Towards Rate Estimation for Transient Surveys I: Assessing Transient Detectability and Volume Sensitivity for iPTF

Abstract

The last couple of decades have seen an emergence of transient detection facilities in various avenues of time domain astronomy which has provided us with a rich dataset of transients. The rates of these transients have implications in star formation, progenitor models, evolution channels and cosmology measurements. The crucial component of any rate calculation is the detectability and space-time volume sensitivity of a survey to a particular transient type as a function of many intrinsic and extrinsic parameters. Fully sampling that multi-dimensional parameter space is challenging. Instead, we present a scheme to assess the detectability of transients using supervised machine learning. The data product is a classifier that determines the detection likelihood of sources resulting from an image subtraction pipeline associated with time domain survey telescopes, taking into consideration the intrinsic properties of the transients and the observing conditions. We apply our method to assess the space-time volume sensitivity of type Ia supernovae (SNe~Ia) in the intermediate Palomar Transient Factory (iPTF) and obtain the result,$⟨VT{⟩}_{\mathrm{Ia}}=2.93\pm 0.21\times 10^{-2}\mathrm{Gp}{\mathrm{c}}^3\mathrm{yr}$. With rate estimates in the literature, this volume sensitivity gives a count of $680-1160$ SNe~Ia detectable by iPTF which is consistent with the archival data. With a view toward wider applicability of this technique we do a preliminary computation for long-duration type IIp supernovae (SNe~IIp) and find $⟨VT{⟩}_{\mathrm{IIp}}=7.80\pm 0.76\times 10^{-4}\mathrm{Gp}{\mathrm{c}}^3\mathrm{yr}$. This classifier can be used for computationally fast space-time volume sensitivity calculation of any generic transient type using their lightcurve properties. Hence, it can be used as a tool to facilitate calculation of transient rates in a range of time-domain surveys, given suitable training sets.

arXiv comment13 pages, 9 figures, accepted for publication in ApJ

References in corpus (30)

• Scikit-learn: Machine Learning in Python
• The NumPy array: a structure for efficient numerical computation
• The Astropy Project: Building an inclusive, open-science project and status of the v2.0 core package
• Nine-Year Wilkinson Microwave Anisotropy Probe (WMAP) Observations: Cosmological Parameter Results
• Binary companions of evolved stars in APOGEE DR14: Search method and catalog of ~5,000 companions
• Multi-messenger Observations of a Binary Neutron Star Merger
• Gravitational Waves and Gamma-rays from a Binary Neutron Star Merger: GW170817 and GRB 170817A
• LSST: from Science Drivers to Reference Design and Anticipated Data Products
• Correcting for the Effects of Interstellar Extinction
• Improved cosmological constraints from a joint analysis of the SDSS-II and SNLS supernova samples
• First Results from the Catalina Real-time Transient Survey
• The Palomar Transient Factory: System Overview, Performance and First Results
• SALT2: using distant supernovae to improve the use of Type Ia supernovae as distance indicators
• Nearby Supernova Rates from the Lick Observatory Supernova Search. III. The Rate-Size Relation, and the Rates as a Function of Galaxy Hubble Type and Colour
• Type-Ia supernova rates and the progenitor problem, a review
• The Sloan Digital Sky Survey-II Supernova Survey: Search Algorithm and Follow-up Observations
• Estimating the Contribution of Dynamical Ejecta in the Kilonova Associated with GW170817
• Absolute-Magnitude Distributions of Supernovae
• A Measurement of the Rate of type-Ia Supernovae at Redshift $z\approx$ 0.1 from the First Season of the SDSS-II Supernova Survey
• Extinction and the Radial Distribution of Supernova Properties in Their Parent Galaxies
• Using Machine Learning for Discovery in Synoptic Survey Imaging
• Counting And Confusion: Bayesian Rate Estimation With Multiple Populations
• High Redshift Supernovae in the Hubble Deep Field
• Supernovae in the Subaru Deep Field: An Initial Sample, and Type Ia Rate, out to Redshift 1.6
• Intermediate Palomar Transient Factory: Realtime Image Subtraction Pipeline
• The ASAS-SN Bright Supernova Catalog -- IV. 2017
• The Relative Specific Type Ia Supernovae Rate From Three Years of ASAS-SN
• The volumetric rate of calcium-rich transients in the local universe
• iPTF Archival Search for Fast Optical Transients
• Real-Time Recovery Efficiencies and Performance of the Palomar Transient Factory's Transient Discovery Pipeline

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