Discovery of transient high-energy gamma-ray emission from the BL Lacertae object 5C 3.178

We report the serendipitous discovery of transient, point-like high energy gamma-ray emission coincident with the position of the suspected BL Lac object 5C 3.178. The source was detected using the Fermi Large Area Telescope (LAT) at a significance level of ~8 sigma during a 200 day period which began on MJD 55882. The observed gamma-ray emission is characterised by a moderate 0.2-300 GeV flux F(0.2-300 GeV)=$(8.22\pm2.04)\times10^{-9}$ ph cm$^{-2}$s$^{-1}$ and a hard power law spectrum with spectral index $\Gamma=1.76\pm0.09$. These properties appear consistent with the known sub-population of TeV gamma-ray-emitting BL Lac objects, implying that the source may be detectable using atmospheric Cherenkov telescope arrays. Moreover, the temporal variability of the source suggests that real-time searches of the Fermi-LAT all-sky dataset for weak emission on ~200 day timescales may represent a rewarding strategy.Comment: 5 pages, 4 figures. Accepted for publication in Astronomy & Astrophysic

Very High Energy Gamma-Rays from Binary Systems

This thesis presents a study of the very high energy (VHE) gamma-ray emission from X-ray binary systems using the H.E.S.S. imaging atmospheric Cherenkov array. The historical background and basic principles of ground-based gamma-ray astronomy are briefly reviewed and an overview of the design and capabilities of the H.E.S.S. telescope system is presented. The broadband observational properties of X-ray binary systems and their relevance in a broader astrophysical context is also discussed. A review of the radiative emission mechanisms which relate to VHE gamma-ray emission in X-ray binaries is presented, with emphasis given to the leptonic emission processes of synchrotron radiation and inverse-Compton scattering. Intrinsic absorption processes which act to attenuate the emitted flux of VHE gamma-rays are also discussed. Three computer models are introduced which simulate aspects of the gamma-ray emission and absorption in X-ray binary systems. A detailed analysis of the VHE gamma-ray emission from the X-ray binary LS 5039 is presented and the relevant procedures for data selection, gamma-hadron separation and background estimation are discussed in some detail. Methods for the determination of detection significance and the calculation of gamma-ray fluxes are also reviewed and results are derived which apply specifically to LS 5039. A detailed temporal analysis of the gamma-ray signal from LS 5039 is presented, applying tests for secular, excess and periodic variability. Strong evidence is found for modulation of the observed gamma-ray flux on the orbital period of ~3.9 days. Following a brief discussion of the procedures required for spectral analysis of VHE gamma-ray data, results are presented for LS 5039 which reveal evidence for spectral variability which is correlated with the observed gamma-ray flux and therefore, the orbital phase of the binary system. The spectral and temporal characteristics of LS 5039 are then compared with the predictions of theoretical models in an attempt to explain the observed behaviour. Contemporaneous X-ray and VHE gamma-ray observations of three galactic microquasars using the Rossi X-ray Timing Explorer and H.E.S.S. are presented. Although no gamma-ray detections are reported, the observations permit the derivation of upper limits to the VHE gamma-ray flux which correspond to episodes of known X-ray behaviour. The X-ray characteristics of each target are compared with pre-existing observational data to infer the presence or otherwise of relativistic outflows at the H.E.S.S. observation epochs. The implications of the gamma-ray non-detections are then discussed in the context of these inferred system properties. The results of a survey of the VHE gamma-ray emission associated with the positions of 125 known X-ray binaries are presented. Although no conclusive detections were obtained, tentative indications were found for a population of faint, spectrally hard gamma-ray sources associated with high-mass X-ray binary systems. The inferred characteristics of the indicated population show broad agreement with the measured properties of known gamma-ray-emitting X-ray binary systems like LS 5039

Modeling with the Crowd: Optimizing the Human-Machine Partnership with Zooniverse

LSST and Euclid must address the daunting challenge of analyzing the unprecedented volumes of imaging and spectroscopic data that these next-generation instruments will generate. A promising approach to overcoming this challenge involves rapid, automatic image processing using appropriately trained Deep Learning (DL) algorithms. However, reliable application of DL requires large, accurately labeled samples of training data. Galaxy Zoo Express (GZX) is a recent experiment that simulated using Bayesian inference to dynamically aggregate binary responses provided by citizen scientists via the Zooniverse crowd-sourcing platform in real time. The GZX approach enables collaboration between human and machine classifiers and provides rapidly generated, reliably labeled datasets, thereby enabling online training of accurate machine classifiers. We present selected results from GZX and show how the Bayesian aggregation engine it uses can be extended to efficiently provide object-localization and bounding-box annotations of two-dimensional data with quantified reliability. DL algorithms that are trained using these annotations will facilitate numerous panchromatic data modeling tasks including morphological classification and substructure detection in direct imaging, as well as decontamination and emission line identification for slitless spectroscopy. Effectively combining the speed of modern computational analyses with the human capacity to extrapolate from few examples will be critical if the potential of forthcoming large-scale surveys is to be realized.Comment: 5 pages, 1 figure. To appear in Proceedings of the International Astronomical Unio

Tolerance for local and global differences in the integration of shape information

© 2015 ARVO. Shape is a critical cue to object identity. In psychophysical studies, radial frequency (RF) patterns, paths deformed from circular by a sinusoidal modulation of radius, have proved valuable stimuli for the demonstration of global integration of local shape information. Models of the mechanism of integration have focused on the periodicity in measures of curvature on the pattern, despite the fact that other properties covary. We show that patterns defined by rectified sinusoidal modulation also exhibit global integration and are indistinguishable from conventional RF patterns at their thresholds for detection, demonstrating some indifference to the modulating function. Further, irregular patterns incorporating four different frequencies of modulation are globally integrated, indicating that uniform periodicity is not critical. Irregular patterns can be handed in the sense that mirror images cannot be superimposed. We show that mirror images of the same irregular pattern could not be discriminated near their thresholds for detection. The same irregular pattern and a pattern with four cycles of a constant frequency of modulation completing 2p radians were, however, perfectly discriminated, demonstrating the existence of discrete representations of these patterns by which they are discriminated. It has previously been shown that RF patterns of different frequencies are perfectly discriminated but that patterns with the same frequency but different numbers of cycles of modulation were not. We conclude that such patterns are identified, near threshold, by the set of angles subtended at the center of the pattern by adjacent points of maximum convex curvature

Rapid prediction of lab-grown tissue properties using deep learning

The interactions between cells and the extracellular matrix are vital for the self-organisation of tissues. In this paper we present proof-of-concept to use machine learning tools to predict the role of this mechanobiology in the self-organisation of cell-laden hydrogels grown in tethered moulds. We develop a process for the automated generation of mould designs with and without key symmetries. We create a large training set with $N=6500$ cases by running detailed biophysical simulations of cell-matrix interactions using the contractile network dipole orientation (CONDOR) model for the self-organisation of cellular hydrogels within these moulds. These are used to train an implementation of the \texttt{pix2pix} deep learning model, reserving $740$ cases that were unseen in the training of the neural network for training and validation. Comparison between the predictions of the machine learning technique and the reserved predictions from the biophysical algorithm show that the machine learning algorithm makes excellent predictions. The machine learning algorithm is significantly faster than the biophysical method, opening the possibility of very high throughput rational design of moulds for pharmaceutical testing, regenerative medicine and fundamental studies of biology. Future extensions for scaffolds and 3D bioprinting will open additional applications.Comment: 26 Pages, 11 Figure