Self-Interacting Dark Matter Subhalos in the Milky Way's Tides

We study evolution of self-interacting dark matter (SIDM) subhalos in the Milky Way (MW) tidal field. The interaction between the subhalos and the MW's tides lead to more diverse dark matter distribution in the inner region, compared to their cold dark matter counterparts. We test this scenario with two MW satellite galaxies, Draco and Fornax, opposite extremes in the inner dark matter content, and find that they can be accommodated within the SIDM model proposed to explain the diverse rotation curves of spiral galaxies in the field.Comment: 6 pages, 3 figures. Updated figures and text. Accepted for publication in PR

The impact of baryonic discs on the shapes and profiles of self-interacting dark matter halos

We employ isolated N-body simulations to study the response of self-interacting dark matter (SIDM) halos in the presence of the baryonic potentials. Dark matter self-interactions lead to kinematic thermalization in the inner halo, resulting in a tight correlation between the dark matter and baryon distributions. A deep baryonic potential shortens the phase of SIDM core expansion and triggers core contraction. This effect can be further enhanced by a large self-scattering cross section. We find the final SIDM density profile is sensitive to the baryonic concentration and the strength of dark matter self-interactions. Assuming a spherical initial halo, we also study evolution of the SIDM halo shape together with the density profile. The halo shape at later epochs deviates from spherical symmetry due to the influence of the non-spherical disc potential, and its significance depends on the baryonic contribution to the total gravitational potential, relative to the dark matter one. In addition, we construct a multi-component model for the Milky Way, including an SIDM halo, a stellar disc and a bulge, and show it is consistent with observations from stellar kinematics and streams.Comment: 10 pages, 8 figures, submitted to MNRAS, accepted for publication in MNRA

Personalising the evaluation of substance misuse treatment: A new approach to outcome measurement

Patient involvement in healthcare, in general, and in substance misuse in particular, has become a topic of paramount importance (Rutter et al., 2004). Patient involvement can be conceptualised as listening to the patients’ perspective and encouraging patients to take an active role in the care they are receiving. This approach is advocated by international authorities in health and social care such as the United Kingdom’s NICE, which recommends “person-centred care” that takes into account the patient’s “needs, preferences and strengths” (Crawford, 2011). According to Orford (2008), the perspectives of patients in substance misuse treatment tend to be overlooked and their involvement with treatment is limite

The effects of subgrid models on the properties of giant molecular clouds in galaxy formation simulations

Recent cosmological hydrodynamical simulations are able to reproduce numerous statistical properties of galaxies that are consistent with observational data. Yet, the adopted subgrid models strongly affect the simulation outcomes, limiting the predictive power of these simulations. In this work, we perform a suite of isolated galactic disk simulations under the {\it SMUGGLE} framework and investigate how different subgrid models affect the properties of giant molecular clouds (GMCs). We employ {\sc astrodendro}, a hierarchical clump-finding algorithm, to identify GMCs in the simulations. We find that different choices of subgrid star formation efficiency, $\epsilon_{\rm ff}$, and stellar feedback channels, yield dramatically different mass and spatial distributions for the GMC populations. Without feedback, the mass function of GMCs has a shallower power-law slope and extends to higher mass ranges compared to runs with feedback. Moreover, higher $\epsilon_{\rm ff}$ results in faster molecular gas consumption and steeper mass function slopes. Feedback also suppresses power in the two-point correlation function (TPCF) of the spatial distribution of GMCs. Specifically, radiative feedback strongly reduces the TPCF on scales below 0.2~kpc, while supernova feedback reduces power on scales above 0.2~kpc. Finally, runs with higher $\epsilon_{\rm ff}$ exhibit a higher TPCF than runs with lower $\epsilon_{\rm ff}$, because the dense gas is depleted more efficiently thereby facilitating the formation of well-structured supernova bubbles. We argue that comparing simulated and observed GMC populations can help better constrain subgrid models in the next-generation of galaxy formation simulations.Comment: 12 pages, 8 figures, MNRAS in pres

Artificial Intelligence: Practice and Implications for Journalism

The increasing presence of artificial intelligence and automated technology is changing journalism. While the term artificial intelligence dates back to the 1950s, and has since acquired several meanings, there is a general consensus around the nature of AI as the theory and development of computer systems able to perform tasks normally requiring human intelligence. Since many of the AI tools journalists are now using come from other disciplines—computer science, statistics, and engineering, for example—they tend to be general purpose. Now that journalists are using AI in the newsroom, what must they know about these technologies, and what must technologists know about journalistic standards when building them? On June 13, 2017, the Tow Center for Digital Journalism and the Brown Institute for Media Innovation convened a policy exchange forum of technologists and journalists to consider how artificial intelligence is impacting newsrooms and how it can be better adapted to the field of journalism. The gathering explored questions like: How can journalists use AI to assist the reporting process? Which newsroom roles might AI replace? What are some areas of AI that news organizations have yet to capitalize on? Will AI eventually be a part of the presentation of every news story

Personalising outcome measurement in substance misuse treatment: the feasibility and psychometrics of two individualised outcome measures

Individualised information in substance misuse treatment complements standardised outcome measures. However, few studies investigate the use of individualised measures and their robustness in terms of quantifying outcomes. In this study, we analysed the psychometrics and feasibility of two individualised outcome measures (PQ and PSYCHLOPS). We followed a cross-sectional methodology, administering the individualised measures and three additional standardised measures (TOP, a measure of psychological health within addiction services; PHQ-9; CORE-OM) to a sample of 93 patients entering substance misuse treatment in four clinical services. The results showed high levels of patient acceptability of the two individualised measures (response rates > 95%). The internal reliability was good for both PQ and PSYCHLOPS (Cronbach’s alpha, .79 and .72, respectively). Convergent validity of PQ with standardised measures was weak: Pearson’s r values for TOP (psychological health), PHQ-9 and CORE-OM were .21, .22 and .27, respectively. In contrast, convergent validity of PSYCHLOPS was moderate: r = .40, .39 and .50, respectively. Convergence between PQ and PSYCHLOPS was weak (r = .28). Experience of previous treatment episodes was associated with higher PQ and PSYCHLOPS scores; PSYCHLOPS but not PQ scores were higher among those opting to complete the questionnaires in written rather than verbal format. Our findings demonstrated that PQ and PSYCHLOPS are reliable and feasible individualised outcome measures for use in substance misuse treatment units, although the lack of strong convergent validity indicates that they may be measuring different underlying constructs. Optimal outcome measurement may involve combining individualised and standardised measures.EU FEDER COMPETE: POCI-01-0145-FEDER- 007294info:eu-repo/semantics/publishedVersio

Simulating the interstellar medium and stellar feedback on a moving mesh: Implementation and isolated galaxies

We introduce the Stars and MUltiphase Gas in GaLaxiEs -- SMUGGLE model, an explicit and comprehensive stellar feedback model for the moving-mesh code arepo. This novel sub-resolution model resolves the multiphase gas structure of the interstellar medium and self-consistently generates gaseous outflows. The model implements crucial aspects of stellar feedback including photoionization, radiation pressure, energy and momentum injection from stellar winds and from supernovae. We explore this model in high-resolution isolated simulations of Milky Way-like disc galaxies. Stellar feedback regulates star formation to the observed level and naturally captures the establishment of a Kennicutt-Schmidt relation. This result is achieved independent of the numerical mass and spatial resolution of the simulations. Gaseous outflows are generated with average mass loading factors of the order of unity. Strong outflow activity is correlated with peaks in the star formation history of the galaxy with evidence that most of the ejected gas eventually rains down onto the disc in a galactic fountain flow that sustains late-time star formation. Finally, the interstellar gas in the galaxy shows a distinct multiphase distribution with a coexistence of cold, warm and hot phases.Comment: 29 pages, 15 figures, 3 tables, 1 appendix. Accepted for publication in MNRAS. Updated manuscript to match the published versio

Semi-automated detection of tagged animals from camera trap images using artificial intelligence

The use of technology in ecology and conservation offers unprecedented opportunities to survey and monitor wildlife remotely, for example by using camera traps. However, such solutions typically cause challenges stemming from the big datasets gathered, such as millions of camera trap images. Artificial intelligence is a proven, powerful tool to automate camera trap image analyses, but this is so far largely been restricted to species identification from images. Here, we develop and test an artificial intelligence algorithm that allows discrimination of individual animals carrying a tag (in this case a patagial yellow tag on vultures) from a large array of camera trap images. Such a tool could assist scientists and practitioners using similar patagial tags on vultures, condors and other large birds worldwide. We show that the overall performance of such an algorithm is relatively good, with 88.9% of all testing images (i.e. those not used for training or validation) correctly classified using a cut-off discrimination of 0.4. Specifically, performance was high for correctly classifying images with a tag (95.2% of all positive images correctly classified), but less so for images without a tag (87.0% of all negative images). The correct classification of images with a tag was, however, significantly higher when the tag code was at least partly readable compared with the other cases. Overall, this study underscores the potential of artificial intelligence for assisting scientists and practitioners in analysing big datasets from camera traps.Peer reviewe

Modeling Galactic Conformity with the Color-Halo Age Relation in the Illustris Simulation

Comparisons between observational surveys and galaxy formation models find that the mass of dark matter haloes can largely explain galaxies' stellar mass. However, it remains uncertain whether additional environmental variables, generally referred to as assembly bias, are necessary to explain other galaxy properties. We use the Illustris Simulation to investigate the role of assembly bias in producing galactic conformity by considering 18,000 galaxies with $M_{stellar}$ > $2 \times 10^9$ $M_{\odot}$. We find a significant signal of galactic conformity: out to distances of about 10 Mpc, the mean red fraction of galaxies around redder galaxies is higher than around bluer galaxies at fixed stellar mass. Dark matter haloes exhibit an analogous conformity signal, in which the fraction of haloes formed at earlier times (old haloes) is higher around old haloes than around younger ones at fixed halo mass. A plausible interpretation of galactic conformity can be given as a combination of the halo conformity signal with the galaxy color-halo age relation: at fixed stellar mass, particularly toward the low-mass end, Illustris' galaxy colors correlate with halo age, with the reddest galaxies (often satellites) being preferentially found in the oldest haloes. In fact, we can explain the galactic conformity effect with a simple semi-empirical model, by assigning stellar mass based on halo mass (abundance matching) and by assigning galaxy color based on halo age (age matching). We investigate other interpretations for the galactic conformity, particularly its dependence on the isolation criterion and on the central-satellite information. Regarding comparison to observations, we conclude that the adopted selection/isolation criteria, projection effects, and stacking techniques can have a significant impact on the measured amplitude of the conformity signal.Comment: 15 pages, 8 figures; accepted for publication in MNRAS (minor revisions to match accepted version