Abstract
We investigate Lyα, [O iii] λ5007, Hα, and [C ii] 158 μm emission from 1124 galaxies at z = 4.9–7.0. Our sample is composed of 1092 Lyα emitters (LAEs) at z = 4.9, 5.7, 6.6, and 7.0 identified by Subaru/Hyper-Suprime-Cam (HSC) narrowband surveys covered by Spitzer Large Area Survey with Hyper-Suprime-Cam (SPLASH) and 34 galaxies at z = 5.148–7.508 with deep ALMA [C ii] 158 μm data in the literature. Fluxes of strong rest-frame optical lines of [O iii] and Hα (Hβ) are constrained by significant excesses found in the SPLASH 3.6 and 4.5 μm photometry. At z = 4.9, we find that the rest-frame Hα equivalent width and the Lyα escape fraction fLyα positively correlate with the rest-frame Lyα equivalent width . The correlation is similarly found at z ∼ 0–2, suggesting no evolution of the correlation over z ≃ 0–5. The typical ionizing photon production efficiency of LAEs is log(ξion/[Hz erg−1]) ≃ 25.5, significantly (60%–100%) higher than those of LBGs at a given UV magnitude. At z = 5.7–7.0, there exists an interesting turnover trend that the [O iii]/Hα flux ratio increases in and then decreases out to . We also identify an anticorrelation between a ratio of [C ii] luminosity to star formation rate (L[C ii]/SFR) and at the >99% confidence level.. We carefully investigate physical origins of the correlations with stellar-synthesis and photoionization models and find that a simple anticorrelation between and metallicity explains self-consistently all of the correlations of Lyα, Hα, [O iii]/Hα, and [C ii] identified in our study, indicating detections of metal-poor (∼0.03 Z⊙) galaxies with .
1. Introduction
Probing physical conditions of the interstellar medium (ISM) is fundamental in understanding star formation and gas reprocessing in galaxies across cosmic time. Recent ALMA observations are uncovering interesting features of the ISM in high-redshift galaxies. Early observations found surprisingly weak [C ii] 158 μm emission in Lyα emitters (LAEs) at z ∼ 6−7 ([C ii] deficit; e.g., Ouchi et al. 2013; Ota et al. 2014; Maiolino et al. 2015; Schaerer et al. 2015). On the other hand, recent studies detected strong [C ii] emission in galaxies at z = 5–7, whose [C ii] luminosities are comparable to local star-forming galaxies (e.g., Capak et al. 2015; Pentericci et al. 2016; Bradač et al. 2017). A theoretical study discusses that the [C ii] deficit can be explained by very low metallicity (0.05 Z⊙) in the ISM (Vallini et al. 2015; Olsen et al. 2017). Thus, estimating metallicities of the high-redshift galaxies is crucial to our understanding of the origin of the [C ii] deficit.
The ISM property is also important for cosmic reionization. Observations by the Planck satellite and high-redshift UV luminosity functions (LFs) suggest that faint and abundant star-forming galaxies dominate the reionization process (e.g., Robertson et al. 2015). Furthermore, Ishigaki et al. (2018) claim that the ionizing photon budget of star-forming galaxies is sufficient for reionizing the universe with the escape fraction of ionizing photons of and the faint limit of the UV LF of Mtrunc > −12.5 for an assumed constant ionizing photon production efficiency of log(ξion/[Hz erg−1]) = 25.34, which is the number of Lyman continuum photons per UV (1500 Å) luminosity (see also Faisst 2016). On the other hand, Giallongo et al. (2015) argue that faint active galactic nuclei (AGNs) are important contributors to the reionization from their estimates of number densities and ionizing emissivities (see Madau & Haardt 2015; Parsa et al. 2018). One caveat in these two contradictory results is that properties of ionizing sources (i.e., fesc and ξion) are not guaranteed to be the same as the typically assumed values. Various studies constrain ionizing photon production efficiencies of star-forming galaxies to be log(ξion/[Hz erg−1]) = 24.8–25.3 at z ∼ 0−2 (e.g., Izotov et al. 2017; Matthee et al. 2017a; Shivaei et al. 2018; see also Sobral et al. 2018). Recently, Bouwens et al. (2016) report log(ξion/[Hz erg−1]) = 25.3–25.8 for Lyman break galaxies (LBGs) at z ∼ 4−5, relatively higher than the canonical value (i.e., 25.2; Robertson et al. 2015). Nakajima et al. (2016) also estimate ξion of 15 LAEs at z = 3.1–3.7, which is 0.2–0.5 dex higher than those of typical LBGs at similar redshifts. Since the faint star-forming galaxies are expected to be strong line emitters, it is important to estimate ξion of LAEs at higher redshift, as their ISM properties are likely more similar to the ionizing sources.
Metallicities and ionizing photon production efficiencies of galaxies can be estimated from rest-frame optical emission lines such as Hα, Hβ, [O iii] λλ4959, 5007, and [O ii] λλ3726, 3729. However, at z ≳ 4, some of these emission lines are redshifted into the mid-infrared, where they cannot be observed with ground-based telescopes. Thus, we need new future space telescopes (e.g., JWST) to investigate rest-frame optical emission lines of high-redshift galaxies. On the other hand, recent studies reveal that the redshifted emission lines significantly affect infrared broadband photometry (e.g., Stark et al. 2013; Smit et al. 2014, 2015; Faisst et al. 2016a; Rasappu et al. 2016; Roberts-Borsani et al. 2016; Castellano et al. 2017). Thus, infrared broadband photometry can be useful to estimate the rest-frame optical emission line fluxes that are not accessible with the ground-based telescopes before the JWST era.
The Subaru/Hyper-Suprime-Cam Subaru strategic program (HSC-SSP) survey started in early 2014, and its first data release took place in 2017 February (Miyazaki et al. 2012; Aihara et al. 2018a, 2018b; see also Furusawa et al. 2018; Kawanomoto et al. 2017; Komiyama et al. 2018; Miyazaki et al. 2018). The HSC-SSP survey provides a large high-redshift galaxy sample, especially LAEs selected with the narrowband (NB) filters. The NB816 and NB921 imaging data are already taken in the HSC-SSP survey. In addition, the NB718 and NB973 data are taken in the Cosmic HydrOgen Reionization Unveiled with Subaru (CHORUS) project (PI: A. K. Inoue; A. K. Inoue et al. 2018, in preparation), which is an independent program of the HSC-SSP survey. Spitzer Large Area Survey with Hyper-Suprime-Cam (SPLASH; PI: P. Capak; P. Capak et al. 2018, in preparation)19 has obtained the Spitzer/Infrared Array Camera (IRAC) images overlapped with these NB data, which allow us to conduct statistical studies of the rest-frame optical emission in the high-redshift LAEs. Furthermore, the number of galaxies observed with ALMA is increasing, which will improve our understanding of the [C ii] deficit. Thus, in this study we investigate the ISM properties of high-redshift galaxies by measuring the Lyα, [O iii] λ5007, Hα, Hβ, and [C ii] emission line strength (Figure 1).
This paper is one in a series of papers from twin programs devoted to scientific results on high-redshift galaxies based on the HSC-SSP survey data. One program is our LAE study with the large-area NB images complemented by spectroscopic observations, named Systematic Identification of LAEs for Visible Exploration and Reionization Research Using Subaru HSC (SILVERRUSH; Higuchi et al. 2018; Inoue et al. 2018; Konno et al. 2018; Ouchi et al. 2018; Shibuya et al. 2018a, 2018b). The other one is a luminous LBG study, named Great Optically Luminous Dropout Research Using Subaru HSC (GOLDRUSH; Harikane et al. 2018; Ono et al. 2018; Toshikawa et al. 2018).
This paper is organized as follows. We present our sample and imaging data sets in Section 2, and we describe methods to estimate line fluxes in Section 3. We show results in Section 4, discuss our results in Section 5, and summarize our findings in Section 6. Throughout this paper we use the recent Planck cosmological parameter sets constrained with the temperature power spectrum, temperature–polarization cross spectrum, polarization power spectrum, low-l polarization, cosmic microwave background lensing, and external data (TT, TE, EE+lowP+lensing+ext result; Planck Collaboration et al. 2016): Ωm = 0.3089, ΩΛ = 0.6911, Ωb = 0.049, h = 0.6774, and σ8 = 0.8159. We assume a Chabrier (2003) initial mass function (IMF). All magnitudes are in the AB system (Oke & Gunn 1983).
2. Sample
2.1. LAE Sample
We use LAE samples at z = 4.9, 5.7, 6.6, and 7.0 selected with the NB filters of NB718, NB816, NB921, and NB973, respectively. Figure 2 shows redshift windows where strong rest-frame optical emission lines enter in the Spitzer/IRAC 3.6 μm ([3.6]) and 4.5 μm ([4.5]) bands. At z = 4.9, the Hα line is redshifted into the [3.6] band, with no strong emission line into the [4.5] band. Thus, we can estimate the Hα flux at z = 4.9 from the IRAC band photometry. At z = 5.7 and 6.6, since the [O iii] λ5007 + Hβ and Hα lines affect the [3.6] and [4.5] bands, respectively, the IRAC photometry can infer the [O iii]/Hα ratio. At z = 7.0, we can estimate the ratio of [O iii] λ5007 to Hβ, which enters the [3.6] and [4.5] bands, respectively.
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Standard image High-resolution imageIn this study, we use LAEs in the UD-COSMOS and UD-SXDS fields, where the deep optical to mid-infrared imaging data are available. These two fields are observed with in the ultradeep (UD) layer of the HSC-SSP survey. The HSC data are reduced by the HSC-SSP collaboration with hscPipe (Bosch et al. 2018), which is the HSC data reduction pipeline based on the Large Synoptic Survey Telescope (LSST) pipeline (Ivezic et al. 2008; Axelrod et al. 2010; Jurić et al. 2015). The astrometric and photometric calibrations are based on the data of the Panoramic Survey Telescope and Rapid Response System (Pan-STARRS) 1 imaging survey (Schlafly et al. 2012; Tonry et al. 2012; Magnier et al. 2013). In addition, NB718 and NB973 imaging data taken in the CHORUS project are available in the UD-COSMOS field. The UD-COSMOS and UD-SXDS fields are covered in the JHKs and JHK bands with VISTA/VIRCAM and UKIRT/WFCAM in the UltraVISTA survey (McCracken et al. 2012) and the UKIDSS/UDS project (Lawrence et al. 2007), respectively. Here we utilize the second data release (DR2) of UltraVISTA and the 10th data release (DR10) of UKIDSS/UDS. The SPLASH covers both UD-COSMOS and UD-SXDS fields in the IRAC [3.6] and [4.5] bands (P. Capak et al. 2018, in preparation; V. Mehta et al. 2018, in preparation). The total area coverage of the UD-COSMOS and UD-SXDS fields is 4 deg2. Table 1 summarizes the imaging data used in this study.
Table 1. Summary of Imaging Data Used in This Study
Field | Subaru | VISTA/UKIRT | Spitzer | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
g | r | i | z | y | NB718 | NB816 | NB921 | NB973 | J | H | Ks/K | [3.6] | [4.5] | |
5σ Limiting Magnitudea | ||||||||||||||
UD-COSMOS | 27.13 | 26.84 | 26.46 | 26.10 | 25.28 | 26.11 | 25.98 | 26.17 | 25.05 | 25.32 | 25.05 | 25.16 | 25.11 | 24.89 |
UD-SXDS | 27.15 | 26.68 | 26.53 | 25.96 | 25.15 | ⋯ | 25.40 | 25.36 | ⋯ | 25.28 | 24.75 | 25.01 | 25.30 | 24.88 |
Aperture Correctionb | ||||||||||||||
UD-COSMOS | 0.31 | 0.22 | 0.23 | 0.20 | 0.37 | 0.28 | 0.25 | 0.23 | 0.23 | 0.27 | 0.20 | 0.18 | 0.52 | 0.55 |
UD-SXDS | 0.24 | 0.25 | 0.23 | 0.28 | 0.19 | ⋯ | 0.14 | 0.34 | ⋯ | 0.15 | 0.15 | 0.15 | 0.52 | 0.55 |
Notes.
a5σ limiting magnitudes measured in 15, 20, and 30 diameter apertures in , JHKs(K), and [3.6][4.5] images, respectively. bAperture corrections of 2'' and 3'' diameter apertures in the and [3.6][4.5] images, respectively. Values in the [3.6] and [4.5] bands are taken from Ono et al. (2010a).Download table as: ASCIITypeset image
The LAE samples at z = 5.7 and 6.6 are selected in Shibuya et al. (2018b) based on the HSC-SSP survey data in both the UD-COSMOS and UD-SXDS fields. A total of 426 and 495 LAEs are selected at z = 5.7 and 6.6, respectively, with the following color criteria:
for z = 5.7 and
for z = 6.6. The subscripts "5σ" and "3σ" indicate the 5 and 3 mag limits for a given filter, respectively. Since our LAEs are selected based on the HSC data, our sample is larger and brighter than previous Subaru/Suprime-Cam studies such as Ono et al. (2010a).
We use LAE samples at z = 4.9 and 7.0 selected based on the NB718 and NB973 images in the CHORUS project and the HSC-SSP survey data in the UD-COSMOS field. A total of 141 and 30 LAEs are selected at z = 4.9 and 7.0, respectively, with the following color criteria:
for z = 4.9 and
for z = 7.0, where ri is the magnitude in the ri band whose flux is defined with r and i band fluxes as , and is the 3σ error of the color. Details of the sample selection will be presented in H. Zhang et al. (2018, in preparation) for NB718 and R. Itoh et al. (2018, in preparation) for NB973.
Out of 1092 LAEs in the sample, 805 LAEs are covered with the JHKs(K)[3.6][4.5] images, and 96 LAEs are spectroscopically confirmed with Lyα emission, Lyman break features, or rest-frame UV absorption lines (Shibuya et al. 2018a). In addition to the confirmed LAEs listed in Shibuya et al. (2018a), we spectroscopically identified HSC J021843-050915 at z = 6.513 in our Magellan/LDSS3 observation in October 2016 (PI: M. Rauch). We show some examples of the spectra around Lyα in Figure 3, including HSC J021843-050915. Tables 2 summarizes 50 spectroscopically confirmed LAEs at z = 5.7 and 6.6 without severe blending in the IRAC images (see Section 3.1). Based on spectroscopy in Shibuya et al. (2018a), the contamination rate of our z = 5.7 and 6.6 samples is 0%–30% and appears to depend on the magnitude (Konno et al. 2018). We will discuss the effect of the contamination in Section 3.2.
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Standard image High-resolution imageTable 2. Examples of Spectroscopically Confirmed LAEs Used in This Study
ID | R.A. (J2000) | Decl. (J2000) | zspec | EWLyα0 | MUV | [3.6] | [4.5] | [3.6]–[4.5] | References |
---|---|---|---|---|---|---|---|---|---|
(1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | (10) |
HSC J021828-051423 | 02:18:28.87 | −05:14:23.01 | 5.737 | −20.4 ± 0.5 | >26.0 | >25.9 | ⋯ | H18 | |
HSC J021724-053309 | 02:17:24.02 | −05:33:09.61 | 5.707 | −21.3 ± 0.2 | 25.3 ± 0.3 | 25.4 ± 0.3 | −0.1 ± 0.4 | H18 | |
HSC J021859-052916 | 02:18:59.92 | −05:29:16.81 | 5.674 | −22.6 ± 0.1 | 24.6 ± 0.2 | 25.1 ± 0.3 | −0.5 ± 0.3 | H18 | |
HSC J021827-044736 | 02:18:27.44 | −04:47:36.98 | 5.703 | −20.2 ± 0.6 | 25.6 ± 0.4 | >25.9 | <−0.3 | H18 | |
HSC J021830-051457 | 02:18:30.53 | −05:14:57.81 | 5.688 | −20.4 ± 0.5 | >26.0 | >25.9 | ⋯ | H18 | |
HSC J021624-045516 | 02:16:24.70 | −04:55:16.55 | 5.706 | −20.9 ± 0.3 | >26.0 | >25.9 | ⋯ | H18 | |
HSC J100058+014815 | 10:00:58.00 | +01:48:15.14 | 6.604 | −22.4 ± 0.2 | 24.0 ± 0.1 | 25.3 ± 0.3 | −1.3 ± 0.3 | S15 | |
HSC J021757-050844 | 02:17:57.58 | −05:08:44.63 | 6.595 | −21.4 ± 0.5 | 24.7 ± 0.2 | 25.4 ± 0.3 | −0.7 ± 0.4 | O10 | |
HSC J100109+021513 | 10:01:09.72 | +02:15:13.45 | 5.712 | −20.7 ± 0.3 | 23.3 ± 0.0 | 22.7 ± 0.0 | 0.6 ± 0.1 | M12 | |
HSC J100129+014929 | 10:01:29.07 | +01:49:29.81 | 5.707 | −21.4 ± 0.2 | 24.8 ± 0.2 | 25.7 ± 0.5 | −0.9 ± 0.5 | M12 | |
HSC J100123+015600 | 10:01:23.84 | +01:56:00.46 | 5.726 | −20.8 ± 0.3 | >26.1 | >25.9 | ⋯ | M12 | |
HSC J021843-050915 | 02:18:43.62 | −05:09:15.63 | 6.510 | −22.0 ± 0.3 | 25.0 ± 0.2 | 25.5 ± 0.4 | −0.5 ± 0.4 | This | |
HSC J021703-045619 | 02:17:03.46 | −04:56:19.07 | 6.589 | −21.4 ± 0.5 | >26.0 | >25.9 | ⋯ | O10 | |
HSC J021702-050604 | 02:17:02.56 | −05:06:04.61 | 6.545 | −20.5 ± 0.7 | >26.0 | >25.9 | ⋯ | O10 | |
HSC J021819-050900 | 02:18:19.39 | −05:09:00.65 | 6.563 | −20.8 ± 0.7 | >26.0 | >25.9 | ⋯ | O10 | |
HSC J021654-045556 | 02:16:54.54 | −04:55:56.94 | 6.617 | −21.2 ± 0.6 | >26.0 | >25.9 | ⋯ | O10 | |
HSC J095952+013723 | 09:59:52.13 | +01:37:23.24 | 5.724 | −20.9 ± 0.2 | >26.1 | >25.9 | ⋯ | M12 | |
HSC J095952+015005 | 09:59:52.03 | +01:50:05.95 | 5.744 | −21.5 ± 0.1 | 25.6 ± 0.3 | 25.5 ± 0.4 | 0.1 ± 0.5 | M12 | |
HSC J021737-043943 | 02:17:37.96 | −04:39:43.02 | 5.755 | −21.0 ± 0.3 | 25.1 ± 0.2 | 25.9 ± 0.5 | −0.8 ± 0.6 | H18 | |
HSC J100015+020056 | 10:00:15.66 | +02:00:56.04 | 5.718 | −20.5 ± 0.3 | >26.1 | >25.9 | ⋯ | M12 | |
HSC J021734-044558 | 02:17:34.57 | −04:45:58.95 | 5.702 | −21.2 ± 0.2 | 25.6 ± 0.4 | >25.9 | <−0.2 | O08 | |
HSC J100131+023105 | 10:01:31.08 | +02:31:05.77 | 5.690 | −20.5 ± 0.4 | >26.1 | >25.9 | ⋯ | M12 | |
HSC J100301+020236 | 10:03:01.15 | +02:02:36.04 | 5.682 | −22.0 ± 0.1 | 24.3 ± 0.1 | 24.1 ± 0.1 | 0.2 ± 0.1 | M12 | |
HSC J021654-052155 | 02:16:54.60 | −05:21:55.52 | 5.712 | −20.1 ± 0.6 | >26.0 | >25.9 | ⋯ | O08 | |
HSC J021748-053127 | 02:17:48.46 | −05:31:27.02 | 5.690 | −20.9 ± 0.3 | >26.0 | >25.9 | ⋯ | O08 | |
HSC J100127+023005 | 10:01:27.77 | +02:30:05.83 | 5.696 | −21.0 ± 0.2 | 25.4 ± 0.3 | >25.9 | <−0.5 | M12 | |
HSC J021745-052936 | 02:17:45.24 | −05:29:36.01 | 5.688 | >112.1 | >−19.4 | 25.6 ± 0.4 | >25.9 | <−0.3 | O08 |
HSC J095954+021039 | 09:59:54.77 | +02:10:39.26 | 5.662 | −21.0 ± 0.2 | >26.1 | >25.9 | ⋯ | M12 | |
HSC J095919+020322 | 09:59:19.74 | +02:03:22.02 | 5.704 | −19.8 ± 0.6 | >26.1 | >25.9 | ⋯ | M12 | |
HSC J095954+021516 | 09:59:54.52 | +02:15:16.50 | 5.688 | −20.7 ± 0.3 | >26.1 | >25.9 | ⋯ | M12 | |
HSC J100005+020717 | 10:00:05.06 | +02:07:17.01 | 5.704 | −20.0 ± 0.6 | >26.1 | >25.9 | ⋯ | M12 | |
HSC J021804-052147 | 02:18:04.17 | −05:21:47.25 | 5.734 | −21.4 ± 0.2 | >26.0 | >25.9 | ⋯ | H18 | |
HSC J100022+024103 | 10:00:22.51 | +02:41:03.25 | 5.661 | −21.3 ± 0.2 | 25.7 ± 0.4 | >25.9 | <−0.1 | M12 | |
HSC J021848-051715 | 02:18:48.23 | −05:17:15.45 | 5.741 | −21.2 ± 0.2 | >26.0 | >25.9 | ⋯ | H18 | |
HSC J100030+021714 | 10:00:30.41 | +02:17:14.73 | 5.695 | −19.9 ± 0.6 | >26.1 | >25.9 | ⋯ | M12 | |
HSC J021558-045301 | 02:15:58.49 | −04:53:01.75 | 5.718 | −20.1 ± 0.6 | >26.0 | >25.9 | ⋯ | H18 | |
HSC J100131+014320 | 10:01:31.11 | +01:43:20.50 | 5.728 | −20.2 ± 0.5 | 26.1 ± 0.5 | >25.9 | <0.2 | M12 | |
HSC J095944+020050 | 09:59:44.07 | +02:00:50.74 | 5.688 | −20.4 ± 0.4 | 25.6 ± 0.4 | >25.9 | <−0.3 | M12 | |
HSC J021709-050329 | 02:17:09.77 | −05:03:29.18 | 5.709 | −20.1 ± 0.6 | >26.0 | >25.9 | ⋯ | H18 | |
HSC J021803-052643 | 02:18:03.87 | −05:26:43.45 | 5.747 | >69.8 | >−19.4 | >26.0 | >25.9 | ⋯ | H18 |
HSC J021805-052704 | 02:18:05.17 | −05:27:04.06 | 5.746 | >68.4 | >−19.4 | >26.0 | >25.9 | ⋯ | O08 |
HSC J021739-043837 | 02:17:39.25 | −04:38:37.21 | 5.720 | −19.6 ± 0.7 | >26.0 | >25.9 | ⋯ | H18 | |
HSC J021857-045648 | 02:18:57.32 | −04:56:48.88 | 5.681 | −19.5 ± 0.7 | >26.0 | >25.9 | ⋯ | H18 | |
HSC J021639-051346 | 02:16:39.89 | −05:13:46.75 | 5.702 | −19.6 ± 0.7 | >26.0 | >25.9 | ⋯ | O08 | |
HSC J021805-052026 | 02:18:05.28 | −05:20:26.90 | 5.742 | −20.5 ± 0.4 | >26.0 | >25.9 | ⋯ | H18 | |
HSC J100058+013642 | 10:00:58.41 | +01:36:42.89 | 5.688 | >72.9 | >−19.2 | >26.1 | >25.9 | ⋯ | M12 |
HSC J100029+015000 | 10:00:29.58 | +01:50:00.78 | 5.707 | −19.8 ± 0.6 | >26.1 | >25.9 | ⋯ | M12 | |
HSC J021911-045707 | 02:19:11.03 | −04:57:07.48 | 5.704 | >53.3 | >−19.5 | >26.0 | >25.9 | ⋯ | H18 |
HSC J021628-050103 | 02:16:28.05 | −05:01:03.85 | 5.691 | >43.5 | >−19.4 | >26.0 | >25.9 | ⋯ | H18 |
HSC J100107+015222 | 10:01:07.35 | +01:52:22.88 | 5.668 | −20.2 ± 0.5 | >26.1 | >25.9 | ⋯ | M12 |
Note. Column (1): object ID; same as Shibuya et al. (2018a). Column (2): right ascension. Column (3): declination. Column (4): spectroscopic redshift of the Lyα emission line, Lyman break feature, or rest-frame UV absorption line. Column (5): rest-frame Lyα EW or its 2σ lower limit. Column (6): absolute UV magnitude or its 2σ lower limit. Columns (7) and (8): total magnitudes in the [3.6] and [4.5] bands, respectively. The lower limit is 2σ. Column (9): [3.6]–[4.5] color. Column (10): reference (O08: Ouchi et al. 2008; O10: Ouchi et al. 2010; M12: Mallery et al. 2012; S15: Sobral et al. 2015; S17: Shibuya et al. 2018a; H18: Higuchi et al. 2018; This: this work, see Section 2.1).
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We derive the rest-frame equivalent widths (EWs) of Lyα () of our LAEs, in the same manner as in Shibuya et al. (2018b). We use colors of , , , and for z = 4.9, 5.7, 6.6, and 7.0 LAEs, respectively. We assume the redshift of the central wavelength of each NB filter. A full description of the calculation is provided in Section 8 in Shibuya et al. (2018b). We find that our calculations for most of the LAEs are consistent with previous studies, except for CR7. The difference in the EW estimates for CR7 comes from different y-band magnitudes, probably due to the differences of the instrument, filter, and photometry. We adopt the estimate in Sobral et al. (2015) to compare with the previous studies. Note that some of the rest-frame Lyα EW are lower than 20 Å, roughly corresponding to the color selection criteria in Shibuya et al. (2018b), because of the difference in the color bands used for the EW calculations. While the 20 Å EW threshold in the selection corresponds to the color criteria in and at z = 5.7 and 6.6, respectively, our Lyα EWs are calculated from , . Thus, LAEs with are galaxies faint in i (z) and bright in NB816 and z (NB921 and y) at z = 5.7 (6.6). In order to investigate the effect of the AGNs, we also conduct analyses removing LAEs with log(LLyα/erg s−1) > 43.4, because Konno et al. (2016) reveal that LAEs brighter than log(LLyα/erg s−1) = 43.4 are AGNs at z = 2.2. We find that results do not change, indicating that the effect of the AGNs is not significant.
2.2. [C ii] 158 μm Sample
In addition to our HSC LAE samples, we compile previous ALMA and PdBI observations targeting [C ii] 158 μm in galaxies at z > 5. We use results of 34 galaxies from Kanekar et al. (2013), Ouchi et al. (2013), Ota et al. (2014), Schaerer et al. (2015), Maiolino et al. (2015), Watson et al. (2015), Capak et al. (2015), Willott et al. (2015), Knudsen et al. (2016), Inoue et al. (2016), Pentericci et al. (2016), Bradač et al. (2017), Smit et al. (2018), Matthee et al. (2017b), Carniani et al. (2017), and Carniani et al. (2018). Kanekar et al. (2013) used PdBI, and the others studies used ALMA. We take [C ii] luminosities, total star formation rates (SFRs), and Lyα EWs from these studies. The properties of these galaxies are summarized in Table 3. For the [C ii] luminosity of Himiko, we adopt the reanalysis result of Carniani et al. (2018). Himiko and CR7 overlap with the LAE sample in Section 2.1. We do not include objects with AGN signatures, e.g., HZ5 in Capak et al. (2015), in our sample.
Table 3. List of Galaxies Used in Our [C ii] Study
Name | zspec | logL[C ii] | logSFRtot | References | ||
---|---|---|---|---|---|---|
(1) | (2) | (3) | (4) | (5) | (6) | (7) |
HCM6A | 6.56 | 25.1 | 35.2 | <7.81 | 1.00 | K13, H02 |
IOK-1 | 6.965 | 43.0 | 63.9 | <7.53 | 1.38 | O14, O12 |
z8-GND-5296 | 7.508 | 8.0 | 27.6 | <8.55 | 1.37 | S15, F12 |
BDF-521 | 7.109 | 64.0 | 120.3 | <7.78 | 0.78 | M15, V11 |
BDF-3299 | 7.008 | 50.0 | 75.8 | <7.30 | 0.76 | M15, V11 |
SDF46975 | 6.844 | 43.0 | 63.1 | <7.76 | 1.19 | M15, O12 |
A1689-zD1 | 7.5 | <27.0 | <93.1 | <7.95 | W15 | |
HZ1 | 5.690 | 8.40 ± 0.32 | C15, M12 | |||
HZ2 | 5.670 | 6.9 ± 2.0 | 6.9 ± 2.0 | 8.56 ± 0.41 | C15 | |
HZ3 | 5.546 | <3.6 | <3.6 | 8.67 ± 0.28 | C15 | |
HZ4 | 5.540 | 8.98 ± 0.22 | C15, M12 | |||
HZ6 | 5.290 | 9.15 ± 0.17 | C15, M12 | |||
HZ7 | 5.250 | 9.8 ± 5.5 | 9.8 ± 5.5 | 8.74 ± 0.24 | C15 | |
HZ8 | 5.148 | 8.41 ± 0.18 | C15, M12 | |||
HZ9 | 5.548 | 9.21 ± 0.09 | C15, M12 | |||
HZ10 | 5.659 | 9.13 ± 0.13 | C15, M12 | |||
CLM1 | 6.176 | 50.0 | 59.2 | 8.38 ± 0.06 | 1.57 ± 0.05 | W15, C03 |
WMH5 | 6.076 | 13.0 ± 4.0 | 14.8 ± 4.6 | 8.82 ± 0.05 | 1.63 ± 0.05 | W15, W13 |
A383-5.1 | 6.029 | 138.0 | 154.9 | 6.95 ± 0.15 | 0.51 | K16, St15 |
SXDF-NB1006-2 | 7.215 | >15.4 | >38.4 | <7.92 | I16, S12 | |
COSMOS13679 | 7.154 | 15.0 | 30.9 | 7.87 ± 0.10 | 1.38 | P16 |
NTTDF6345 | 6.701 | 15.0 | 21.7 | 8.27 ± 0.07 | 1.18 | P16 |
UDS16291 | 6.638 | 6.0 | 8.6 | 7.86 ± 0.10 | 1.20 | P16 |
COSMOS24108 | 6.629 | 27.0 | 38.7 | 8.00 ± 0.10 | 1.46 | P16 |
RXJ1347-1145 | 6.765 | 26.0 ± 4.0 | 37.8 ± 5.8 | B16 | ||
COS-3018555981 | 6.854 | <2.9 | <4.3 | 8.67 ± 0.05 | S17, L17 | |
COS-2987030247 | 6.816 | 8.56 ± 0.06 | S17, L17 | |||
CR7 | 6.604 | 211.0 ± 20.0 | 301.6 ± 28.6 | 8.30 ± 0.09 | 1.65 ± 0.02 | M17, So15 |
NTTDF2313 | 6.07 | 0 | 0 | <7.65 | 1.08 | C17 |
BDF2203 | 6.12 | 3.0 | 3.5 | 8.10 ± 0.09 | 1.20 | C17 |
GOODS3203 | 6.27 | 5.0 | 6.2 | <8.08 | 1.26 | C17 |
COSMOS20521 | 6.36 | 10.0 | 12.8 | <7.68 | 1.15 | C17 |
UDS4821 | 6.561 | 48.0 | 67.3 | <7.83 | 1.11 | C17 |
Himiko | 6.595 | 8.08 ± 0.07 | 1.31 ± 0.03 | C18, O13 |
Note. Column (1): object name. Column (2): redshift determined with Lyα, Lyman break, rest-frame UV absorption lines, or [C ii] 158 μm. Column (3): rest-frame Lyα EW not corrected for the intergalactic medium (IGM) absorption. Column (4): rest-frame Lyα EW corrected for the IGM absorption with Equations (13)–(17). Column (5): [C ii] 158 μm luminosity or its 3σ upper limit in units of L⊙. Column (6): total SFR in units of M⊙ yr−1. Column (7): reference (H02: Hu et al. 2002; C03: Cuby et al. 2003; V11: Vanzella et al. 2011; O12: Ono et al. 2012; S12: Shibuya et al. 2012; M12: Mallery et al. 2012; W13: Willott et al. 2013; K13: Kanekar et al. 2013; F13: Finkelstein et al. 2013; O13: Ouchi et al. 2013; O14: Ota et al. 2014; S15: Schaerer et al. 2015; St15: Stark et al. 2015a; M15: Maiolino et al. 2015; W15: Watson et al. 2015; C15: Capak et al. 2015; W15: Willott et al. 2015; So15: Sobral et al. 2015; K16: Knudsen et al. 2016; I16: Inoue et al. 2016; P16: Pentericci et al. 2016; B17: Bradač et al. 2017; S17: Smit et al. 2018; L17: Laporte et al. 2017; M17: Matthee et al. 2017b; C17: Carniani et al. 2017; C18: Carniani et al. 2018).
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3. Method
In this section, we estimate rest-frame optical line fluxes of the LAEs by comparing observed SEDs and model SEDs.
3.1. Removing Severely Blended Sources
Since point-spread functions (PSFs) of IRAC images are relatively large (∼17), source confusion and blending are significant for some LAEs. In order to remove effects of the neighbor sources on the photometry, we first generate residual IRAC images where only the LAEs under analysis are left. We perform a T-PHOT second-pass run with an option of exclfile (Merlin et al. 2016) to leave the LAEs in the IRAC images. T-PHOT exploits information from high-resolution prior images, such as position and morphology, to extract photometry from lower-resolution data where blending is a concern. As high-resolution prior images in the T-PHOT run, we use HSC grizyNB stacked images whose PSFs are ∼07. The high-resolution image is convolved with a transfer kernel to generate model images for the low-resolution data (here the IRAC images), allowing the flux in each source to vary. This model image was in turn fitted to the real low-resolution image. In this way, all sources are modeled and those close to the LAEs are effectively removed such that these cleaned images can be used to generate reliable stacked images of the LAEs (Figure 4). We then visually inspect all of our LAEs and exclude 97 objects owing to the presence of bad residual features close to the targets that can possibly affect the photometry. Finally, we use the 107, 213, 273, and 20 LAEs at z = 4.9, 5.7, 6.6, and 7.0 for our analysis, respectively. Note that using the HSC images as the prior does not have a significant impact on our photometry, as far as we are interested in the total flux of the galaxy, rather than individual components. For example, our IRAC color measurement of CR7 is −1.3 ± 0.3, consistent with that of Bowler et al. (2017), who use the high-resolution Hubble image (PSF ∼ 02) as a prior.
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Standard image High-resolution image3.2. Stacking Analysis
To investigate the connection between the ISM properties and Lyα emission, we divide our LAE samples into subsamples by the Lyα EW bins at z = 4.9, 5.7, and 6.6. In addition, we make a subsample of representing a typical LAE sample at each redshift. Tables 4 and 5 summarize the EW ranges and number of LAEs in the subsamples at z = 4.9 and 5.7, 6.6, and 7.0, respectively. We cut out 12'' × 12'' images of the LAEs in HSC (), VIRCAM JHKs (WFCAM JHK), and IRAC [3.6][4.5] bands in the UD-COSMOS (UD-SXDS) field. Then, we generate median-stacked images of the subsamples in each band with IRAF task imcombine. Figures 5 and 6 show the stacked images of the subsamples. Aperture magnitudes are measured in 3''- and 2''-diameter circular apertures in the IRAC and the other images, respectively. To account for flux falling outside these apertures, we apply aperture corrections summarized in Table 1, which are derived from samples of isolated point sources. We measure limiting magnitudes of the stacked images by making 1000 median-stacked sky noise images, each of which is made of the same number of randomly selected sky noise images as the LAEs in the subsamples. In addition to this stacking analysis, we measure fluxes of individual LAEs that are detected in the IRAC [3.6] and/or [4.5] bands, but our main results are based on the stacked images. In Figure 7, we plot the IRAC colors ([3.6]–[4.5]) of the stacked subsamples and individual LAEs. At z = 4.9 and 5.7, the IRAC color decreases with increasing Lyα EW. At z = 6.6, the color decreases with increasing Lyα EW from ∼7 to ∼30 Å and then increases from ∼30 to ∼130 Å.
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Standard image High-resolution imageTable 4. Summary of the Subsamples at z = 4.9
Redshift | N | [3.6]–[4.5] | logξion | fLyα | |||||
---|---|---|---|---|---|---|---|---|---|
(1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | (10) |
z = 4.9 | 20.0 | 1000.0 | 99 | 63.8 | −20.6 | −0.81 ± 0.16 | ( | ||
0.0 | 20.0 | 8 | 16.9 | −21.4 | −0.26 ± 0.16 | ( | |||
20.0 | 70.0 | 58 | 43.0 | −20.9 | −0.75 ± 0.14 | ( | |||
70.0 | 1000.0 | 41 | 117.5 | −20.0 | <−1.04 | >1860 | >25.50 (>25.55) | >0.55 |
Note. Column (1): redshift of the LAE subsample. Column (2): lower limit of the rest-frame Lyα EW of the subsample. Column (3): upper limit of the rest-frame Lyα EW of the subsample. Column (4): number of sources in the subsample. Column (5): median value of the rest-frame Lyα EWs in the subsample. Column (6): median value of the UV magnitudes in the subsample. Column (7): IRAC [3.6]–[4.5] color. Column (8): Hα EWs in the subsample. Column (9): ionizing photon production efficiency in units of Hz erg−1 with . The value in parentheses is the ionizing photon production efficiency with , inferred from this study. Column (10): Lyα escape fraction.
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Table 5. Summary of the Subsamples at z = 5.7, 6.6, and 7.0
Redshift | N | [3.6]–[4.5] | [O iii] λ5007/Hα | [O iii] λ5007/Hβ | |||||
---|---|---|---|---|---|---|---|---|---|
(1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | (10) |
z = 5.7 | 20.0 | 1000.0 | 202 | 88.8 | −19.9 | −0.55 ± 0.20 | >460 | ||
0.0 | 10.0 | 6 | 5.8 | −22.0 | 0.30 ± 0.08 | ⋯ | |||
10.0 | 20.0 | 5 | 14.7 | −22.0 | 0.06 ± 0.22 | ⋯ | |||
20.0 | 40.0 | 21 | 33.7 | −20.9 | −0.37 ± 0.20 | >330 | |||
40.0 | 100.0 | 107 | 75.7 | −20.2 | −0.38 ± 0.28 | >340 | |||
100.0 | 1000.0 | 74 | 125.9 | −19.4 | <−0.64 | >1.27 | >3.63 | >490 | |
z = 6.6 | 20.0 | 1000.0 | 230 | 40.1 | −20.3 | <−0.85 | >1.18 | >3.37 | >540 |
0.0 | 10.0 | 17 | 6.5 | −21.6 | −0.40 ± 0.14 | >310 | |||
10.0 | 30.0 | 92 | 23.6 | >−20.3 | <−1.11 | >2.01 | >5.75 | >640 | |
30.0 | 100.0 | 148 | 44.1 | >−20.3 | <−0.56 | >0.55 | >1.58 | >390 | |
100.0 | 1000.0 | 16 | 126.2 | >−20.3 | −0.30 ± 0.54 | ⋯ | |||
z = 7.0 | 20.0 | 1000.0 | 20 | 88.1 | −20.4 | <−0.85 | <0.86 | <2.46 | ⋯ |
Note. Column (1): redshift of the LAE subsample. Column (2): lower limit of the rest-frame Lyα EW of the subsample. Column (3): upper limit of the rest-frame Lyα EW of the subsample. Column (4): number of sources in the subsample. Column (5): median value of the rest-frame Lyα EWs in the subsample. Column (6): median value of the UV magnitudes in the subsample. The lower limit indicates that more than half of the LAEs in that subsample are not detected in the rest-frame UV band. Column (7): IRAC [3.6]–[4.5] color. Column (8): [O iii] λ5007/Hα line flux ratio of the subsample. For the z = 7.0 subsample, the [O iii]/Hα ratio is calculated from the [O iii]/Hβ ratio assuming Hα/Hβ = 2.86. Column (9): [O iii] λ5007/Hβ line flux ratio of the subsample. For the z = 5.7 and 6.6 subsamples, the [O iii]/Hβ ratios are calculated from the [O iii]/Hα ratio assuming Hα/Hβ = 2.86. Column (10): lower limit of the rest-frame [O iii] λ5007 EW assuming no emission line in the [4.5] band.
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We discuss a sample selection effect on the IRAC color results. Since our sample is selected based on the NB excess, we cannot select low Lyα EW galaxies with UV continua much fainter than the detection limit. Thus, the median UV magnitude is brighter in the lower subsample (see Tables 4 and 5). We use LAEs of limited UV magnitudes of −21 mag < MUV < −20 mag and divide them into subsamples based on the Lyα EW. We stack images of the subsamples and measure the IRAC colors in the same manner as described above. We find a similar decreasing trend of the IRAC color with increasing Lyα EW at z = 5.7. At z = 4.9 and 6.6, we cannot find the trends owing to the small number of the galaxies in the subsamples. In order to investigate the effects further, a larger LAE sample and/or deep mid-infrared data (e.g., obtained by JWST) are needed.
We also discuss effects of contamination on the stacked IRAC images. As explained in Section 2.1, the contamination fraction in our LAE sample is 0%–30% and appears to depend on the magnitude. Low-redshift emitter contaminants do not make the IRAC excess, because no strong emission lines enter in the IRAC bands. Thus, if the LAE sample contains significant contamination, the IRAC color excess becomes weaker. Here we roughly estimate the effect of the contamination, assuming a flat continuum of the contaminant in the IRAC bands and maximum 30% contamination rate. If the color excess of LAEs is [3.6]–[4.5] = −0.5 (i.e., the flux ratio of f[3.6]/f[4.5] = 1.6), the 30% contamination makes the mean color excess weaker by 0.1 mag. Similarly, if the color excess of LAEs is [3.6]–[4.5] = −1.0 (i.e., flux ratio f[3.6]/f[4.5] = 2.5), the contamination makes the mean color excess weaker by 0.2 mag. Although we use the median-stack images, which are different from the mean stack and not simple, the maximum effect could be 0.1–0.2 mag. This effect is comparable to the uncertainties of our [3.6]–[4.5] color measurements. Thus, the effect of the contamination could not be significant.
In some z = 6.6 subsamples, LAEs are marginally detected in the stacked images bluer than the Lyman break. The fluxes in the bluer bands are ≳7 times fainter than that in the y band. These marginal detections could be due to the contamination of the low-redshift emitters (e.g., [O iii] emitters), because the ∼7 times fainter fluxes in the gri bands can be explained by the ∼15% contamination with flat continua. However, we cannot exclude possibilities of the unrelated contamination in the line of sight and/or Lyman continuum leakage. Larger spectroscopic samples are needed to distinguish these possibilities.
3.3. Model SED
We generate the model SEDs at z = 4.9, 5.7, 6.6, and 7.0 using BEAGLE (Chevallard & Charlot 2016). In BEAGLE, we use the combined stellar population + photoionization model presented in Gutkin et al. (2016). Stellar emission is based on an updated version of the population synthesis code of Bruzual & Charlot (2003), while gas emission is computed with the standard photoionization code CLOUDY (Ferland et al. 2013) following the prescription of Charlot & Longhetti (2001). The IGM absorption is considered following a model of Inoue et al. (2014). In BEAGLE we vary the total mass of stars formed, ISM metallicity (Zneb), ionization parameter (logUion), star formation history, stellar age, and V-band attenuation optical depth (τV), while we fix the dust-to-metal ratio (ξd) to 0.3 (e.g., De Vis et al. 2017) and adopt the Calzetti et al. (2000) dust extinction curve. The choice of the extinction law does not affect our conclusions, because our SED fittings infer dust-poor populations such as τV = 0.0–0.1. Here we adopt a constant star formation history and vary the four adjustable parameters of the model in vast ranges, −2.0 < log(Zneb/Z⊙) < 0.2 (with a step of 0.1 dex), −3.0 < logUion < −1.0 (with a step of 0.1 dex), 6.0 < log(Age/yr) < 9.1 (with a step of 0.1 dex), and τV = [0, 0.05, 0.1, 0.2, 0.4, 0.8, 1.6, 2]. The lower limit of the ionization parameter is consistent with recent observations for high-redshift galaxies (e.g., Kojima et al. 2017). The upper limit of the ionization parameter is set to the very high value, because recent observations suggest an increase of the ionization parameter toward high redshift (Nakajima et al. 2013). The upper limit of the stellar age corresponds to the cosmic age at z = 4.9 (9.08 Gyr). These parameter ranges cover previous results for high-redshift LAEs (e.g., Ono et al. 2010a, 2010b). We assume that the stellar metallicity is the same as the ISM metallicity, with interpolation of original templates. We fix the stellar mass as M* = 108 M⊙, which will be scaled later. In generating model SEDs, we remove emission lines at 4000 Å < λrest < 7000 Å, because we estimate line fluxes of the LAEs by measuring the difference between the observed photometry (emission line contaminated) and the model continuum (no emission lines). We also calculate the Lyα EW of each model SED assuming the Case B recombination without considering the resonance scattering (Osterbrock 1989), which will be compared with the observed .
3.4. Line Flux Estimate
We estimate rest-frame optical emission line fluxes by comparing the stacked SEDs (Section 3.2) with the model SEDs (Section 3.3). We use seven (zyJHKs[4.5]EWLyα), six (zyJHKs(K)EWLyα), five (yJHKs(K)EWLyα), and four (JHKsEWLyα) observational data points to constrain the model SEDs at z = 4.9, 5.7, 6.6, and 7.0, respectively. First, from the all models, we remove models whose Lyα EWs are lower than the minimum of each subsample. We keep models with higher than the maximum EW of each subsample, because the EWs in the models could be overestimated, as we do not account for the enhanced absorption by dust of resonantly scattered Lyα photons in the neutral ISM. Then, the model SEDs are normalized to the fluxes of the stacked images in bands redder than the Lyα emission and free from the strong rest-frame optical emission lines (i.e., zyJHKs[4.5], zyJHKs(K), yJHKs(K), and JHKs for z = 4.9, 5.7, 6.6, and 7.0 LAEs, respectively) by the least-squares fits. We then calculate the χ2 value of each model with these band fluxes and adopt the least χ2 model as the best-fit model.
Figure 8 shows examples of the best-fit SEDs with the observed magnitudes. The uncertainty of the model is computed with the models in the 1σ confidence interval. We calculate the flux differences between the stacked SEDs and the model SEDs in the [3.6] band at z = 4.9 and the [3.6] and [4.5] bands at z = 5.7, 6.6, and 7.0. The flux differences are corrected for dust extinction with the τV values in the models, assuming the Calzetti et al. (2000) extinction curve.
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Standard image High-resolution imageWe estimate the Hα, Hβ, and [O iii] λ5007 line fluxes from these flux differences. Here we consider Hα, Hβ, [O iii] λλ4959, 5007, [N ii] λ6584, and [S ii] λλ6717, 6731 emission lines, because the other emission lines redshifted into the [3.6][4.5] bands are weak in the metallicity range of 0.02 < Z/Z⊙ < 2.5 (Anders & Fritze-v. Alvensleben 2003). We use averaged filter throughputs of the [3.6] and [4.5] bands at the wavelength of each redshifted emission line calculated with the redshift distributions of the LAE samples. We assume the Case B recombination with an electron density of ne = 100 cm−3 and an electron temperature of Te = 10,000 K (Hα/Hβ = 2.86; Osterbrock 1989) and typical line ratios of [O iii] λ4959/[O iii] λ5007 = 0.3 (Kojima et al. 2017), [N ii]/Hα = 0.068, and [S ii]/Hα = 0.095 for subsolar (0.2 Z⊙) metallicity (Anders & Fritze-v. Alvensleben 2003; see also Faisst et al. 2018). Note that these assumptions do not affect our final results, because the statistical uncertainties of the line fluxes or ratios in our study are larger than 10%. For example, recent observations suggest relatively high electron densities of ne ∼ 100–1000 cm−3 (Shimakawa et al. 2015; Onodera et al. 2016; Sanders et al. 2016; Kashino et al. 2017), but our conclusions do not change if we adopt ne = 1000 cm−3. The uncertainties of the emission-line fluxes include both the photometric errors and the SED model uncertainties.
We check the reliability of this flux estimation method. We use galaxies at z = 1.2–1.6 whose [O iii] λ5007 and Hα emission lines are redshifted into the J125 and H160 bands, respectively. From the 3D-HST catalogs (Brammer et al. 2012; Skelton et al. 2014; Momcheva et al. 2016), we select 211 galaxies at zspec = 1.20–1.56 in the GOODS-South field with [O iii] and Hα emission lines detected at >3σ levels. In addition to the spectroscopic data in Momcheva et al. (2016), we estimate the [O iii] and Hα fluxes from the broadband magnitudes following the method described above. We divide the galaxies into subsamples and plot the median and 1σ scatter of the [O iii]/Hα ratio of each subsample in Figure 9. Furthermore, we plot the [O iii]/Hα ratios of two LAEs at z = 2.2, COSMOS-30679 (Nakajima et al. 2013) and COSMOS-12805 (Kojima et al. 2017), whose [O iii] and Hα lines enter in the H and Ks bands, respectively. We also measure magnitudes of COSMOS-30679 and COSMOS-12805 in our grizyJHKs[3.6][4.5] images. Although the uncertainties of the ratios estimated from photometry are large, they agree with those from spectroscopy within a factor of ∼1.5. Thus, this flux estimation method is valid.
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Standard image High-resolution image4. Results
4.1. Properties of z = 4.9 LAEs
4.1.1. Inferred Hα EW
The left panel in Figure 10 shows rest-frame Hα EWs () as a function of Lyα EWs at z = 4.9. The Hα EW increases from ∼600 to >1900 Å with increasing Lyα EW. of the subsample () is ∼600 Å, relatively higher than results of M* ∼ 1010 M⊙ galaxies at z ∼ 5 (300–400 Å; Faisst et al. 2016a), because our galaxies may be less massive (log(M*/M⊙) ∼ 8−9) than the galaxies in Faisst et al. (2016a). On the other hand, the high Lyα EW () subsample has , which is ≳5 times higher than that of the M* ∼ 1010 M⊙ galaxies. Based on photoionization model calculations in Inoue (2011), this high value indicates very young stellar age of <10 Myr or very low metallicity of <0.02 Z⊙ (right panel in Figure 10). The individual galaxies are largely scattered beyond the typical uncertainty, probably due to varieties of the stellar age and metallicity.
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Standard image High-resolution imageWe compare the Hα EWs of the z = 4.9 LAEs with those of galaxies at other redshifts. Sobral et al. (2014) report median Hα EWs of 30–200 Å for galaxies with log(M*/M⊙) = 9.0–11.5 at z = 0.40–2.23. Our Hα EWs are more than two times higher than the galaxies in Sobral et al. (2014). The high EW (∼1400 Å) of our LAEs is comparable to an extrapolation of the scaling relation in Sobral et al. (2014), , for z = 4.9 and log(M*/M⊙) = 8.15 (see Section 4.1.5). This good agreement indicates that this scaling relation may hold at z ∼ 5 and the lower stellar mass.Fumagalli et al. (2012) estimate Hα EWs of galaxies at z ∼ 1 to be EWHα = 10–100 Å, which are lower than ours. The high Hα EWs of our z = 4.9 LAEs are comparable to those of galaxies at z ∼ 6.7 (Smit et al. 2014).
4.1.2. Lyα Escape Fraction
We estimate the Lyα escape fraction, fLyα, which is the ratio of the observed Lyα luminosity to the intrinsic one, by comparing Lyα with Hα. Because Lyα photons are resonantly scattered by neutral hydrogen (H i) gas in the ISM, the Lyα escape fraction depends on kinematics and distribution of the ISM, as well as the metallicity of the ISM. The Lyα escape fraction can be estimated by the following equation:
where subscripts "int" and "obs" refer to the intrinsic and observed luminosities, respectively. Here we assume the Case B recombination (Brocklehurst 1971). The intrinsic Hα luminosities are derived from the dust-corrected Hα fluxes, estimated in Section 3.4.
We plot the estimated Lyα escape fractions as a function of Lyα EW in Figure 11. The Lyα escape fraction increases from ∼10% to >50% with increasing from 20 to 100 Å, whose trend is identified for the first time at z = 4.9. In addition, the escape fractions at z = 4.9 agree very well with those at z = 2.2 at given (Sobral et al. 2017). Sobral et al. (2017) suggest a possible nonevolution of the relation from z ∼ 0 to z = 2.2. We confirm this redshift-independent relation up to z = 4.9. In Figure 11, we also plot the following relation in Sobral & Matthee (2018), which fits our and previous results:
We will discuss implications of these results in Section 5.3.
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Standard image High-resolution image4.1.3. Ionizing Photon Production Efficiency
We estimate the ionizing photon production efficiencies of the z = 4.9 LAEs from their Hα fluxes and UV luminosities. The definition of the ionizing photon production efficiency is as follows:
where N(H0) is the production rate of the ionizing photon, which can be estimated from the Hα luminosity using a conversion factor by Leitherer & Heckman (1995),
where is the ionizing photon escape fraction and Nobs(H0) is the ionizing photon production rate with .
The left panel in Figure 12 shows estimated ξion values as a function of UV magnitude. We calculate the values of the ξion in two cases: , following previous studies such as Bouwens et al. (2016), and , inferred from our analysis in Section 4.1.4. The ionizing photon production efficiency is estimated to be for the subsample with . This value is systematically higher than those of LBGs at similar redshift and UV magnitude (log(ξion/[Hz erg−1]) ≃ 25.3; Bouwens et al. 2016) and the canonical values (log(ξion/[Hz erg−1]) ≃25.2–25.3) by 60%–100%. These higher ξion in our LAEs may be due to the younger age (see Section 4.1.1) or higher ionization parameter (Nakajima et al. 2013; Nakajima & Ouchi 2014).
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Standard image High-resolution imageWe also compare ξion of our LAEs with studies at different redshifts in the right panel in Figure 12. Our estimates for the z = 4.9 LAEs are comparable to those of LBGs at higher redshift, z = 5.1–5.4 (Bouwens et al. 2016), and of bright galaxies at z ∼ 5.7–7.0 (Matthee et al. 2017c). Our estimates are higher than those of galaxies at z ∼ 2 (Matthee et al. 2017a; Shivaei et al. 2018).
4.1.4. Ionizing Photon Escape Fraction
We estimate the ionizing photon escape fraction of the z = 4.9 LAEs from the Hα flux and the SED fitting result, following a method in Ono et al. (2010a). We can measure the ionizing photon production rate with the zero escape fraction, Nobs(H0), from Equation (8). On the other hand, we can estimate N(H0) from the SED fitting. Thus, the ionizing photon escape fraction is
We estimate only for the subsample of , whose SED is well determined. We plot of our z = 4.9 LAEs in the left panel of Figure 13. The estimated escape fraction is , which is comparable to local Lyman continuum emitters (Izotov et al. 2016a, 2016b; Puschnig et al. 2017; Verhamme et al. 2017). Note that this estimate largely depends on the stellar population model. Stanway et al. (2016) report that binary star populations produce a higher number of ionizing photons, exceeding the single-star population flux by 50%–60%. Thus, we take the 60% systematic uncertainty into account, resulting in the escape fraction of . The validity of this method will be tested in future work.
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Standard image High-resolution image4.1.5. Star Formation Main Sequence
We can derive the SFR and the stellar mass, M*, from the SED fitting. The SFR, stellar mass, and specific SFR are estimated to be
respectively, for the subsample, where SFR, M*, and SFR/M* are in units of M⊙ yr−1, M⊙, and yr−1, respectively. We plot the result in the right panel of Figure 13. At the fixed stellar mass, the SFR of the LAEs is higher than the extrapolation of the relations measured with LBGs (Lee et al. 2012; Steinhardt et al. 2014; Salmon et al. 2015). Thus, the LAEs may have a higher SFR than other galaxies with similar stellar masses, as also suggested by Ono et al. (2010a) at z ∼ 6−7 and Hagen et al. (2016) at z ∼ 2. However, some studies infer that LAEs are located on the main sequence (e.g., Kusakabe et al. 2018; Shimakawa et al. 2017) at z ∼ 2−3, so further investigation is needed.
4.2. Properties of z = 5.7, 6.6, and 7.0 LAEs
4.2.1. [O iii] λ5007/Hα and [O iii] λ5007/Hβ Ratios
We plot the [O iii] λ5007/Hα ([O iii] λ5007/Hβ) flux ratios of the subsamples and individual LAEs at z = 5.7 and 6.6 (z = 7.0) in Figure 14. The [O iii]/Hα ratios of the subsamples are typically ∼1 but vary as a function of . At z = 5.7, the ratio increases from ∼0.5 to 2.5 with increasing Lyα EW from to 80 Å. On the other hand, at z = 6.6, the ratio increases with increasing from 7 to 20 Å and then decreases to ∼130 Å, showing the turnover trend at the 2.3σ confidence level. The [O iii]/Hα ratio depends on the ionization parameter and metallicity. The low [O iii]/Hα ratio in the high-EW subsample at z = 6.6, whose ionization parameter is expected to be high, indicates the low metallicity in the high-EW subsample. At z = 7.0, the [O iii]/Hβ ratio is lower than 2.8. The ratios of individual galaxies are largely scattered, which may be due to varieties of the ionization parameter and metallicity.
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Standard image High-resolution imageWe compare the [O iii]/Hα flux ratios at z = 5.7, 6.6, and 7.0. The [O iii]/Hα ratio of the z = 7.0 subsample is estimated from the [O iii]/Hβ ratio assuming the Case B recombination (Hα/Hβ = 2.86). Here we use the intrinsic Lyα EW, , which is corrected for the IGM absorption. Konno et al. (2018), Ota et al. (2017), and Konno et al. (2014) measure the IGM transmission, , at z = 6.6, 7.0, and 7.3, relative to the one at z =5.7, as , , and , respectively. Thus, we estimate the intrinsic rest-frame Lyα EW, , from the observed rest-frame Lyα EW, , at a given redshift, by interpolating these measurements as follows:
The typical uncertainty of these corrections is 20% (Konno et al. 2018).
The [O iii]/Hα ratios of the z = 5.7, 6.6, and 7.0 LAEs are presented in Figure 15. We do not find significant redshift evolution of the ratio from z = 5.7 to 7.0 after the IGM correction. We also plot the ratios of galaxies at z = 2.5 and 0.3 from Trainor et al. (2016) and Cowie et al. (2011), respectively, by making subsamples based on the Lyα EW. The ratios of the z = 0–2.5 galaxies are also comparable to our results at z = 5.7–7.0 (see also Wold et al. 2017).
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Standard image High-resolution image4.2.2. Metallicity–EWLyα Anticorrelation
We investigate physical quantities explaining our observed [O iii]/Hα ratios as a function of Lyα EW. We simply parameterize the metallicity, Zneb, the ionization parameter, Uion, and the stellar age with the Lyα EW in units of Å as follows:
where Zneb and Age are in units of Z⊙ and yr, respectively. We find that the quadratic function of Equation (18) can describe the observed [O iii]/Hα results better than a linear function. With these equations, we calculate the metallicity, the ionization parameter, and the stellar age for given Lyα EW. BEAGLE can predict the [O iii]/Hα ratio for the parameter sets of (Zneb, Uion, Age). We fit our observational results of the [O iii]/Hα ratios with this model and constrain the parameters in Equation (18)–(20) using the Markov Chain Monte Carlo (MCMC) parameter estimation technique. Here we assume the electron density of ne = 100 cm−1, and this assumption does not have significant impacts on our analysis. Because the critical density of [O iii] λ5007 is very high, 6.4 × 105 cm−3, the [O iii]/Hα ratio does not significantly change in the observed range of the electron density (e.g., ne ∼ 100–1000 cm−3; Shimakawa et al. 2015; Onodera et al. 2016; Sanders et al. 2016; Kashino et al. 2017).
The results are presented in Figure 16. The best-fit relations with 1σ errors are
Although the ionization parameter and the stellar age are not well determined, we have constrained the metallicity–Lyα EW relation well. The result suggests an anticorrelation between the metallicity and the Lyα EW, implying the very metal-poor ISM (∼0.03 Z⊙) in the galaxies with (see also Nagao et al. 2007; Hashimoto et al. 2017). This anticorrelation is supported by results of Faisst et al. (2016b), based on the rest-frame UV absorption lines, and consistent with a lower limit for the metallicity of z = 2.2 LAEs in Nakajima et al. (2012).
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Standard image High-resolution imageThese results can be understood as follows. We find the turnover trend of the [O iii]/Hα ratio with increasing . This turnover trend can be reproduced only by the metallicity change, if we assume that the quantities of (Zneb, Uion, Age) are simple monotonic functions of . Figure 17 shows the BEAGLE calculations of [O iii]/Hα as a function of metallicity, in parameter ranges of −2.0 < log(Zneb/Z⊙) < 0.2, −3.0 <logUion < −1.0, and 6.0 < log(Age/yr) < 9.1. At fixed ionization parameter and stellar age, the ratio increases with decreasing metallicity from ∼1 to 0.4 Z⊙ and then decreases with metallicity from ∼0.4 to 0.01 Z⊙, making the turnover trend similarly seen in the [O iii]/Hα– plane (see also Nagao et al. 2006; Maiolino et al. 2008). On the other hand, the ratio monotonically increases with increasing ionization parameter. The ratio does not significantly depend on the stellar age (<0.1 dex), since the number of ionizing photons saturates at Age ≳ 10 Myr. Thus, we can constrain the relation from the observed [O iii]/Hα ratios.
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Standard image High-resolution image4.3. [C ii] 158 μm–Lyα Relation
In Figure 18, we plot the observed ratios of the [C ii] luminosity to SFR, L[C ii]/SFR, and [C ii] luminosities as functions of Lyα EW corrected for the IGM absorption, . We conduct the Kendall correlation test using the cenken function in the NADA library from the R-project statistics package and find anticorrelations in both and planes at 2.9σ (99.6%)and 2.3σ (97.9%) significance levels, respectively. The L[C ii]/SFR ratio of local star-forming galaxies is log(L[C ii]/SFR)/[L⊙/(M⊙ yr−1)] ≃ 7 (De Looze et al. 2014). We find that the typical L[C ii]/SFR ratio of the galaxies at z > 5 with is lower than those of the local star-forming galaxies by a factor of ∼3, indicating the [C ii] deficit. Thus, we statistically confirm the [C ii] deficit in high- galaxies for the first time. We discuss physical origins of the anticorrelation in Section 5.1. In Figure 18, we also plot the following power-law functions:
where L[C ii], SFR, and are in units of L⊙, M⊙ yr−1, and Å, respectively. These anticorrelations cannot be explained only by the SFR difference of the galaxies, because there is no significant trend between and SFR in our sample. We divide our sample into subsamples of Å, 10–100 Å, and 100–1000 Å and find that median SFRs of subsamples are comparable (within a factor of ∼2). Carniani et al. (2017) also report the anticorrelation in the plane, although the slope is shallower than ours. As discussed in Carniani et al. (2017), their shallower slope is probably due to the fact that they use individual subcomponents extracted from galaxies. Using subcomponents allows us to investigate physical properties of each clump but could make the correlation weaker if accuracy of the measurements is not enough.
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Standard image High-resolution image5. Discussion
5.1. Fiducial Model Reproducing the Lyα, [O iii], Hα, and [C ii]
We have constrained the relations of , , and from the [O iii]/Hα ratios in Section 4.2.2. Hereafter, we call this model "the fiducial model." In this section, we investigate whether the fiducial model can also reproduce our other observational results.
In Section 4.1.1, we find that the Hα EW positively correlates with the Lyα EW at z = 4.9. The Hα EW depends on the metallicity and the stellar age, as shown in the right panel of Figure 10. Since we do not have a good constraint on the stellar age, as shown in Figure 16, we assume log(Age/yr) = 6.5 and 6 < log(Age/yr) < 7 as the best value and the uncertainty, respectively, which are typical for LAEs (Ono et al. 2010a, 2010b) and are consistent with Equation (20). We derive the parameter sets of given from this fiducial model, and we calculate Hα EWs using BEAGLE. In the left panel of Figure 10, we plot the prediction of the fiducial model with the dark-gray curve. We find that the fiducial model agrees well with the observed relation.
The ratio of L[C ii]/SFR negatively correlates with the Lyα EW, as shown in Section 4.3. Vallini et al. (2015) present the following formula describing their simulation results:
where L[C ii], SFR, and Zneb are in units of L⊙, M⊙ yr−1, and Z⊙, respectively (see also Lagache et al. 2018). By substituting Equation (21) into this equation, we can obtain an relation. Here we assume the typical SFR of our sample, SFR = 10 M⊙ yr−1, but the choice of the SFR does not have a significant impact on the discussion. We plot this relation (i.e., the fiducial model) with the dark-gray curve in Figure 19. The fiducial model nicely explains the anticorrelation, indicating that the [C ii] deficit in high Lyα EW galaxies may be due to the low metallicity.
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Standard image High-resolution imageThere are two other possibilities for the anticorrelation. One is the density-bounded nebula in high-redshift galaxies. Recently there is growing evidence that high-redshift galaxies have high ionization parameters with intense radiation (Nakajima & Ouchi 2014). Such intense radiation ionizes C ii and H i in the H i region and photodissociation region (PDR), making the density-bounded nebula, decreasing the [C ii] emissivity, and increasing the transmission of Lyα (see also discussions in Vallini et al. 2015). Thus, if the ionizing parameter positively correlates with the Lyα EW, we could explain the anticorrelation by this density-bounded nebula scenario. The other is a very high density of the PDR. The critical density of the [C ii] line is ∼3000 cm−3. A very high density PDR (>3000 cm−3) enhances more rapid collisional de-excitations for the forbidden [C ii] line and decreases the [C ii] emissivity. Indeed, Díaz-Santos et al. (2013, 2014) report an anticorrelation between the [C ii]-to-FIR luminosity ratio (L[C ii]/LFIR) and the FIR luminosity surface density (ΣFIR) for local starburst galaxies, which may be due to high ionization parameters or collisional de-excitations in high-ΣFIR galaxies (Spilker et al. 2016). Although we find that the relation can explain the anticorrelation, these two scenarios are still possible.
Nevertheless, we find that the predictions from the fiducial model with the anticorrelation agree well with our observational results of the Hα EW and L[C ii]/SFR. These good agreements suggest a picture that galaxies with high (low) Lyα EWs have the high (low) Lyα escape fractions and are metal-poor (metal-rich) with the high (low) ionizing photon production efficiencies and the weak (strong) [C ii] emission (Figure 20).
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Standard image High-resolution image5.2. Predicted C iii] λλ1907, 1909 EWs of LAEs
The C iii] λλ1907, 1909 lines are believed to be the second most frequent emission lines in the UV rest-frame spectra of SFGs after Lyα. Recent observations with the VIMOS Ultra Deep Survey and the MUSE Hubble Ultra Deep Field Survey allow us to investigate statistical properties of the C iii] emission at 1 < z < 4 (Le Fèvre et al. 2017; Maseda et al. 2017; Nakajima et al. 2018). These studies reveal that the C iii] EW () positively correlates with Lyα EW (Stark et al. 2014; Le Fèvre et al. 2017). Here we investigate whether the fiducial model can reproduce the observed correlation between and . Since the C iii] EW depends on the metallicity, ionization parameter, and stellar age, we can predict the C iii] EW with Equations (21)–(23) using BEAGLE.
Figure 21 shows predicted C iii] EWs with observational results. Although the uncertainty is large owing to the poor constraints on the ionization parameter and stellar age, the prediction reproduces the positive correlation at . Thus, LAEs with would be strong C iii] emitters. Beyond , the C iii] EW decreases owing to low carbon abundance, suggested by Nakajima et al. (2018).
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Standard image High-resolution image5.3. Implication for Cosmic Reionization
In Section 4.1.2, we find that the Lyα escape fraction given the Lyα EW, , does not change significantly with redshift from z = 0 to 4.9. Thus, the ratio of the EW to the escape fraction, , also does not change with redshift. Since the Lyα EW and the Lyα escape fraction are proportional to the ratio of the Lyα luminosity to the UV luminosity (EWLyα ∝ LLyα/LUV) and that of the Lyα luminosity to the Hα luminosity (fLyα ∝ LLyα/LHα), the ratio of the EW to the escape fraction is proportional to the ionizing photon production efficiency, as follows:
Thus, the redshift-independent relation indicates that the ionizing photon production efficiency depends on the Lyα EW, but not on the redshift. If this is redshift independent even at z > 5, galaxies in the reionization epoch have ξion values comparable to those of LAEs at lower redshift with similar Lyα EWs. Thus, this redshift-independent relation justifies studies of low-redshift analogs to understand physical properties of the ionizing sources at the epoch of the cosmic reionization.
We discuss the contributions of star-forming galaxies to the cosmic reionization based on the results of the z = 4.9 LAEs. If we assume that the faint star-forming galaxies at the reionization epoch have similar properties to the EWLyα0 > 20 Å LAEs at z = 4.9, the ionizing photon production efficiency is and the ionizing photon escape fraction is . Based on recent UV LFs measurements, the ionizing photon budget is explained only by star-forming galaxies if (Ishigaki et al. 2018). Our z = 4.9 LAE results suggest , indicating that the photon budget can be explained only by the star-forming galaxies, with a minor contribution from faint AGNs (see also Khaire et al. 2016; Onoue et al. 2017).
6. Summary
We have investigated ISM properties from 1092 LAEs at z = 4.9, 5.7, 6.6, and 7.0, using wide and deep mid-infrared images obtained in SPLASH and stellar-synthesis and photoionization models. The deep Spitzer data constrain the strengths of the rest-frame optical emission lines, which are not accessible from the ground telescopes at z > 4. In addition, we study the connection between the Lyα emission and [C ii] 158 μm emission using ALMA and PdBI [C ii] observations targeting 34 galaxies at z = 5.148–7.508 in the literature. Our major findings are summarized below.
- 1.The Hα EW increases with increasing Lyα EW at z = 4.9. The Hα EW of the 0 Å < EWLyα0 < 20 Å subsample is ∼600 Å, relatively higher than the results of M* ∼ 1010 M⊙ galaxies. On the other hand, the Hα EW of the subsample is higher than ∼1900 Å, indicating the very young stellar age of <10 Myr or the very low metallicity of <0.02 Z⊙ (Figure 10; Section 4.1.1).
- 2.We find that the Lyα escape fraction, fLyα, positively correlates with the Lyα EW, , at z = 4.9. This relation does not show redshift evolution at z = 0–4.9, indicating that the ionizing photon production efficiency depends on the Lyα EW, but not on the redshift. This result justifies the studies of low-redshift analogs to understand physical properties of the ionizing sources at the epoch of the cosmic reionization (Figure 11; Sections 4.1.2 and 5.3).
- 3.The ionizing photon production efficiency of the LAE subsample is log(ξion/[Hz erg−1]) = 25.5, significantly higher than those of LBGs at a similar redshift and UV magnitude, as well as higher than the canonical values by 60%–100%. The ionizing photon escape fraction is estimated to be . From our measured ξion and , we find that the ionizing photon budget for the reionization can be explained by the star-forming galaxies, if they have similar properties to our LAEs at z = 4.9 (Figure 12; Sections 4.1.3, 4.1.4, and 5.3).
- 4.We estimate [O iii] λ5007/Hα flux ratios of the LAEs as a function of Lyα EW at z = 5.7 and 6.6. At z = 5.7, the ratio increases from 0.5 to 2.5 with increasing Lyα EW from to 80 Å. On the other hand, at z = 6.6, the ratio increases with increasing from 7 to 20 Å and then decreases to 130 Å, showing the turnover trend at the 2.3σ confidence level. The [O iii] λ5007/Hβ flux ratio of the z = 7.0 LAEs is lower than 2.8. All of the observed [O iii]/Hα and [O iii]/Hβ ratios can be understood by the anticorrelation between the metallicity and the Lyα EW. High Lyα EW (∼200 Å) subsamples are expected to be very metal-poor, Zneb ∼ 0.03 Z⊙ (Figures 14, 15, and 16; Section 4.2).
- 5.
- 6.We find that the fiducial model with the anticorrelation can explain the results of the and relations. These good agreements suggest a picture that galaxies with high (low) Lyα EWs have the high (low) Lyα escape fractions and are metal-poor (metal-rich) with the high (low) ionizing photon production efficiencies and the weak (strong) [C ii] emission (Figures 10, 15, and 19; Section 5.1).
We thank the anonymous referee for a careful reading and valuable comments that improved the clarity of the paper. We are grateful to Richard Ellis, Anna Feltre, Max Gronke, Toshihiro Kawaguchi, Kimihiko Nakajima, Kazuhiro Shimasaku, and David Sobral for their useful comments and discussions. We thank Stefano Carniani for providing their data points.
The Hyper-Suprime-Cam (HSC) collaboration includes the astronomical communities of Japan and Taiwan, and Princeton University. The HSC instrumentation and software were developed by the National Astronomical Observatory of Japan (NAOJ), the Kavli Institute for the Physics and Mathematics of the Universe (Kavli IPMU), the University of Tokyo, the High Energy Accelerator Research Organization (KEK), the Academia Sinica Institute for Astronomy and Astrophysics in Taiwan (ASIAA), and Princeton University. Funding was contributed by the FIRST program from Japanese Cabinet Office, the Ministry of Education, Culture, Sports, Science and Technology (MEXT), the Japan Society for the Promotion of Science (JSPS), Japan Science and Technology Agency (JST), the Toray Science Foundation, NAOJ, Kavli IPMU, KEK, ASIAA, and Princeton University.
The Pan-STARRS1 Surveys (PS1) have been made possible through contributions of the Institute for Astronomy, the University of Hawaii, the Pan-STARRS Project Office, the Max-Planck Society and its participating institutes, the Max Planck Institute for Astronomy, Heidelberg, the Max Planck Institute for Extraterrestrial Physics, Garching, Johns Hopkins University, Durham University, the University of Edinburgh, Queen's University Belfast, the Harvard-Smithsonian Center for Astrophysics, the Las Cumbres Observatory Global Telescope Network Incorporated, the National Central University of Taiwan, the Space Telescope Science Institute, the National Aeronautics and Space Administration under grant no. NNX08AR22G issued through the Planetary Science Division of the NASA Science Mission Directorate, the National Science Foundation under grant no. AST-1238877, the University of Maryland, and Eotvos Lorand University (ELTE).
This paper makes use of software developed for the Large Synoptic Survey Telescope. We thank the LSST Project for making their code available as free software at http://dm.lsst.org.
This work is supported by World Premier International Research Center Initiative (WPI Initiative), MEXT, Japan, and KAKENHI (15H02064) Grant-in-Aid for Scientific Research (A) through Japan Society for the Promotion of Science (JSPS). Y.H. acknowledges support from the Advanced Leading Graduate Course for Photon Science (ALPS) grant and the JSPS through the JSPS Research Fellowship for Young Scientists. J.C. and S.C. acknowledge support from the European Research Council (ERC) via an Advanced Grant under grant agreement no. 321323-NEOGAL. S.T. acknowledge support from the ERC Consolidator Grant funding scheme (project ConTExt, grant No. 648179). The Cosmic Dawn Center is funded by the Danish National Research Foundation.