open access publication

Preprint, 2024

Euclid: Improving the efficiency of weak lensing shear bias calibration. Pixel noise cancellation and the response method on trial

arXiv, ISSN 2331-8422, 10.48550/arxiv.2401.08239

Contributors

Jansen, H. [1] [2] Tewes, M. [1] Schrabback, Tim 0000-0002-6987-7834 [1] [2] Aghanim, N. [3] Amara, A. [4] Andreon, Stefano 0000-0002-2041-8784 [5] Auricchio, Natalia 0000-0003-4444-8651 [6] Baldi, Marco 0000-0003-4145-1943 [6] [7] [8] Branchini, Enzo 0000-0002-0808-6908 [9] [10] Brescia, Massimo 0000-0001-9506-5680 [11] [12] Brinchmann, Jarle 0000-0003-4359-8797 [13] Camera, Stefano 0000-0003-3399-3574 [14] [15] [16] Capobianco, Vito 0000-0002-3309-7692 [16] Carbone, Carmelita 0000-0003-0125-3563 [17] Cardone, V F [18] [19] Carretero, Jorge 0000-0002-3130-0204 [20] [21] Casas, S [22] Castellano, Marco 0000-0001-9875-8263 [19] Cavuoti, Stefano 0000-0002-3787-4196 [12] [23] Cimatti, A [7] Congedo, Giuseppe 0000-0003-2508-0046 [24] Conversi, Luca 0000-0002-6710-8476 [25] [26] Copin, Yannick 0000-0002-5317-7518 [27] Corcione, Leonardo 0000-0002-6497-5881 [16] Courbin, Frederic Yves Michel 0000-0003-0758-6510 [28] Courtois, H M [29] Da Silva, António José Cunha 0000-0002-6385-1609 [30] Degaudenzi, H. [31] Dinis, João 0000-0001-5075-1601 [30] Dubath, F. [31] Dupac, Xavier [26] Farina, Maria 0000-0002-3089-7846 [32] Farrens, S. [33] Ferriol, Sylvain [27] Frailis, Marco 0000-0002-7400-2135 [34] Franceschi, Enrico 0000-0002-0585-6591 [6] Fumana, Marco 0000-0001-6787-5950 [17] Galeotta, Samuele 0000-0002-3748-5115 [34] Gillis, Bryan R 0000-0002-4478-1270 [24] Giocoli, Carlo 0000-0002-9590-7961 [6] [8] Grazian, Andrea [35] Grupp, Frank U 0000-0001-7300-9303 [36] [37] Haugan, S. V. H. [38] Hoekstra, H. [39] Holmes, Warren A [40] Hormuth, F [41] Hornstrup, Allan 0000-0002-3363-0936 [42] [43] Hudelot, Patrick [44] [45] Jahnke, K. [46] Joachimi, B. [47] Kermiche, Sabrina 0000-0002-0302-5735 [48] Kiessling, Alina [40] Kilbinger, Martin [33] Kitching, Thomas D [47] Kubik, Bogna [27] Kurki-Suonio, Hannu 0000-0002-4618-3063 [49] [50] Ligori, Sebastiano 0000-0003-4172-4606 [16] Lilje, P. B. [38] Lindholm, Valtteri 0000-0003-2317-5471 [49] [50] Lloro, Ivan [51] Maiorano, Elisabetta 0000-0003-2593-4355 [6] Mansutti, Oriana 0000-0001-5758-4658 [34] Marggraf, O. [1] Markovic, Katarina 0000-0001-6764-073X [40] Martinet, N [52] Marulli, Federico 0000-0002-8850-0303 [6] [7] [8] Massey, Richard J Massey Richard J 0000-0002-6085-3780 [53] Medinaceli, E [6] Mei, S. [54] Melchior, M [55] Mellier, Yannick [44] [45] [56] Meneghetti, Massimo 0000-0003-1225-7084 [6] [8] Merlin, Emiliano 0000-0001-6870-8900 [19] Meylan, Georges [28] Miller, L. [57] Moresco, Michele 0000-0002-7616-7136 [6] [7] Moscardini, Lauro 0000-0002-3473-6716 [6] [7] [8] Munari, Emiliano 0000-0002-1751-5946 [34] Nakajima, R. [1] Niemi, S. -M. [58] Padilla, C. [21] Paltani, S. [31] Pasian, Fabio 0000-0002-4869-3227 [34] Pedersen, K [59] Pettorino, Valeria 0000-0002-4203-9320 [33] Pires, S. [33] Polenta, G. [60] Poncet, Maurice [61] Raison, Frédéric [36] Renzi, Alessandro 0000-0001-9856-1970 [62] [63] Rhodes, Jason D [40] Riccio, Giuseppe 0000-0001-7020-1172 [12] Romelli, Erik 0000-0003-3069-9222 [34] Roncarelli, Mauro 0000-0001-9587-7822 [6] Rossetti, E. [7] Saglia, Roberto P 0000-0003-0378-7032 [36] [37] Sapone, D. [64] Sartoris, Barbara 0000-0003-1337-5269 [34] [37] Schneider, P. [1] Secroun, Aurélia 0000-0003-0505-3710 [48] Seidel, G. [46] Serrano, Santiago 0000-0002-0211-2861 [65] [66] [67] Sirignano, Chiara 0000-0002-0995-7146 [62] [63] Sirri, G. [8] Skottfelt, J. [68] Stanco, L. [63] Tallada-Crespí, Pau 0000-0002-1336-8328 [20] [69] Tereno, Ismael Alexandre Borges 0000-0002-4537-6218 [30] Toledo-Moreo, Rafael 0000-0002-2997-4859 [70] Torradeflot, Fra Ncesc 0000-0003-1160-1517 [20] [69] Tutusaus, Isaac 0000-0002-3199-0399 [71] Valentijn, Edwin A 0000-0003-1032-6680 [72] Valenziano, Luca 0000-0002-1170-0104 [6] [8] Vassallo, T 0000-0001-6512-6358 [34] [37] Veropalumbo, A [5] [10] Wang, Y. [73] Weller, Jochen 0000-0002-8282-2010 [36] [37] Zamorani, Giovanni 0000-0002-2318-301X [6] Zoubian, J. [48] Colodro-Conde, Carlos [74] Scottez, Vivien [45] [75]

Affiliations

  1. [1] University of Bonn
  2. [NORA names: Germany; Europe, EU; OECD];
  3. [2] Universität Innsbruck
  4. [NORA names: Austria; Europe, EU; OECD];
  5. [3] French National Centre for Scientific Research
  6. [NORA names: France; Europe, EU; OECD];
  7. [4] University of Portsmouth
  8. [NORA names: United Kingdom; Europe, Non-EU; OECD];
  9. [5] Brera Astronomical Observatory
  10. [NORA names: Italy; Europe, EU; OECD];

Abstract

To obtain an accurate cosmological inference from upcoming weak lensing surveys such as the one conducted by Euclid, the shear measurement requires calibration using galaxy image simulations. We study the efficiency of different noise cancellation methods that aim at reducing the simulation volume required to reach a given precision in the shear measurement. Explicitly, we compared fit methods with different noise cancellations and a method based on responses. We used GalSim to simulate galaxies both on a grid and at random positions in larger scenes. Placing the galaxies at random positions requires their detection, which we performed with SExtractor. On the grid, we neglected the detection step and, therefore, the potential detection bias arising from it. The shear of the simulated images was measured with the fast moment-based method KSB, for which we note deviations from purely linear shear measurement biases. For the estimation of uncertainties, we used bootstrapping as an empirical method. We find that each method we studied on top of shape noise cancellation can further increase the efficiency of calibration simulations. The improvement depends on the considered shear amplitude range and the type of simulations (grid-based or random positions). The response method on a grid for small shears provides the biggest improvement. In the more realistic case of randomly positioned galaxies, we still find an improvement factor of 70 for small shears using the response method. Alternatively, the runtime can be lowered by a factor of 7 already using pixel noise cancellation on top of shape noise cancellation. Furthermore, we demonstrate that the efficiency of shape noise cancellation can be enhanced in the presence of blending if entire scenes are rotated instead of individual galaxies.

Keywords

Euclid, GalSim, KSB, SExtractor, accurate cosmological inferences, amplitude range, bias, bias calibration, blends, calibrated simulations, calibration, cancellation, cancellation method, cases, cosmological inference, detection, detection bias, detection step, deviation, efficiency, estimates of uncertainty, estimation, factors, galaxies, grid, image simulations, images, improvement, improvement factor, individual galaxies, inference, measurement bias, measurements, method, noise, noise cancellation, noise cancellation method, pixel, position, potential detection bias, precision, presence, presence of blending, random positions, range, response, response method, runtime, scene, shape, shear, shear measurements, simulated galaxies, simulated images, simulation, simulation volume, steps, survey, trials, uncertainty, volume, weak lensing surveys

Data Provider: Digital Science