arXiv research

New method calibrates photometric redshift PDFs more accurately.

The Baire metric induces an ultrametric on a dataset and is of linear computational complexity, contrasted with the standard quadratic time agglomerative hierarchical clustering algorithm. We apply the Baire distance to spectrometric and photometric redshifts from the Sloan Digital Sky Survey using, in this work, about…

It is well known in astronomy that propagating non-Gaussian prediction uncertainty in photometric redshift estimates is key to reducing bias in downstream cosmological analyses. Similarly, likelihood-free inference approaches, which are beginning to emerge as a tool for cosmological analysis, require a characterization…

It is shown that the redshift between two Cauchy surfaces in a globally hyperbolic spacetime equals the ratio of the associated contact forms on the space of light rays of that spacetime.

With the availability of the huge amounts of data produced by current and future large multi-band photometric surveys, photometric redshifts have become a crucial tool for extragalactic astronomy and cosmology. In this paper we present a novel method, called Weak Gated Experts (WGE), which allows to derive photometric …

In this paper we explore the applicability of the unsupervised machine learning technique of Self Organizing Maps (SOM) to estimate galaxy photometric redshift probability density functions (PDFs). This technique takes a spectroscopic training set, and maps the photometric attributes, but not the redshifts, to a two di…

The Baire metric induces an ultrametric on a dataset and is of linear computational complexity, contrasted with the standard quadratic time agglomerative hierarchical clustering algorithm. In this work we evaluate empirically this new approach to hierarchical clustering. We compare hierarchical clustering based on the …

We present a large catalog of optically selected galaxy clusters from the application of a new Gaussian Mixture Brightest Cluster Galaxy (GMBCG) algorithm to SDSS Data Release 7 data. The algorithm detects clusters by identifying the red sequence plus Brightest Cluster Galaxy (BCG) feature, which is unique for galaxy c…

Tabular foundation models outperform other methods in conditional density estimation across various datasets.

CHARM creates mock halo catalogs from dark matter density fields using neural networks.

PICZL improves photometric redshifts for AGN in all-sky surveys.

Approximate Bayesian Computation (ABC) is a method to obtain a posterior distribution without a likelihood function, using simulations and a set of distance metrics. For that reason, it has recently been gaining popularity as an analysis tool in cosmology and astrophysics. Its drawback, however, is a slow convergence r…

Genetic algorithms optimize neural networks for cosmological data analysis.

We consider solutions to the linear wave equation on a suitable globally hyperbolic subset of an extreme Reissner-Nordstrom spacetime, arising from regular initial data prescribed on a Cauchy hypersurface crossing the future event horizon. We obtain boundedness, decay, non-decay and blow-up results. Our estimates hold …

Rubin LSST DESC uses AI/ML for dark energy research.

High-dimensional, large-sample astrophysical databases of galaxy clusters, such as the Chandra Deep Field South COMBO-17 database, provide measurements on many variables for thousands of galaxies and a range of redshifts. Current understanding of galaxy formation and evolution rests sensitively on relationships between…

We study the problem of stability and instability of extreme Reissner-Nordstrom spacetimes for linear scalar perturbations. Specifically, we consider solutions to the linear wave equation on a suitable globally hyperbolic subset of such a spacetime, arising from regular initial data prescribed on a Cauchy hypersurface …

Study cosmic structures using Topological Data Analysis and Persistence Energy.

This paper is motivated by the non-linear stability problem for the expanding region of Kerr de Sitter cosmologies in the context of Einstein's equations with positive cosmological constant. We show that under dynamically realistic assumptions the conformal Weyl curvature of the spacetime decays towards future null inf…

Conditional density estimation generalizes regression by modeling a full density f(yjx) rather than only the expected value E(yjx). This is important for many tasks, including handling multi-modality and generating prediction intervals. Though fundamental and widely applicable, nonparametric conditional density estimat…

We construct a large class of dynamical vacuum black hole spacetimes whose exterior geometry asymptotically settles down to a fixed Schwarzschild or Kerr metric. The construction proceeds by solving a backwards scattering problem for the Einstein vacuum equations with characteristic data prescribed on the event horizon…

Proposes a method to improve learning when training data is not representative.

We propose a deep-learning approach based on generative adversarial networks (GANs) to reduce noise in weak lensing mass maps under realistic conditions. We apply image-to-image translation using conditional GANs to the mass map obtained from the first-year data of Subaru Hyper Suprime-Cam (HSC) survey. We train the co…

The massive wave equation $\Box_g ψ- α\fracΛ{3} ψ= 0$ is studied on a fixed Kerr-anti de Sitter background $(\mathcal{M},g_{M,a,Λ})$. We first prove that in the Schwarzschild case (a=0), $ψ$ remains uniformly bounded on the black hole exterior provided that $α< {9/4}$, i.e. the Breitenlohner-Freedman bound holds. Our p…

Designing a photometric system to best fulfil a set of scientific goals is a complex task, demanding a compromise between conflicting requirements and subject to various constraints. A specific example is the determination of stellar astrophysical parameters (APs) - effective temperature, metallicity etc. - across a wi…

To draw inferences about gamma-ray burst (GRB) source populations based on Swift observations, it is essential to understand the detection efficiency of the Swift burst alert telescope (BAT). This study considers the problem of modeling the Swift/BAT triggering algorithm for long GRBs, a computationally expensive proce…

Unified method for input, data, and model uncertainty in neural networks.

Bayesian Gaussian Processes improve exoplanet transit and Hubble constant inference.

This paper benchmarks uncertainty disentanglement across various tasks.

Large-scale surveys make huge amounts of photometric data available. Because of the sheer amount of objects, spectral data cannot be obtained for all of them. Therefore it is important to devise techniques for reliably estimating physical properties of objects from photometric information alone. These estimates are nee…

Unified Bayesian framework for quantifying GNN uncertainty.

Paper recovers uncertainty from dynamic valuation rules.

Proposes a new criterion for reliable uncertainty estimation in deep neural networks.

Proposes a method to quantify uncertainty in graph neural networks for node classification.

Survey on uncertainty in ML and DL, covering sources, quantification, and decision-making.

Unified Uncertainty Calibration improves AI predictions by combining different types of uncertainty.

New method combines ODE filters and numerical quadrature to propagate model uncertainty.

Paper decomposes risk into aleatoric and epistemic uncertainties and generates predictive uncertainty measures.

Estimating how uncertain an AI system is in its predictions is important to improve the safety of such systems. Uncertainty in predictive can result from uncertainty in model parameters, irreducible data uncertainty and uncertainty due to distributional mismatch between the test and training data distributions. Differe…

New method estimates model uncertainty in regression.

Introduces hierarchical uncertainty using U-sequences.

Connects robust optimization to conformal prediction for uncertainty sets.

We propose orthogonality as a necessary condition for disentangling aleatoric and epistemic uncertainty.

This work introduces a method to decompose uncertainty in in-context learning for large language models.

Cooperative model disentangles data uncertainties.

Framework disentangles deep feature uncertainty for efficient inference.

This study introduces axioms to assess regression uncertainty measures.

Study improves AI's handling of uncertainty.