p_ANN_galaxy, p_ANN_QSO, p_ANN_star
PS           g, r, i, z, g - r, g - i, g - z, r - i, r - z, i - z
WISE      W1, W2, W3, W4, W1 - W2, W1 - W3, W1 - W4, W2 - W3, W2 - W4, W3 - W4
GAIA       g_mean_mag, parallax, pmra, pmdec, bp_mean_mag, rp_mean_mag, bp_rp_excess_factor
The exact column names can be found in the script “features.py” in the Github repository.

Description This data release contains the light curve models, raw forced photometry, and derived parameters for Type II SNe analysed in the paper "Inferring CSM Properties of Type II SNe Using a Magnitude-Limited ZTF Sample". The dataset includes photometric observations from the Zwicky Transient Facility (ZTF) Bright Transient Survey (BTS) and the resulting model fits with multi-output Gaussian Process regression used to infer circumstellar material (CSM) properties around Type II SN progenitors. Contents 1. ZTF Forced Photometry Raw forced photometry files for 493 Type II (incl. Type II, Type IIb and Type IIn) SNe from the ZTF Bright Transient Survey Data format: Within Hinds_25_lcs, CSV files containing time series photometry in g, r, and i bands Includes observation dates (MJD), flux, flux uncertainties, filter_ids and quality flags Filenames follow the convention: [ZTFID]_BTS_fnu.csv 2. Light Curve Models Best-fit light curve models in mag space for each supernova using CSM interaction models Models generated using the methodology described in the associated paper Data format: Within Hinds_25_lcs, CSV files containing light curve predictions Filenames follow the convention: [ZTFID]_LC.csv  3. Parameter Table Comprehensive table of derived parameters for all SNe in the sample Includes physical parameters: rise time, absolute magnitude, CSM mass and CSM radius, among others Data format: machine-readable table (produced with astropy) Filename: Hinds_ea_25.dat Methods The photometric data were obtained through the ZTF forced photometry service, see A. A. Miller et al., (in prep.). Light curve modelling using multi-output Gaussian Processes. Full details of the methodology can be found in the associated paper. Citation If you use this data in your research, please cite: Hinds et al. (2025). "Inferring CSM Properties of Type II SNe Using a Magnitude-Limited ZTF Sample."