Parallel Attribute Computation for Distributed Component Forests

Component trees are powerful image processing tools to analyze the connected components of an image. One attractive strategy consists in building the nested relations at first and then deriving the components' attributes afterward, such that the user can switch between different attribute functions without having to re-compute the entire tree. Only sequential algorithms allow such an approach, while no parallel algorithm is available. In this paper, we extend a recent method using distributed memory techniques to enable posterior attribute computation in a parallel or distributed manner. This novel approach significantly reduces the computational time needed for combining several attribute functions interactively in Giga and Tera-Scale data sets

Distributed Connected Component Filtering and Analysis in 2-D and 3-D Tera-Scale Data Sets

Connected filters and multi-scale tools are region-based operators acting on the connected components of an image. Component trees are image representations to efficiently perform these operations as they represent the inclusion relationship of the connected components hierarchically. This paper presents disccofan (DIStributed Connected COmponent Filtering and ANalysis), a new method that extends the previous 2-D implementation of the Distributed Component Forests (DCFs) to handle 3-D processing and higher dynamic range data sets. disccofan combines shared and distributed memory techniques to efficiently compute component trees, user-defined attributes filters, and multi-scale analysis. Compared to similar methods, disccofan is faster and scales better on low and moderate dynamic range images, and is the only method with a speed-up larger than 1 on a realistic, astronomical floating-point data set. It achieves a speed-up of 11.20 using 48 processes to compute the DCF of a 162 Gigapixels, single-precision floating-point 3-D data set, while reducing the memory used by a factor of 22. This approach is suitable to perform attribute filtering and multi-scale analysis on very large 2-D and 3-D data sets, up to single-precision floating-point value

Distributed Component Forests in 2-D:Hierarchical Image Representations Suitable for Tera-Scale Images

The standard representations known as component trees, used in morphological connected attribute filtering and multi-scale analysis, are unsuitable for cases in which either the image itself or the tree do not fit in the memory of a single compute node. Recently, a new structure has been developed which consists of a collection of modified component trees, one for each image tile. It has to-date only been applied to fairly simple image filtering based on area. In this paper, we explore other applications of these distributed component forests, in particular to multi-scale analysis such as pattern spectra, and morphological attribute profiles and multi-scale leveling segmentations

Accurately predicting the escape fraction of ionizing photons using restframe ultraviolet absorption lines

The fraction of ionizing photons that escape high-redshift galaxies sensitively determines whether galaxies reionized the early universe. However, this escape fraction cannot be measured from high-redshift galaxies because the opacity of the intergalactic medium is large at high redshifts. Without methods to indirectly measure the escape fraction of high-redshift galaxies, it is unlikely that we will know what reionized the universe. Here, we analyze the far-ultraviolet (UV) H I (Lyman series) and low-ionization metal absorption lines of nine low-redshift, confirmed Lyman continuum emitting galaxies. We use the H I covering fractions, column densities, and dust attenuations measured in a companion paper to predict the escape fraction of ionizing photons. We find good agreement between the predicted and observed Lyman continuum escape fractions (within $1.4\sigma$) using both the H I and ISM absorption lines. The ionizing photons escape through holes in the H I, but we show that dust attenuation reduces the fraction of photons that escape galaxies. This means that the average high-redshift galaxy likely emits more ionizing photons than low-redshift galaxies. Two other indirect methods accurately predict the escape fractions: the Ly$\alpha$ escape fraction and the optical [O III]/[O II] flux ratio. We use these indirect methods to predict the escape fraction of a sample of 21 galaxies with rest-frame UV spectra but without Lyman continuum observations. Many of these galaxies have low escape fractions ($f_{\rm esc} \le 1$\%), but 11 have escape fractions $>1$\%. The methods presented here will measure the escape fractions of high-redshift galaxies, enabling future telescopes to determine whether star-forming galaxies reionized the early universe.Comment: Accepted for publication in A&A. 12 pages, 5 figure

Inferring the properties of the sources of reionization using the morphological spectra of the ionized regions

High-redshift 21-cm observations will provide crucial insights into the physical processes of the Epoch of Reionization. Next-generation interferometers such as the Square Kilometer Array will have enough sensitivity to directly image the 21-cm fluctuations and trace the evolution of the ionizing fronts. In this work, we develop an inferential approach to recover the sources and IGM properties of the process of reionization using the number and, in particular, the morphological pattern spectra of the ionized regions extracted from realistic mock observations. To do so, we extend the Markov Chain Monte Carlo analysis tool 21CMMC by including these 21-cm tomographic statistics and compare this method to only using the power spectrum. We demonstrate that the evolution of the number-count and morphology of the ionized regions as a function of redshift provides independent information to disentangle multiple reionization scenarios because it probes the average ionizing budget per baryon. Although less precise, we find that constraints inferred using 21-cm tomographic statistics are more robust to the presence of contaminants such as foreground residuals. This work highlights that combining power spectrum and tomographic analyses more accurately recovers the astrophysics of reionization.Comment: 28 pages, 16 figures. Accepted to MNRA

Constraining the X-ray heating and reionization using 21-cm power spectra with Marginal Neural Ratio Estimation

Cosmic Dawn (CD) and Epoch of Reionization (EoR) are epochs of the Universe which host invaluable information about the cosmology and astrophysics of X-ray heating and hydrogen reionization. Radio interferometric observations of the 21-cm line at high redshifts have the potential to revolutionize our understanding of the universe during this time. However, modeling the evolution of these epochs is particularly challenging due to the complex interplay of many physical processes. This makes it difficult to perform the conventional statistical analysis using the likelihood-based Markov-Chain Monte Carlo (MCMC) methods, which scales poorly with the dimensionality of the parameter space. In this paper, we show how the Simulation-Based Inference (SBI) through Marginal Neural Ratio Estimation (MNRE) provides a step towards evading these issues. We use 21cmFAST to model the 21-cm power spectrum during CD-EoR with a six-dimensional parameter space. With the expected thermal noise from the Square Kilometre Array (SKA), we are able to accurately recover the posterior distribution for the parameters of our model at a significantly lower computational cost than the conventional likelihood-based methods. We further show how the same training dataset can be utilized to investigate the sensitivity of the model parameters over different redshifts. Our results support that such efficient and scalable inference techniques enable us to significantly extend the modeling complexity beyond what is currently achievable with conventional MCMC methods.Comment: 15 pages, 9 figures. Accepted for publication in MNRA

Epilithic biomass in a large gravel-bed river (the Garonne, France): a manifestation of eutrophication?

In order to evaluate the impact of outputs of the city of Toulouse (740 000 inhabitants) on the epilithic communities colonizing pebble banks in the river Garonne, a large gravel-bed river (eighth order), dry mass (DM), ash-free dry mass (AFDM) and chlorophyll-a (chla) epilithic biomass per unit area were measured and autotrophic index (AI) (i.e. ratio AFDM/chla) was calculated at four stations. This river is morphologically characterized by a succession of pools and riffles and by highly fluctuating hydraulic conditions. At the four stations studied (223 km apart), the means of AFDM values varied between 17.1 and 31.1 g m−2 of colonized surface and the chla concentration varied between 112 and 254 mg m−2. However, there were no significant differences in AFDM per unit area between the parts of the river upstream and downstream of the Toulouse area (Mann–Whitney U-test statistic), nor between the four stations (Kruskal–Wallis test statistic), and the AI did not allow the description of changes in periphyton communities between sampling locations. This study showed that epilithic biomass should be considered as the typical microbial community of the river rather than as a manifestation of eutrophication