Multi-component Decomposition of Cosmic Infrared Background Fluctuations

被引:4
|
作者
Feng, Chang [1 ,2 ]
Cooray, Asantha [2 ]
Bock, Jamie [3 ,4 ]
Chang, Tzu-Ching [3 ,4 ,5 ]
Dore, Olivier [3 ,4 ]
Santos, Mario G. [6 ,7 ,8 ]
Silva, Marta B. [9 ]
Zemcov, Michael [4 ,10 ]
机构
[1] Univ Illinois, Dept Phys, 1110 W Green St, Urbana, IL 61801 USA
[2] Univ Calif Irvine, Dept Phys & Astron, Irvine, CA 92697 USA
[3] CALTECH, 1200 E Calif Blvd, Pasadena, CA 91125 USA
[4] CALTECH, Jet Prop Lab, 4800 Oak Grove Dr, Pasadena, CA 91109 USA
[5] Acad Sinica, Inst Astron & Astrophys, Roosevelt Rd, Taipei 10617, Taiwan
[6] Univ Western Cape, Dept Phys & Astron, Cape Town, South Africa
[7] SKA South Africa, Pk,Pk Rd, ZA-7405 Pinelands, South Africa
[8] Univ Lisbon, Inst Astrofis & Ciencias Espaco, OAL, PT-1349018 Lisbon, Portugal
[9] Univ Groningen, Kapteyn Astron Inst, Landleven 12, NL-9747 AD Groningen, Netherlands
[10] Rochester Inst Technol, Rochester, NY 14623 USA
来源
ASTROPHYSICAL JOURNAL | 2019年 / 875卷 / 02期
基金
新加坡国家研究基金会;
关键词
dark ages; reionization; first stars; Galaxy: formation; infrared: diffuse background; methods: data analysis; stars: Population II; stars: Population III; LENSING POWER SPECTRUM; COVARIANCE;
D O I
10.3847/1538-4357/ab0d8e
中图分类号
P1 [天文学];
学科分类号
0704 ;
摘要
The near-infrared background between 0.5 and 2 mu m contains a wealth of information related to radiative processes in the universe. Infrared background anisotropies encode the redshift-weighted total emission over cosmic history, including any spatially diffuse and extended contributions. The anisotropy power spectrum is dominated by undetected galaxies at small angular scales and a diffuse background of Galactic emission at large angular scales. In addition to these known sources, the infrared background also arises from intrahalo light (IHL) at z < 3 associated with tidally stripped stars during galaxy mergers. Moreover, it contains information on the very first galaxies from the epoch of reionization (EoR). The EoR signal has a spectral energy distribution (SED) that goes to zero near optical wavelengths due to Lyman absorption, while other signals have spectra that vary smoothly with frequency. Due to differences in SEDs and spatial clustering, these components may be separated in a multi-wavelength-fluctuation experiment. To study the extent to which EoR fluctuations can be separated in the presence of IHL, and extragalactic and Galactic foregrounds, we develop a maximum likelihood technique that incorporates a full covariance matrix among all the frequencies at different angular scales. We apply this technique to simulated deep imaging data over a 2 x 45 deg(2) sky area from 0.75 to 5 mu m in 9 bands and find that such a "frequency tomography" can successfully reconstruct both the amplitude and spectral shape for representative EoR, IHL, and the foreground signals.
引用
收藏
页数:13
相关论文
共 50 条
  • [21] Effect of soil moisture on the multi-component decomposition of wetland macrophytes
    Song Xu
    Guojian He
    Hongwei Fang
    Siwen Liu
    Sen Bai
    Hydrobiologia, 2023, 850 : 503 - 517
  • [22] Cosmic far-infrared background fluctuations revealed by the FIRBACK observing program
    Lagache, G
    Puget, JL
    EXTRAGALACTIC INFRARED BACKGROUND AND ITS COSMOLOGICAL IMPLICATIONS, 2001, (204): : 263 - 263
  • [23] Effect of soil moisture on the multi-component decomposition of wetland macrophytes
    Xu, Song
    He, Guojian
    Fang, Hongwei
    Liu, Siwen
    Bai, Sen
    HYDROBIOLOGIA, 2023, 850 (03) : 503 - 517
  • [24] Decomposition in multi-component AlCoCrCuFeNi high-entropy alloy
    Singh, S.
    Wanderka, N.
    Murty, B. S.
    Glatzel, U.
    Banhart, J.
    ACTA MATERIALIA, 2011, 59 (01) : 182 - 190
  • [25] Empirical Mode Decomposition for Fault Diagnosis of Multi-Component Systems
    Syan, Chanan S.
    Ramsoobag, Geeta
    2018 ANNUAL RELIABILITY AND MAINTAINABILITY SYMPOSIUM (RAMS), 2018,
  • [26] Multi-component signal decomposition techniques for structural health monitoring
    Chang, CC
    Sun, Z
    Poon, CW
    Sze, KW
    SMART STRUCTURES AND MATERIALS 2005: SENSORS AND SMART STRUCTURES TECHNOLOGIES FOR CIVIL, MECHANICAL, AND AEROSPACE, PTS 1 AND 2, 2005, 5765 : 873 - 880
  • [27] A multi-component decomposition algorithm for event-related potentials
    Yin, Gang
    Zhang, Jun
    Tian, Yin
    Yao, DeZhong
    JOURNAL OF NEUROSCIENCE METHODS, 2009, 178 (01) : 219 - 227
  • [28] LIGHT SCATTERING ARISING FROM COMPOSITION FLUCTUATIONS IN MULTI-COMPONENT SYSTEMS
    KIRKWOOD, JG
    GOLDBERG, RJ
    JOURNAL OF CHEMICAL PHYSICS, 1950, 18 (01): : 54 - 57
  • [29] Development of a Multi-component Infrared Gas Sensor Detection System
    Wang Qing
    Liu Yong-ping
    Li Wei-long
    2019 3RD INTERNATIONAL CONFERENCE ON MACHINE VISION AND INFORMATION TECHNOLOGY (CMVIT 2019), 2019, 1229
  • [30] Multi-component background learning automates signal detection for spectroscopic data
    Sebastian E. Ament
    Helge S. Stein
    Dan Guevarra
    Lan Zhou
    Joel A. Haber
    David A. Boyd
    Mitsutaro Umehara
    John M. Gregoire
    Carla P. Gomes
    npj Computational Materials, 5