Pan-STARRS Pixel Analysis: Source Detection and Characterization

Magnier, E. A.; Sweeney, William E.; Chambers, K.; Flewelling, H.; Huber, M. E.; Price, P. A.; Waters, C.; Denneau, L.; Draper, P. W.; Jedicke, Robert; Hodapp, Klaus W.; Kaiser, N.; Kudritzki, Rolf‐Peter; Metcalfe, N.; Stubbs, C. W.; Wainscoast, R. J.

doi:10.3847/1538-4365/abb82c

Cited by 87 publications

(53 citation statements)

References 43 publications

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“…The Pan-STARRS analysis pipeline computes PSF models at individual positions of stack images over a grid of about 8 steps, and interpolates these models to predict the PSF FWHM across the sky (Magnier et al 2020). This introduces deviations between the modeled and true PSF, since the latter varies on very small scales due to stacking of individual exposures with variable FWHM.…”

Section: Strong Lensing Simulationsmentioning

confidence: 99%

“…Focusing the CNN lens search on a subset of the three billion sources detected in the PS1 3π survey requires a preselection of sources based on their properties released in public catalogs. For this purpose, we computed the photometry of mock lensed systems in the same way as the PS1 image processing pipeline (Magnier et al 2020). Fixed aperture photometry is particularly important to measure reliable colors.…”

Section: Photometry Of Mock Lens Systemsmentioning

confidence: 99%

“…Unsurprisingly, the scatter only rises above these average values for the large aperture magnitudes of fainter objects, which mostly enclose background noise. These residual biases up to 0.1-0.2 mag for 22.0 mag likely indicate small differences in the local background subtraction between both methods, or the contamination from neighbors which were subtracted by the PS1 pipeline (Magnier et al 2020) but not with SExtractor. Nonetheless, these offsets remain minor compared to other uncertainties in the analysis.…”

Section: Photometry Of Mock Lens Systemsmentioning

confidence: 99%

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Holismokes

Cañameras

Schuldt

Suyu

et al. 2020

A&A

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We present a systematic search for wide-separation (with Einstein radius θE ≳ 1.5″), galaxy-scale strong lenses in the 30 000 deg2 of the Pan-STARRS 3π survey on the Northern sky. With long time delays of a few days to weeks, these types of systems are particularly well-suited for catching strongly lensed supernovae with spatially-resolved multiple images and offer new insights on early-phase supernova spectroscopy and cosmography. We produced a set of realistic simulations by painting lensed COSMOS sources on Pan-STARRS image cutouts of lens luminous red galaxies (LRGs) with redshift and velocity dispersion known from the sloan digital sky survey (SDSS). First, we computed the photometry of mock lenses in gri bands and applied a simple catalog-level neural network to identify a sample of 1 050 207 galaxies with similar colors and magnitudes as the mocks. Second, we trained a convolutional neural network (CNN) on Pan-STARRS gri image cutouts to classify this sample and obtain sets of 105 760 and 12 382 lens candidates with scores of pCNN > 0.5 and > 0.9, respectively. Extensive tests showed that CNN performances rely heavily on the design of lens simulations and the choice of negative examples for training, but little on the network architecture. The CNN correctly classified 14 out of 16 test lenses, which are previously confirmed lens systems above the detection limit of Pan-STARRS. Finally, we visually inspected all galaxies with pCNN > 0.9 to assemble a final set of 330 high-quality newly-discovered lens candidates while recovering 23 published systems. For a subset, SDSS spectroscopy on the lens central regions proves that our method correctly identifies lens LRGs at z ∼ 0.1–0.7. Five spectra also show robust signatures of high-redshift background sources, and Pan-STARRS imaging confirms one of them as a quadruply-imaged red source at zs = 1.185, which is likely a recently quenched galaxy strongly lensed by a foreground LRG at zd = 0.3155. In the future, high-resolution imaging and spectroscopic follow-up will be required to validate Pan-STARRS lens candidates and derive strong lensing models. We also expect that the efficient and automated two-step classification method presented in this paper will be applicable to the ∼4 mag deeper gri stacks from the Rubin Observatory Legacy Survey of Space and Time (LSST) with minor adjustments.

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Section: Strong Lensing Simulationsmentioning

confidence: 99%

Section: Photometry Of Mock Lens Systemsmentioning

confidence: 99%

Section: Photometry Of Mock Lens Systemsmentioning

confidence: 99%

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Holismokes

Cañameras

Schuldt

Suyu

et al. 2020

A&A

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“…However, as Fig. 7 shows, the PSF of CRTS co-adds is a factor of 2 wider than the others: with the average FWHM being 3 .1 (computed from SEXTRACTOR), as compared to 1 .39 for PS1 (Magnier et al 2016), and 1 .32 for SDSS (Ross et al 2011). This trade-off between deeper and sharper images will have to be resolved by the end user based on their requirements in image subtractions and transient searches.…”

Section: Comparing With Other Surveysmentioning

confidence: 98%

Deep co-added sky from Catalina Sky Survey images

Singhal

Bhalerao

Mahabal

et al. 2021

Monthly Notices of the Royal Astronomical Society

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A number of synoptic sky surveys are underway or being planned. Typically they are done with small telescopes and relatively short exposure times. A search for transient or variable sources involves comparison with deeper baseline images, ideally obtained through the same telescope and camera. With that in mind we have stacked images from the 0.68 m Schmidt telescope on Mt. Bigelow taken over ten years as part of the Catalina Sky Survey. In order to generate deep reference images for the Catalina Real-time Transient Survey, close to 0.8 million images over 8000 fields and covering over 27000 sq. deg. have gone into the deep stack that goes up to 3 magnitudes deeper than individual images. CRTS system does not use a filter in imaging, hence there is no standard passband in which the optical magnitude is measured. We estimate depth by comparing these wide-band unfiltered co-added images with images in the g-band and find that the image depth ranges from 22.0–24.2 across the sky, with a 200-image stack attaining an equivalent AB magnitude sensitivity of 22.8. We compared various state-of-the-art software packages for co-adding astronomical images and have used SWarp for the stacking. We describe here the details of the process adopted. This methodology may be useful in other panoramic imaging applications, and to other surveys as well. The stacked images are available through a server at Inter-University Centre for Astronomy and Astrophysics (IUCAA).

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“…PS1-MDS ran from 2009 July to 2014 July using 25% of the observing time on Pan-STARRS1 and consisted of 10 deep-drilling fields with a three-day cadence in any of five bands: grizy (Chambers et al 2016). Images were processed and calibrated using the Image Processing Pipeline (Magnier et al 2020a(Magnier et al , 2020b(Magnier et al , 2020cWaters et al 2020). With the exception of y, PS1-MDS reached typical depths of 23.3mag per visit (Chambers et al 2016); the y filter has lower throughput and cadence, so we exclude it from our analysis.…”

Section: Data Setmentioning

confidence: 99%

Photometric Classification of 2315 Pan-STARRS1 Supernovae with Superphot

Hosseinzadeh

Dauphin

Villar

et al. 2020

ApJ

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The classification of supernovae (SNe) and its impact on our understanding of explosion physics and progenitors have traditionally been based on the presence or absence of certain spectral features. However, current and upcoming wide-field time-domain surveys have increased the transient discovery rate far beyond our capacity to obtain even a single spectrum of each new event. We must therefore rely heavily on photometric classificationconnecting SN light curves back to their spectroscopically defined classes. Here, we present Superphot, an opensource Python implementation of the machine-learning classification algorithm of Villar et al., and apply it to 2315 previously unclassified transients from the Pan-STARRS1 Medium Deep Survey for which we obtained spectroscopic host-galaxy redshifts. Our classifier achieves an overall accuracy of 82%, with completenesses and purities of >80% for the best classes (SNe Ia and superluminous SNe). For the worst performing SN class (SNe Ibc), the completeness and purity fall to 37% and 21%, respectively. Our classifier provides 1257 newly classified SNeIa, 521 SNeII, 298 SNeIbc, 181 SNeIIn, and 58 SLSNe. These are among the largest uniformly observed samples of SNe available in the literature and will enable a wide range of statistical studies of each class.

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Pan-STARRS Pixel Analysis: Source Detection and Characterization

Cited by 87 publications

References 43 publications

Holismokes

Holismokes

Deep co-added sky from Catalina Sky Survey images

Photometric Classification of 2315 Pan-STARRS1 Supernovae with Superphot

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