The PAndAS Field of Streams: stellar structures in the Milky Way halo toward Andromeda and Triangulum

We reveal the highly structured nature of the Milky Way stellar halo within the footprint of the PAndAS photometric survey from blue main sequence and main sequence turn-off stars. We map no fewer than five stellar structures within a heliocentric range of ~5 to 30 kpc. Some of these are known (the Monoceros Ring, the Pisces/Triangulum globular cluster stream), but we also uncover three well-defined stellar structures that could be, at least partly, responsible for the so-called Triangulum/Andromeda and Triangulum/Andromeda 2 features. In particular, we trace a new faint stellar stream located at a heliocentric distance of ~17 kpc. With a surface brightness of \Sigma_V ~ 32-32.5 mag/arcsec^2, it follows an orbit that is almost parallel to the Galactic plane north of M31 and has so far eluded surveys of the Milky Way halo as these tend to steer away from regions dominated by the Galactic disk. Investigating our follow-up spectroscopic observations of PAndAS, we serendipitously uncover a radial velocity signature from stars that have colors and magnitudes compatible with the stream. From the velocity of eight likely member stars, we show that this stellar structure is dynamically cold, with an unresolved velocity dispersion that is lower than 7.1 km/s at the 90-percent confidence level. Along with the width of the stream (300-650 pc), its dynamics points to a dwarf-galaxy-accretion origin. The numerous stellar structures we can map in the Milky Way stellar halo between 5 and 30 kpc and their varying morphology is a testament to the complex nature of the stellar halo at these intermediate distances.Comment: 11 pages, 8 figures, accepted for publication in the ApJ, Figure 3 is the money plo

Conscious Intentionality in Perception, Imagination, and Cognition

Participants in the cognitive phenomenology debate have proceeded by (a) proposing a bifurcation of theoretical options into inflationary and non-inflationary theories, and then (b) providing arguments for/against one of these theories. I suggest that this method has failed to illuminate the commonalities and differences among conscious intentional states of different types, in the absence of a theory of the structure of these states. I propose such a theory. In perception, phenomenal-intentional properties combine with somatosensory properties to form P-I property clusters that serve as phenomenal modes of presentations of particulars. In imagination, somatosensory properties are replaced with phenomenal-intentional properties whose intentional objects are somatosensory properties, thus resulting in imaginative facsimiles of perceptual P-I property clusters. Such structures can then be used as phenomenal prototypes that pick out individuals and kinds. Sets of such prototypes constitute a subject’s conception of individuals and kinds. Combined with a few additional elements, these imaginative P-I property clusters serve as the building-blocks of conscious cognitive states. Different ways of carving up theoretical space classify my theory either as inflationary or as non-inflationary. I conclude that the theory is anti-inflationary in letter but inflationary in spirit

The most complete and detailed X-ray view of the SNR Puppis A

With the purpose of producing the first detailed full view of Puppis A in X-rays, we carried out new XMM-Newton observations covering the missing regions in the southern half of the supernova remnant (SNR) and combined them with existing XMM-Newton and Chandra data. The new images were produced in the 0.3-0.7, 0.7-1.0 and 1.0-8.0 energy bands. We investigated the SNR morphology in detail, carried out a multi-wavelength analysis and estimated the flux density and luminosity of the whole SNR. The complex structure observed across the remnant confirms that Puppis A evolves in an inhomogeneous, probably knotty interstellar medium. The southwestern corner includes filaments that perfectly correlate with radio features suggested to be associated with shock/cloud interaction. In the northern half of Puppis A the comparison with Spitzer infrared images shows an excellent correspondence between X-rays and 24 and 70 microns emission features, while to the south there are some matched and other unmatched features. X-ray flux densities of 12.6 X 10^-9, 6.2 X 10^-9, and 2.8 X 10^-9 erg cm^-2 s^-1 were derived for the 0.3-0.7, 0.7-1.0 and 1.0-8.0 keV bands, respectively. At the assumed distance of 2.2 kpc, the total X-ray luminosity between 0.3 and 8.0 keV is 1.2 X 10^37 erg s^-1. We also collected and updated the broad-band data of Puppis A between radio and GeV gamma-ray range, producing its spectral energy distribution. To provide constraints to the high-energy emission models, we re-analyzed radio data, estimating the energy content in accelerated particles to be Umin=4.8 X 10^49 erg and the magnetic field strength B=26 muG.Comment: Article accepted to be published in the Astronomy and Astrophysics Main Journa

Recombinator Networks: Learning Coarse-to-Fine Feature Aggregation

Deep neural networks with alternating convolutional, max-pooling and decimation layers are widely used in state of the art architectures for computer vision. Max-pooling purposefully discards precise spatial information in order to create features that are more robust, and typically organized as lower resolution spatial feature maps. On some tasks, such as whole-image classification, max-pooling derived features are well suited; however, for tasks requiring precise localization, such as pixel level prediction and segmentation, max-pooling destroys exactly the information required to perform well. Precise localization may be preserved by shallow convnets without pooling but at the expense of robustness. Can we have our max-pooled multi-layered cake and eat it too? Several papers have proposed summation and concatenation based methods for combining upsampled coarse, abstract features with finer features to produce robust pixel level predictions. Here we introduce another model --- dubbed Recombinator Networks --- where coarse features inform finer features early in their formation such that finer features can make use of several layers of computation in deciding how to use coarse features. The model is trained once, end-to-end and performs better than summation-based architectures, reducing the error from the previous state of the art on two facial keypoint datasets, AFW and AFLW, by 30\% and beating the current state-of-the-art on 300W without using extra data. We improve performance even further by adding a denoising prediction model based on a novel convnet formulation.Comment: accepted in CVPR 201