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Title: Toward Rate Estimation for Transient Surveys. I. Assessing Transient Detectability and Volume Sensitivity for iPTF

Abstract

The past couple of decades have seen an emergence of transient detection facilities in various avenues of time-domain astronomy that have provided us with a rich data set 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 spacetime volume sensitivity of a survey to a particular transient type as a function of many intrinsic and extrinsic parameters. Fully sampling that multidimensional 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 spacetime volume sensitivity of type Ia supernovae (SNe Ia) in the intermediate Palomar Transient Factory (iPTF) and obtain the result, ⟨ VTIa = (2.93 ± 0.21) × 10 -2 Gpc 3 yr. With rate estimates in the literature, this volume sensitivity gives a count of 680-1160 SNe Ia detectable by iPTF, which is consistentmore » 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 ⟨ VTIIa = (7.80 ± 0.76) × 10 -4 Gpc 3 yr. This classifier can be used for computationally fast spacetime volume sensitivity calculation of any generic transient type using their light-curve 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.« less

Authors:
ORCiD logo [1]; ORCiD logo [2];  [1];  [3]; ORCiD logo [1]; ORCiD logo [3]
  1. Univ. of Wisconsin, Milwaukee, WI (United States). Dept. of Physics
  2. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Univ. of California, Berkeley, CA (United States). Dept. of Astronomy
  3. California Inst. of Technology (CalTech), Pasadena, CA (United States). Cahill Centre for Astrophysics
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1567170
Grant/Contract Number:  
AC02-05CH11231
Resource Type:
Accepted Manuscript
Journal Name:
The Astrophysical Journal (Online)
Additional Journal Information:
Journal Name: The Astrophysical Journal (Online); Journal Volume: 881; Journal Issue: 2; Journal ID: ISSN 1538-4357
Publisher:
Institute of Physics (IOP)
Country of Publication:
United States
Language:
English
Subject:
79 ASTRONOMY AND ASTROPHYSICS

Citation Formats

Chatterjee, Deep, Nugent, Peter E., Brady, Patrick R., Cannella, Chris, Kaplan, David L., and Kasliwal, Mansi M. Toward Rate Estimation for Transient Surveys. I. Assessing Transient Detectability and Volume Sensitivity for iPTF. United States: N. p., 2019. Web. doi:10.3847/1538-4357/ab2b9c.
Chatterjee, Deep, Nugent, Peter E., Brady, Patrick R., Cannella, Chris, Kaplan, David L., & Kasliwal, Mansi M. Toward Rate Estimation for Transient Surveys. I. Assessing Transient Detectability and Volume Sensitivity for iPTF. United States. doi:10.3847/1538-4357/ab2b9c.
Chatterjee, Deep, Nugent, Peter E., Brady, Patrick R., Cannella, Chris, Kaplan, David L., and Kasliwal, Mansi M. Wed . "Toward Rate Estimation for Transient Surveys. I. Assessing Transient Detectability and Volume Sensitivity for iPTF". United States. doi:10.3847/1538-4357/ab2b9c.
@article{osti_1567170,
title = {Toward Rate Estimation for Transient Surveys. I. Assessing Transient Detectability and Volume Sensitivity for iPTF},
author = {Chatterjee, Deep and Nugent, Peter E. and Brady, Patrick R. and Cannella, Chris and Kaplan, David L. and Kasliwal, Mansi M.},
abstractNote = {The past couple of decades have seen an emergence of transient detection facilities in various avenues of time-domain astronomy that have provided us with a rich data set 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 spacetime volume sensitivity of a survey to a particular transient type as a function of many intrinsic and extrinsic parameters. Fully sampling that multidimensional 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 spacetime volume sensitivity of type Ia supernovae (SNe Ia) in the intermediate Palomar Transient Factory (iPTF) and obtain the result, ⟨VT⟩Ia = (2.93 ± 0.21) × 10-2 Gpc3 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⟩IIa = (7.80 ± 0.76) × 10-4 Gpc3 yr. This classifier can be used for computationally fast spacetime volume sensitivity calculation of any generic transient type using their light-curve 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.},
doi = {10.3847/1538-4357/ab2b9c},
journal = {The Astrophysical Journal (Online)},
number = 2,
volume = 881,
place = {United States},
year = {2019},
month = {8}
}

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