Learning Galaxy Intrinsic Alignment Correlations

• URL: http://arxiv.org/abs/2404.13702v1
• Date: Sun, 21 Apr 2024 16:16:56 GMT
• Title: Learning Galaxy Intrinsic Alignment Correlations
• Authors: Sneh Pandya, Yuanyuan Yang, Nicholas Van Alfen, Jonathan Blazek, Robin Walters,
• Abstract summary: The intrinsic alignments (IA) of galaxies represent the correlation of galaxy shapes due to gravitational tidal interactions and galaxy formation processes. We present a deep learning approach to emulate galaxy position-position, position-orientation, and orientation-orientation correlation function measurements and uncertainties.
• Score: 16.805775045014578
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The intrinsic alignments (IA) of galaxies, regarded as a contaminant in weak lensing analyses, represents the correlation of galaxy shapes due to gravitational tidal interactions and galaxy formation processes. As such, understanding IA is paramount for accurate cosmological inferences from weak lensing surveys; however, one limitation to our understanding and mitigation of IA is expensive simulation-based modeling. In this work, we present a deep learning approach to emulate galaxy position-position ($\xi$), position-orientation ($\omega$), and orientation-orientation ($\eta$) correlation function measurements and uncertainties from halo occupation distribution-based mock galaxy catalogs. We find strong Pearson correlation values with the model across all three correlation functions and further predict aleatoric uncertainties through a mean-variance estimation training procedure. $\xi(r)$ predictions are generally accurate to $\leq10\%$. Our model also successfully captures the underlying signal of the noisier correlations $\omega(r)$ and $\eta(r)$, although with a lower average accuracy. We find that the model performance is inhibited by the stochasticity of the data, and will benefit from correlations averaged over multiple data realizations. Our code will be made open source upon journal publication.

Related papers

• Geometric deep learning for galaxy-halo connection: a case study for galaxy intrinsic alignments [1.2231689895452238]
  We propose a Deep Generative Model trained on the IllustrisTNG-100 simulation to sample 3D galaxy shapes and orientations. The model is able to learn and predict features such as galaxy orientations that are statistically consistent with the reference simulation.
  arXiv  Detail & Related papers  (2024-09-27T13:55:10Z)
• SimBIG: Field-level Simulation-Based Inference of Galaxy Clustering [2.3988372195566443]
  We present the first simulation-based inference ( SBI) of cosmological parameters from field-level analysis of galaxy clustering. We apply SimBIG to a subset of the BOSS CMASS galaxy sample using a convolutional neural network with weight averaging to perform massive data compression of the galaxy field. This work not only presents competitive cosmological constraints but also introduces novel methods for leveraging additional cosmological information in upcoming galaxy surveys like DESI, PFS, and Euclid.
  arXiv  Detail & Related papers  (2023-10-23T18:05:32Z)
• On the Identifiability and Estimation of Causal Location-Scale Noise Models [122.65417012597754]
  We study the class of location-scale or heteroscedastic noise models (LSNMs) We show the causal direction is identifiable up to some pathological cases. We propose two estimators for LSNMs: an estimator based on (non-linear) feature maps, and one based on neural networks.
  arXiv  Detail & Related papers  (2022-10-13T17:18:59Z)
• The SZ flux-mass ($Y$-$M$) relation at low halo masses: improvements with symbolic regression and strong constraints on baryonic feedback [2.436653298863297]
  AGN and supernovae feedback can affect measurements of integrated SZ flux of halos from CMB surveys. We search for analogues of the $Y-M$ relation which are more robust to feedback processes for low masses. Our results can be useful for using upcoming SZ surveys to constrain the nature of baryonic feedback.
  arXiv  Detail & Related papers  (2022-09-05T18:00:00Z)
• Inverting brain grey matter models with likelihood-free inference: a tool for trustable cytoarchitecture measurements [62.997667081978825]
  characterisation of the brain grey matter cytoarchitecture with quantitative sensitivity to soma density and volume remains an unsolved challenge in dMRI. We propose a new forward model, specifically a new system of equations, requiring a few relatively sparse b-shells. We then apply modern tools from Bayesian analysis known as likelihood-free inference (LFI) to invert our proposed model.
  arXiv  Detail & Related papers  (2021-11-15T09:08:27Z)
• Primordial non-Gaussianity from the Completed SDSS-IV extended Baryon Oscillation Spectroscopic Survey I: Catalogue Preparation and Systematic Mitigation [3.2855185490071444]
  We investigate the large-scale clustering of the final spectroscopic sample of quasars from the recently completed extended Baryon Oscillation Spectroscopic Survey (eBOSS) We develop a neural network-based approach to mitigate spurious fluctuations in the density field caused by spatial variations in the quality of the imaging data used to select targets for follow-up spectroscopy.
  arXiv  Detail & Related papers  (2021-06-25T16:01:19Z)
• Understanding the Under-Coverage Bias in Uncertainty Estimation [58.03725169462616]
  quantile regression tends to emphunder-cover than the desired coverage level in reality. We prove that quantile regression suffers from an inherent under-coverage bias. Our theory reveals that this under-coverage bias stems from a certain high-dimensional parameter estimation error.
  arXiv  Detail & Related papers  (2021-06-10T06:11:55Z)
• Spatially relaxed inference on high-dimensional linear models [48.989769153211995]
  We study the properties of ensembled clustered inference algorithms which combine spatially constrained clustering, statistical inference, and ensembling to aggregate several clustered inference solutions. We show that ensembled clustered inference algorithms control the $delta$-FWER under standard assumptions for $delta$ equal to the largest cluster diameter.
  arXiv  Detail & Related papers  (2021-06-04T16:37:19Z)
• SLOE: A Faster Method for Statistical Inference in High-Dimensional Logistic Regression [68.66245730450915]
  We develop an improved method for debiasing predictions and estimating frequentist uncertainty for practical datasets. Our main contribution is SLOE, an estimator of the signal strength with convergence guarantees that reduces the computation time of estimation and inference by orders of magnitude.
  arXiv  Detail & Related papers  (2021-03-23T17:48:56Z)
• Model-Based Multi-Agent RL in Zero-Sum Markov Games with Near-Optimal Sample Complexity [67.02490430380415]
  We show that model-based MARL achieves a sample complexity of $tilde O(|S||B|(gamma)-3epsilon-2)$ for finding the Nash equilibrium (NE) value up to some $epsilon$ error. We also show that such a sample bound is minimax-optimal (up to logarithmic factors) if the algorithm is reward-agnostic, where the algorithm queries state transition samples without reward knowledge.
  arXiv  Detail & Related papers  (2020-07-15T03:25:24Z)
• A Precise High-Dimensional Asymptotic Theory for Boosting and Minimum-$\ell_1$-Norm Interpolated Classifiers [3.167685495996986]
  This paper establishes a precise high-dimensional theory for boosting on separable data. Under a class of statistical models, we provide an exact analysis of the universality error of boosting. We also explicitly pin down the relation between the boosting test error and the optimal Bayes error.
  arXiv  Detail & Related papers  (2020-02-05T00:24:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.