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Davide Bianchi

Publications and source records attributed to Davide Bianchi.

At least 91 records · Page 5Linked to original sources
arXiv2024

Neumann cut-offs and essential self-adjointness on complete Riemannian manifolds with boundary

We generalize some fundamental results for noncompact Riemannian manfolds without boundary, that only require completeness and no curvature assumptions, to manifolds with boundary: let $M$ be a smooth Riemannian manifold with boundary $\partial M$ and let $\hat{C}^\infty_c(M)$ denote the space of smooth compactly supported cut-off functions with vanishing normal derivative, Neumann cut-offs. We show, among other things, that under completeness: - $\hat{C}^\infty_c(M)$ is dense in $W^{1,p}(\mathring{M})$ for all $p\in (1,\infty)$; this generalizes a classical result by Aubin [2] for $\partial M=\emptyset$. - $M$ admits a sequence of first order cut-off functions in $\hat{C}^\infty_c(M)$; for $\partial M=\emptyset$ this result can be traced back to Gaffney [7]. - the Laplace-Beltrami operator with domain of definition $\hat{C}^\infty_c(M)$ is essentially self-adjoint; this is a generalization of a classical result by Strichartz [20] for $\partial M=\emptyset$.

math.DG↗
arXiv2024

The DESI One-Percent survey: exploring the Halo Occupation Distribution of Emission Line Galaxies with AbacusSummit simulations

The One-Percent survey of the Dark Energy Spectroscopic Instrument collected ~ 270k emission line galaxies (ELGs) at 0.8 < z < 1.6. The high completeness of the sample allowed the clustering to be measured down to scales never probed before, 0.04 Mpc/h in rp for the projected 2-point correlation function (2PCF) and 0.17 Mpc/h in galaxy pair separation s for the 2PCF monopole and quadrupole. The most striking feature of the measurements is a strong signal at the smallest scales, below 0.2 Mpc/h in rp and 1 Mpc/h in s. We analyze these data in the halo occupation distribution framework. We consider different distributions for central galaxies, a standard power law for satellites with no condition on the presence of a central galaxy and explore several extensions of these models. For all considered models, the mean halo mass of the sample is found to be log10 ~ 11.9. We obtain a satellite mean occupation function which agrees with physically motivated ELG models only if we introduce central-satellite conformity, meaning that the satellite occupation is conditioned by the presence of central galaxies of the same type. To achieve in addition a good modeling of the clustering between 0.1 and 1 Mpc/h in rp, we allow for ELG positioning outside of the halo virial radius and find 0.5% of ELGs residing in the outskirts of halos. Furthermore, the satellite velocity dispersion inside halos is found to be ~ 30% larger than that of the halo dark matter particles. These are the main findings of our work. We investigate assembly bias as a function of halo concentration, local density or local density anisotropies and observe no significant change in our results. We split the data sample in two redshift bins and report no significant evolution with redshift. Lastly, changing the cosmology in the modeling impacts only slightly our results.

astro-ph.CO↗
arXiv2023

Optimal constraints on Primordial non-Gaussianity with the eBOSS DR16 quasars in Fourier space

We present constraints on the amplitude of local Primordial Non-Gaussianities (PNG), $f_{\rm NL}$, using the quasar sample in the Sloan Digital Sky Survey IV extended Baryon Oscillation Spectroscopic Survey Data Release 16. We analyze the power spectrum monopole, testing for the presence of scale dependent galaxy bias induced by local PNG. Our analysis makes use of optimal redshift weights that maximize the response of the quasar sample to the possible presence of non zero PNG. We find $-4<f_{\rm NL}<27$ at $68\%$ confidence level, which is among the strongest bounds with Large Scale Structure data. The optimal analysis reduces the error bar by about 10% compared to the standard one, an amount which is smaller than what was expected from a Fisher matrix analysis. This, and the reduced improvement over previous releases of the same catalog, suggest the presence of still unknown systematic effects in the data. If the quasars have a lower response to local PNG, our optimal constraint becomes $-23<f_{\rm NL}<21$ at $68\%$, with an improvement of $30\%$ over standard analyses. We also show how to use the optimal weights to put data-driven priors on the sample's response to local PNG.

astro-ph.CO↗
arXiv2023

The DESI One-Percent Survey: Exploring the Halo Occupation Distribution of Luminous Red Galaxies and Quasi-Stellar Objects with AbacusSummit

We present the first comprehensive Halo Occupation Distribution (HOD) analysis of the DESI One-Percent survey Luminous Red Galaxy (LRG) and Quasi-Stellar Object (QSO) samples. We constrain the HOD of each sample and test possible HOD extensions by fitting the redshift-space galaxy 2-point correlation functions in 0.15 < r < 32 Mpc/h in a set of fiducial redshift bins. We use AbacusSummit cubic boxes at Planck 2018 cosmology as model templates and forward model galaxy clustering with the AbacusHOD package. We achieve good fits with a standard HOD model with velocity bias, and we find no evidence for galaxy assembly bias or satellite profile modulation at the current level of statistical uncertainty. For LRGs in 0.4 < z < 0.6, we infer a satellite fraction of fsat = 11+-1%, a mean halo mass of log10 Mh = 13.40+0.02-0.02, and a linear bias of blin = 1.93+0.06-0.04. For LRGs in 0.6 < z < 0.8, we find fsat = 14+-1%, log10 Mh = 13.24+0.02-0.02, and blin = 2.08+0.03-0.03. For QSOs, we infer fsat = 3+8-2%, log10 Mh = 12.65+0.09-0.04, and blin = 2.63+0.37-0.26 in redshift range 0.8 < z < 2.1. Using these fits, we generate a large suite of high-fidelity galaxy mocks. We also study the redshift-evolution of the DESI LRG sample from z = 0.4 up to z = 1.1, revealing significant and interesting trends in mean halo mass, linear bias, and satellite fraction.

astro-ph.CO↗
arXiv2023

Uniformly convex neural networks and non-stationary iterated network Tikhonov (iNETT) method

We propose a non-stationary iterated network Tikhonov (iNETT) method for the solution of ill-posed inverse problems. The iNETT employs deep neural networks to build a data-driven regularizer, and it avoids the difficult task of estimating the optimal regularization parameter. To achieve the theoretical convergence of iNETT, we introduce uniformly convex neural networks to build the data-driven regularizer. Rigorous theories and detailed algorithms are proposed for the construction of convex and uniformly convex neural networks. In particular, given a general neural network architecture, we prescribe sufficient conditions to achieve a trained neural network which is component-wise convex or uniformly convex; moreover, we provide concrete examples of realizing convexity and uniform convexity in the modern U-net architecture. With the tools of convex and uniformly convex neural networks, the iNETT algorithm is developed and a rigorous convergence analysis is provided. Lastly, we show applications of the iNETT algorithm in 2D computerized tomography, where numerical examples illustrate the efficacy of the proposed algorithm.

math.NA↗
arXiv2021

Constructive approach to the monotone rearrangement of functions

We detail a simple procedure (easily convertible to an algorithm) for constructing from quasi-uniform samples of $f$ a sequence of linear spline functions converging to the monotone rearrangement of $f$, in the case where $f$ is an almost everywhere continuous function defined on a bounded set $Ω$ with negligible boundary. Under additional assumptions on $f$ and $Ω$, we prove that the convergence of the sequence is uniform. We also show that the same procedure applies to arbitrary measurable functions too, but with the substantial difference that in this case the procedure has only a theoretical interest and cannot be converted to an algorithm.

math.NA↗
arXiv2021

Asymptotic spectra of large (grid) graphs with a uniform local structure (part II): numerical applications

In the current work we are concerned with sequences of graphs having a grid geometry, with a uniform local structure in a bounded domain $Ω\subset {\mathbb R}^d$, $d\ge 1$. When $Ω=[0,1]$, such graphs include the standard Toeplitz graphs and, for $Ω=[0,1]^d$, the considered class includes $d$-level Toeplitz graphs. In the general case, the underlying sequence of adjacency matrices has a canonical eigenvalue distribution, in the Weyl sense, and it has been shown in the theoretical part of this work that we can associate to it a symbol $\boldsymbol{\mathfrak{f}}$. The knowledge of the symbol and of its basic analytical features provides key information on the eigenvalue structure in terms of localization, spectral gap, clustering, and global distribution. In the present paper, many different applications are discussed and various numerical examples are presented in order to underline the practical use of the developed theory. Tests and applications are mainly obtained from the approximation of differential operators via numerical schemes such as Finite Differences (FDs), Finite Elements (FEs), and Isogeometric Analysis (IgA). Moreover, we show that more applications can be taken into account, since the results presented here can be applied as well to study the spectral properties of adjacency matrices and Laplacian operators of general large graphs and networks, whenever the involved matrices enjoy a uniform local structure.

math.NA↗
arXiv2021

Compatibility, embedding and regularization of non-local random walks on graphs

Several variants of the graph Laplacian have been introduced to model non-local diffusion processes, which allow a random walker to {\textquotedblleft jump\textquotedblright} to non-neighborhood nodes, most notably the transformed path graph Laplacians and the fractional graph Laplacian. From a rigorous point of view, this new dynamics is made possible by having replaced the original graph $G$ with a weighted complete graph $G'$ on the same node-set, that depends on $G$ and wherein the presence of new edges allows a direct passage between nodes that were not neighbors in $G$. We show that, in general, the graph $G'$ is not compatible with the dynamics characterizing the original model graph $G$: the random walks on $G'$ subjected to move on the edges of $G$ are not stochastically equivalent, in the wide sense, to the random walks on $G$. From a purely analytical point of view, the incompatibility of $G'$ with $G$ means that the normalized graph $\hat{G}$ can not be embedded into the normalized graph $\hat{G}'$. Eventually, we provide a regularization method to guarantee such compatibility and preserving at the same time all the nice properties granted by $G'$.

math.NA↗
arXiv2021

Graph approximation and generalized Tikhonov regularization for signal deblurring

Given a compact linear operator $\K$, the (pseudo) inverse $\K^\dagger$ is usually substituted by a family of regularizing operators $\R_α$ which depends on $\K$ itself. Naturally, in the actual computation we are forced to approximate the true continuous operator $\K$ with a discrete operator $\K^{(n)}$ characterized by a finesses discretization parameter $n$, and obtaining then a discretized family of regularizing operators $\R_α^{(n)}$. In general, the numerical scheme applied to discretize $\K$ does not preserve, asymptotically, the full spectrum of $\K$. In the context of a generalized Tikhonov-type regularization, we show that a graph-based approximation scheme that guarantees, asymptotically, a zero maximum relative spectral error can significantly improve the approximated solutions given by $\R_α^{(n)}$. This approach is combined with a graph based regularization technique with respect to the penalty term.

math.NA↗
arXiv2021

Graph Laplacian for image deblurring

Image deblurring is relevant in many fields of science and engineering. To solve this problem, many different approaches have been proposed and among the various methods, variational ones are extremely popular. These approaches are characterized by substituting the original problem with a minimization one where the functional is composed of two terms, a data fidelity term and a regularization term. In this paper we propose, in the classical $\ell^2-\ell^1$ minimization with the non-negativity constraint of the solution, the use of the graph Laplacian as regularization operator. Firstly, we describe how to construct the graph Laplacian from the observed noisy and blurred image. Once the graph Laplacian has been built, we solve efficiently the proposed minimization problem splitting the convolution operator and the graph Laplacian by the alternating direction method of multipliers (ADMM). Some selected numerical examples show the good performances of the proposed algorithm.

math.NA↗
arXiv2020

The Completed SDSS-IV extended Baryon Oscillation Spectroscopic Survey: exploring the Halo Occupation Distribution model for Emission Line Galaxies

We study the modelling of the Halo Occupation Distribution (HOD) for the eBOSS DR16 Emission Line Galaxies (ELGs). Motivated by previous theoretical and observational studies, we consider different physical effects that can change how ELGs populate haloes. We explore the shape of the average HOD, the fraction of satellite galaxies, their probability distribution function (PDF), and their density and velocity profiles. Our baseline HOD shape was fitted to a semi-analytical model of galaxy formation and evolution, with a decaying occupation of central ELGs at high halo masses. We consider Poisson and sub/super-Poissonian PDFs for satellite assignment. We model both NFW and particle profiles for satellite positions, also allowing for decreased concentrations. We model velocities with the virial theorem and particle velocity distributions. Additionally, we introduce a velocity bias and a net infall velocity. We study how these choices impact the clustering statistics while keeping the number density and bias fixed to that from eBOSS ELGs. The projected correlation function, $w_p$, captures most of the effects from the PDF and satellites profile. The quadrupole, $ξ_2$, captures most of the effects coming from the velocity profile. We find that the impact of the mean HOD shape is subdominant relative to the rest of choices. We fit the clustering of the eBOSS DR16 ELG data under different combinations of the above assumptions. The catalogues presented here have been analysed in companion papers, showing that eBOSS RSD+BAO measurements are insensitive to the details of galaxy physics considered here. These catalogues are made publicly available.

astro-ph.CO↗
arXiv2020

Hybrid-basis inference for large-scale galaxy clustering: combining spherical and Cartesian Fourier analyses

Future precision cosmology from large-scale structure experiments including the Dark Energy Spectroscopic Instrument (DESI) and Euclid will probe wider and deeper cosmic volumes than those covered by previous surveys. The Cartesian power spectrum analysis of anisotropic galaxy clustering based on the Fourier plane wave basis makes a number of assumptions, including the local plane-parallel approximation, that will no longer be valid on very large scales and may degrade cosmological constraints. We propose an approach that utilises a hybrid basis: on the largest scales, clustering statistics are decomposed into spherical Fourier modes which respect the natural geometry of both survey observations and physical effects along the line of sight, such as redshift-space distortions, the Alcock--Paczyńsky and light-cone effects; on smaller scales with far more clustering modes, we retain the computational benefit of the power spectrum analysis aided by fast Fourier transforms. This approach is particularly suited to the likelihood analysis of local primordial non-Gaussianity $f_\textrm{NL}$ through the scale-dependent halo bias, and we demonstrate its applicability with $N$-body simulations. We also release our public code Harmonia (https://github.com/MikeSWang/Harmonia) for galaxy clustering likelihood inference in spherical Fourier or hybrid-basis analyses.

astro-ph.CO↗
arXiv2020

Analysis of the spectral symbol associated to discretization schemes of linear self-adjoint differential operators

Given a linear self-adjoint differential operator $\mathcal{L}$ along with a discretization scheme (like Finite Differences, Finite Elements, Galerkin Isogeometric Analysis, etc.), in many numerical applications it is crucial to understand how good the (relative) approximation of the whole spectrum of the discretized operator $\mathcal{L}^{(\mathbf{n})}$ is, compared to the spectrum of the continuous operator $\mathcal{L}$. The theory of Generalized Locally Toeplitz sequences allows to compute the spectral symbol function $ω$ associated to the discrete matrix $\mathcal{L}^{(\mathbf{n})}$. We prove that the symbol $ω$ can measure, asymptotically, the maximum spectral relative error $\mathcal{E}\geq 0$. It measures how the scheme is far from a good relative approximation of the whole spectrum of $\mathcal{L}$, and it suggests a suitable (possibly non-uniform) grid such that, if coupled to an increasing refinement of the order of accuracy of the scheme, guarantees $\mathcal{E}=0$.

math.NA↗
arXiv2020

Generalized Structure Preserving Preconditioners for Frame-Based Image Deblurring

We are interested in fast and stable iterative regularization methods for image deblurring problems with space invariant blur. The associated coefficient matrix has a Block Toeplitz Toeplitz Blocks (BTTB) like structure plus a small rank correction depending on the boundary conditions imposed on the imaging model. In the literature, several strategies have been proposed in the attempt to define proper preconditioner for iterative regularization methods that involve such linear systems. Usually, the preconditioner is chosen to be a Block Circulant with Circulant Blocks (BCCB) matrix because it can be efficiently exploit Fast Fourier Transform (FFT) for any computation, including the (pseudo-)inversion. Nevertheless, for ill-conditioned problems, it is well known that BCCB preconditioners cannot provide a strong clustering of the eigenvalues. Moreover, in order to get an effective preconditioner, it is crucial to preserve the structure of the coefficient matrix. On the other hand, thresholding iterative methods have been recently successfully applied to image deblurring problems, exploiting the sparsity of the image in a proper wavelet domain. Motivated by the results of recent papers, we combine a nonstationary preconditioned iteration with the modified linearized Bregman algorithm (MLBA) and proper regularization operators. Several numerical experiments shows the performances of our methods in terms of quality of the restorations.

math.NA↗
arXiv2020

Asymptotic spectra of large (grid) graphs with a uniform local structure

We are concerned with sequences of graphs having a grid geometry, with a uniform local structure in a bounded domain $Ω\subset {\mathbb R}^d$, $d\ge 1$. We assume $Ω$ to be Lebesgue measurable with regular boundary and contained, for convenience, in the cube $[0,1]^d$. When $Ω=[0,1]$, such graphs include the standard Toeplitz graphs and, for $Ω=[0,1]^d$, the considered class includes $d$-level Toeplitz graphs. In the general case, the underlying sequence of adjacency matrices has a canonical eigenvalue distribution, in the Weyl sense, and we show that we can associate to it a symbol $f$. The knowledge of the symbol and of its basic analytical features provide many informations on the eigenvalue structure, of localization, spectral gap, clustering, and distribution type. Few generalizations are also considered in connection with the notion of generalized locally Toeplitz sequences and applications are discussed, stemming e.g. from the approximation of differential operators via numerical schemes.

math.NA↗
arXiv2020

Qualitative properties of bounded subsolutions of nonlinear PDEs

We study decay and compact support properties of positive and bounded solutions of $Δ_{p} u \geq Λ(u)$ on the exterior of a compact set of a complete manifold with rotationally symmetry. In the same setting, we also give a new characterization of stochastic completeness for the $p$-Laplacian in terms of a global $W^{1,p}$-regularity of such solutions. One of the tools we use is a nonlinear version of the Feller property which we investigate on general Riemannian manifolds and which we establish under integral Ricci curvature conditions.

math.AP↗
arXiv2019

Confronting missing observations with probability weights: Fourier space and generalised formalism

Due to instrumental limitations, the nature of which vary from case to case, spectroscopic galaxy redshift surveys usually do not collect redshifts for all galaxies in the population of potential targets. Especially problematic is the entanglement between this incompleteness and the true cosmological signal, arising from the fact that the proportion of successful observations is typically lower in regions with higher density of galaxies. The result is a fictitious suppression of the galaxy clustering that, if not properly corrected, can impact severely on cosmological-parameter inference. Recent developments in the field have shown that an unbiased estimate of the 2-point correlation function in the presence of missing observations can be obtained by weighting each pair by its inverse probability of being targeted. In this work we expand on the concept of probability weights by developing a more mature statistical formalism, which provides us with a deeper understanding of their fundamental properties. We take advantage of this novel perspective to handle the problem of estimating the inverse probability, specifically, we discuss how to efficiently determine the weights from a finite set of realisations of the targeting and how to model exactly the resulting sampling effects. This allows us to derive an inverse-probability-based estimator for the power spectrum, which is the main result of this work, but also to improve robustness and computational efficiency of the already existing configuration-space estimator. Finally, we propose a strategy to further extend the concept of inverse probability, providing examples of how traditional missing-observation countermeasures can be included in this more general picture. The effectiveness of the different models and weighting schemes discussed in this work is demonstrated using realisations of an idealised simple survey strategy.

astro-ph.CO↗