Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userThe RWST, a comprehensive statistical description of the non-Gaussian structures in the ISM
127
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
The interstellar medium (ISM) is a complex non-linear system governed by gravity and magneto-hydrodynamics, as well as radiative, thermodynamical, and chemical processes. Our understanding of it mostly progresses through observations and numerical simulations, and a quantitative comparison between these two approaches requires a generic and comprehensive statistical description. The goal of this paper is to build such a description, with the purpose to permit an efficient comparison independent of any specific prior or model. We start from the Wavelet Scattering Transform (WST), a low-variance statistical description of non-Gaussian processes, developed in data science, that encodes long-range interactions through a hierarchical multiscale approach based on the Wavelet transform. We perform a reduction of the WST through a fit of its angular dependencies, allowing to gather most of the information it contains into a few components whose physical meanings are identified, and that describe, e.g., isotropic and anisotropic behaviours. The result of this paper is the Reduced Wavelet Scattering Transform (RWST), a statistical description with a small number of coefficients that characterizes complex structures arising from non-linear phenomena, free from any specific prior. The RWST coefficients encode moments of order up to four, have reduced variances, and quantify the couplings between scales. To show the efficiency and generality of this description, we apply it successfully to three kinds of processes: fractional Brownian motions, MHD simulations, and Herschel observations in a molecular cloud. With fewer than 100 coefficients when probing 6 scales and 8 angles on 256*256 maps, we were able with the RWST to perform quantitative comparisons, to infer relevant physical properties, and to produce realistic synthetic fields.
rate research
Read More
The statistical characterization of the diffuse magnetized ISM and Galactic foregrounds to the CMB poses a major challenge. To account for their non-Gaussian statistics, we need a data analysis approach capable of efficiently quantifying statistical couplings across scales. This information is encoded in the data, but most of it is lost when using conventional tools, such as one-point statistics and power spectra. The wavelet scattering transform (WST), a low-variance statistical descriptor of non-Gaussian processes introduced in data science, opens a path towards this goal. We applied the WST to noise-free maps of dust polarized thermal emission computed from a numerical simulation of MHD turbulence. We analyzed normalized complex Stokes maps and maps of the polarization fraction and polarization angle. The WST yields a few thousand coefficients; some of them measure the amplitude of the signal at a given scale, and the others characterize the couplings between scales and orientations. The dependence on orientation can be fitted with the reduced WST (RWST), an angular model introduced in previous works. The RWST provides a statistical description of the polarization maps, quantifying their multiscale properties in terms of isotropic and anisotropic contributions. It allowed us to exhibit the dependence of the map structure on the orientation of the mean magnetic field and to quantify the non-Gaussianity of the data. We also used RWST coefficients, complemented by additional constraints, to generate random synthetic maps with similar statistics. Their agreement with the original maps demonstrates the comprehensiveness of the statistical description provided by the RWST. This work is a step forward in the analysis of observational data and the modeling of CMB foregrounds. We also release PyWST, a Python package to perform WST/RWST analyses at: https://github.com/bregaldo/pywst.
We present a revised version of our automated technique using Gaussian processes (GPs) to detect Damped Lyman-$alpha$ absorbers (DLAs) along quasar (QSO) sightlines. The main improvement is to allow our Gaussian process pipeline to detect multiple DLAs along a single sightline. Our DLA detections are regularised by an improved model for the absorption from the Lyman-$alpha$ forest which improves performance at high redshift. We also introduce a model for unresolved sub-DLAs which reduces mis-classifications of absorbers without detectable damping wings. We compare our results to those of two different large-scale DLA catalogues and provide a catalogue of the processed results of our Gaussian process pipeline using 158 825 Lyman-$alpha$ spectra from SDSS data release 12. We present updated estimates for the statistical properties of DLAs, including the column density distribution function (CDDF), line density ($dN/dX$), and neutral hydrogen density ($Omega_{textrm{DLA}}$).
We present the characteristics of the Damped Lyman-$alpha$ (DLA) systems found in the data release DR16 of the extended Baryon Oscillation Spectroscopic Survey (eBOSS) of the Sloan Digital Sky Survey (SDSS). DLAs were identified using the convolutional neural network (CNN) of~cite{Parks2018}. A total of 117,458 absorber candidates were found with $2 leq zdla leq 5.5$ and $19.7 leq lognhi leq 22$, including 57,136 DLA candidates with $lognhi geq 20.3$. Mock quasar spectra were used to estimate DLA detection efficiency and the purity of the resulting catalog. Restricting the quasar sample to bright forests, i.e. those with mean forest fluxes $meanflux>2timesfluxunit$, the completeness and purity are greater than 90% for DLAs with column densities in the range $20.1leq lognhi leq 22$.
We use a semi-analytical model for the substructure of dark matter haloes to assess the too-big-to-fail (TBTF) problem. The model accurately reproduces the average subhalo mass and velocity functions, as well as their halo-to-halo variance, in N-body simulations. We construct thousands of realizations of Milky Way (MW) size host haloes, allowing us to investigate the TBTF problem with unprecedented statistical power. We examine the dependence on host halo mass and cosmology, and explicitly demonstrate that a reliable assessment of TBTF requires large samples of hundreds of host haloes. We argue that previous statistics used to address TBTF suffer from the look-elsewhere effect and/or disregard certain aspects of the data on the MW satellite population. We devise a new statistic that is not hampered by these shortcomings, and, using only data on the 9 known MW satellite galaxies with $V_{rm max}>15{rm kms}^{-1}$, demonstrate that $1.4^{+3.3}_{-1.1}%$ of MW-size host haloes have a subhalo population in statistical agreement with that of the MW. However, when using data on the MW satellite galaxies down to $V_{rm max}=8{rm kms}^{-1}$, this MW consistent fraction plummets to $<5times10^{-4}$ (at 68% CL). Hence, if it turns out that the inventory of MW satellite galaxies is complete down to 8km/s, then the maximum circular velocities of MW satellites are utterly inconsistent with $Lambda$CDM predictions, unless baryonic effects can drastically increase the spread in $V_{rm max}$ values of satellite galaxies compared to that of their subhaloes.
We study structure formation in a set of cosmological simulations to uncover the scales in the initial density field that gave rise to the formation of present-day structures. Our simulations share a common primordial power spectrum (here Lambda-CDM), but the introduction of hierarchical variations of the phase information allows us to systematically study the scales that determine the formation of structure at later times. We consider the variance in z=0 statistics such as the matter power spectrum and halo mass function. We also define a criterion for the existence of individual haloes across simulations, and determine what scales in the initial density field contain sufficient information for the non-linear formation of unique haloes. We study how the characteristics of individual haloes such as the mass and concentration, as well as the position and velocity, are affected by variations on different scales, and give scaling relations for haloes of different mass. Finally, we use the example of a cluster-mass halo to show how our hierarchical parametrisation of the initial density field can be used to create variants of particular objects. With properties such as mass, concentration, kinematics and substructure of haloes set on distinct and well-determined scales, and its unique ability to introduce variations localised in real space, our method is a powerful tool to study structure formation in cosmological simulations.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
