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Register a new userMeasuring Quasar Variability with Pan-STARRS1 and SDSS
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We measure quasar variability using the Panoramic Survey Telescope and Rapid Response System 1 Survey (Pan-STARRS1 or PS1) and the Sloan Digital Sky Survey (SDSS) and establish a method of selecting quasars via their variability in 10,000 square degree surveys. We use 100,000 spectroscopically confirmed quasars that have been well measured in both PS1 and SDSS and take advantage of the decadal time scales that separate SDSS measurements and PS1 measurements. A power law model fits the data well over the entire time range tested, 0.01 to 10 years. Variability in the current PS1-SDSS dataset can efficiently distinguish between quasars and non-varying objects. It improves the purity of a griz quasar color cut from 4.1% to 48% while maintaining 67% completeness. Variability will be very effective at finding quasars in datasets with no u band and in redshift ranges where exclusively photometric selection is not efficient. We show that quasars rest-frame ensemble variability, measured as a root mean squared in delta magnitudes, is consistent with V(z, L, t) = A0 (1+z)^0.37 (L/L0)^-0.16 (t/1yr)^0.246 , where L0 = 10^46 ergs^-1 and A0 = 0.190, 0.162, 0.147 or 0.141 in the gP1 , rP1 , iP1 or zP1 filter, respectively. We also fit across all four filters and obtain median variability as a function of z, L and lambda as V(z, L, lambda, t) = 0.079(1 + z)^0.15 (L/L0 )^-0.2 (lambda/1000 nm)^-0.44 (t/1yr)^0.246 .
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The classification of supernovae (SNe) and its impact on our understanding of the 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 classification, connecting SN light curves back to their spectroscopically defined classes. Here we present Superphot, an open-source 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.
Pan-STARRS1 has carried out a set of distinct synoptic imaging sky surveys including the $3pi$ Steradian Survey and the Medium Deep Survey in 5 bands ($grizy_{P1}$). The mean 5$sigma$ point source limiting sensitivities in the stacked 3$pi$ Steradian Survey in $grizy_{P1}$ are (23.3, 23.2, 23.1, 22.3, 21.4) respectively. The upper bound on the systematic uncertainty in the photometric calibration across the sky is 7-12 millimag depending on the bandpass. The systematic uncertainty of the astrometric calibration using the Gaia frame comes from a comparison of the results with Gaia: the standard deviation of the mean and median residuals ($ Delta ra, Delta dec $) are (2.3, 1.7) milliarcsec, and (3.1, 4.8) milliarcsec respectively. The Pan-STARRS system and the design of the PS1 surveys are described and an overview of the resulting image and catalog data products and their basic characteristics are described together with a summary of important results. The images, reduced data products, and derived data products from the Pan-STARRS1 surveys are available to the community from the Mikulski Archive for Space Telescopes (MAST) at STScI.
Automated classification of supernovae (SNe) based on optical photometric light curve information is essential in the upcoming era of wide-field time domain surveys, such as the Legacy Survey of Space and Time (LSST) conducted by the Rubin Observatory. Photometric classification can enable real-time identification of interesting events for extended multi-wavelength follow-up, as well as archival population studies. Here we present the complete sample of 5,243 SN-like light curves (in griz) from the Pan-STARRS1 Medium-Deep Survey (PS1-MDS). The PS1-MDS is similar to the planned LSST Wide-Fast-Deep survey in terms of cadence, filters and depth, making this a useful training set for the community. Using this dataset, we train a novel semi-supervised machine learning algorithm to photometrically classify 2,315 new SN-like light curves with host galaxy spectroscopic redshifts. Our algorithm consists of a random forest supervised classification step and a novel unsupervised step in which we introduce a recurrent autoencoder neural network (RAENN). Our final pipeline, dubbed SuperRAENN, has an accuracy of 87% across five SN classes (Type Ia, Ibc, II, IIn, SLSN-I). We find the highest accuracy rates for Type Ia SNe and SLSNe and the lowest for Type Ibc SNe. Our complete spectroscopically- and photometrically-classified samples break down into: 62.0% Type Ia (1839 objects), 19.8% Type II (553 objects), 4.8% Type IIn (136 objects), 11.7% Type Ibc (291 objects), and 1.6% Type I SLSNe (54 objects). Finally, we discuss how this algorithm can be modified for online LSST data streams.
[Abbreviated] We search for scaling relations between the fundamental AGN parameters and rest-frame UV/optical variability properties for a sample of $sim$90 X-ray selected AGNs covering a wide redshift range from the XMM-COSMOS survey, with optical light curves in four bands provided by the Pan-STARRS1 (PS1) Medium Deep Field 04 survey. To estimate the variability amplitude we utilize the normalized excess variance ($sigma_{mathrm{rms}}^{2}$) and probe variability on rest-frame timescales of several months and years by calculating $sigma_{mathrm{rms}}^{2}$ from different parts of our light curves. In addition, we derive the rest-frame optical PSD for our sources using continuous-time autoregressive moving average (CARMA) models. We observe that the excess variance and the PSD amplitude are strongly anti-correlated with wavelength, bolometric luminosity and Eddington ratio. There is no evidence for a dependency of the variability amplitude on black hole mass and redshift. These results suggest that the accretion rate is the fundamental physical quantity determining the rest-frame UV/optical variability amplitude of quasars on timescales of months and years. The optical PSD of all of our sources is consistent with a broken power law showing a characteristic bend at rest-frame timescales ranging between $sim$100 and $sim$300 days. The break timescale exhibits no significant correlation with any of the fundamental AGN parameters. The low frequency slope of the PSD is consistent with a value of $-1$ for most of our objects, whereas the high frequency slope is characterized by a broad distribution of values between $sim-2$ and $sim-4$. These findings unveil significant deviations from the simple damped random walk model, frequently used in previous optical variability studies. We find a weak tendency for AGNs with higher black hole mass having steeper high frequency PSD slopes.
We present the result of a search for orphan Gamma-Ray Burst (GRB) afterglows in the Pan-STARRS1 (PS1) COSMOS survey. There is extensive theoretical and observational evidence suggesting that GRBs are collimated jets; the direct observation of orphan GRB afterglows would further support this model. An optimal survey strategy is designed by coupling the PS1 survey with the Subaru/Hyper-Suprime-Cam (HSC) survey. The PS1 COSMOS survey, one of the survey fields in the PS1 Medium Deep Survey (PS1/MDS), searches a field of 7 deg$^2$ from December 2011 to January 2014, reaching a limiting magnitude R $sim$ 23. The dense cadence of PS1/MDS is crucial for identifying transients, and the deep magnitude reached by the HSC survey (R $sim$ 26) is important for evaluating potential GRB hosts. A transient classification method is employed to select potential orphan GRB afterglow candidates. After a thorough analysis of the transient and host galaxy properties, we conclude that there are no candidates in this survey field. The null result implies that the consideration of jet structures is essential for further orphan GRB afterglow surveys.
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