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Register a new userA Search for Fast Optical Transients in the Pan-STARRS1 Medium-Deep Survey: M Dwarf Flares, Asteroids, Limits on Extragalactic Rates, and Implications for LSST
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[Abridged] We present a search for fast optical transients (~0.5 hr-1 day) using repeated observations of the Pan-STARRS1 Medium-Deep Survey (PS1/MDS) fields. Our search takes advantage of the consecutive g/r-band observations (16.5 min in each filter), by requiring detections in both bands, with non-detections on preceding and subsequent nights. We identify 19 transients brighter than 22.5 AB mag (S/N>10). Of these, 11 events exhibit quiescent counterparts in the deep PS1/MDS templates that we identify as M4-M9 dwarfs. The remaining 8 transients exhibit a range of properties indicative of main-belt asteroids near the stationary point of their orbits. With identifications for all 19 transients, we place an upper limit of R_FOT(0.5hr)<0.12 deg^-2 d^-1 (95% confidence level) on the sky-projected rate of extragalactic fast transients at <22.5 mag, a factor of 30-50 times lower than previous limits; the limit for a timescale of ~day is R_FOT<2.4e-3 deg^-2 d^-1. To convert these sky-projected rates to volumetric rates, we explore the expected peak luminosities of fast optical transients powered by various mechanisms, and find that non-relativistic events are limited to M~-10 mag (M~-14 mag) for a timescale of ~0.5 hr (~day), while relativistic sources (e.g., GRBs, magnetar-powered transients) can reach much larger luminosities. The resulting volumetric rates are <13 (M~-10 mag), <0.05 (M~-14 mag) and <1e-6 Mpc^-3 yr^-1 (M~-24 mag), significantly above the nova, supernova, and GRB rates, respectively, indicating that much larger surveys are required to provide meaningful constraints. Motivated by the results of our search we discuss strategies for identifying fast optical transients in the LSST main survey, and reach the optimistic conclusion that the veil of foreground contaminants can be lifted with the survey data, without the need for expensive follow-up observations.
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We present a systematic search for periodically varying quasars and supermassive black hole binary (SMBHB) candidates in the Pan-STARRS1 (PS1) Medium Deep Surveys MD09 field. From a color-selected sample of 670 quasars extracted from a multi-band deep-stack catalog of point sources, we locally select variable quasars and look for coherent periods with the Lomb-Scargle periodogram. Three candidates from our sample demonstrate strong variability for more than ~3 cycles, and their PS1 light curves are well fitted to sinusoidal functions. We test the persistence of the candidates apparent periodic variations detected during the 4.2 years of the PS1 survey with archival photometric data from the SDSS Stripe 82 survey or new monitoring with the Large Monolithic Imager at the Discovery Channel Telescope. None of the three periodic candidates (including PSO J334.2028+1.4075) remain persistent over the extended baseline of 7 - 14 years, corresponding to a detection rate of < 1 in 670 quasars in a search area of 5 deg^2. Even though SMBHBs should be a common product of the hierarchal growth of galaxies, and periodic variability in SMBHBs has been theoretically predicted, a systematic search for such signatures in a large optical survey is strongly limited by its temporal baseline and the red noise associated with normal quasar variability. We show that follow-up long-term monitoring (>5 cycles) is crucial to our search for these systems.
We present a systematic search for periodically varying quasar and supermassive black hole binary (SMBHB) candidates in the Pan-STARRS1 Medium Deep Survey. From $sim9,000$ color-selected quasars in a $sim50$ deg$^{2}$ sky area, we initially identify $26$ candidates with more than $1.5$ cycles of variation. We extend the baseline of observations via our imaging campaign with the Discovery Channel Telescope and the Las Cumbres Observatory network and reevaluate the candidates using a more rigorous, maximum likelihood method. Using a range of statistical criteria and assuming the Damped Random Walk model for normal quasar variability, we identify one statistically significant periodic candidate. We also investigate the capabilities of detecting SMBHBs by the Large Synoptic Survey Telescope using our study with MDS as a benchmark and explore any complementary, multiwavelength evidence for SMBHBs in our sample.
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.
Photometric classification of supernovae (SNe) is imperative as recent and upcoming optical time-domain surveys, such as the Large Synoptic Survey Telescope (LSST), overwhelm the available resources for spectrosopic follow-up. Here we develop a range of light curve classification pipelines, trained on 518 spectroscopically-classified SNe from the Pan-STARRS1 Medium-Deep Survey (PS1-MDS): 357 Type Ia, 93 Type II, 25 Type IIn, 21 Type Ibc, and 17 Type I SLSNe. We present a new parametric analytical model that can accommodate a broad range of SN light curve morphologies, including those with a plateau, and fit this model to data in four PS1 filters (griz). We test a number of feature extraction methods, data augmentation strategies, and machine learning algorithms to predict the class of each SN. Our best pipelines result in 90% average accuracy, 70% average purity, and 80% average completeness for all SN classes, with the highest success rates for Type Ia SNe and SLSNe and the lowest for Type Ibc SNe. Despite the greater complexity of our classification scheme, the purity of our Type Ia SN classification, 95%, is on par with methods developed specifically for Type Ia versus non-Type Ia binary classification. As the first of its kind, this study serves as a guide to developing and training classification algorithms for a wide range of SN types with a purely empirical training set, particularly one that is similar in its characteristics to the expected LSST main survey strategy. Future work will implement this classification pipeline on ~3000 PS1/MDS light curves that lack spectroscopic classification.
There has been speculation of a class of relativistic explosions with an initial Lorentz factor smaller than that of classical Gamma-Ray Bursts (GRBs). These dirty fireballs would lack prompt GRB emission but could be pursued via their optical afterglow, appearing as transients that fade overnight. Here we report a search for such transients (transients that fade by 5-$sigma$ in magnitude overnight) in four years of archival photometric data from the intermediate Palomar Transient Factory (iPTF). Our search criteria yielded 45 candidates. Of these, two were afterglows to GRBs that had been found in dedicated follow-up observations to triggers from the Fermi GRB Monitor (GBM). Another (iPTF14yb; Cenko et al. 2015) was a GRB afterglow discovered serendipitously. Two were spurious artifacts of reference image subtraction and one was an asteroid. The remaining 37 candidates have red stellar counterparts in external catalogs. The photometric and spectroscopic properties of the counterparts identify these transients as strong flares from M dwarfs of spectral type M3-M7 at distances of d ~ 0.15-2.1 kpc; two counterparts were already spectroscopically classified as late-type M stars. With iPTF14yb as the only confirmed relativistic outflow discovered independently of a high-energy trigger, we constrain the all-sky rate of transients that peak at m = 18 and fade by $Delta$2 mag in $Delta$3 hr to be 680 per year with a 68% confidence interval of 119-2236 per year. This implies that the rate of visible dirty fireballs is at most comparable to that of the known population of long-duration GRBs.
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