Neil Wyn Evans

Mismatch and misalignment: dark haloes and satellites of disc galaxies

We study the phase-space distribution of satellite galaxies associated with late-type galaxies in the GIMIC suite of simulations. GIMIC consists of resimulations of five cosmologically representative regions from the Millennium Simulation, which have higher resolution and incorporate baryonic physics. Whilst the disc of the galaxy is well aligned with the inner regions (r ∼ 0.1r200) of the dark matter halo, both in shape and angular momentum, there can be substantial misalignments at larger radii (r ∼r200). Misalignments of >45 ◦ are seen in ∼30 per cent of our sample. We find that the satellite population aligns with the shape (and angular momentum) of the outer dark matter halo. However, the alignment with the galaxy is weak owing to the mismatch between the disc and dark matter halo. Roughly 20 per cent of the satellite systems with 10 bright galaxies within r200 exhibit a polar spatial alignment with respect to the galaxy – an orientation reminiscent of the classical satellites of the Milky Way. We find that a small fraction (∼10 per cent) of satellite systems show evidence for rotational support which we attribute to group infall. There is a bias towards satellites on prograde orbits relative to the spin of the dark matter halo (and to a lesser extent with the angular momentum of the disc). This preference towards co-rotation is stronger in the inner regions of the halo where the most massive satellites accreted at relatively early times are located. We attribute the anisotropic spatial distribution and angular momentum bias of the satellites at z = 0 to their directional accretion along the major axes of the dark matter halo. The satellite galaxies have been accreted relatively recently compared to the dark matter mass and have experienced less phase-mixing and relaxation – the memory of their accretion history can remain intact to z = 0. Understanding the phase-space distribution of the z = 0 satellite population is key for studies that estimate the host halo mass from the line-of-sight velocities and projected positions of satellite galaxies. We quantify the effects of such systematics in estimates of the host halo mass from the satellite population.

Skinny Milky Way please, says Sagittarius

Motivated by recent observations of the Sagittarius stream, we devise a rapid algorithm to generate faithful representations of the centroids of stellar tidal streams formed in a disruption of a progenitor of an arbitrary mass in an arbitrary potential. Our method works by releasing swarms of test particles at the Lagrange points around the satellite and subsequently evolving them in a combined potential of the host and the progenitor. We stress that the action of the progenitors gravity is crucial to making streams that look almost indistinguishable from the N-body realizations, as indeed ours do. The method is tested on mock stream data in three different Milky Way potentials with increasing complexity, and is shown to deliver unbiased inference on the Galactic mass distribution out to large radii. When applied to the observations of the Sagittarius stream, our model gives a natural explanation of the streams apocentric distances and the differential orbital precession. We, therefore, provide a new independent measurement of the Galactic mass distribution beyond 50 kpc. The Sagittarius stream model favours a light Milky Way with the mass 4.1 +/- 0.4 x 10^11 M_sun at 100 kpc, which can be extrapolated to 5.6 +/- 1.2 x 10^11 M_sun at 200 kpc. Such a low mass for the Milky Way Galaxy is in good agreement with estimates from the kinematics of halo stars and from the satellite galaxies (once Leo I is removed from the sample). It entirely removes the Too Big To Fail Problem.

A 'Baedecker' for the dark matter annihilation signal

We provide a ``Baedecker or travel guide to the directions on the sky where the dark matter annihilation signal may be expected. We calculate the flux of high energy gamma-rays from annihilation of neutralino dark matter in the centre of the Milky Way and the three nearest dwarf spheroidals (Sagittarius, Draco and Canis Major), using realistic models of the dark matter distribution. Other investigators have used cusped dark halo profiles (such as the Navarro-Frenk-White) to claim a significant signal. This ignores the substantial astrophysical evidence that the Milky Way is not dark-matter dominated in the inner regions. We show that the annihilation signal from the Galactic Centre falls by two orders of magnitude on substituting a cored dark matter density profile for a cusped one. The present and future generation of high energy gamma-ray detectors, whether atmospheric Cerenkov telescopes or space missions like GLAST, lack the sensitivity to detect any of the monochromatic gamma-ray annihilation lines. The continuum gamma-ray signal above 1 GeV and above 50 GeV may however be detectable either from the dwarf spheroidals or from the Milky Way itself. If the density profiles of the dwarf spheroidals are cusped, then the best prospects are for detecting Sagittarius and Canis Major. However, if the dwarf spheroidals have milder, cored profiles, then the annihilation signal is not detectable. For GLAST, an attractive strategy is to exploit the wide field of view and observe the Milky Way at medium latitudes, as suggested by Stoehr et al. This is reasonably robust against changes in the density profile.

Clustering of ultrahigh-energy cosmic rays and their sources

The sky distribution of cosmic rays with energies above the GZK cutoff holds important clues to their origin. The AGASA data, although consistent with isotropy, shows evidence for small-angle clustering, and it has been argued that such clusters are aligned with BL Lacertae objects, implicating these as sources. It has also been suggested that clusters can arise if the cosmic rays come from the decays of very massive relic particles in the Galactic halo, due to the expected clumping of cold dark matter. We examine these claims and show that both are in fact not justified.

Lightcurve Classification in Massive Variability Surveys

This paper pioneers the use of neural networks to provide a fast and automatic way to classify lightcurves in massive photometric datasets. As an example, we provide a working neural network that can distinguish microlensing lightcurves from other forms of variability, such as eruptive, pulsating, cataclysmic and eclipsing variable stars. The network has five input neurons, a hidden layer of five neurons and one output neuron. The five input variables for the network are extracted by spectral analysis from the lightcurve datapoints and are optimised for the identification of a single, symmetric, microlensing bump. The output of the network is the posterior probability of microlensing. The committee of neural networks successfully passes tests on noisy data taken by the MACHO collaboration. When used to process 5000 lightcurves on a typical tile towards the bulge, the network cleanly identifies the single microlensing event. When fed with a sub-sample of 36 lightcurves identified by the MACHO collaboration as microlensing, the network corroborates this verdict in the case of 27 events, but classifies the remaining 9 events as other forms of variability. For some of these discrepant events, it looks as though there are secondary bumps or the bump is noisy or not properly contained. Neural networks naturally allow for the possibility of novelty detection -- that is, new or unexpected phenomena which we may want to follow up. The advantages of neural networks for microlensing rate calculations, as well as the future developments of massive variability surveys, are both briefly discussed.

On the run: mapping the escape speed across the Galaxy with SDSS

We measure the variation of the escape speed of the Galaxy across a range of

Simple J-factors and D-factors for indirect dark matter detection

sim

Hamiltonians of spherical Galaxies in action-angle coordinates

40 kpc in Galactocentric radius. The local escape speed is found to be

Cuts and penalties: Comment on ''Clustering of ultrahigh energy cosmic rays and their sources''

521^{+46}_{-30},mathrm{km,s^{-1}}

Hypervelocity runaways from the Large Magellanic Cloud

, in good agreement with other studies. We find that this has already fallen to