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Palomar Gattini-IR: Survey overview, data processing system, on-sky performance and first results

De, Kishalay and Hankins, Matthew J. and Kasliwal, Mansi M. and Moore, Anna M. and Ofek, Eran O. and Adams, Scott M. and Ashley, Michael C. B. and Babul, Aliya-Nur and Bagdasaryan, Ashot and Burdge, Kevin B. and Burnham, Jill and Dekany, Richard G. and Declacroix, Alexander and Galla, Antony and Greffe, Tim and Hale, David and Jencson, Jacob E. and Lau, Ryan M. and Mahabal, Ashish and McKenna, Daniel and Sharma, Manasi and Shopbell, Patrick L. and Smith, Roger M. and Soon, Jamie and Sokoloski, Jennifer and Soria, Roberto and Travouillon, Tony (2020) Palomar Gattini-IR: Survey overview, data processing system, on-sky performance and first results. Publications of the Astronomical Society of the Pacific, 132 (1008). Art. No. 025001. ISSN 1538-3873. https://resolver.caltech.edu/CaltechAUTHORS:20191122-085137309

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Abstract

Palomar Gattini-IR is a new wide-field, near-infrared (NIR) robotic time domain survey operating at Palomar Observatory. Using a 30 cm telescope mounted with a H2RG detector, Gattini-IR achieves a field of view (FOV) of 25 sq. deg. with a pixel scale of 8.”7 in J-band. Here, we describe the system design, survey operations, data processing system and on-sky performance of Palomar Gattini-IR. As a part of the nominal survey, Gattini-IR scans ≈7500 square degrees of the sky every night to a median 5σ depth of 15.7 AB mag outside the Galactic plane. The survey covers ≈15,000 square degrees of the sky visible from Palomar with a median cadence of 2 days. A real-time data processing system produces stacked science images from dithered raw images taken on sky, together with point-spread function (PSF)-fit source catalogs and transient candidates identified from subtractions within a median delay of ≈4 hr from the time of observation. The calibrated data products achieve an astrometric accuracy (rms) of ≈0.”7 with respect to Gaia DR2 for sources with signal-to-noise ratio > 10, and better than ≈0.”35 for sources brighter than ≈12 Vega mag. The photometric accuracy (rms) achieved in the PSF-fit source catalogs is better than ≈3% for sources brighter than ≈12 Vega mag and fainter than the saturation magnitude of ≈8.5 Vega mag, as calibrated against the Two Micron All Sky Survey catalog. The detection efficiency of transient candidates injected into the images is better than 90% for sources brighter than the 5σ limiting magnitude. The photometric recovery precision of injected sources is 3% for sources brighter than 13 mag, and the astrometric recovery rms is ≈0.”9. Reference images generated by stacking several field visits achieve depths of ≳16.5 AB mag over 60% of the sky, while it is limited by confusion in the Galactic plane. With a FOV ≈40× larger than any other existing NIR imaging instrument, Gattini-IR is probing the reddest and dustiest transients in the local universe such as dust obscured supernovae in nearby galaxies, novae behind large columns of extinction within the galaxy, reddened microlensing events in the Galactic plane and variability from cool and dust obscured stars. We present results from transients and variables identified since the start of the commissioning period.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1088/1538-3873/ab6069DOIArticle
https://arxiv.org/abs/1910.13319arXivDiscussion Paper
ORCID:
AuthorORCID
De, Kishalay0000-0002-8989-0542
Hankins, Matthew J.0000-0001-9315-8437
Kasliwal, Mansi M.0000-0002-5619-4938
Moore, Anna M.0000-0002-2894-6936
Ofek, Eran O.0000-0002-6786-8774
Adams, Scott M.0000-0001-5855-5939
Burdge, Kevin B.0000-0002-7226-836X
Jencson, Jacob E.0000-0001-5754-4007
Mahabal, Ashish0000-0003-2242-0244
Soria, Roberto0000-0002-4622-796X
Additional Information:© 2020 The Astronomical Society of the Pacific. Received 2019 October 28; accepted 2019 December 10; published 2020 January 13. We thank A. Fruchter, F. Masci, S. R. Kulkarni, C. Steidel and M. J. Graham for valuable discussions on this work. We thank the anonymous referee for a careful reading of the manuscript that significantly helped improve the quality of the manuscript. M.M.K. and E.O. acknowledge the US-Israel Bi-national Science Foundation Grant 2016227. M.M.K. and J.L.S. acknowledge the Heising-Simons foundation for support via a Scialog fellowship of the Research Corporation. M.M.K. and A.M.M. acknowledge the Mt Cuba foundation. J. Soon is supported by an Australian Government Research Training Program (RTP) Scholarship. SED Machine is based upon work supported by the National Science Foundation under Grant No. 1106171. K.D. and M.J.H. thank the hospitality of the astrophysics group at the Weizmann Institute of Science, Rehovot, Israel, where part of this work was carried out. This work was supported by the GROWTH (Global Relay of Observatories Watching Transients Happen) project funded by the National Science Foundation under PIRE Grant No 1545949. GROWTH is a collaborative project among the California Institute of Technology (USA), University of Maryland College Park (USA), University of Wisconsin Milwaukee (USA), Texas Tech University (USA), San Diego State University (USA), University of Washington (USA), Los Alamos National Laboratory (USA), Tokyo Institute of Technology (Japan), National Central University (Taiwan), Indian Institute of Astrophysics (India), Indian Institute of Technology Bombay (India), Weizmann Institute of Science (Israel), The Oskar Klein Centre at Stockholm University (Sweden), Humboldt University (Germany), Liverpool John Moores University (UK) and University of Sydney (Australia). The High Performance Wireless Research & Education Network (HPWREN; https://hpwren.ucsd.edu) is a project at the University of California, San Diego and the National Science Foundation (grant numbers 0087344 (in 2000), 0426879 (in 2004), and 0944131 (in 2009)). This publication makes use of data products from the Two Micron All Sky Survey, which is a joint project of the University of Massachusetts and the Infrared Processing and Analysis Center/California Institute of Technology, funded by NASA and the National Science Foundation. This work has made use of data from the European Space Agency (ESA) mission Gaia (https://www.cosmos.esa.int/gaia), processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www.cosmos.esa.int/web/gaia/dpac/consortium). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement. This work has also made use of the Pan-STARRS1 (PS1) Surveys (http://www.ifa.hawaii.edu/pswww/) and the PS1 public science archive (https://panstarrs.stsci.edu). Software: astropy (Astropy Collaboration et al. 2013), matplotlib (Hunter 2007), scipy (Virtanen et al. 2019), pandas (McKinney 2010), SExtractor (Bertin & Arnouts 1996), scamp (Bertin 2006), PSFEx (Bertin 2011), pysedm (Rigault et al. 2019), pyraf-dbsp (Bellm & Sesar 2016), spextool (Cushing et al. 2004), xtellcor (Vacca et al. 2003).
Group:Infrared Processing and Analysis Center (IPAC), Astronomy Department
Funders:
Funding AgencyGrant Number
Binational Science Foundation (USA-Israel)2016227
Heising-Simons FoundationUNSPECIFIED
Research CorporationUNSPECIFIED
Mt. Cuba Astronomical FoundationUNSPECIFIED
Australian GovernmentUNSPECIFIED
NSFAST-1106171
NSFAST-1545949
NSFOAC-0087344
NSFOAC-0426879
NSFOAC-0944131
NASAUNSPECIFIED
Gaia Multilateral AgreementUNSPECIFIED
Subject Keywords:astronomical databases: miscellaneous – catalogs – infrared: general – methods: data analysis – surveys – techniques: image processing – techniques: photometric
Issue or Number:1008
Record Number:CaltechAUTHORS:20191122-085137309
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20191122-085137309
Official Citation:Kishalay De et al 2020 PASP 132 025001
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:100010
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:22 Nov 2019 17:21
Last Modified:09 Mar 2020 13:19

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