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

Chatterjee, Deep and Nugent, Peter E. and Brady, Patrick R. and Cannella, Chris and Kaplan, David L. and Kasliwal, Mansi M. (2019) Towards Rate Estimation for Transient Surveys I: Assessing Transient Detectability and Volume Sensitivity for iPTF. Astrophysical Journal, 881 (2). Art. No. 128. ISSN 1538-4357. https://resolver.caltech.edu/CaltechAUTHORS:20190722-083139256

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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, ⟨VT⟩_(Ia) = 2.93±0.21×10^(−2)Gpc^3yr. 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⟩_(IIp) = 7.80±0.76×10^(−4)Gpc^3yr. 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.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.3847/1538-4357/ab2b9cDOIArticle
https://arxiv.org/abs/1906.09309arXivDiscussion Paper
ORCID:
AuthorORCID
Chatterjee, Deep0000-0003-0038-5468
Nugent, Peter E.0000-0002-3389-0586
Brady, Patrick R.0000-0002-4611-9387
Cannella, Chris0000-0003-2667-7290
Kaplan, David L.0000-0001-6295-2881
Kasliwal, Mansi M.0000-0002-5619-4938
Additional Information:© 2019 The American Astronomical Society. Received 2019 May 15; revised 2019 June 18; accepted 2019 June 19; published 2019 August 21. This work was supported by Global Relay of Observatories Watching Transients Happen (GROWTH) project under the National Science Foundation (NSF) grant No. 1545949. The research used resources of the National Energy Research Scientific Computing Center (NERSC), a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. D.C. acknowledges the use of computing facilities provided by NERSC and by Leonard E. Parker Center for Gravitation, Cosmology and Astrophysics at University of Wisconsin–Milwaukee. The latter is supported by NSF Awards PHY-1626190 and PHY-1607585. P.E.N. acknowledges support from the DOE through DE-FOA-0001088, Analytical Modeling for Extreme-Scale Computing Environments. D.C. would like to thank Shaon Ghosh, Jolien Creighton, Siddharth Mohite, Angela Van Sistine, and Lin Yan for helpful discussions. We thank the anonymous referee for helpful comments. Software: SExtractor (Bertin & Arnouts 1996), HOTPANTS (Becker 2015), Astropy (Astropy Collaboration et al. 2018), sncosmo (Barbary 2014), scikit-learn (Pedregosa et al. 2011), Matplotlib (Hunter 2007), scipy (Jones et al. 2001), numpy (van der Walt et al. 2011), pandas (McKinney 2010), jupyter (https://jupyter.org/), SQLAlchemy (https://www.sqlalchemy.org/).
Group:Astronomy Department
Funders:
Funding AgencyGrant Number
NSFAST-1545949
Department of Energy (DOE)DE-AC02-05CH11231
NSFPHY-1626190
NSFPHY-1607585
Department of Energy (DOE)DE-FOA-0001088
Subject Keywords:Supernovae – Transient detection – Type Ia supernovae – Type II supernovae
Issue or Number:2
Record Number:CaltechAUTHORS:20190722-083139256
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20190722-083139256
Official Citation:Deep Chatterjee et al 2019 ApJ 881 128
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:97308
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:22 Jul 2019 16:16
Last Modified:09 Mar 2020 13:18

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