BRIGHT GALAXIES AT HUBBLE'S REDSHIFT DETECTION FRONTIER: PRELIMINARY RESULTS AND DESIGN FROM THE REDSHIFT z ∼ 9–10 BoRG PURE-PARALLEL HST SURVEY

As discussed in Section 2, pure-parallel imaging is not dithered, potentially affecting the data quality of the photometry. Thanks to a follow-up program of a Y-dropout overdensity in BoRG[z8] (see Trenti et al. 2012), we have both pure-parallel and dithered observations of overlapping area in the same IR filters. Previously, we combined all available data to maximize the depth of the observations, arriving at identifying the brightest dropouts of the region at very high S/N (object borg_1437+5043_1137 with ${J}_{{\rm{125}}}$ = 25.76 ± 0.07 mag detected at ${\rm{S}}/{{\rm{N}}}_{125}\sim 20;\;$Schmidt et al. 2014a). Here, we re-processed the original pure-parallel observations in F125W (${t}_{\mathrm{exp}}=2500$ s) with our latest pipeline, and separately we analyzed the dithered (primary GO) follow-up observations, combining a total of ${t}_{\mathrm{exp}}=2300\;{\rm{s}}$ of data and using a pixel scale of 008 pixel⁻¹ with the goal of obtaining the closest analog possible to the pure-parallel image. Visual inspection of the two science images, which are shown in the top panels of Figure 2, immediately highlights the near equivalence of the pure-parallel data to the dithered ones. To quantify the photometric accuracy, we selected 400 empty sky regions in each image and performed aperture photometry (radius $r=0\buildrel{\prime\prime}\over{.} 32$), obtaining the noise distribution shown in the bottom panel of Figure 2. The variance σ of the distribution for the pure-parallel and dithered data set are 0.10 and 0.11, respectively, corresponding to ${m}_{\mathrm{lim}}=28.7$ (pure-parallel) and ${m}_{\mathrm{lim}}=28.6$ (dithered) in the ${J}_{{\rm{125}}}$-band, in agreement with the exposure time calculator estimate of ${\rm{\Delta }}{m}_{\mathrm{lim}}=0.06$ due to the slight difference in exposure time. Furthermore, within statistical uncertainty, the two distributions are equivalent (Kolmogorov–Smirnov statistics p-value 99%). These tests demonstrate that the lack of dithering has essentially no impact on the photometry from pure-parallel data, which we can consider equivalent to that of dithered data with the same exposure time within our analysis uncertainty of ${\rm{\Delta }}m\lesssim 0.1$.

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To construct source catalogs we ran SExtractor in dual-image mode. For each field, we combined all the frames taken in F140W and F160W with AstroDrizzle to create a detection image and a combined weight map, which has been normalized for correlated noise (see Section 3). As a necessary condition for inclusion in the catalog, we required objects to have at least nine contiguous pixels with signal-to-noise ratio per pixel ${\rm{S}}/{\rm{N}}\geqslant 0.7$. Subsequently, we post-process the catalog to retain only sources with isophotal ${\rm{S}}/{\rm{N}}\geqslant 8$ in the detection image.

Photometry was performed in each filter via SExtractor dual-mode using the detection image to define source positions and isophotal contours. As in BoRG[z8], we adopt MAG_AUTO as the total magnitude of each source, while the signal-to-noise S/N is defined as

$$\begin{eqnarray*}&&\displaystyle \frac{{\rm{S}}}{{\rm{N}}}=\displaystyle \frac{\mathrm{FLUX}\_\mathrm{ISO}}{\mathrm{FLUXERR}\_\mathrm{ISO}}\end{eqnarray*}$$

(see Stiavelli 2009). Finally, colors are calculated from SExtractor isophotal magnitudes (MAG_ISO), without applying PSF matching, following the established practice for the BoRG survey (see Trenti et al. 2012). To account for Galactic extinction in each field, the official magnitude zeropoints²² (Zpt${}_{{\rm{F}}350\mathrm{LP}}=26.9435$ mag, Zpt${}_{{\rm{F}}105{\rm{W}}}=26.2687$ mag, Zpt${}_{{\rm{F}}125{\rm{W}}}=26.2303$ mag, Zpt${}_{{\rm{F}}140{\rm{W}}}=26.4524$ mag, Zpt${}_{{\rm{F}}160{\rm{W}}}=25.9463$ mag) have been corrected using the maps by Schlafly & Finkbeiner (2011).²³

To be included in the catalogs, objects need to have segmentation maps that are associated to pixels with non-zero weight maps in all five filters of the survey. For example, we excluded sources that fall on the gap between the two CCD detectors and thus lack photometry in F350LP.

Finally, we used external persistence maps,²⁴ which are released shortly after the observations, to flag any source in the catalog that appears to be either spurious or affected by persistent charge. Specifically, for each image and for each filter, we created a mask that includes all pixels in the released map with persistence value above $0.01{{\rm{e}}}^{-}/{\rm{s}}$, and flagged all sources in the catalog that include at least one persistent pixel.

For an optimal use of the full photometric information from our five bands survey, we ran the BPZ code by Benítez (2000) (see also Coe et al. 2006) on all detected sources. We use spectral energy distribution (SED) model templates as described in Benitez et al. (2014) (but see also Rafelski et al. 2015). Originally based on PEGASE models including emission lines (Fioc & Rocca-Volmerange 1997), these SEDs are recalibrated to match the observed photometry of galaxies with spectroscopic redshifts from FIREWORKS (Wuyts et al. 2008). They include five early types, two late types, and four starbursts. BPZ allows for interpolation between adjacent templates. These 11 templates were selected to encompass the ranges of metallicities, extinctions, and star formation histories derived from galaxy observations at low and high redshift. Because of the degeneracy between redshift and intrinsic galaxy properties such as age, dust content, and presence of emission lines, photometric redshift estimates for classes of rare objects with properties similar to galaxies at $z\gt 6$ are affected by uncertainties that are difficult to quantify. Therefore, rather than relying only on photo-z to identify high-z objects, we opt primarily for a Lyman-break selection, as discussed below. Given the challenges and uncertainty associated to the definition of an informed prior on the relative likelihood of solutions that have dropout-like colors but are lower-redshift interlopers, we make the minimal assumption of adopting a flat prior on the redshift distribution.

For the purposes of deriving LFs, we consider as optimal our choice to construct catalogs starting from the Lyman-break selection, since it allows us to calculate the source recovery efficiency as a function of input magnitude and redshift, which is then used to constrain the number density of high-z sources.

However, the Lyman Break Galaxy (LBG) selection is based on a binary decision outcome regarding inclusion of candidates in the high-z source catalog, neglecting the impact of photometric uncertainties that scatter objects in and/or out of the selection region (Su et al. 2011). Therefore, to investigate whether we are missing objects, we employed an alternative selection by searching for sources in our photometric catalogs that have high-z solutions. Following McLure et al. (2013), we required non-detection in the optical (${\rm{S}}/{{\rm{N}}}_{350}\lt 2$) as a necessary condition, and impose stellarity CLASS_STAR $\lt \quad 0.95$, as well. Then, we selected sources that have the peak of the redshift probability distribution function at $z\gt 7$.

In addition, we evaluated the sensitivity of the source selection to the construction of catalogs with a combination of F140W and F160W. For this, we produced alternative catalogs using only F160W as detection image. The results of this selection are summarized in the Appendix.

The selection of the highest redshift galaxies in BoRG[z9–10] relies primarily on one color (${J}_{{\rm{125}}}$–${H}_{{\rm{160}}}$; see Figure 8), associated with non-detection in the bluer bands (F350LP and F105W). F140W is used to verify whether the object is detected in a second, independent band, and to refine the photometric redshift estimates. Figure 3 shows the expected redshift distribution of the dropouts for a flat input distribution in one representative field, obtained through artificial source recovery simulations (Oesch et al. 2007, 2009, 2012 and Section 6.1). The figure clearly indicates that the color criteria adopted select sources at $z\gtrsim 9.5$. Our sample consists of two of them:

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borg_2134–0708_774 is a galaxy with magnitude ${H}_{{\rm{160}}}$ = 25.35 and a very red ${J}_{{\rm{125}}}$–${H}_{{\rm{160}}}$ color (${J}_{{\rm{125}}}$-${H}_{{\rm{160}}}$ = 1.74 ). The photometric redshift probability distribution peaks at z = 10.0, implying ${M}_{\mathrm{AB}}\sim -22.2$, albeit there is a broad wing of lower z solutions. The source is clearly resolved and shows extended structure in both the ${{JH}}_{{\rm{140}}}$ and ${H}_{{\rm{160}}}$-band images. Its intrinsic half-light radius is ${r}_{{\rm{e}}}=0\buildrel{\prime\prime}\over{.} 23$, after correction for the broadening introduced by the point-spread function (PSF). Interestingly, the dropout is in close proximity ($1\buildrel{\prime\prime}\over{.} 46$ center to center) to a foreground galaxy of magnitude ${H}_{{\rm{160}}}$ = 23.22. The photometric redshift distribution for the foreground is very broad, but given its compact size, it is likely at $z\gtrsim 0.5$ and thus can provide at least some gravitational lensing magnification. We estimated the possible range of magnification using the modeling framework developed by Barone-Nugent et al. (2015) and Mason et al. (2015b). Both methods suggest that the magnification is modest ($\mu \lesssim 1.5$) assuming a typical mass-to-light ratio, because the foreground galaxy is relatively faint. The maximum $\mu \sim 1.5$ is expected if the foreground galaxy is at $z\sim 2$, while we predict $\mu =1.2\pm 0.1$ in case of a deflector at $z\sim 0.8$, which is the redshift at which the lensing optical depth peaks (see Mason et al. 2015b).

borg_2140+0241_37 is comparably bright (${H}_{{\rm{160}}}$ = 24.94) to borg_2134–0708_774, and it is only detected in the two reddest bands of the survey. Therefore, the photometric redshift has a strong preference for $z\gt 10$ solutions. Like borg_2134–0708_774, this object is also close in projection ($1\buildrel{\prime\prime}\over{.} 02$ center to center) to a foreground brighter galaxy (${H}_{{\rm{160}}}$ = 24.05), also expected to be at $z\gtrsim 0.5$ because of the compact size, and therefore a potential lens. The lensing magnification predicted by our modeling is $\mu =1.2\pm 0.1$, essentially identical to that for borg_2134–0708_774 (see above), because the smaller angular separation compensates for the lower luminosity of the lensing galaxy. The dropout galaxy has an extended structure, especially in the ${H}_{{\rm{160}}}$-band image (${r}_{{\rm{e}}}=0\buildrel{\prime\prime}\over{.} 37$). Such spatial extension of the source is larger than that expected for a typical $z\sim 10$ candidate (${r}_{{\rm{e}}}\lesssim 0\buildrel{\prime\prime}\over{.} 2$), although still marginally smaller than confirmed low-z contaminants in CANDELS (${r}_{{\rm{e}}}\gtrsim 0\buildrel{\prime\prime}\over{.} 4;$ see Holwerda et al. 2015).

The Lyman-break selection for ${Y}_{{\rm{105}}}$–${{JH}}_{{\rm{140}}}$ dropouts peaks at z = 8.72, with a 95% confidence region from z = 7.75 up to z = 9.68 (see Figure 3). We identify three candidates with postage stamps and p(z) shown in Figure 5, all with the most likely redshift at or below the theoretical median of the distribution. However, not only is the sample size very small, but a skewed distribution is expected when the galaxy LF evolves rapidly over the redshift range covered by the selection (Muñoz & Loeb 2008).

borg_0116+1425_630 This object is exceptionally bright for a $z\gt 8$ candidate (${H}_{{\rm{160}}}$ = 24.53 mag, corresponding to ${M}_{\mathrm{AB}}\sim -22.8$). It is detected at high S/N (${\rm{S}}/{{\rm{N}}}_{140}=12.6$, ${\rm{S}}/{{\rm{N}}}_{160}=16.1$) and has a best photometric redshift solution z = 8.4 and compact size (${r}_{{\rm{e}}}=0\buildrel{\prime\prime}\over{.} 17$ after accounting for the ${H}_{{\rm{160}}}$ PSF). This source is about 0.5 mag brighter than the four $z\geqslant 7$ candidates in the EGS field presented by Roberts-Borsani et al. (2015), one of which shows an emission line consistent with Lyα at z = 8.68 (Zitrin et al. 2015; and two others have Lyα emission at z = 7.73 and $z=7.48;$ see Oesch et al. 2015; Roberts-Borsani et al. 2015). Our new source thus appears to be an ideal candidate for spectroscopic follow-up, with the potential to elucidate how galaxy formation proceeds for the brightest sources well into the epoch of reionization. The photometric redshift for the galaxy shows two peaks (Figure 5), but the lower redshift ($z\sim 1.8$) early-type SED is disfavored by the current data and by the compact size (see Holwerda et al. 2015). Possibly, part of the emitted flux of such a bright source could hint at the presence of an active galactic nucleus (Oesch et al. 2014).

borg_0956+2848_85 is the ${Y}_{{\rm{105}}}$–${{JH}}_{{\rm{140}}}$ candidate with the highest photometric redshift solution (z = 8.7). Despite being relatively faint (${H}_{{\rm{160}}}$ = 26.41) it is confidently detected because of the long exposure times (e.g., 4400 s in ${H}_{{\rm{160}}}$). The ${Y}_{{\rm{105}}}$–${{JH}}_{{\rm{140}}}$ drop is also the most prominent in the sample (${Y}_{{\rm{105}}}$–${{JH}}_{{\rm{140}}}$ = 2.1 ). Finally, its compact size (${r}_{{\rm{e}}}=0\buildrel{\prime\prime}\over{.} 08$ after accounting for the ${H}_{{\rm{160}}}$ PSF) strengthens the rejection of the alternative (already disfavored) photometric redshift solution at $z\sim 1.8$.

borg_2229–0945_548 has a very significant drop in F105W (${Y}_{{\rm{105}}}$–${{JH}}_{{\rm{140}}}$= 2.04), which leads to a photo-z distribution sharply peaked at z = 8.4. The dropout galaxy, which has ${H}_{{\rm{160}}}$ = 25.12, is in very close proximity to a brighter $z\sim 2.1$ passive galaxy (${H}_{{\rm{160}}}$ = 22.58; center to center distance equal to 148, see bottom panel in Figure 5). Based on modeling of the foreground deflector following Barone-Nugent et al. (2015) and Mason et al. (2015b), we estimate a gravitational lensing magnification of the dropout flux $\mu \sim 1.3\pm 0.3$ or $\mu \sim 1.9\pm 0.7$, respectively.

While we optimized BoRG[z9–10] for searching objects at $z\gt 8$, the multi-band nature of the survey allows us to augment the sample of $z\quad \sim$ 7–8 objects as well. We identify candidates in this redshift range as ${Y}_{{\rm{105}}}$–${J}_{{\rm{125}}}$ dropouts. Four galaxies satisfy the selection requirements, with a wide range of luminosities (${m}_{\mathrm{AB}}\quad \sim$ 25–26.1 in the ${H}_{{\rm{160}}}$-band). As shown in Figure 6, the best photometric redshift solutions lie in the range $7.3\lt z\lt 8.0$. One caveat to this selection is that, unlike the earlier BoRG[z8] survey, which relied on the medium-band filter F098M for Y-band imaging, the use of F105W implies a partial overlap with F125W, resulting in a potentially higher contaminant fraction for ${Y}_{{\rm{105}}}$–${J}_{{\rm{125}}}$ LBGs (Stanway et al. 2008). However, the analysis of the size distribution of the sample appears reassuring because all four objects have ${r}_{{\rm{e}}}\leqslant 0\buildrel{\prime\prime}\over{.} 25$, and only one has ${r}_{{\rm{e}}}\gt 0\buildrel{\prime\prime}\over{.} 2$ after accounting for the PSF shape. Thus we have confidence that even in the absence of longer wavelength observations and despite the partial overlap of F105W with F125W, the purity of the sample that we constructed is high (see Section 6.2).

Following the prescription of Oesch et al. (2007, 2009, 2012), we ran simulations of artificial source recovery to derive the completeness function, C(m), and magnitude-dependent redshift selection function, $S(z,m)$, at $z\sim 9$ and $z\sim 10$ for each BoRG[z9–10] field. A detailed discussion of the simulations is presented by Oesch et al. (2012) (see also Bradley et al. 2012; Schmidt et al. 2014a for previous applications of the method to the BoRG[z8] survey). To summarize the method, artificial sources with a range of input magnitudes, sizes, SED, and redshifts are added to the science images. Then dropout catalogs are constructed following the steps described in Sections 3.2 and 4. From these catalogs we construct the completeness C(m) and source selection $S(z,m)$ functions. The procedure is carried out for each individual field (see Figure 9 for borg_0116+1425 field, representative of all those in our data set). The sum of all completeness-weighted selection functions, integrated over redshift, then determines the effective comoving volume probed as a function of source brightness:

$$\begin{eqnarray*}&&{V}_{{\rm{eff}}}(m)={\displaystyle \int }_{0}^{\infty }S(z,m)C(m)\displaystyle \frac{{dV}}{{dz}}{dz}.\end{eqnarray*}$$

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${V}_{{\rm{eff}}}(m)$ takes into account all aspects of the selection of high-z sources, including (1) loss of volume due to foreground sources and/or areas affected by persistence; (2) decreased detection efficiency as the survey magnitude limit is approached; (3) effects of photometric scatter which can move artificial sources at high-z outside the parameter space for selection. ${V}_{{\rm{eff}}}(m)$ is shown in Figure 9, from which it is immediately clear that the recovery efficiency of BoRG[z9–10] drops around ${m}_{\mathrm{AB}}=26.2$ in the ${H}_{{\rm{160}}}$-band for sources detected at ${\rm{S}}/{\rm{N}}\geqslant 8$. At bright magnitudes, the effective volume of the search for the $z\sim 9$ and $z\sim 10$ samples is respectively (2.5–3.5) × 10⁵ Mpc³ comoving.

Catalogs of high-z candidates selected from broadband imaging are potentially affected by contamination of low-z interlopers with similar colors, which may be Galactic stars, low/intermediate redshift galaxies, or spurious sources (Bouwens et al. 2015a).

In general stellar contamination is not a significant source of concern for resolved sources detected at ${\rm{S}}/{\rm{N}}\gtrsim 10$, since in that case the SExtractor CLASS_STAR parameter can reliably discriminate between stars and galaxies (Bouwens et al. 2015a). All sources in our main samples have CLASS_STAR $\lt \quad 0.5$ (see Table 2), which is comfortably different from CLASS_STAR $\gt \quad 0.9$ typically measured for point sources. In addition, stars with red IR colors have a surface density that remains relatively flat in the magnitude range $21\lesssim {m}_{\mathrm{AB}}\lesssim 26$ (Holwerda et al. 2014).

The color–color criteria we adopted for the selection of ${Y}_{{\rm{105}}}$–${{JH}}_{{\rm{140}}}$ and ${J}_{{\rm{125}}}$–${H}_{{\rm{160}}}$ dropouts exclude the contamination from dwarf stars based on the colors of these sources. On the other hand, the sample of ${Y}_{{\rm{105}}}$–${J}_{{\rm{125}}}$ dropouts is potentially contaminated by cold red stars. From archival near-IR HST data sets (Ryan et al. 2011; Holwerda et al. 2014), we estimate that $n\sim 2$ T dwarf stars may enter our selection box within the current survey area. From the values of CLASS_STAR and r_e for ${Y}_{{\rm{105}}}$–${J}_{{\rm{125}}}$ dropouts we conclude that only borg_0835+0310_145 is compact enough to be a potential stellar interloper of the $z\sim 7.5$ dropout sample.

We do not expect that contamination by spurious sources is a concern because of our requirement of ${\rm{S}}/{\rm{N}}\gt 8$ in the detection images combined with stringent color cuts. Schmidt et al. (2014a) analyzed BoRG[z8] data to characterize the noise distribution, and found that on average, one spurious source for each WFC3 pointing is detected at ${\rm{S}}/{\rm{N}}\gt 8$ because of hot pixels or detector persistence. However, the colors of these spurious sources are not as red as $z\gt 8$ dropouts because hot pixels or persistence affect all IR bands. In fact, Schmidt et al. (2014a) found that no spurious source was identified as a dropout within the 350 arcmin² of the full BoRG[z8] data set. Applying the conclusions of that study to the BoRG[z9–10] data analyzed here, we expect $n\lesssim 0.3$ spurious sources in our samples. Note also that since images in all filters are acquired for each pointing in a single visit (lasting less than 8 hr), contamination by transients such as supernova events is negligible since only a $z\gt 8$ event would have the right colors to enter into our selection.

Finally, we discuss the most significant source of contamination: foreground galaxies with colors similar to $z\gt 8$ sources. Two main classes of objects may enter our LBG selection: intermediate-age galaxies at $z\quad \sim$ 1–3 with a prominent Balmer break, and strong line emitters, which have IR broadband flux dominated by nebular lines (e.g., Hα, [${\rm{O}}\;{\rm{II}}$], and [${\rm{O}}\;{\rm{III}}$]; Atek et al. 2011; Pacifici et al. 2015) and an undetected faint continuum flux at optical wavelengths (Bouwens et al. 2015a). Spectroscopic follow-up of $z\sim 8$ dropouts in BoRG[z8] has found no evidence of strong emitters after targeting 15 sources (Treu et al. 2012, 2013; Barone-Nugent et al. 2015) selected from ∼350 arcmin². Even more stringently, Bouwens et al. (2015a) estimate that the extreme line emitters capable of contaminating a dropout sample at $z\sim 8$ have a density of $\sim {10}^{-3}$ arcmin⁻². We thus estimate that extreme line emission is not a significant concern, although one caveat is that the only $z\gtrsim 10$ candidate identified in the UDF field may be an [${\rm{O}}\;{\rm{III}}$] emitter at $z\sim 2.2$ (see Brammer et al. 2013). The primary source of contaminants in our sample is thus expected to be intermediate redshift galaxies with a strong Balmer break. Estimating the precise contamination fraction from these sources is very challenging, but we expect it to be in the range of ∼30% based on the analysis of spectral templates. This figure is comparable to previous estimates of the BoRG[z8] sample purity at the 20%–30% level (Trenti et al. 2011; Bradley et al. 2012; Schmidt et al. 2014a). This rate is larger than the typical contamination rate $\lesssim 10\%$ found in Lyman-break samples from legacy fields such as HUDF and CANDELS, which have extensive multi-observatory coverage (Bouwens et al. 2015a). In the absence of observations with Spitzer/IRAC which would help to discriminate between intermediate-z ellipticals and $z\gt 8$ starbursts, it is possible to use size as a proxy for the ${H}_{{\rm{160}}}$–[4.5] color (Holwerda et al. 2015), and reassuringly our dropouts sources are generally too compact to be contaminants. One caveat is that the size proxy is unable to discriminate against contamination from sources with unusual colors at $z\quad \sim$ 6–7, since they would be similarly compact. Of course, the availability of observations over a wider range of wavelengths would help to identify the presence, if any, of such population in our samples.

The presence of a non-negligible, but not overwhelmingly large, contamination fraction can be inferred from the analysis of the redshift probability distribution for the candidates in our sample, as well. For sources at $z\sim 10$, we measure that the probability of being at $z\lt 8$ is p = 39%, while for $z\sim 9$ dropouts the photo-z estimates are more peaked at high redshift, and there is only an average probability p = 11% for the candidates to be at $z\lt 7$. These estimates are in agreement with the study by Pirzkal et al. (2013), where a ∼21% contamination fraction is derived for typical samples of galaxies at $8\lt z\lt 12$ identified from HST imaging.

Combining all the different approaches to the contamination issue, we assume 30% as a baseline estimate of the contamination rate, which is close to the weighted average from the photo-z${\rm{[}}(0.39\times 3+0.11\times 2)/5\sim 0.28$]. Thus, we would expect that one to two of the five sources reported in this paper may be low-redshift interlopers.

Combining the effective volume and contamination estimates, we derive a step-wise LF for the $z\sim 9$ and $z\sim 10$ samples, which we report in Table 4 and plot in Figure 10. The determination is severely limited by the large Poisson uncertainty, but the comparison with existing constraints on the LF, shown as gray points in the figure, is informative. Our determination of the bright end of the $z\sim 9$ LF is consistent with the latest measurement by Bouwens et al. (2015b) at ${M}_{\mathrm{AB}}\gt -21.5$, and at ${M}_{\mathrm{AB}}=-22.2$ the measured number density of ${3.7}_{-3.1}^{+8.3}\times {10}^{-6}{\mathrm{Mpc}}^{-3}{\mathrm{mag}}^{-1}$ is within the predictions of the LF model of Mason et al. (2015a), which is successful in describing the LF evolution with redshift. The striking difference with previous searches and with theoretical predictions is the detection of the exceptionally bright candidate borg_0116+1425_630, which, if confirmed, would argue against an exponential decline at the bright end and point instead of a power-law LF. One possibility is, of course, that such object is a contaminant, but intriguingly the recent spectroscopic confirmation at $z\sim 8.7$ of a ${m}_{\mathrm{AB}}\sim 25$ source (Zitrin et al. 2015) may suggest that intense starbursts of the order of $50\;{M}_{\odot }{\mathrm{yr}}^{-1}$ could become relatively more common as the redshift increases, and merger-driven activity increases over smooth gas accretion.

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Table 4.  BoRG[z9–10] Step-wise Rest-frame UV LF at $z\sim 8.7$ and $z\sim 10$

z	${M}_{\mathrm{UV},\mathrm{AB}}$	ϕ(10−6 Mpc−3 mag−1)
8.7	−22.97	${3.3}_{-2.7}^{+7.7}$
	−22.17	${3.7}_{-3.1}^{+8.3}$
	−21.37	${6.9}_{-5.6}^{+16.2}$
	−20.57	$\lt 110$
10	−23.13	$\lt 4.7$
	−22.33	${5.4}_{-3.5}^{+7.6}$
	−21.53	$\lt 12$

Note. Upper limits are 1σ.

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At $z\sim 10$, the situation is similar. At face value, our LF determination appears too high compared to expectations for objects with ${m}_{\mathrm{AB}}\lt 25.5$. One possibility to explain our results would be significant lensing magnification because both candidates are close in proximity to brighter foreground sources. At the current stage and without follow-up studies of the two candidates to increase the confidence on their $z\sim 10$ nature, it is difficult to draw firmer conclusions. Of course, the full BoRG[z9–10] data set will allow us to investigate whether this initial overabundance of very bright candidates is confirmed or not and to systematically account for the effect of lensing magnification on the LF via Bayesian methods (Schmidt et al. 2014a; Mason et al. 2015b).

As an additional consistency check for the number of detections reported here compared to theoretical expectations, we estimated the number density by integrating the Mason et al. (2015b) LF model (see also Trenti et al. 2010; Tacchella et al. 2013) over the effective volume of the survey. For $z\sim 9$, we estimate a total of $\langle n\rangle ={1.1}_{-0.7}^{+1.5}$ detections, consistent with the n = 2.1 observed after accounting for 30% contamination. For $z\sim 10$, the expectation is $\langle n\rangle ={0.1}_{-0.08}^{+0.25}$ detections, so this is in mild ($\sim 2\sigma$) tension with the observed number after accounting again for 30% contamination. If compared to expectations from the $z\sim 8$ LF, which would predict $\langle n\rangle \sim 11$ detections in the survey, our result of an contamination-corrected sample size of $n\sim 3.5$ sources indicates a decline in bright galaxies with increasing redshift, confirming the clear trend previously established observationally and by theoretical modeling (e.g., Oesch et al. 2012; Bouwens et al. 2015a; Mason et al. 2015a).

Finally, regarding the sample of four objects at $z\sim 7.3{\rm{\mbox{--}}}8$, we defer the study of the LF until the full data set has been acquired, since the new area (∼130 arcmin²) is only a modest improvement over the existing BoRG[z8] data (∼350 arcmin²) and the determination from the combination of all HST archival data (∼1000 arcmin²; Bouwens et al. 2015b).

Catalogs constructed using F160W only as detection image have been processed to search for candidates at $z\gt 7$ following the main text analysis based on F140W+F160W selection. We identified three new sources at $z\gt 7$, that were not included in the main samples because of slightly different colors or because a hot pixel was present within their segmentation maps. Interestingly, one of these new candidates, borg_1209+4543_1696, is identified as a ${Y}_{{\rm{105}}}$–${{JH}}_{{\rm{140}}}$ dropout, with a photometric redshift distribution strongly peaked at z = 8.56 and ${H}_{{\rm{160}}}$ ∼ 25.0. The reason why the source is not in the main catalog is because of the presence of an hot pixel, correctly identified by the reduction pipeline, in the outskirts of the dropout image. The pixel is included in the segmentation map of the F160W+F140W detection image, thereby excluding the galaxy from further analysis owing to our choice to discard any source with one or more pixels having zero weight (infinite rms). With the selection in F160W only, the galaxy segmentation map avoids inclusion of the hot pixel, and SExtractor photometry is performed without incurring in unbound errors. Since the defect is located in the outer parts of the image of the galaxy, we consider this source a credible high-z candidate, albeit its relatively large size would indicate the possibility that the low-z solution is preferred. The other two sources are less interesting and have a high probability of being contaminants, with colors near/at the boundary of the selection region.

We analyzed the photometric redshift probability distribution for each galaxy in our catalogs, constructed following the procedure introduced in Section 3.3. No new candidates at $z\gt 7$ have been identified this way.

Three sources listed in Table 5 and shown in Figure 11 have a p(z) distribution peaked at $z\quad \sim$ 1–2 and magnitudes ${H}_{{\rm{160}}}$ ∼ 25–26.5. The existing data make it difficult to evaluate whether this is an effect of photometric scatter versus the presence of a population of interlopers with similar near-IR colors, especially because the compact effective radii of these sources would corroborate the high-z photometric solution.

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Table 5.  BoRG[z9–10] Additional High-z Candidates

Obj ID	α(J2000)	δ(J2000)	${H}_{{\rm{160}}}$	Colors				S/N					re	Stellarity	Photo-z
	(deg)	(deg)	(AB mag)	${Y}_{{\rm{105}}}$–${J}_{{\rm{125}}}$	${J}_{{\rm{125}}}$–${H}_{{\rm{160}}}$	${Y}_{{\rm{105}}}$–${{JH}}_{{\rm{140}}}$	${{JH}}_{{\rm{140}}}$–${H}_{{\rm{160}}}$	F350LP	${Y}_{{\rm{105}}}$	${J}_{{\rm{125}}}$	${{JH}}_{{\rm{140}}}$	${H}_{{\rm{160}}}$
0751+2917_1211	117.7138	+29.2929	26.97 ± 0.35	0.53 ± 0.20	−0.66 ± 0.23	0.43 ± 0.20	−0.56 ± 0.23	−0.1	6.3	10.3	10.0	5.2	ps	0.93	7.3
1209+4543_1696	182.3859	45.7250	24.96 ± 0.40	1.47 ± 0.51	0.31 ± 0.13	2.15 ± 0.51	−0.37 ± 0.10	0.1	2.2	10.4	16.2	15.2	0.28	0.03	8.6
0853+0310_912	133.1798	+3.1730	24.99 ± 0.16	0.94 ± 0.13	0.48 ± 0.25	1.10 ± 0.24	0.32 ± 0.12	0.6	4.8	10.4	12.7	13.4	0.18	0.02	1.6
0925+1360_965	141.2946	+14.0056	26.23 ± 0.28	0.71 ± 0.35	0.13 ± 0.25	0.90 ± 0.34	0.06 ± 0.24	1.1	3.6	6.3	6.9	6.1	0.10	0.01	1.1
1048+1518_1140	161.9818	+15.2961	26.40 ± 0.20	1.04 ± 0.63	0.68 ± 0.29	1.52 ± 0.60	0.20 ± 0.21	−0.1	1.9	4.5	7.3	7.4	0.08	0.19	1.9
0119–3411_22	19.7015	−34.1830	25.06 ± 0.15	0.84 ± 0.31	0.38 ± 0.17	1.10 ± 0.28	0.12 ± 0.13	0.4	4.0	7.5	12.7	11.8	0.14	0.04	1.5

Note. Coordinates and photometric properties of the additional dropout candidates that are discussed in the Appendix. Columns 2–3: α and δ coordinates in degrees. Column 4: total magnitude in the ${H}_{{\rm{160}}}$ band from SExtractor MAG_AUTO. Columns 5–6: IR colors from SExtractor MAG_ISO. Columns 7–11: S/N in each band. Column 12: effective radius r_e in arcseconds measured by SExtractor and corrected for PSF. Column 13: stellarity index in the ${H}_{{\rm{160}}}$-band image from SExtractor CLASS_STAR. Column 14: photometric redshift obtained from BPZ.

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The selection of $z\quad \sim$ 7.5–8 sources in Section 4 has been optimized for the BoRG[z9–10] filter set. Here, we select sources following the criteria adopted for legacy fields data by Bouwens et al. (2015a), with the main difference being the availability of a larger number of blue (non-detection) bands from surveys such as GOODS/CANDELS (Grogin et al. 2011). The selection criteria are:

$$\begin{eqnarray*}&&S/{N}_{350}\lt 1.5\\ &&S/{N}_{125}\geqslant 6\\ &&S/{N}_{140}\geqslant 6\\ &&S/{N}_{160}\geqslant 4\\ &&{m}_{105}-{m}_{125}\gt 0.45\\ &&{m}_{105}-{m}_{125}\gt 0.75\cdot ({m}_{125}-{m}_{160})+0.525\\ &&{m}_{125}-{m}_{160}\lt 0.5.\end{eqnarray*}$$

With this selection, we identified one additional ${Y}_{{\rm{105}}}$–${J}_{{\rm{125}}}$ dropout, borg_0119–3411_22, which is quite bright (${H}_{{\rm{160}}}$ = 25.06). In the near-IR color–color space, the source lies close to the boundaries of the selection box for high-z objects. Therefore, it is not surprising that the photometric redshift distribution shows both a low and an high-redshift peak (Figure 11), with the low-z solution marginally preferred. The uncertainty in the nature of this source is compounded by the relatively short exposure time in F350LP, which was constrained by readout conflicts with the primary observations associated to this opportunity.

The conclusion from these additional searches is that the failure to identify credible additional candidates both with an alternate color selection and with a photo-z analysis (Section 3.3) confirms that the selection criteria adopted in the main text are robust.