Selection of burst-like transients and stochastic variables using multi-brand image differencing in the Pan-STARRS1 Medium Deep Survey

We present a novel method for the light-curve characterization of Pan-STARRS1 Medium Deep Survey (PS1 MDS) extragalactic sources into stochastic variables (SVs) and burst-like (BL) transients, using multi-band image-differencing time-series data. We select detections in difference images associated with galaxy hosts using a star/galaxy catalog extracted from the deep PS1 MDS stacked images, and adopt a maximum a posteriori formulation to model their difference-flux time-series in four Pan-STARRS1 photometric bands gP1, rP1, iP1, and zP1. We use three deterministic light-curve models to fit BL transients; a Gaussian, a Gamma distribution, and an analytic supernova (SN) model, and one stochastic light-curve model, the Ornstein-Uhlenbeck process, in order to fit variability that is characteristic of active galactic nuclei (AGNs). We assess the quality of fit of the models band-wise and source-wise, using their estimated leave-out-one cross-validation likelihoods and corrected Akaike information criteria. We then apply a K-means clustering algorithm on these statistics, to determine the source classification in each band. The final source classification is derived as a combination of the individual filter classifications, resulting in two measures of classification quality, from the averages across the photometric filters of (1) the classifications determined from the closest K-means cluster centers, and (2) the square distances from the clustering centers in the K-means clustering spaces. For a verification set of AGNs and SNe, we show that SV and BL occupy distinct regions in the plane constituted by these measures. We use our clustering method to characterize 4361 extragalactic image difference detected sources, in the first 2.5 yr of the PS1 MDS, into 1529 BL, and 2262 SV, with a purity of 95.00% for AGNs, and 90.97% for SN based on our verification sets. We combine our light-curve classifications with their nuclear or off-nuclear host galaxy offsets, to define a robust photometric sample of 1233 AGNs and 812 SNe. With these two samples, we characterize their variability and host galaxy properties, and identify simple photometric priors that would enable their real-time identification in future wide-field synoptic surveys.

Quiet-Sun Hα Transients and Corresponding Small-scale Transition Region and Coronal Heating

Rapid blue- and redshifted excursions (RBEs and RREs) are likely to be the on-disk counterparts of Type II spicules. Recently, heating signatures from RBEs/RREs have been detected in IRIS slit-jaw images dominated by transition region (TR) lines around network patches. Additionally, signatures of Type II spicules have been observed in Atmospheric Imaging Assembly (AIA) diagnostics. The full-disk, ever-present nature of the AIA diagnostics should provide us with sufficient statistics to directly determine how important RBEs and RREs are to the heating of the TR and corona. We find, with high statistical significance, that at least 11% of the low coronal brightenings detected in a quiet-Sun region in He ii 304 Å can be attributed to either RBEs or RREs as observed in Hα, and a 6% match of Fe IX 171 Å detected events to RBEs or RREs with very similar statistics for both types of Hα features. We took a statistical approach that allows for noisy detections in the coronal channels and provides us with a lower, but statistical significant, bound. Further, we consider matches based on overlapping features in both time and space, and find strong visual indications of further correspondence between coronal events and co-evolving but non-overlapping, RBEs and RREs.

cAMP/PKA-dependent increases in Ca Sparks, oscillations and SR Ca stores in retinal arteriolar myocytes after exposure to vasopressin.

PURPOSE: To investigate the effects of arginine vasopressin (AVP) on Ca(2+) sparks and oscillations and on sarcoplasmic reticulum (SR) Ca(2+) content in retinal arteriolar myocytes. METHODS: Fluo-4-loaded smooth muscle in intact segments of freshly isolated porcine retinal arteriole was imaged by confocal laser microscopy. SR Ca(2+) store content was assessed by recording caffeine-induced Ca(2+) transients with microfluorimetry and fura-2. RESULTS: The frequencies of Ca(2+) sparks and oscillations were increased both during exposure to, and 10 minutes after washout of AVP (10 nM). Caffeine transients were increased in amplitude 10 and 90 minutes after a 3-minute application of AVP. Both AVP-induced Ca(2+) transients and the enhancement of caffeine responses after AVP washout were inhibited by SR 49059, a V(1a) receptor blocker. Forskolin, an activator of adenylyl cyclase, also persistently enhanced caffeine transients. Rp-8-HA-cAMPS, a membrane-permeant PKA inhibitor, prevented enhancement of caffeine transients by both AVP and forskolin. Forskolin, but not AVP, produced a reversible, Rp-8-HA-cAMPS insensitive reduction in basal [Ca(2+)](i). CONCLUSIONS: AVP activates a cAMP/PKA-dependent pathway via V(1a) receptors in retinal arteriolar smooth muscle. This effect persistently increases SR Ca(2+) loading, upregulating Ca(2+) sparks and oscillations, and may favor prolonged agonist activity despite receptor desensitization.

Contemporary and 'messy' rural in-migration processes: Comparing counterurban and lateral rural migration.

This paper questions the ongoing dominant coverage given to counterurbanisation in the rural population literature. It is argued that this provides only a partial account of the true diversity of contemporary migration processes operating in rural areas and has the potential to fuse together different in-migration processes. Specifically, lateral rural migration has been under-researched to date. Using empirical data from a survey of 260 migrant households to 3 UK case study areas (in Scotland, Wales, and Northern Ireland), the significance of lateral rural migration is revealed and compared with counterurban migration and migrants. The last change of address shows that 59% relocated from an urban area (participating in a counterurban flow) whilst 41% moved from another rural location (lateral rural flow). The boundary between migration processes can, however, be blurred: Some moves are an example of both counterurbanisation and lateral rural flows. Incorporating lifetime migration histories data demonstrates the contemporary complexity and messiness of rural in-migration processes. For example, 26% of these migrant households only ever undertook a lateral rural move during their lifetime. For others, the direction of migration has changed numerous times and intertwined with each move are aspects of life course, return, and inter-regional migration. Comparing the survey characteristics and motivations of counterurban and lateral rural migrants, alongside interview material, highlights important similarities and differences. The paper concludes by calling on rural population geographers to more fully engage with the complexity, totality, and indeed messiness of contemporary rural in-migration processes.

Machine learning for transient discovery in Pan-STARRS1 difference imaging

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 similar to 32 000 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 20 x 20 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 per cent 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 per cent, the classifier initially suggests a missed detection rate of around 10 per cent. 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 per cent.

Selecting superluminous supernovae in faint galaxies from the first year of the Pan-STARRS1 Medium Deep Survey

The Pan-STARRS1 (PS1) survey has obtained imaging in five bands (grizyP1) over 10 Medium Deep Survey (MDS) fields covering a total of 70 square degrees. This paper describes the search for apparently hostless supernovae (SNe) within the first year of PS1 MDS data with an aim of discovering superluminous supernovae (SLSNe). A total of 249 hostless transients were discovered down to a limiting magnitude of MAB ∼ 23.5, of which 76 were classified as Type Ia supernovae (SNe Ia). There were 57 SNe with complete light curves that are likely core-collapse SNe (CCSNe) or type Ic SLSNe and 12 of these have had spectra taken. Of these 12 hostless, non-Type Ia SNe, 7 were SLSNe of type Ic at redshifts between 0.5 and 1.4. This illustrates that the discovery rate of type Ic SLSNe can be maximized by concentrating on hostless transients and removing normal SNe Ia. We present data for two possible SLSNe; PS1-10pm (z = 1.206) and PS1-10ahf (z = 1.1), and estimate the rate of type Ic SLSNe to be between 3+3−2×10−53+3−2×10−53+3−2×10−5 and 8+2−1×10−58+2−1×10−58+2−1×10−5 that of the CCSN rate within 0.3 ≤ z ≤ 1.4 by applying a Monte Carlo technique. The rate of slowly evolving, type Ic SLSNe (such as SN2007bi) is estimated as a factor of 10 lower than this range.

Current transients in the small salient-pole alternator caused by sudden short-circuit and synchronisation events

Small salient-pole machines, in the range 30 kVA to 2 MVA, are often used in distributed generators, which in turn are likely to form the major constituent of power generation in power system islanding schemes or microgrids. In addition to power system faults, such as short-circuits, islanding contains an inherent risk of out-of-synchronism re-closure onto the main power system. To understand more fully the effect of these phenomena on a small salient-pole alternator, the armature and field currents from tests conducted on a 31.5 kVA machine are analysed. This study demonstrates that by resolving the voltage difference between the machine terminals and bus into direct and quadrature axis components, interesting properties of the transient currents are revealed. The presence of saliency and short time-constants cause intriguing differences between machine events such as out-of-phase synchronisations and sudden three-phase short-circuits.

Escalators, Elevators and Travelators: The Occupational Mobility of Migrants to South-East England

In a meritocratic society it is assumed that the chance of achieving occupational mobility (OM) is not strongly influenced by one's starting position in terms of class or ethnicity. This paper seeks to explain the drivers of the high levels of OM achieved by one ethnically defined group: the Scots. Educational attainment is shown to be particularly important. A second level of interest is the changing role of internal migrants to a global city in the face of increased international skilled immigration. We investigate whether there is any evidence that the OM of internal migrants is being hindered as a result. The evidence points instead to immobile local labour being more disadvantaged occupationally than mobile labour from peripheral regions of the state.