ABSTRACT
The mass and structural assembly of galaxies is a matter of intense debate. Current theoretical models predict the existence of a linear relationship between galaxy size (Re) and the host ...dark matter halo virial radius (Rh). By making use of semi-empirical models compared to the size distributions of central galaxies from the Sloan Digital Sky Survey, we provide robust constraints on the normalization and scatter of the Re−Rh relation. We explore the parameter space of models in which the Re−Rh relation is mediated by either the spin parameter or the concentration of the host halo, or a simple constant the nature of which is in principle unknown. We find that the data require extremely tight relations for both early-type and late-type galaxies (ETGs, LTGs), especially for more massive galaxies. These constraints challenge models based solely on angular momentum conservation, which predict significantly wider distributions of galaxy sizes and no trend with stellar mass, if taken at face value. We discuss physically motivated alterations to the original models that bring the predictions into better agreement with the data. We argue that the measured tight size distributions of SDSS disc galaxies can be reproduced by semi-empirical models in which the Re−Rh connection is mediated by the stellar specific angular momenta jstar. We find that current cosmological models of galaxy formation broadly agree with our constraints for LTGs, and justify the strong link between Re and jstar that we propose, however the tightness of the Re−Rh relation found in such ab initio theoretical models for ETGs is in tension with our semi-empirical findings.
Abstract
We carry out a systematic investigation of the total mass density profile of massive (log Mstar/M⊙ ∼ 11.5) early-type galaxies and its dependence on redshift, specifically in the range 0 ≲ z ...≲ 1. We start from a large sample of Sloan Digital Sky Survey early-type galaxies with stellar masses and effective radii measured assuming two different profiles, de Vaucouleurs and Sérsic. We assign dark matter haloes to galaxies via abundance matching relations with standard ΛCDM profiles and concentrations. We then compute the total, mass-weighted density slope at the effective radius γ΄, and study its redshift dependence at fixed stellar mass. We find that a necessary condition to induce an increasingly flatter γ΄ at higher redshifts, as suggested by current strong lensing data, is to allow the intrinsic stellar profile of massive galaxies to be Sérsic and the input Sérsic index n to vary with redshift as n(z) ∝ (1 + z)δ, with δ ≲ −1. This conclusion holds irrespective of the input Mstar–Mhalo relation, the assumed stellar initial mass function (IMF), or even the chosen level of adiabatic contraction in the model. Secondary contributors to the observed redshift evolution of γ΄ may come from an increased contribution at higher redshifts of adiabatic contraction and/or bottom-light stellar IMFs. The strong lensing selection effects we have simulated seem not to contribute to this effect. A steadily increasing Sérsic index with cosmic time is supported by independent observations, though it is not yet clear whether cosmological hierarchical models (e.g. mergers) are capable of reproducing such a fast and sharp evolution.
ABSTRACT
With the advent of future big-data surveys, automated tools for unsupervised discovery are becoming ever more necessary. In this work, we explore the ability of deep generative networks for ...detecting outliers in astronomical imaging data sets. The main advantage of such generative models is that they are able to learn complex representations directly from the pixel space. Therefore, these methods enable us to look for subtle morphological deviations which are typically missed by more traditional moment-based approaches. We use a generative model to learn a representation of expected data defined by the training set and then look for deviations from the learned representation by looking for the best reconstruction of a given object. In this first proof-of-concept work, we apply our method to two different test cases. We first show that from a set of simulated galaxies, we are able to detect ${\sim}90{{\ \rm per\ cent}}$ of merging galaxies if we train our network only with a sample of isolated ones. We then explore how the presented approach can be used to compare observations and hydrodynamic simulations by identifying observed galaxies not well represented in the models. The code used in this is available at https://github.com/carlamb/astronomical-outliers-WGAN.
ABSTRACT
Hydrodynamical simulations of galaxy formation and evolution attempt to fully model the physics that shapes galaxies. The agreement between the morphology of simulated and real galaxies, and ...the way the morphological types are distributed across galaxy scaling relations are important probes of our knowledge of galaxy formation physics. Here, we propose an unsupervised deep learning approach to perform a stringent test of the fine morphological structure of galaxies coming from the Illustris and IllustrisTNG (TNG100 and TNG50) simulations against observations from a subsample of the Sloan Digital Sky Survey. Our framework is based on PixelCNN, an autoregressive model for image generation with an explicit likelihood. We adopt a strategy that combines the output of two PixelCNN networks in a metric that isolates the small-scale morphological details of galaxies from the sky background. We are able to quantitatively identify the improvements of IllustrisTNG, particularly in the high-resolution TNG50 run, over the original Illustris. However, we find that the fine details of galaxy structure are still different between observed and simulated galaxies. This difference is mostly driven by small, more spheroidal, and quenched galaxies that are globally less accurate regardless of resolution and which have experienced little improvement between the three simulations explored. We speculate that this disagreement, that is less severe for quenched discy galaxies, may stem from a still too coarse numerical resolution, which struggles to properly capture the inner, dense regions of quenched spheroidal galaxies.
Abstract Predicting plasma evolution within a Tokamak reactor is crucial to realizing the goal of sustainable fusion. Capabilities in forecasting the spatio-temporal evolution of plasma rapidly and ...accurately allow us to quickly iterate over design and control strategies on current Tokamak devices and future reactors. Modelling plasma evolution using numerical solvers is often expensive, consuming many hours on supercomputers, and hence, we need alternative inexpensive surrogate models. We demonstrate accurate predictions of plasma evolution both in simulation and experimental domains using deep learning-based surrogate modelling tools, viz., Fourier neural operators (FNO). We show that FNO has a speedup of six orders of magnitude over traditional solvers in predicting the plasma dynamics simulated from magnetohydrodynamic models, while maintaining a high accuracy (Mean Squared Error in the normalised domain ≈ 10 − 5 ). Our modified version of the FNO is capable of solving multi-variable Partial Differential Equations, and can capture the dependence among the different variables in a single model. FNOs can also predict plasma evolution on real-world experimental data observed by the cameras positioned within the MAST Tokamak, i.e. cameras looking across the central solenoid and the divertor in the Tokamak. We show that FNOs are able to accurately forecast the evolution of plasma and have the potential to be deployed for real-time monitoring. We also illustrate their capability in forecasting the plasma shape, the locations of interactions of the plasma with the central solenoid and the divertor for the full (available) duration of the plasma shot within MAST. The FNO offers a viable alternative for surrogate modelling as it is quick to train and infer, and requires fewer data points, while being able to do zero-shot super-resolution and getting high-fidelity solutions.
It is generally agreed that the galaxies in our Universe form and evolve within haloes of dark matter. The formation and evolution of the dark matter density field is believed to leave a profound ...imprint on the luminous matter that traces galaxy properties. Although the precise way dark matter haloes shape galaxies is currently hotly debated, the structural, morphological and dynamical evolution of galaxies are considered important probes of the interplay between galaxies and their dark matter haloes. The aim of this thesis is to study galaxy evolution through the lens of galaxy structure and morphology by taking a holistic approach which encompasses data-driven and existing physical models. In particular, I devise semi-empirical models for galaxy structure, which have been introduced only recently, and I also include novel deep learning methods in the modelling stack. Firstly, I will use statistical modelling to derive empirical relationships between galaxies and their dark matter haloes, setting constraints on the physical processes arising from dark matter that set galaxy structure and dynamics. Secondly, I take state-of-the-art hydrodynamical simulations of galaxy formation that meet these constraints, and I evaluate the small-scale structural details of simulated galaxies against real observations. By treating this problem as an unsupervised Out of Distribution detection task, I show that simulations are improving over the years, but they are yet to agree perfectly with observational data. Thirdly, I further test the semi-empirical models above on the fast structural growth of Massive Galaxies and on the weak dependence of their size on the large-scale environment, and provide predictive trends for future observations. Finally, in the spirit of transferring knowledge from Astronomy and Astrophysics to other fields, I apply similar modelling techniques to Medicine to assess the effectiveness of current management strategies for hypertension.
ABSTRACT
The masses of supermassive black holes at the centres of local galaxies appear to be tightly correlated with the mass and velocity dispersions of their galactic hosts. However, the local ...Mbh–Mstar relation inferred from dynamically measured inactive black holes is up to an order-of-magnitude higher than some estimates from active black holes, and recent work suggests that this discrepancy arises from selection bias on the sample of dynamical black hole mass measurements. In this work, we combine X-ray measurements of the mean black hole accretion luminosity as a function of stellar mass and redshift with empirical models of galaxy stellar mass growth, integrating over time to predict the evolving Mbh–Mstar relation. The implied relation is nearly independent of redshift, indicating that stellar and black hole masses grow, on average, at similar rates. Matching the de-biased local Mbh–Mstar relation requires a mean radiative efficiency ε ≳ 0.15, in line with theoretical expectations for accretion on to spinning black holes. However, matching the ‘raw’ observed relation for inactive black holes requires ε ∼ 0.02, far below theoretical expectations. This result provides independent evidence for selection bias in dynamically estimated black hole masses, a conclusion that is robust to uncertainties in bolometric corrections, obscured active black hole fractions, and kinetic accretion efficiency. For our fiducial assumptions, they favour moderate-to-rapid spins of typical supermassive black holes, to achieve ε ∼ 0.12–0.20. Our approach has similarities to the classic Soltan analysis, but by using galaxy-based data instead of integrated quantities we are able to focus on regimes where observational uncertainties are minimized.
OBJECTIVES:To assess the impact of variable drug response and measurement error on SBP control.
METHODS:We simulated a treat-to-target strategy for populations with different pretreatment SBP, ...whereby medications were added sequentially until measured SBP (mSBP) less than 140 mmHg. Monte Carlo simulations determined variability of both drug response (drugeff ± σdrug; 10 ± 5 mmHg base case) and measurement error (σmeas; 10 mmHg base case) of true SBP (tSBP). The primary outcome measure was the proportion of individuals who achieved target less than 140 mmHg.
RESULTS:Decision-making based on mSBP resulted in 35.0% of individuals with initial tSBP 150 mmHg being either inappropriately given, or inappropriately denied a second drug. When the simulation was run for multiple drug titrations, measurement error limited tSBP control for all populations tested. A strategy of drug titration based on a second measurement for individuals at risk of incorrect decisions (mSBP 120–150 mmHg; σmeas 15 mmHg) reduced the proportion above target from 40.1 to 30.0% when initial tSBP 160 mmHg. When the measurement variability for the second reading was reduced below that usually seen in clinical practice (σmeas 5 mmHg), the proportion above target decreased further to 17.4%.
CONCLUSION:In this simulation, measurement error had the greatest impact on the proportion of individuals achieving their SBP target. Efforts to reduce this error through repeated measures, alternative measurement techniques or changing thresholds, are promising strategies to reduce cardiovascular morbidity and mortality and should be investigated in clinical trials. Here we have shown that Monte Carlo simulations are a useful technique to investigate the influence of uncertainty for different hypertension management strategies.
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