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Impact of Source-Lens Clustering on Cosmic Shear Analysis

Study assesses how clustering affects cosmic shear measurements and cosmological insights.

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Cosmic Shear is a technique used by astronomers to study the universe by looking at how light from distant galaxies is distorted as it travels through the gravitational fields of massive objects, such as galaxy clusters. This distortion gives us clues about the distribution of matter in the universe, including dark matter, which does not emit light and is difficult to detect.

As surveys are conducted to gather more data on cosmic shear, the understanding of how this technique works continues to improve. With the advent of new surveys, there is a need to refine models and methods used in cosmic shear analysis to ensure accurate results. One area that has gained attention is the clustering of source galaxies with the surrounding matter, a phenomenon known as source-lens clustering (SLC). This study aims to quantify how this clustering affects measurements and the conclusions drawn from them.

What is Source-Lens Clustering?

Source-lens clustering refers to the way distant source galaxies are not distributed randomly but are instead clustered in relation to the large-scale structure of the universe. This means that the closer galaxies can affect the perceived characteristics of those further away, leading to biases in the shear measurements.

Typically, analysis assumes that these source galaxies are spread out evenly across the sky. However, in reality, they tend to cluster in regions of higher matter density, which can impact the observed shear signal. As these source galaxies are affected by the Gravitational Lensing of the foreground mass, their positions can create correlations that may not be accounted for in conventional analyses.

Importance of Accurate Measurements

Accurate measurements are essential for cosmological studies. Cosmic shear measurements help determine key parameters of the universe, such as its expansion rate and the nature of dark energy. However, if the effects of source-lens clustering are not considered, the results could lead to incorrect conclusions about the universe’s makeup.

As data from new surveys become available, the focus must shift towards ensuring that models accommodate these effects to provide more reliable cosmological measurements. This investigation into the impact of source-lens clustering is crucial as the precision of cosmic shear analyses increases with modern surveys.

Examining the Effects on Cosmological Parameters

To understand how source-lens clustering impacts cosmological analyses, researchers use simulated datasets that mimic real observations. These simulations help to investigate the effects of clustering when measuring Shear Correlation Functions.

Shear correlation functions are statistical tools used to analyze the shape distortions caused by gravitational lensing. By comparing the results from clustered galaxies with those from uniformly distributed sources, it is possible to quantify the impact of source-lens clustering on measurements of cosmic shear.

Findings of the Study

Through analysis, it was found that when considering Nuisance Parameters, which account for various other biases in the data, the effect of source-lens clustering on inferred cosmological parameters was minimal for current surveys. Although there were small fluctuations in the shear signal due to clustering, these shifts were largely compensated for by adjustments in the nuisance parameters.

Specifically, while some parameters showed slight shifts in their estimated values, the overall conclusions drawn from the cosmic shear analysis remained intact. This underscores the importance of including nuisance parameters in the analysis to mitigate potential biases from clustering effects.

Implications for Future Surveys

As we look towards the future with upcoming surveys, such as Stage IV surveys, there will be a significant increase in the amount of cosmic shear data available for analysis. This presents an opportunity to further refine models and parameters to ensure that features like source-lens clustering are adequately integrated into the analysis framework.

Future analyses must also consider more advanced methods to account for any residual effects of source-lens clustering that might arise as the precision of measurements improves. This will be key to maintaining accuracy in interpreting data from large-scale cosmic surveys.

The Role of Simulations in Research

Simulations play a vital role in understanding cosmic shear and the impacts of source-lens clustering. By creating realistic models of galaxy distributions and their corresponding lensing effects, researchers can better predict how these phenomena interact in the real universe. The use of simulations allows for comprehensive analysis, helping to identify biases before conducting real observations.

Conclusion

In summary, source-lens clustering is an important factor when analyzing cosmic shear data. While its effects are manageable with the inclusion of nuisance parameters, ongoing research is critical to ensure that future studies remain accurate as they gather increasingly precise measurements from new surveys. Understanding and accounting for clustering effects will help refine the tools needed for cosmic analysis, ultimately leading to deeper insights into the structure and evolution of the universe.

With continued investigation and advancements in methods, researchers will be better equipped to understand the complex behaviors of galaxies as they relate to the larger framework of cosmic shear and the mysteries of the universe.

Original Source

Title: Euclid and KiDS-1000: Quantifying the impact of source-lens clustering on cosmic shear analyses

Abstract: The transition from current Stage-III surveys such as the Kilo-Degree Survey (KiDS) to the increased area and redshift range of Stage IV surveys such as Euclid will significantly increase the precision of weak lensing analyses. However, with increasing precision, the accuracy of model assumptions needs to be evaluated. In this study, we quantify the impact of the correlated clustering of weak lensing source galaxies with the surrounding large-scale structure, known as source-lens clustering (SLC), which is commonly neglected. For this, we use simulated cosmological datasets with realistically distributed galaxies and measure shear correlation functions for both clustered and uniformly distributed source galaxies. Cosmological analyses are performed for both scenarios to quantify the impact of SLC on parameter inference for a KiDS-like and a Euclid-like setting. We find for Stage III surveys, SLC has a minor impact when accounting for nuisance parameters for intrinsic alignments and shifts of tomographic bins, as these nuisance parameters absorb the effect of SLC, thus changing their original meaning. For KiDS (Euclid), the inferred intrinsic alignment amplitude $A_{IA}$ changes from $0.11_{-0.46}^{+0.44}$ ($-0.009_{-0.080}^{+0.079}$) for data without SLC to $0.28_{-0.44}^{+0.42}$ ($0.022_{-0.082}^{+0.081}$) with SLC. However, fixed nuisance parameters lead to shifts in $S_8$ and $\Omega_{m}$, emphasizing the need for including SLC in the modelling. For Euclid, we find that $\sigma_8$, $\Omega_m$, and $w_0$ are shifted by $0.19$, $0.12$, and $0.12\, \sigma$, respectively, when including free nuisance parameters, and by $0.20$, $0.16$, and $0.32\,\sigma$ when fixing the nuisance parameters. Consequently, SLC on its own has only a small impact on the inferred parameter inference when using uninformative priors for nuisance parameters.

Authors: L. Linke, S. Unruh, A. Wittje, T. Schrabback, S. Grandis, M. Asgari, A. Dvornik, H. Hildebrandt, H. Hoekstra, B. Joachimi, R. Reischke, J. L. van den Busch, A. H. Wright, P. Schneider, N. Aghanim, B. Altieri, A. Amara, S. Andreon, N. Auricchio, C. Baccigalupi, M. Baldi, S. Bardelli, D. Bonino, E. Branchini, M. Brescia, J. Brinchmann, S. Camera, V. Capobianco, C. Carbone, V. F. Cardone, J. Carretero, S. Casas, F. J. Castander, M. Castellano, S. Cavuoti, A. Cimatti, G. Congedo, C. J. Conselice, L. Conversi, Y. Copin, F. Courbin, H. M. Courtois, A. Da Silva, H. Degaudenzi, J. Dinis, M. Douspis, F. Dubath, X. Dupac, S. Dusini, M. Farina, S. Farrens, S. Ferriol, P. Fosalba, M. Frailis, E. Franceschi, M. Fumana, S. Galeotta, B. Gillis, C. Giocoli, A. Grazian, F. Grupp, L. Guzzo, S. V. H. Haugan, W. Holmes, I. Hook, F. Hormuth, A. Hornstrup, P. Hudelot, K. Jahnke, E. Keihänen, S. Kermiche, A. Kiessling, M. Kilbinger, T. Kitching, B. Kubik, K. Kuijken, M. Kümmel, M. Kunz, H. Kurki-Suonio, S. Ligori, P. B. Lilje, V. Lindholm, I. Lloro, D. Maino, E. Maiorano, O. Mansutti, O. Marggraf, K. Markovic, N. Martinet, F. Marulli, R. Massey, H. J. McCracken, E. Medinaceli, S. Mei, Y. Mellier, M. Meneghetti, E. Merlin, G. Meylan, M. Moresco, L. Moscardini, E. Munari, R. Nakajima, R. C. Nichol, S. -M. Niemi, J. W. Nightingale, C. Padilla, S. Paltani, F. Pasian, K. Pedersen, V. Pettorino, S. Pires, G. Polenta, M. Poncet, L. A. Popa, F. Raison, R. Rebolo, A. Renzi, J. Rhodes, G. Riccio, E. Romelli, M. Roncarelli, R. Saglia, Z. Sakr, D. Sapone, B. Sartoris, M. Schirmer, A. Secroun, G. Seidel, S. Serrano, C. Sirignano, G. Sirri, L. Stanco, J. -L. Starck, P. Tallada-Crespí, A. N. Taylor, I. Tereno, R. Toledo-Moreo, F. Torradeflot, I. Tutusaus, L. Valenziano, T. Vassallo, G. Verdoes Kleijn, A. Veropalumbo, Y. Wang, J. Weller, G. Zamorani, E. Zucca, C. Burigana, A. Pezzotta, C. Porciani, V. Scottez, M. Viel, A. M. C. Le Brun

Last Update: 2024-12-02 00:00:00

Language: English

Source URL: https://arxiv.org/abs/2407.09810

Source PDF: https://arxiv.org/pdf/2407.09810

Licence: https://creativecommons.org/licenses/by/4.0/

Changes: This summary was created with assistance from AI and may have inaccuracies. For accurate information, please refer to the original source documents linked here.

Thank you to arxiv for use of its open access interoperability.

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