Cosmological Information in Perturbative Forward Modeling
Physical Review D(2024)SCI 2区
Inst Adv Study | CERN
Abstract
We study how well perturbative forward modeling can constrain cosmological parameters compared to conventional analyses. We exploit the fact that in perturbation theory the field-level posterior can be computed analytically in the limit of small noise. In the idealized case where the only relevant parameter for the nonlinear evolution is the nonlinear scale, we argue that information content in this posterior is the same as in the $n$-point correlation functions computed at the same perturbative order. In the real universe other parameters can be important, and there are possibly enhanced effects due to nonlinear interactions of long and short wavelength fluctuations that can either degrade the signal or increase covariance matrices. We identify several different parameters that control these enhancements and show that for some shapes of the linear power spectrum they can be large. This leads to degradation of constraints in the standard analyses, even though the effects are not dramatic for a $\Lambda$CDM-like cosmology. The aforementioned long-short couplings do not affect the field-level inference which remains optimal. Finally, we show how in these examples calculation of the perturbative posterior motivates new estimators that are easier to implement in practice than the full forward modelling but lead to nearly optimal constraints on cosmological parameters.
MoreTranslated text
Key words
Cosmological Parameters
PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Try using models to generate summary,it takes about 60s
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Related Papers
Cosmology with Persistent Homology: a Fisher Forecast
JOURNAL OF COSMOLOGY AND ASTROPARTICLE PHYSICS 2024
被引用0
Bootstrapping Lagrangian Perturbation Theory for the Large Scale Structure
JOURNAL OF COSMOLOGY AND ASTROPARTICLE PHYSICS 2024
被引用0
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
去 AI 文献库 对话