Deep learning in quasar physics

被引：0

作者：

Nia, F. Rastegar ^{[1
,2
,3
,4
]}

Mirtorabi, M. T. ^{[1
]}

Moradi, R. ^{[4
,5
,6
]}

Wang, Y. ^{[4
,5
,6
]}

Sadr, A. Vafaei ^{[7
,8
]}

机构：

[1] Alzahra Univ Vanak, Phys Dept, Tehran 1993891176, Iran

[2] Univ Roma La Sapienza, ICRA, Piazzale Aldo Moro 5, I-00185 Rome, Italy

[3] Univ Roma La Sapienza, Dipartimento Fis, Piazzale Aldo Moro 5, I-00185 Rome, Italy

[4] ICRANet, Piazza Repubbl 10, I-65122 Pescara, Italy

[5] Sapienza Univ Roma, ICRA, Dipartimento Fis, I-00185 Rome, Italy

[6] INAF, I-00136 Rome, Italy

[7] Univ Geneva, Dept Phys Theor, Geneva, Switzerland

[8] Univ Geneva, Ctr Astroparticle Phys, Geneva, Switzerland

来源：

SIXTEENTH MARCEL GROSSMANN MEETING | 2023年

关键词：

Quasar; Deep learning; CNN; SDSS; PHOTOMETRIC REDSHIFTS; STELLAR SPECTRA; NEURAL-NETWORKS; GALAXIES; CLASSIFICATION;

D O I：

暂无

中图分类号：

P1 [天文学];

学科分类号：

0704 ;

摘要：

In view of increasing data volume of existing and upcoming telescopes/detectors we here apply the 1-dimensional convolutional neural network (CNN) to estimate the redshift of (high-)redshifts quasars in Sloan Digital Sky Survey IV (SDSS-IV) quasar catalog from DR16 of eBOSS. Our CNN takes the flux of the quasars as an array and their redshift as labels. We here evidence that new structure of the network, and augmenting the training set, provide a high precision result in estimating the redshift of quasars.

引用

页码：382 / 390

页数：9

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