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Your search keyword '"V. Yankelevich"' showing total 2 results
Start Over Author "V. Yankelevich" Publication Type Academic Journals
2 results on '"V. Yankelevich"'

1. Euclid: Forecasts for k-cut 3×2 Point Statistics

Author

Peter L. Taylor, T. Kitching, V. F. Cardone, A. Ferté, E. M. Huff, F. Bernardeau, J. Rhodes, A. C. Deshpande, I. Tutusaus, Alkistis Pourtsidou, S. Camera, C. Carbone, S. Casas, M. Martinelli, V. Pettorino, Z. Sakr, D. Sapone, V. Yankelevich, N. Auricchio, A. Balestra, C. Bodendorf, D. Bonino, A. Boucaud, Enzo Branchini, M. Brescia, V. Capobianco, J. Carretero, M. Castellano, S. Cavuoti, A. Cimatti, R. Cledassou, G. Congedo, L. Conversi, L. Corcione, Mark Cropper, E. Franceschi, B. Garilli, B. Gillis, C. Giocoli, L. Guzzo, S. V. H. Haugan, W. Holmes, F. Hormuth, Knud Jahnke, S. Kermiche, M. Kilbinger, M. Kunz, H. Kurki-Suonio, S. Ligori, Per B. Lilje, I. Lloro, O. Marggraf, K. Markovic, R. Massey, S. Mei, E. Medinaceli, M. Meneghetti, G. Meylan, M. Moresco, B. Morin, Lauro Moscardini, S. Niemi, C. Padilla, F. Pasian, S. Paltani, K. Pedersen, S. Pires, Will J. Percival, G. Polenta, M. Poncet, L. Popa, F. Raison, M. Roncarelli, E. Rossetti, R. Saglia, Peter Schneider, A. Secroun, G. Seidel, S. Serrano, C. Sirignano, G. Sirri, F. Sureau, P. Tallada Crespí, D. Tavagnacco, A. N. Taylor, H. I. Teplitz, I. Tereno, R. Toledo-Moreo, E. A. Valentijn, L. Valenziano, T. Vassallo, Yun Wang, Jochen Weller, A. Zacchei, and J. Zoubian

Subjects
Astronomy, QB1-991, Astrophysics, QB460-466
Published
2021
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2. Euclid: Forecasts for $k$-cut $3 \times 2$ Point Statistics

Author

Peter L. Taylor, T. Kitching, V. F. Cardone, A. Ferté, E. M. Huff, F. Bernardeau, J. Rhodes, A. C. Deshpande, I. Tutusaus, A. Pourtsidou, S. Camera, C. Carbone, S. Casas, M. Martinelli, V. Pettorino, Z. Sakr, D. Sapone, V. Yankelevich, N. Auricchio, A. Balestra, C. Bodendorf, D. Bonino, A. Boucaud, E. Branchini, M. Brescia, V. Capobianco, J. Carretero, M. Castellano, S. Cavuoti, A. Cimatti, R. Cledassou, G. Congedo, L. Conversi, L. Corcione, M. Cropper, E. Franceschi, B. Garilli, B. Gillis, C. Giocoli, L. Guzzo, S. V. H. Haugan, W. Holmes, F. Hormuth, K. Jahnke, S. Kermiche, M. Kilbinger, M. Kunz, H. Kurki-Suonio, S. Ligori, P. B. Lilje, I. Lloro, O. Marggraf, K. Markovic, R. Massey, E. Medinaceli, S. Mei, M. Meneghetti, G. Meylan, M. Moresco, B. Morin, L. Moscardini, S. Niemi, C. Padilla, F. Pasian, S. Paltani, K. Pedersen, W. J. Percival, S. Pires, G. Polenta, M. Poncet, L. Popa, F. Raison, M. Roncarelli, E. Rossetti, R. Saglia, P. Schneider, A. Secroun, G. Seidel, S. Serrano, C. Sirignano, G. Sirri, F. Sureau, P. Tallada Crespí, D. Tavagnacco, A. N. Taylor, H. I. Teplitz, I. Tereno, R. Toledo-Moreo, E. A. Valentijn, L. Valenziano, T. Vassallo, Y. Wang, J. Weller, A. Zacchei, and J. Zoubian

Subjects
Astronomy, QB1-991, Astrophysics, QB460-466
Abstract

Modelling uncertainties at small scales, i.e. high $k$ in the power spectrum $P(k)$, due to baryonic feedback, nonlinear structure growth and the fact that galaxies are biased tracers poses a significant obstacle to fully leverage the constraining power of the Euclid wide-field survey. $k$-cut cosmic shear has recently been proposed as a method to optimally remove sensitivity to these scales while preserving usable information. In this paper we generalise the $k$-cut cosmic shear formalism to $3 \times 2$ point statistics and estimate the loss of information for different $k$-cuts in a $3 \times 2$ point analysis of the {\it Euclid} data. Extending the Fisher matrix analysis of Euclid Collaboration: Blanchard et al. (2019), we assess the degradation in constraining power for different $k$-cuts. We find that taking a $k$-cut at $2.6 \ h \ {\rm Mpc} ^{-1}$ yields a dark energy Figure of Merit (FOM) of 1018. This is comparable to taking a weak lensing cut at $\ell = 5000$ and a galaxy clustering and galaxy-galaxy lensing cut at $\ell = 3000$ in a traditional $3 \times 2$ point analysis. We also find that the fraction of the observed galaxies used in the photometric clustering part of the analysis is one of the main drivers of the FOM. Removing $50 \% \ (90 \%)$ of the clustering galaxies decreases the FOM by $19 \% \ (62 \%)$. Given that the FOM depends so heavily on the fraction of galaxies used in the clustering analysis, extensive efforts should be made to handle the real-world systematics present when extending the analysis beyond the luminous red galaxy (LRG) sample. This paper is published on behalf of the Euclid Consortium: http://euclid-ec.org

Published
2021
Full Text
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