Quantifying systematics from the shear inversion on weak-lensing peak counts

Lin, Chieh-An; Kilbinger, M.

doi:10.1051/0004-6361/201730872

Cited by 8 publications

(5 citation statements)

References 50 publications

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“…Higher-order statistics, especially peak counts (e.g. Peel et al 2017;Shan et al 2018;Martinet et al 2018;Lin & Kilbinger 2018), do a better job but still leave room for improvement when distinguishing between a large number of models and in the presence of noise. Most commonly used methods to characterise observational data are naturally based on physical models.…”

Section: Methodsmentioning

confidence: 99%

On the dissection of degenerate cosmologies with machine learning

Merten¹,

Giocoli

Baldi

et al. 2019

Monthly Notices of the Royal Astronomical Society

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Based on the DUSTGRAIN-pathfinder suite of simulations, we investigate observational degeneracies between nine models of modified gravity and massive neutrinos. Three types of machine learning techniques are tested for their ability to discriminate lensing convergence maps by extracting dimensional reduced representations of the data. Classical map descriptors such as the power spectrum, peak counts and Minkowski functionals are combined into a joint feature vector and compared to the descriptors and statistics that are common to the field of digital image processing. To learn new features directly from the data we use a Convolutional Neural Network (CNN). For the mapping between feature vectors and the predictions of their underlying model, we implement two different classifiers; one based on a nearest-neighbour search and one that is based on a fully connected neural network. We find that the neural network provides a much more robust classification than the nearest-neighbour approach and that the CNN provides the most discriminating representation of the data. It achieves the cleanest separation between the different models and the highest classification success rate of 59% for a single source redshift. Once we perform a tomographic CNN analysis, the total classification accuracy increases significantly to 76% with no observational degeneracies remaining. Visualising the filter responses of the CNN at different network depths provides us with the unique opportunity to learn from very complex models and to understand better why they perform so well.

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Section: Methodsmentioning

confidence: 99%

On the dissection of degenerate cosmologies with machine learning

Merten¹,

Giocoli

Baldi

et al. 2019

Monthly Notices of the Royal Astronomical Society

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“…Additionally to the models listed, there are many other proposals, and the interested reader can find more details in the corresponding papers[537][538][539][540][541][542][543][544] …”

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confidence: 99%

Cosmology Intertwined: A Review of the Particle Physics, Astrophysics, and Cosmology Associated with the Cosmological Tensions and Anomalies

Abdalla,

Abellán,

Aboubrahim

et al. 2022

Preprint

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This White Paper has been prepared to fulfill SNOWMASS 2021 requirements and it extends the material previously summarized in the four Letters of Interest [1][2][3][4]. The Particle Physics Community Planning Exercise (a.k.a. SNOWMASS ) is organized by the Division of Particles and Fields of the American Physical Society, and this is an effort to bring together the community of theoretical physicists and cosmologists and identify promising opportunities to address the questions described.This White Paper was initiated with the aim of identifying the opportunities in the cosmological field for the next decade, and strengthening the coordination of the community. It is addressed to identifying the most promising directions of investigation, and rather than attempting a long review of the current status of the whole field of research, it focuses on the upcoming theoretical opportunities and challenges described. The White Paper is a collaborative effort led by Eleonora Di Valentino and Luis Anchordoqui, and it is organized in the topics listed below, each of them coordinated by the scientists indicated (alphabetical order):

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“…However it is worth mentioning that one could either; leverage these uncertainties to define the data covariance in a Bayesian manner (as opposed to MC which is fast but may not necessarily be fully principled, or MCMC which is O(10 6 ) times slower than our MAP approach) before then running a standard likelihood analysis ; or perform a grid search in parameter space using these uncertainties again as the data covariance. Correctly accounting the uncertainties introduced during mass-mapping has been shown to be an important consideration for the future prospects of statistics such as this (Lin & Kilbinger 2018).…”

Section: Analysis Of Peak Statisticmentioning

confidence: 99%

Sparse Bayesian mass mapping with uncertainties: peak statistics and feature locations

Price

McEwen

Cai

et al. 2019

Monthly Notices of the Royal Astronomical Society

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Weak lensing convergence maps -upon which higher order statistics can be calculated -can be recovered from observations of the shear field by solving the lensing inverse problem. For typical surveys this inverse problem is ill-posed (often seriously) leading to substantial uncertainty on the recovered convergence maps. In this paper we propose novel methods for quantifying the Bayesian uncertainty in the location of recovered features and the uncertainty in the cumulative peak statistic -the peak count as a function of signal to noise ratio (SNR). We adopt the sparse hierarchical Bayesian mass-mapping framework developed in previous work, which provides robust reconstructions and principled statistical interpretation of reconstructed convergence maps without the need to assume or impose Gaussianity. We demonstrate our uncertainty quantification techniques on both Bolshoi N-body (cluster scale) and Buzzard V-1.6 (large scale structure) N-body simulations. For the first time, this methodology allows one to recover approximate Bayesian upper and lower limits on the cumulative peak statistic at well defined confidence levels.

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Quantifying systematics from the shear inversion on weak-lensing peak counts

Cited by 8 publications

References 50 publications

On the dissection of degenerate cosmologies with machine learning

On the dissection of degenerate cosmologies with machine learning

Cosmology Intertwined: A Review of the Particle Physics, Astrophysics, and Cosmology Associated with the Cosmological Tensions and Anomalies

Sparse Bayesian mass mapping with uncertainties: peak statistics and feature locations

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