DeepLSS: breaking parameter degeneracies in large scale structure with deep learning analysis of combined probes

• URL: http://arxiv.org/abs/2203.09616v1
• Date: Thu, 17 Mar 2022 21:08:31 GMT
• Title: DeepLSS: breaking parameter degeneracies in large scale structure with deep learning analysis of combined probes
• Authors: Tomasz Kacprzak and Janis Fluri
• Abstract summary: We show that a deep learning analysis of combined probes of weak gravitational lensing and galaxy clustering, which we call DeepS, can effectively break these degeneracies. These degeneracies lead to a significant gain in constraining power for $sigma_8$ and $Omega_m$, with the figure of merit improved by 15x. These results indicate that the fully numerical, map-based forward modeling approach to cosmological inference with machine learning may play an important role in upcoming LSS surveys.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In classical cosmological analysis of large scale structure surveys with 2-pt functions, the parameter measurement precision is limited by several key degeneracies within the cosmology and astrophysics sectors. For cosmic shear, clustering amplitude $\sigma_8$ and matter density $\Omega_m$ roughly follow the $S_8=\sigma_8(\Omega_m/0.3)^{0.5}$ relation. In turn, $S_8$ is highly correlated with the intrinsic galaxy alignment amplitude $A_{\rm{IA}}$. For galaxy clustering, the bias $b_g$ is degenerate with both $\sigma_8$ and $\Omega_m$, as well as the stochasticity $r_g$. Moreover, the redshift evolution of IA and bias can cause further parameter confusion. A tomographic 2-pt probe combination can partially lift these degeneracies. In this work we demonstrate that a deep learning analysis of combined probes of weak gravitational lensing and galaxy clustering, which we call DeepLSS, can effectively break these degeneracies and yield significantly more precise constraints on $\sigma_8$, $\Omega_m$, $A_{\rm{IA}}$, $b_g$, $r_g$, and IA redshift evolution parameter $\eta_{\rm{IA}}$. The most significant gains are in the IA sector: the precision of $A_{\rm{IA}}$ is increased by approximately 8x and is almost perfectly decorrelated from $S_8$. Galaxy bias $b_g$ is improved by 1.5x, stochasticity $r_g$ by 3x, and the redshift evolution $\eta_{\rm{IA}}$ and $\eta_b$ by 1.6x. Breaking these degeneracies leads to a significant gain in constraining power for $\sigma_8$ and $\Omega_m$, with the figure of merit improved by 15x. We give an intuitive explanation for the origin of this information gain using sensitivity maps. These results indicate that the fully numerical, map-based forward modeling approach to cosmological inference with machine learning may play an important role in upcoming LSS surveys. We discuss perspectives and challenges in its practical deployment for a full survey analysis.

Related papers

• 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)
• A Unified Framework for Uniform Signal Recovery in Nonlinear Generative Compressed Sensing [68.80803866919123]
  Under nonlinear measurements, most prior results are non-uniform, i.e., they hold with high probability for a fixed $mathbfx*$ rather than for all $mathbfx*$ simultaneously. Our framework accommodates GCS with 1-bit/uniformly quantized observations and single index models as canonical examples. We also develop a concentration inequality that produces tighter bounds for product processes whose index sets have low metric entropy.
  arXiv  Detail & Related papers  (2023-09-25T17:54:19Z)
• Bayesian deep learning for cosmic volumes with modified gravity [0.0]
  This study aims at extracting cosmological parameters from modified gravity (MG) simulations through deep neural networks endowed with uncertainty estimations. We train both BNNs with real-space density fields and power-spectra from a suite of 2000 dark matter only particle mesh $N$-body simulations. BNNs excel in accurately predicting parameters for $Omega_m$ and $sigma_8$ and their respective correlation with the MG parameter.
  arXiv  Detail & Related papers  (2023-09-01T17:59:06Z)
• Smoothing the Landscape Boosts the Signal for SGD: Optimal Sample Complexity for Learning Single Index Models [43.83997656986799]
  We focus on the task of learning a single index model $sigma(wstar cdot x)$ with respect to the isotropic Gaussian distribution in $d$ dimensions. We show that online SGD on a smoothed loss learns $wstar$ with $n gtrsim dkstar/2$ samples.
  arXiv  Detail & Related papers  (2023-05-18T01:10:11Z)
• 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)
• Topology-aware Generalization of Decentralized SGD [89.25765221779288]
  This paper studies the generalizability of decentralized Valpha-10 stability descent (D-SGD) We prove that the generalizability of D-SGD has a positive correlation with connectivity in initial training phase.
  arXiv  Detail & Related papers  (2022-06-25T16:03:48Z)
• Sharper Rates and Flexible Framework for Nonconvex SGD with Client and Data Sampling [64.31011847952006]
  We revisit the problem of finding an approximately stationary point of the average of $n$ smooth and possibly non-color functions. We generalize the $smallsfcolorgreen$ so that it can provably work with virtually any sampling mechanism. We provide the most general and most accurate analysis of optimal bound in the smooth non-color regime.
  arXiv  Detail & Related papers  (2022-06-05T21:32:33Z)
• Approximate Function Evaluation via Multi-Armed Bandits [51.146684847667125]
  We study the problem of estimating the value of a known smooth function $f$ at an unknown point $boldsymbolmu in mathbbRn$, where each component $mu_i$ can be sampled via a noisy oracle. We design an instance-adaptive algorithm that learns to sample according to the importance of each coordinate, and with probability at least $1-delta$ returns an $epsilon$ accurate estimate of $f(boldsymbolmu)$.
  arXiv  Detail & Related papers  (2022-03-18T18:50:52Z)
• Augmenting astrophysical scaling relations with machine learning : application to reducing the SZ flux-mass scatter [2.0223261087090303]
  We study the Sunyaev-Zeldovich flux$-$cluster mass relation ($Y_mathrmSZ-M$) We find a new proxy for cluster mass which combines $Y_mathrmSZ$ and concentration of ionized gas. We show that the dependence on $c_mathrmgas$ is linked to cores of clusters exhibiting larger scatter than their outskirts.
  arXiv  Detail & Related papers  (2022-01-04T19:00:01Z)
• Improved Sample Complexity for Incremental Autonomous Exploration in MDPs [132.88757893161699]
  We learn the set of $epsilon$-optimal goal-conditioned policies attaining all states that are incrementally reachable within $L$ steps. DisCo is the first algorithm that can return an $epsilon/c_min$-optimal policy for any cost-sensitive shortest-path problem.
  arXiv  Detail & Related papers  (2020-12-29T14:06:09Z)
• Curse of Dimensionality on Randomized Smoothing for Certifiable Robustness [151.67113334248464]
  We show that extending the smoothing technique to defend against other attack models can be challenging. We present experimental results on CIFAR to validate our theory.
  arXiv  Detail & Related papers  (2020-02-08T22:02:14Z)

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.