Redshift equals ratio of contact forms in spacetime.
problem Understanding redshift in spacetime geometry.
method Relating redshift to contact forms on light ray space.
result Redshift is a geometric ratio of contact forms.
The Weyl curvature in expanding black hole cosmologies decays towards infinity.
problem Non-linear stability of expanding regions in Kerr de Sitter cosmologies.
method Establishes decay estimates for Weyl fields under realistic assumptions.
result Uniform, optimal, and consistent decay estimates for Weyl fields.
New method calibrates photometric redshift PDFs more accurately.
problem Inaccurate photometric redshift uncertainties lead to systematic errors.
method Local re-calibration using feature-space regression of Probability Integral Transform (PIT) distributions.
result Calibrated PDFs are more accurate at all locations in feature space.
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…
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 …
qABC accelerates ABC for cosmological redshift distributions.
problem Slow convergence rate in Approximate Bayesian Computation (ABC).
method qABC uses Quantile Regression to model quantiles of distance measures as a function of input parameters, accelerating ABC convergence.
result qABC converges to same posterior as basic ABC but uses only 20% of simulations, achieving a fivefold gain in execution time.
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 …
PICZL improves photometric redshifts for AGN in all-sky surveys.
problem Challenges in accurately computing photo-z for AGN due to interplay of SMBH and host galaxy emissions.
method PICZL uses an ensemble of CNNs with cross-channel integration of image and catalog data, leveraging Gaussian mixture models.
result PICZL achieves a photo-z variance of 4.5% and outlier fraction of 5.6% on a validation sample of 8098 AGN, outperforming previous methods.
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…
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 …
CHARM creates mock halo catalogs from dark matter density fields using neural networks.
problem Creating accurate mock halo catalogs for cosmological studies is computationally expensive.
method CHARM uses multi-stage neural spline flow networks to learn the mapping from dark matter density fields to halo catalogs.
result Mock halo catalogs have the same statistical properties as those from high-resolution N-body simulations.
The paper introduces tools for nonparametric conditional density estimation in astronomy.
problem Estimating photometric redshifts and likelihood-free cosmological inference with uncertainty quantification.
method Nonparametric conditional density estimation (CDE) tools in Python and R.
result Comprehensive statistical tools and software for CDE in astronomy.
Tabular foundation models outperform other methods in conditional density estimation across various datasets.
problem Estimating the full conditional distribution of a response given tabular covariates, especially in settings with heteroscedasticity, multimodality, or asymmetric uncertainty.
method Benchmarked three tabular foundation model variants (TabPFN and TabICL) against six CDE baselines on 39 real-world datasets.
result Tabular foundation models achieve the best CDE loss, log-likelihood, and CRPS across all sample sizes, outperforming other methods.
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.
problem Improving supervised learning when training data is not representative (covariate shift).
method Conditioning on propensity scores to balance covariates within strata.
result Significantly improved target prediction and AUC (0.958) on supernovae classification challenge.
Genetic algorithms optimize neural networks for cosmological data analysis.
problem Inaccurate results from neural networks due to poor hyperparameter selection.
method Used genetic algorithms to optimize hyperparameters of neural networks.
result Genetic algorithms improve neural network performance in cosmological data analysis.
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…
Study cosmic structures using Topological Data Analysis and Persistence Energy.
problem Investigate cosmic web evolution in ΛCDM cosmologies. method Apply LITE method to embed persistence diagrams into vector spaces and analyze cosmic structures.
result Discover a correlation between Persistence Energy and redshift values.
New study finds environment significantly suppresses star formation in galaxies, contrary to previous beliefs.
problem Understanding the role of environment in galaxy formation and evolution.
method Applied causal inference framework to IllustrisTNG simulations.
result Environment suppresses star formation by a factor of ~100, contrary to previous beliefs.
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…
Study robustness of global feature effect explanations in machine learning models.
problem Vulnerability of global feature effect explanations to data and model perturbations.
method Theoretical bounds and experimental evaluation of partial dependence plots and accumulated local effects.
result Quantifies the gap between best and worst-case scenarios of misinterpreting machine learning predictions globally.
GADGET framework decomposes global feature effects using recursive partitioning.
problem Misleading global feature effects when feature interactions are present.
method Generalized additive decomposition of global effects (GADGET) based on recursive partitioning.
result Minimizes interaction-related heterogeneity of local feature effects.
Deep learning reduces noise in weak lensing mass maps using GANs.
problem Noise reduction in weak lensing mass maps.
method Generative adversarial networks (GANs) applied to Subaru Hyper Suprime-Cam data.
result GANs successfully reproduce non-Gaussian information in denoised maps, showing stronger cosmological dependence.
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…
Graph neural networks help assess how global changes affect plant-pollinator networks.
problem Interpreting GNN results to understand how global changes impact plant-pollinator networks.
method Simulation study and application on Spipoll dataset to assess effects of global changes on pollination networks.
result GNNs can detect interactive effects between covariates and plant genera on pollination network connectivity.
We study the effect of globalization on the Korean market, one of the emerging markets. Some characteristics of the Korean market are different from those of the mature market according to the latest market data, and this is due to the influence of foreign markets or investors. We concentrate on the market network stru…
Model forecasts global stock market volatility using dynamic graphs and all trading days.
problem Enhance forecasting accuracy and practical utility in global stock market volatility.
method Spatial-temporal graph neural network architecture to capture volatility spillover effect.
result Forecasting performance surpasses baseline models in all scenarios.
Analyzes error sources in global feature effect estimation methods.
problem Unexplored error sources in global feature effect estimation methods.
method Systematic, estimator-level analysis of bias and variance.
result Holdout data is theoretically cleanest, but estimation variance depends on sample size and model characteristics.
Bayesian neural networks detect complex interactions with uncertainty.
problem Estimating global pairwise interaction effects with uncertainty.
method Bayesian neural network for modeling interactions, GEH for estimating interaction effects.
result The method empirically outperforms alternatives and detects interpretable interactions.
Graph Signal Processing improves stock market volatility forecasting.
problem Forecasting realized volatility in a global stock market context.
method Integrating Graph Signal Processing into the HAR model.
result The proposed model outperforms HAR-type benchmarks.
This study investigates global normalization in neural models, showing its effectiveness in search-aware training.
problem Theoretical equivalence of global and local normalization in high-capacity models, practical advantage unclear.
method Continuous relaxation of beam search for training globally normalized recurrent sequence models.
result Globally normalized models are more effective than locally normalized ones in inexact search.
Global solutions and smoothing effects for reaction-diffusion equations on manifolds.
problem Global existence and smoothing effects for reaction-diffusion equations on Riemannian manifolds.
method Functional analytic methods, Sobolev and Poincaré inequalities.
result Existence of global solutions under certain conditions on the manifold.
Inspired by the bankruptcy of Lehman Brothers and its consequences on the global financial system, we develop a simple model in which the Lehman default event is quantified as having an almost immediate effect in worsening the credit worthiness of all financial institutions in the economic network. In our stylized desc…
Decomposes spillover effects under misspecified exposure mappings.
problem Modeling outcomes as functions of own treatment and a misspecified exposure mapping of others' treatments.
method Pseudo-true estimands and local-global extension for structured misspecification.
result Sharp asymptotic decomposition into direct, local, and global components.
Combines local and global smoothing for multivariate density estimation.
problem Non-parametric multivariate density estimation.
method Combines local and global smoothing techniques.
result Simulation shows effectiveness of the method.
This work discovers latent field effects governing interacting dynamical systems.
problem Discovering field effects governing interacting dynamical systems.
method Proposes neural fields to learn latent force fields from observed dynamics, disentangling local object interactions and global field effects.
result Accurately discovers latent field effects in various dynamical systems.
This paper improves a local-to-global principle for Morse quasigeodesics.
problem Quantify the size of local neighborhoods for global Morse behavior.
method Estimates in symmetric space to supplement Kapovich-Leeb-Porti's proof.
result Explicit criteria for local-to-global principle verified.
NormLIME improves feature importance explanations for deep neural networks.
problem Improving local feature explanations for deep learning models.
method NormLIME aggregates local models into global and class-specific interpretations.
result NormLIME outperforms other feature importance metrics in human user studies and numerical experiments.
Researchers develop a method to measure treatment effects in settings with shared states.
problem Measuring treatment effects in settings with shared states like prices, recommendations, or social signals.
method Double machine learning (DML) theorem with conditions for efficient inference under shared-state interference.
result Efficient estimation of average direct effect (ADE) and global average treatment effect (GATE) in various models.
Proposes DR-ME test for interpretable distributional treatment effects.
problem Detects invisible differences in treatment effects on distributional outcomes.
method Semiparametrically efficient finite-location test using kernel witnesses and orthogonal features.
result DR-ME reveals causal-discrepancy coordinates and has noncentral chi-square local power.
New method for estimating global and local parameters using regularized Riesz representers.
problem Estimating global and local parameters in complex models robustly.
method Adaptive inference methods based on ℓ1 regularization, including Riesz representer as a nuisance parameter.
result Non-asymptotic and asymptotic uniform validity for honest confidence bands.
Replica exchange Langevin diffusion accelerates nonconvex optimization.
problem Nonconvex optimization challenges in machine learning.
method Replica exchange Langevin diffusion, discretization analysis.
result Replica exchange accelerates convergence to global minima.
Globally irreducible nodes (i.e. nodes whose branches belong to the same irreducible component) have mild effects on the most common topological invariants of an algebraic curve. In other words, adding a globally irreducible node (simple nodal degeneration) to a curve should not change them a lot. In this paper we stud…
Paper solves optimal portfolio deleveraging with cross asset impacts.
problem Maximize equity while meeting debt/equity requirement with cross asset price impacts.
method Developed successive convex optimization (SCO) and an effective global algorithm integrating SCO, convex relaxation, and branch-and-bound.
result Proposed algorithms find global optimal solutions efficiently.
This paper examines how regional trade agreements affect global trade relationships.
problem The relationship between regional trade agreements and global trade purity.
method Defined and decomposed synthesized trade resistance, separated natural and artificial factors, used expectation maximization algorithm to optimize parameters, and quantified trade purity indicator.
result Regional trade agreements contribute to the relative prosperity of EU and NAFTA countries, but weaken the role of trade unions and accelerate multilateral trade liberalization.
New quantum kernels avoid overfitting by combining local and global components.
problem Exponential concentration in quantum kernels leads to overfitting.
method Local-global quantum kernels combining small subsystem and full-system measurements.
result Demonstrated benign overfitting in local-global quantum kernels.