PAU调查和欧几里得：通过多任务学习改善宽带光度红移

论文标题

PAU调查和欧几里得：通过多任务学习改善宽带光度红移

The PAU Survey & Euclid: Improving broad-band photometric redshifts with multi-task learning

论文作者

Cabayol, L., Eriksen, M., Carretero, J., Casas, R., Castander, F. J., Fernández, E., Garcia-Bellido, J., Gaztanaga, E., Hildebrandt, H., Hoekstra, H., Joachimi, B., Miquel, R., Padilla, C., Pocino, A., Sanchez, E., Serrano, S., Sevilla, I., Siudek, M., Tallada-Crespí, P., Aghanim, N., Amara, A., Auricchio, N., Baldi, M., Bender, R., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Camera, S., Capobianco, V., Carbone, C., Castellano, M., Cavuoti, S., Cimatti, A., Cledassou, R., Congedo, G., Conselice, C. J., Conversi, L., Copin, Y., Corcione, L., Courbin, F., Cropper, M., Da Silva, A., Degaudenzi, H., Douspis, M., Dubath, F., Duncan, C. A. J., Dupac, X., Dusini, S., Farrens, S., Fosalba, P., Frailis, M., Franceschi, E., Franzetti, P., Garilli, B., Gillard, W., Gillis, B., Giocoli, C., Grazian, A., Grupp, F., Haugan, S. V. H., Holmes, W., Hormuth, F., Hornstrup, A., Hudelot, P., Jahnke, K., Kümme, M., Kermiche, S., Kiessling, A., Kilbinger, M., Kohley, R., Kurki-Suonio, H., Ligori, S., Lilje, P. B., Lloro, I., Maiorano, E., Mansutti, O., Marggraf, O., Markovic, K., Marulli, F., Massey, R., Meneghetti, M., Merlin, E., Meylan, G., Moresco, M., Moscardini, L., Munari, E., Nakajima, R., Niemi, S. M., Paltani, S., Pasian, F., Pedersen, K., Pettorino, V., Polenta, G., Poncet, M., Popa, L., Pozzetti, L., Raison, F., Rebolo, R., Rhodes, J., Riccio, G., Rosset, C., Rossetti, E., Saglia, R., Sartoris, B., Schneider, P., Secroun, A., Seide, G., Sirignano, C., Sirri, G., Stanco, L., Taylor, A. N., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Tutusaus, I., Valentijn, E., Valenziano, L., Wang, Y., Weller, J., Zamorani, G., Zoubian, J., Andreon, S., Mei, S., Scottez, V., Tramacere, A.

论文摘要

当前和未来的成像调查需要估计数百万星系的光度红移（照片-Z）。提高照片Z质量是一个主要挑战，但需要提高我们对宇宙学的理解。在本文中，我们探讨了如何利用窄带光度数据和大型成像调查之间的协同作用，以改善宽带光度红移。我们使用多任务学习（MTL）网络通过同时预测宽带照片-Z和窄带光度法来改善宽带照片-Z估计值。狭窄的波段光度法仅在训练场中需要，这还可以在宽场中对没有窄带光度法的星系进行更好的照片-Z预测。该技术通过来自Cosmos领域的加速宇宙调查（PAU）物理学的数据进行了测试。我们发现该方法可以预测photo-z的精确度为13％至幅度i_ {ab} <23;与基线网络相比，离群率也降低了40％。此外，MTL减少了高红移星系的照片-Z偏置，从而改善了用z> 1的层析成像箱的红移分布。将此技术应用于更深的样品对于将来的调查（例如\ euclid或LSST）至关重要。对于模拟数据，用I_ {AB} <23对样品进行培训，该方法将Photo-Z散射降低了16％，所有星系都使用I_ {AB} <25降低。我们还研究了使用PAUS高精度照片-Z扩展训练样本的效果，这将照片-Z散射降低了20％。

Current and future imaging surveys require photometric redshifts (photo-zs) to be estimated for millions of galaxies. Improving the photo-z quality is a major challenge but is needed to advance our understanding of cosmology. In this paper we explore how the synergies between narrow-band photometric data and large imaging surveys can be exploited to improve broadband photometric redshifts. We used a multi-task learning (MTL) network to improve broadband photo-z estimates by simultaneously predicting the broadband photo-z and the narrow-band photometry from the broadband photometry. The narrow-band photometry is only required in the training field, which also enables better photo-z predictions for the galaxies without narrow-band photometry in the wide field. This technique was tested with data from the Physics of the Accelerating Universe Survey (PAUS) in the COSMOS field. We find that the method predicts photo-zs that are 13% more precise down to magnitude i_{AB} < 23; the outlier rate is also 40% lower when compared to the baseline network. Furthermore, MTL reduces the photo-z bias for high-redshift galaxies, improving the redshift distributions for tomographic bins with z>1. Applying this technique to deeper samples is crucial for future surveys such as \Euclid or LSST. For simulated data, training on a sample with i_{AB} <23, the method reduces the photo-z scatter by 16% for all galaxies with i_{AB}<25. We also studied the effects of extending the training sample with photometric galaxies using PAUS high-precision photo-zs, which reduces the photo-z scatter by 20% in the COSMOS field.

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