Palomar gattini-ir: Survey overview, data processing system, on-sky performance and first results
dc.contributor.author | De, Kishalay | |
dc.contributor.author | Hankins, Matthew J. | |
dc.contributor.author | Kasliwal, M M | |
dc.contributor.author | Moore, Anna | |
dc.contributor.author | Ofek, Eran | |
dc.contributor.author | Adams, Scott M | |
dc.contributor.author | Ashley, Michael | |
dc.contributor.author | Babul, Aliya-Nur | |
dc.contributor.author | Bagdasaryan, Ashot | |
dc.contributor.author | Burdge, Kevin B. | |
dc.contributor.author | Burnham, Jill | |
dc.contributor.author | Galla, Antony | |
dc.contributor.author | Soon, Jamie | |
dc.contributor.author | Travouillon, Tony | |
dc.date.accessioned | 2023-04-04T23:49:50Z | |
dc.date.issued | 2020 | |
dc.date.updated | 2022-01-16T07:23:00Z | |
dc.description.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 8farcs7 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 ≈0farcs7 with respect to Gaia DR2 for sources with signal-to-noise ratio > 10, and better than ≈0farcs35 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 ≈0farcs9. 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. | en_AU |
dc.description.sponsorship | M.M.K. and E.O. acknowledge the US-Israel Binational 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. | en_AU |
dc.format.mimetype | application/pdf | en_AU |
dc.identifier.issn | 0004-6280 | en_AU |
dc.identifier.uri | http://hdl.handle.net/1885/288092 | |
dc.language.iso | en_AU | en_AU |
dc.publisher | University of Chicago Press | en_AU |
dc.rights | © 2020. The Astronomical Society of the Pacific. | en_AU |
dc.source | Publications of the Astronomical Society of the Pacific | en_AU |
dc.subject | astronomical databases: miscellaneous | en_AU |
dc.subject | catalogs | en_AU |
dc.subject | infrared: general | en_AU |
dc.subject | methods: data analysis | en_AU |
dc.subject | surveys | en_AU |
dc.subject | techniques: image processing | en_AU |
dc.subject | techniques: photometric | en_AU |
dc.title | Palomar gattini-ir: Survey overview, data processing system, on-sky performance and first results | en_AU |
dc.type | Journal article | en_AU |
local.bibliographicCitation.issue | 1008 | en_AU |
local.bibliographicCitation.lastpage | 31 | en_AU |
local.bibliographicCitation.startpage | 1 | en_AU |
local.contributor.affiliation | De, Kishalay, California Institute of Technology | en_AU |
local.contributor.affiliation | Hankins, Matthew J., California Institute of Technology | en_AU |
local.contributor.affiliation | Kasliwal, M M, California Institute of Technology | en_AU |
local.contributor.affiliation | Moore, Anna, College of Science, ANU | en_AU |
local.contributor.affiliation | Ofek, Eran, Weizmann Institute of Science | en_AU |
local.contributor.affiliation | Adams, Scott M, California Institute of Technology | en_AU |
local.contributor.affiliation | Ashley, Michael, University of New South Wales | en_AU |
local.contributor.affiliation | Babul, Aliya-Nur, Columbia University | en_AU |
local.contributor.affiliation | Bagdasaryan, Ashot, California Institute of Technology | en_AU |
local.contributor.affiliation | Burdge, Kevin B., California Institute of Technology | en_AU |
local.contributor.affiliation | Burnham, Jill, California Institute of Technology | en_AU |
local.contributor.affiliation | Galla, Antony, College of Science, ANU | en_AU |
local.contributor.affiliation | Soon, Jamie, College of Science, ANU | en_AU |
local.contributor.affiliation | Travouillon, Tony, College of Science, ANU | en_AU |
local.contributor.authoremail | u1036159@anu.edu.au | en_AU |
local.contributor.authoruid | Moore, Anna, u1036159 | en_AU |
local.contributor.authoruid | Galla, Antony, u1062088 | en_AU |
local.contributor.authoruid | Soon, Jamie, u6333999 | en_AU |
local.contributor.authoruid | Travouillon, Tony, u1057001 | en_AU |
local.description.embargo | 2099-12-31 | |
local.description.notes | Imported from ARIES | en_AU |
local.identifier.absfor | 401001 - Engineering design | en_AU |
local.identifier.absfor | 510102 - Astronomical instrumentation | en_AU |
local.identifier.absfor | 461208 - Software testing, verification and validation | en_AU |
local.identifier.absseo | 280115 - Expanding knowledge in the information and computing sciences | en_AU |
local.identifier.absseo | 280110 - Expanding knowledge in engineering | en_AU |
local.identifier.absseo | 280120 - Expanding knowledge in the physical sciences | en_AU |
local.identifier.ariespublication | u6269649xPUB894 | en_AU |
local.identifier.citationvolume | 132 | en_AU |
local.identifier.doi | 10.1088/1538-3873/ab6069 | en_AU |
local.identifier.scopusID | 2-s2.0-85078765269 | |
local.identifier.uidSubmittedBy | u6269649 | en_AU |
local.publisher.url | https://iopscience.iop.org/article/10.1088/1538-3873/ab6069 | en_AU |
local.type.status | Published Version | en_AU |
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