Properties of fossil groups in cosmological simulations and galaxy formation models

It has been a long-standing question whether fossil groups are just sampling the tail of the distribution of ordinary groups, or whether they are a physically distinct class of objects, characterized by an unusual and special formation history. To study this question, we here investigate fossil groups identified in the hydrodynamical simulations of the GIMIC project, which consists of resimulations of five regions in the Millennium Simulation (MS) that are characterized by different large-scale densities, ranging from a deep void to a proto-cluster region. For comparison, we also consider semi-analytic models built on top of the MS, as well as a conditional luminosity function approach. We identify galaxies in the GIMIC simulations as groups of stars and use a spectral synthesis code to derive their optical properties. The X-ray luminosity of the groups is estimated in terms of the thermal bremsstrahlung emission of the gas in the host halos, neglecting metallicity effects. We focus on comparing the properties of fossil groups in the theoretical models and observational results, highlighting the differences between them, and trying to identify possible dependencies on environment for which our approach is particularly well set-up. We find that the optical fossil fraction in all of our theoretical models declines with increasing halo mass, and there is no clear environmental dependence. Combining the optical and X-ray selection criteria for fossil groups, the halo mass dependence of the fossil groups seen in optical vanishes. Over the GIMIC halo mass range we resolve best, 9.0\times1012 \sim 4.0\times1013 h-1 M, the central galaxies in the fossil groups show similar properties as those in ordinary groups, in terms of age, metallicity, color, concentration, and mass-to-light ratio. [abridged]Comment: 14 pages, 10 figures; accepted by MNRAS, minor changes to match the accepted versio

A Circular-ribbon Solar Flare Following an Asymmetric Filament Eruption

The dynamic properties of flare ribbons and the often associated filament eruptions can provide crucial information on the flaring coronal magnetic field. This Letter analyzes the GOES-class X1.0 flare on 2014 March 29 (SOL2014-03-29T17:48), in which we found an asymmetric eruption of a sigmoidal filament and an ensuing circular flare ribbon. Initially both EUV images and a preflare nonlinear force-free field model show that the filament is embedded in magnetic fields with a fan-spine-like structure. In the first phase, which is defined by a weak but still increasing X-ray emission, the western portion of the sigmoidal filament arches upward and then remains quasi-static for about five minutes. The western fan-like and the outer spine-like fields display an ascending motion, and several associated ribbons begin to brighten. Also found is a bright EUV flow that streams down along the eastern fan-like field. In the second phase that includes the main peak of hard X-ray (HXR) emission, the filament erupts, leaving behind two major HXR sources formed around its central dip portion and a circular ribbon brightened sequentially. The expanding western fan-like field interacts intensively with the outer spine-like field, as clearly seen in running difference EUV images. We discuss these observations in favor of a scenario where the asymmetric eruption of the sigmoidal filament is initiated due to an MHD instability and further facilitated by reconnection at a quasi-null in corona; the latter is in turn enhanced by the filament eruption and subsequently produces the circular flare ribbon.Comment: 7 pages, 5 figures, accepted to ApJ Letter

Stellar Ages and Metallicities of Central and Satellite Galaxies: Implications for Galaxy Formation and Evolution

Using a large SDSS galaxy group catalogue, we study how the stellar ages and metallicities of central and satellite galaxies depend on stellar mass and halo mass. We find that satellites are older and metal-richer than centrals of the same stellar mass. In addition, the slopes of the age-stellar mass and metallicity-stellar mass relations are found to become shallower in denser environments. This is due to the fact that the average age and metallicity of low mass satellite galaxies increase with the mass of the halo in which they reside. A comparison with the semi-analytical model of Wang et al. (2008) shows that it succesfully reproduces the fact that satellites are older than centrals of the same stellar mass and that the age difference increases with the halo mass of the satellite. This is a consequence of strangulation, which leaves the stellar populations of satellites to evolve passively, while the prolonged star formation activity of centrals keeps their average ages younger. The resulting age offset is larger in more massive environments because their satellites were accreted earlier. The model fails, however, in reproducing the halo mass dependence of the metallicities of low mass satellites, yields metallicity-stellar mass and age-stellar mass relations that are too shallow, and predicts that satellite galaxies have the same metallicities as centrals of the same stellar mass, in disagreement with the data. We argue that these discrepancies are likely to indicate the need to (i) modify the recipes of both supernova feedback and AGN feedback, (ii) use a more realistic description of strangulation, and (iii) include a proper treatment of the tidal stripping, heating and destruction of satellite galaxies. [Abridged]Comment: 20 pages, 12 figures, submitted for publication in MNRA

Levels and trends in child mortality: Report 2022

In total, more than 5.0 million children under age 5, including 2.3 million newborns, along with 2.1 million children and youth aged 5 to 24 years – 43 per cent of whom are adolescents – died in 2021. This tragic and massive loss of life, most of which was due to preventable or treatable causes, is a stark reminder of the urgent need to end preventable deaths of children and young people. Sadly, these deaths were mostly preventable with widespread and effective interventions like improved care around the time of birth, vaccination, nutritional supplementation and water and sanitation programmes.Timely, high-quality and disaggregated data – which allow the most vulnerable children to be identified – are critical to achieving the goal of ending preventable deaths of children. Yet as the COVID-19 pandemic has put into stark light, data of this nature are more the exception than the rule: Just 36 countries have high-quality nationally representative data on under-five mortality for 2021, while about half the world's countries have no data on child mortality in the last five years. These substantial data gaps pose enormous challenges to policy- and decision-making and prolong the need for modelling mortality from what little data are available. To improve the availability, quality and timeliness of data for monitoring the health and survival situation of children and youth, much greater investments must be made to strengthen data systems

Detection of galaxy assembly bias

Assembly bias describes the finding that the clustering of dark matter haloes depends on halo formation time at fixed halo mass. In this paper, we analyse the influence of assembly bias on galaxy clustering using both semi-analytical models (SAMs) and observational data. At fixed stellar mass, SAMs predict that the clustering of {\it central} galaxies depends on the specific star formation rate (sSFR), with more passive galaxies having a higher clustering amplitude. We find similar trends using SDSS group catalogues, and verify that these are not affected by possible biases due to the group finding algorithm. Low mass central galaxies reside in narrow bins of halo mass, so the observed trends of higher clustering amplitude for galaxies with lower sSFR is not driven by variations of the parent halo mass. We argue that the clustering dependence on sSFR represent a direct detection of assembly bias. In addition, contrary to what expected based on clustering of dark matter haloes, we find that low-mass central galaxies in SAMs with larger host halo mass have a {\it lower} clustering amplitude than their counter-parts residing in lower mass haloes. This results from the fact that, at fixed stellar mass, assembly bias has a stronger influence on clustering than the dependence on the parent halo mass.Comment: 6 pages, 4 figures, accepted for publication in MNRAS, Fig.4 update

Outstanding performance of hierarchical alumina microspheres for boron removal in the presence of competing ions

Altres ajuts: acords transformatius de la UABDeveloping efficient materials for the removal of boron from aqueous solutions is becoming an important task to overcome boron pollution. Herein, we present hierarchical alumina microspheres (HAM) as an outstanding adsorbent, synthesized via a microwave-assisted co-precipitation method. The microstructure, morphology, and textural characterization of the HAM particles carried out by X-ray diffraction (XRD), scanning electron microscopy (SEM), and transmission electron microscopy (TEM) revealed hollow γ-Al2O3 particles with a porous dandelion-like shape and an average size of 1.5 μm. The analysis of the adsorption data indicated that the adsorption was homogeneous in a single layer and that chemical adsorption was the controlling step in the process. The adsorption capacity obtained at an initial concentration of 800 mg·L−1 was 51.60 mg·g−1, and the theoretically calculated maximum adsorption capacity using the Langmuir model was 138.50 mg·g−1, which outperforms previously reported adsorbents. The determination of thermodynamic parameters indicated that the adsorption is an exothermic and non-spontaneous process. The XPS spectra of HAM after adsorption indicated the formation of Al-O-B bonds. Of particular interest for industrial applications, the HAM adsorbent showed excellent selectivity for boron in the presence of competing cations or anions and at different ionic strengths. In addition, HAM maintained a high adsorption capacity after five consecutive adsorption/desorption cycles. These findings highlight the potential of HAM as a highly microporous material for boron removal in real industrial applications

The Variation of the Galaxy Luminosity Function with Group Properties

We explore the shape of the galaxy luminosity function (LF) in groups of different mass by creating composite LFs over large numbers of groups. Following previous work using total group luminosity as the mass indicator, here we split our groups by multiplicity and by estimated virial (group halo) mass, and consider red (passive) and blue (star forming) galaxies separately. In addition we utilise two different group catalogues (2PIGG and Yang et al.) in order to ascertain the impact of the specific grouping algorithm and further investigate the environmental effects via variations in the LF with position in groups. Our main results are that LFs show a steepening faint end for early type galaxies as a function of group mass/ multiplicity, with a much suppressed trend (evident only in high mass groups) for late type galaxies. Variations between LFs as a function of group mass are robust irrespective of which grouping catalogue is used, and broadly speaking what method for determining group `mass' is used. We find in particular that there is a significant deficit of low-mass passive galaxies in low multiplicity groups, as seen in high redshift clusters. Further to this, the variation in the LF appears to only occur in the central regions of systems, and in fact seems to be most strongly dependent on the position in the group relative to the virial radius. Finally, distance-rank magnitude relations were considered. Only the Yang groups demonstrated any evidence of a correlation between a galaxy's position relative to the brightest group member and its luminosity. 2PIGG possessed no such gradient, the conclusion being the FOF algorithm suppresses the signal for weak luminosity--position trends and the Yang grouping algorithm naturally enhances it.Comment: 20 pages, 29 figures, accepted for submission to MNRA

Numerical Analysis of Mixed Precision Algorithms and Time-Steppers for Wave Turbulence, and Generative Modeling for Analogs in Data Assimilation

This thesis is comprised of three projects completed to attain a doctoral degree in applied mathematics at the University of Colorado Boulder. The first chapter encompasses the research I conducted with rounding error analysis as the primary mathematical tool. Mixed precision computations have become commonplace in deep learning, where float point numbers are economically stored in low precision but their summations and dot products are computed with higher precision. The rounding error analysis of the dot product and variants of the Householder QR factorization (called QR factorization because of the common symbols used in the factorization A=QR) are performed to investigate the potential use of mixed precision arithmetic in scientific applications outside of deep learning. In particular, their use within some basic iterative eigensolvers for graph clustering are explored. Lastly, rounding error analysis is used to justify the modification of an existing single pass singular value decomposition (SVD) algorithm. Overall, I investigated the numerical stability of some commonplace linear algebra algorithms in a light of the prevalence of big data and data-driven methods. It is important to review and evaluate how the standard algorithms perform with respect to the newer demands of scientific computing applications. The second chapter discusses various time-steppers for wave turbulence-type problems, the dynamics of which are driven by weakly nonlinear interactions within sets of resonant waves. An asymptotic analysis of the difference equations of exponential integrator methods and implicit-explicit (IMEX) methods is used to compare the various methods in the weak, long-time limit. Integrating factor (IF) methods are found to be better suited for wave turbulence than exponential time differencing (ETD) methods in the asymptotic analysis as well as in numerical experiments in two wave turbulence models. A novel near-minimax rational approximation of the exponential is proposed for a robust implementation of IF methods, and shown to be indistinguishable from the exact exponential version in the medium complexity wave turbulence model. Most geophysical fluid systems contain dynamics that are driven by wave resonances, even if those are not the leading dynamics. Therefore, a study of how to best represent this type of energy transfer and how to do so with efficient computations may be useful. The third chapter concerns the use of machine learning techniques within ensemble based data assimilation methods. The use of variational autoencoders (VAEs) as a generative model to create artificial analogs for data assimilation has been shown to match the accuracy of the Ensemble Kalman Filter (EnKF) at a fraction of the cost in a 1D toy problem. Patching of the domain and the use of general autoencoders are studied to make this existing method more robust and feasible for complex systems and these modifications are tested and found to produce highly accurate data assimilation results on the aforementioned toy problem. The implementation of this method in a more realistic geophysical fluid model is ongoing, and is a step towards developing this method for potential uses within real data assimilation applications.</p