研究目的
To propose and assess a new technique for detecting supernovae in high-redshift (U)LIRGs through the variability of the integrated rest-frame infrared light of the entire hosts.
研究成果
The study demonstrates the feasibility of detecting supernovae in high-redshift (U)LIRGs through the variability of the integrated rest-frame infrared light. Two cases are presented where the light curve features are consistent with multiple supernovae overlapping in time. The technique will be relevant for future observations with the James Webb Space Telescope, especially for probing nuclear regions of high-redshift (U)LIRGs where resolution is limited.
研究不足
The study is limited by the current facilities' capability to detect supernovae individually beyond the local universe in rest-frame infrared and longer wavelengths. The technique proposed relies on the variability of the integrated light, which may not provide detailed properties of individual supernovae.
1:Experimental Design and Method Selection:
The study exploits the "IRAC Dark Field" (IDF) observed by the Spitzer Space Telescope for more than 14 years in 3–5 μm and deep far-infrared data from the Herschel Space Observatory to select high-redshift (U)LIRGs. The variability of the integrated rest-frame infrared light is analyzed to detect supernovae.
2:Sample Selection and Data Sources:
A sample of (U)LIRGs with secure optical counterparts is obtained, and their light curves in 3–5 μm are examined. The study also uses data from the Chandra X-ray Observatory for AGN diagnostics.
3:List of Experimental Equipment and Materials:
Spitzer Space Telescope (IRAC), Herschel Space Observatory (SPIRE), Chandra X-ray Observatory, WIYN telescope (ODI), HST Advanced Camera for Surveys (ACS).
4:Experimental Procedures and Operational Workflow:
The study involves photometry on IRAC images, identification of optical counterparts of SPIRE sources, and variability search in the IRAC light curves. The variability is assessed based on continuous changes over >30 days, peak-to-valley variation >0.1 mag, and average photometric error <0.05 mag.
5:1 mag, and average photometric error <05 mag.
Data Analysis Methods:
5. Data Analysis Methods: The variability is analyzed to distinguish between supernovae and AGNs based on light curve behaviors and colors. The study also involves SED fitting to derive photometric redshifts and IR luminosities.
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