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Register a new userMachine learning for transient discovery in Pan-STARRS1 difference imaging
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Efficient identification and follow-up of astronomical transients is hindered by the need for humans to manually select promising candidates from data streams that contain many false positives. These artefacts arise in the difference images that are produced by most major ground-based time domain surveys with large format CCD cameras. This dependence on humans to reject bogus detections is unsustainable for next generation all-sky surveys and significant effort is now being invested to solve the problem computationally. In this paper we explore a simple machine learning approach to real-bogus classification by constructing a training set from the image data of ~32000 real astrophysical transients and bogus detections from the Pan-STARRS1 Medium Deep Survey. We derive our feature representation from the pixel intensity values of a 20x20 pixel stamp around the centre of the candidates. This differs from previous work in that it works directly on the pixels rather than catalogued domain knowledge for feature design or selection. Three machine learning algorithms are trained (artificial neural networks, support vector machines and random forests) and their performances are tested on a held-out subset of 25% of the training data. We find the best results from the random forest classifier and demonstrate that by accepting a false positive rate of 1%, the classifier initially suggests a missed detection rate of around 10%. However we also find that a combination of bright star variability, nuclear transients and uncertainty in human labelling means that our best estimate of the missed detection rate is approximately 6%.
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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.
Identification of anomalous light curves within time-domain surveys is often challenging. In addition, with the growing number of wide-field surveys and the volume of data produced exceeding astronomers ability for manual evaluation, outlier and anomaly detection is becoming vital for transient science. We present an unsupervised method for transient discovery using a clustering technique and the Astronomaly package. As proof of concept, we evaluate 85553 minute-cadenced light curves collected over two 1.5 hour periods as part of the Deeper, Wider, Faster program, using two different telescope dithering strategies. By combining the clustering technique HDBSCAN with the isolation forest anomaly detection algorithm via the visual interface of Astronomaly, we are able to rapidly isolate anomalous sources for further analysis. We successfully recover the known variable sources, across a range of catalogues from within the fields, and find a further 7 uncatalogued variables and two stellar flare events, including a rarely observed ultra fast flare (5 minute) from a likely M-dwarf.
The Pan-STARRS1 survey is collecting multi-epoch, multi-color observations of the sky north of declination -30 deg to unprecedented depths. These data are being photometrically and astrometrically calibrated and will serve as a reference for many other purposes. In this paper we present our determination of the Pan-STARRS photometric system: gp1, rp1, ip1, zp1, yp1, and wp1. The Pan-STARRS photometric system is fundamentally based on the HST Calspec spectrophotometric observations, which in turn are fundamentally based on models of white dwarf atmospheres. We define the Pan-STARRS magnitude system, and describe in detail our measurement of the system passbands, including both the instrumental sensitivity and atmospheric transmission functions. Byproducts, including transformations to other photometric systems, galactic extinction, and stellar locus are also provided. We close with a discussion of remaining systematic errors.
We present a comparison of several Difference Image Analysis (DIA) techniques, in combination with Machine Learning (ML) algorithms, applied to the identification of optical transients associated with gravitational wave events. Each technique is assessed based on the scoring metrics of Precision, Recall, and their harmonic mean F1, measured on the DIA results as standalone techniques, and also in the results after the application of ML algorithms, on transient source injections over simulated and real data. This simulations cover a wide range of instrumental configurations, as well as a variety of scenarios of observation conditions, by exploring a multi dimensional set of relevant parameters, allowing us to extract general conclusions related to the identification of transient astrophysical events. The newest subtraction techniques, and particularly the methodology published in Zackay et al. (2016) are implemented in an Open Source Python package, named properimage, suitable for many other astronomical image analyses. This together with the ML libraries we describe, provides an effective transient detection software pipeline. Here we study the effects of the different ML techniques, and the relative feature importances for classification of transient candidates, and propose an optimal combined strategy. This constitutes the basic elements of pipelines that could be applied in searches of electromagnetic counterparts to GW sources.
High-redshift quasars are currently the only probes of the growth of supermassive black holes and potential tracers of structure evolution at early cosmic time. Here we present our candidate selection criteria from the Panoramic Survey Telescope & Rapid Response System 1 and follow-up strategy to discover quasars in the redshift range 5.7<z<6.2. With this strategy we discovered eight new 5.7<z<6.0 quasars, increasing the number of known quasars at z>5.7 by more than 10%. We additionally recovered 18 previously known quasars. The eight quasars presented here span a large range of luminosities (-27.3 < M_{1450} < -25.4; 19.6 < z_ps1 < 21.2) and are remarkably heterogeneous in their spectral features: half of them show bright emission lines whereas the other half show a weak or no Ly$alpha$ emission line (25% with rest-frame equivalent width of the Ly$alpha$ + Nv line lower than 15{AA}). We find a larger fraction of weak-line emission quasars than in lower redshift studies. This may imply that the weak-line quasar population at the highest redshifts could be more abundant than previously thought. However, larger samples of quasars are needed to increase the statistical significance of this finding.
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