Release Paper

On the Origin of Near-Infrared Extragalactic Background Light Anisotropy

Michael Zemcov, Joseph Smidt, Toshiaki Arai, James Bock, Asantha Cooray, Yan Gong, Min Gyu Kim, Phillip Korngut, Anson Lam, Dae Hee Lee, Toshio Matsumoto, Shuji Matsuura, Uk Won Nam, Gael Roudier, Kohji Tsumura and Takehido Wada

Abstract: Extragalactic background light (EBL) anisotropy traces variations in the total production of photons over cosmic history, and may contain faint, extended components missed in galaxy point source surveys. Infrared EBL fluctuations have been attributed to primordial galaxies and black holes at the epoch of reionization (EOR), or alternately, intra-halo light (IHL) from stars tidally stripped from their parent galaxies at low redshift. We report new EBL anisotropy measurements from a specialized sounding rocket experiment at 1.1 and 1.6 micrometers. The observed fluctuations exceed the amplitude from known galaxy populations, are inconsistent with EOR galaxies and black holes, and are largely explained by IHL emission. The measured fluctuations are associated with an EBL intensity that is comparable to the background from known galaxies measured through number counts, and therefore a substantial contribution to the energy contained in photons in the cosmos.
PDF | Figures | arXiv | ADS

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Data Products

CIBER Power Spectra
CIBER 1.1 x 1.1 micron Auto-Spectra CIBER 1.6 x 1.6 micron Auto-Spectra CIBER 1.1 x 1.6 micron Cross-Spectra	CIBER auto- and cross-power spectra of 1.1 micron, 1.6 micron, and 1.1x1.6 micron images from Figure 1 of Zemcov et al. (2014)
CIBER 1.1 x Spitzer 3.6 micron Cross-Spectra CIBER 1.6 x Spitzer 3.6 micron Cross-Spectra Spitzer 3.6 x 3.6 micron Auto-Spectra	Cross-power spectra of CIBER 1.1 micron and 1.6 micron with Spitzer 3.6 micron, and Spitzer 3.6 micron auto-power spectra. Images were masked to a threshold of L< 16 threshold, corresponding to Figure 1 of Zemcov et al. (2014).

Figures

Figures from Zemcov et al. 2014 (Report and Supplementary Materials)
Figure 1 (pdf) Figure 1 (png)		CIBER and Spitzer auto- and cross-spectra at 1.1, 1.6 and 3.6 μm. We show the CIBER auto-spectra for 1.1×1.1 μm, and 1.6×1.6 μm (panel a), the CIBER 1.1×1.6 μm cross-spectrum (panel b), the CIBER–Spitzer 1.1 × 3.6 μm and 1.6 × 3.6 μm cross-spectra (panel c), and Spitzer 3.6 × 3.6 μm auto-spectra (6) (panel d). At 1.1, 1.6 and 3.6 μm we indicate previous measurements (open circles; note the HST measurements apply a much deeper flux cut at 1.1 and 1.6 μm for masking, and the Spitzer flux cut is somewhat deeper than the cut we are applying at 3.6 μm). The 3.6 μm points use the same data set but are masked to a lower source flux for comparison to our spectrum which is masked to L < 16. The increased depth reduces some of the mid-l power. We show constraints on astrophysical foregrounds, including unmasked stars and z < 5 galaxies, zodiacal light, and diffuse Galactic light. In all cases, we detect a significant excess power at l < 5000 (angular separations θ > 4.3 deg). We model the data using components from IHL and z > 7 first galaxies, obtaining a 95% confidence upper limit on the EOR contribution. The fitted total indicated by the filled band includes all of the astrophysical components plus a bounded systematic error for flat-field variations. The width of the band indicates the 68 % uncertainty interval of the fit including all of the modeling uncertainties. The correlation coefficients for 1.1 × 1.6 μm, 1.1 × 3.6 μm, and 1.6 × 3.6 μm are 0.76 ± 0.10, 0.55 ± 0.14 and 0.31±0.14, respectively, with no statistically significant angular dependence in the correlations (see Section 6.2 of the Supplementary Materials for a description of this calculation).
Figure 2 (pdf) Figure 2 (png)		The electromagnetic spectrum of the near-infrared fluctuations. We show measurements of the fluctuation power from CIBER and Spitzer averaged between 500 < l < 2000 (solid points). Also indicated are previous measurements from AKARI and Spitzer at l = 3000 which use deeper masking thresholds. In all cases we subtract the contribution from the shot noise of unmasked galaxies. We indicate the best fitting Rayleigh-Jeans spectrum to the points from this analysis, estimates for diffuse Galactic light fluctuations, a conservative constraint on zodiacal light fluctuations, and an upper limit on Galactic emission. The known foreground components have both smaller amplitudes and different spectra than the measurements. We show the residual from the best fitting Rayleigh-Jeans spectrum δRJ in the bottom panel, scaled by λ3 to reduce the range. The shortest wavelength measurement at 1.1 μm is 2.0 σ below the fit, indicating a possible short-wavelength departure from a Rayleigh-Jeans spectrum.
Figure S1A (pdf) Figure S1A (png) Figure S1B (pdf) Figure S1B (png)		Imager flight images. The 1.1 μm image (left hand panel) and the 1.6 μm image (right hand panel) for NEP in the third flight are shown. No masking is applied to these images, which are the output of the pipeline presented in Section 3.1. These images are in array coordinates and are not yet astrometrically aligned.
Figure S2A (pdf) Figure S2A (png) Figure S2B (pdf) Figure S2B (png)		Masked flight images. We show the 1.1 μm (left hand panel) and 1.6 μm (right hand panel) images for NEP-3, masked with the algorithm derived in Section 3.2 using the 2MASS catalog. These images can be compared directly to the unmasked images shown in Figure S1.
Figure S3 (pdf) Figure S3 (png)		Mask validation. In this calculation, we generate simulated images from the NEWFIRM catalog including all sources to J' < J, convolved with the full PSF. These images are then masked, and their power spectra are calculated. For simulations including only sources brighter than the J < 17.5 cutoff flux (dashed lines), the residuals are from mask halos, which are below the statistical noise of the measurement (dotted line), estimated from the theoretical sensitivity of the instrument assuming uncorrelated white noise. Simulations with sources below the cutoff flux give power from galaxy clustering fluctuations that exceed the statistical sensitivity. To estimate clustering fluctuations from all galaxies, we extrapolate the J = 20 result by the fraction of the IGL which remains above this magnitude. The total residual galaxy fluctuations are less than the measured fluctuation power at low l. Note the l Cl notation on the y-axis.
Figure S4 (pdf) Figure S4 (png)		Field differenced third flight 1.1 μm image. The 1.1 μm image for the field combination (NEP − ELAIS-N1) in flight third flight is shown. The color scale is significantly smaller than in previous figures to highlight the faint structures in the images. In this combination, 67% of the pixels are masked.
Figure S5 (pdf) Figure S5 (png)		Field differenced flight 1.6 μm image. The 1.6 μm image for the field combination (NEP - ELAIS-N1) in flight third flight is shown, in which 65% of the pixels are masked. As it is referenced to the instrument, this image is rotated by 90◦ compared to Figure S4.
Figure S6 (pdf) Figure S6 (png)		Zoom of field differenced third flight 1.1 μm image. The 1.1 μm image for the field combination (NEP − ELAIS-N1) in flight third flight is shown for a region 12'×12' to show the detailed properties of the mask in a small region.
Figure S7 (pdf) Figure S7 (png)		Zoom of field differenced flight 1.6 μm image. The 1.6 μm image for the field combination (NEP - ELAIS-N1) in flight third flight is shown, again in a 12'×12' region. As it is referenced to the instrument, this image is rotated by 90◦ compared to Figure S6.
Figure S8 (pdf) Figure S8 (png)		Large angular scale structure in a field differenced third flight 1.1 μm image. The 1.1 μm image for the field combination (NEP − ELAIS-N1) in flight third flight are shown. The image has been smoothed with a Gaussian function with FWHM=7.2' (corresponding to l = 3000) to highlight large scale structure in the image.
Figure S9 (pdf) Figure S9 (png)		Large angular scale structure in a field differenced flight 1.6 μm image. The 1.6 μm image for the field combination (NEP - ELAIS-N1) in flight third flight is shown, smoothed with the same Gaussian FWHM=7.2' kernel as used in Figure S8. As it is referenced to the instrument, this image is rotated by 90◦ compared to Figure S8.
Figure S10A (pdf) Figure S10A (png) Figure S10B (pdf) Figure S10B (png)		Mode-coupling matrices for sample fields. We show two example mode-coupling matrices, one for the auto spectrum of the 1.1 μm channel Bootes A − NEP-2 (left panel) and the cross power spectrum of 1.1 μm Bootes A − NEP-2 with Spitzer (right panel). For a given li, the mode coupling operation computes the sum of the product of the uncorrected spectrum with the ith column of this matrix to rectify the bandpower. The strongest effect of the matrix is an overall multiplicative factor on the diagonal, which corrects for loss of power due to the mask. The next largest effect is an anti-correlation from mid-l to low-l, which is at least an order of magnitude smaller than the diagonal elements.
Figure S11 (pdf) Figure S11 (png)		Bl functions and errors for typical CIBER cross-spectra. The top panel shows typical Bl functions used to correct power spectra for the effect of beam apodization using the power spectral formalism of Section 4.2. In auto-spectra, the beam is slightly more compact than in the instrument cross-spectra, since some cross-combinations include broadening in both beams. The bottom panel shows the fractional error in two example Bl, which have only small deviations at l < 104.
Figure S12A (pdf) Figure S12A (png) Figure S12B (pdf) Figure S12B (png) Figure S12C (pdf) Figure S12C (png) Figure S12D (pdf) Figure S12D (png)		Input data sets used in the read noise model construction. The rows are for the 1.1 μm (upper) and 1.6 μm (lower) channels in the second flight for Bootes A, respectively. The columns show the average power spectrum of the input data sets (left) and the standard deviation of the input data sets (right). The structure of the noise is non-trivial in Fourier space and the noise model must adequately capture this behavior. The formulation of the noise model we use reproduces the observed mean and variance per (lx, ly) mode for each combination of instrument, field and flight to accurately model the properties of the instrument.
Figure S13A (pdf) Figure S13A (png) Figure S13B (pdf) Figure S13B (png) Figure S13C (pdf) Figure S13C (png)		Simulation verifying the pipeline estimate of input power spectra. In this figure we show a simulated data image generated from empirical models for the astrophysical emission and noise fluctuations (left panel), the image masked using the same algorithm as for flight data (central panel), and a comparison of the power spectra processed through the pipeline to the fiducial input (right panel). The solid curve corresponds to a power spectrum similar to that measured by CIBER. The image panels show this fluctuation power spectrum summed with empirical models for stars and galaxies, plus a noise model realization. After passing these images through the data analysis pipeline (circles in right panel), we reproduce the input power spectrum with no bias in a single realization. The plotted error bars show the total uncertainty of the measurement from the combination of statistical noise and sample variance. As another test case, we propagate a simulation without a low-l fluctuation component (i.e. the sum of Poisson distributed stars and galaxies, and instrument noise) through the pipeline (dotted line, right panel). Again, the band powers measured after processing match the input spectrum.
Figure S14A (pdf) Figure S14A (png) Figure S14B (pdf) Figure S14B (png) Figure S14C (pdf) Figure S14C (png) Figure S14D (pdf) Figure S14D (png)		The CIBER measured power spectra. The 1.1 μm×1.1 μm and 1.6 μm×1.6 μm auto-power spectra, and the 1.1 μm×1.6 μm cross- power spectrum (blue, red, and purple points, respectively) for each independent field difference in the CIBER second and third flights are shown. We show a model for the contribution from galaxies below our masking threshold as the colored bands, and the best-estimate of galaxy and star fluctuations as the solid curves. Finally, the dashed lines show the sum of the estimated instrumental systematic uncertainties from flat-field error, residual airglow, and mask halos following the color convention for each band combination.
Figure S15A (pdf) Figure S15A (png) Figure S15B (pdf) Figure S15B (png)		The CIBER – Spitzer cross-spectra. The Spitzer 3.6 μm auto-spectrum and the CIBER – Spitzer 3.6 μm cross-spectra in both Bootes A − NEP and Bootes B − ELAIS-N1. Error bars which pass through zero are plotted as upper limits, and band powers with negative expectation values are shown as barred 2σ upper limits. The level of the CIBER autospectrum fluctuations is indicated by the dotted line. The Spitzer auto-spectrum is computed for the entire SDWFS field, and extends to lower l modes than the cross-spectra available with CIBER. The solid colored regions show foreground galaxy models for the 1.1 μm×3.6 μm, 1.6 μm×3.6 μm, and 3.6 μm×3.6 μm combinations. Finally, we indicate the total CIBER instrumental systematic error estimates as dashed lines for each cross-spectrum. Note that Spitzer only detects fluctuations for l ≥ 800, and the clustering power departs from galaxy models at l ∼ 105 due to the greater point source masking depth in Spitzer. The fluctuations clearly depart from galaxy models at l ∼ 104 in the cross-correlation, at higher l than in the auto-spectrum, also due to the greater Spitzer masking depth. Note the lCl/2π convention in these plots.
Figure S16A (pdf) Figure S16A (png) Figure S16B (pdf) Figure S16B (png)		Instrumental systematic uncertainties in the CIBER fluctuations measurement. The CIBER auto- and cross-spectra for Bootes A − NEP in the second flight (left) and Bootes B − ELAIS-N1 in the third flight (left) are shown for 1.1 μm×1.1 μm (blue), 1.6 μm×1.6 μm (red), and 1.1 μm×1.6 μm (purple). In each case, the estimated systematic uncertainty associated with flat field error (dashed), residual airglow (triple dot dash), and residual halos from masked sources (dot dash) are indicated. The total instrumental systematic uncertainty is shown as a solid line for each band and field combination.
Figure S17 (pdf) Figure S17 (png)		Total instrumental systematic uncertainty in the CIBER fluctuations measurement. The CIBER auto- and cross-spectra for the mean of Bootes A - NEP-2 and Bootes B − ELAIS-N1 are shown for 1.1 μm×1.1 μm (blue), 1.6 μm×1.6 μm (red), and 1.1 μm×1.6 μm (purple). The total instrumental systematic uncertainty is shown as a solid line for each band combination. This figure can be compared directly to Figure 1 of the main paper.
Figure S18A (pdf) Figure S18A (png) Figure S18B (pdf) Figure S18B (png)		Effect of mask depth on auto-power spectra. The large panels show auto-spectra of the NEP-3 − ELAIS-N1 field difference at 1.1 (upper) and 1.6 (lower) μm as a function of masking flux threshold for 7 < m < mlim, where mlim is the masking flux. Images corresponding to sample mask thresholds are shown in the left hand column for both bands, corresponding to m ≥ 7 (upper) m ≥ 13 (middle) and m ≥ mlim (lower). As the limiting threshold magnitude is increased, the shot noise component of the power spectra decreases as expected. The low-l amplitude first rapidly decreases with the mask threshold, and then converges to an approximately constant power, as expected for a constant astrophysical fluctuation component.
Figure S19 (pdf) Figure S19 (png)		Validation of flight noise model. The difference between the flight noise and the equivalent noise model realizations as parametrized by the quantity δCldata − δClmodel where δCl is estimated as Equation 8 over l bins is shown. The points give the noise model weighted variance of this difference in the five science fields from both flights for a given readout chain. The plotted uncertainties are the total standard deviation of the noise model assuming the mean goes as 1/sum(1/σ2N) , summing over the science field combinations. The PTE of these residuals (listed in the legend) indicate that the noise model is a good description of the flight data.
Figure S20 (pdf) Figure S20 (png)		Flat-field error cross-correlation test. The cross-spectra between Bootes A − NEP in the second flight and Bootes B − ELAIS-N1 in the third flight are shown. The plotted statistical errors are derived from the noise model and include sample variance. The flat field systematic uncertainties in each case are plotted as dashed or dotted lines. At low l no bandpowers significantly exceed the flat field error, evidence that the systematic error bound is conservative. The probability to exceed χ2 for the null signal hypothesis in these crossspectra are {0.40, 0.06, 0.40, 0.92} (in the order appearing in the legend), indicating that these nominally uncorrelated images are consistent with zero. There is no evidence for additional systematics beyond those already identified in these data.
Figure S21 (pdf) Figure S21 (png)		Single-field cross-spectra. We compute the cross-spectra of individual flight images which share no astrophysical power, but may have common flat-field error. The spectra are computed in array coordinates to maximize the flat-field error. The lines show the flat-field systematic uncertainty estimate generated for each instance as an upper limit. The error bars include both statistical and sample variance. None of the auto-spectra exceed the flat-field error estimate beyond 1σ in any band power, evidence that our estimate is reasonable. The fields used in these power spectra are Bootes A crossed with NEP-2 Bootes B crossed with ELAIS-N1 in the third flight, and Bootes A crossed with ELAIS-N1 in between flights.
Figure S22 (pdf) Figure S22 (png)		Difference cross-spectra between instruments and flights. The cross-power spectra for every combination of Bootes A − NEP-2 and Bootes B − NEP-3. The left plot shows the two combinations of 1.1 μm×1.1 μm and 1.6 μm×1.6 μm between flights, and the right plot all combinations of 1.1 μm×1.6 μm between flights. Lower uncertainties that pass through zero are shown with a standard 1σ upper uncertainty, but as limits on the lower error bar. These cross-spectral combinations suffer additional variance arising from the field differencing used in this analysis, but are consistent with fluctuations detected in auto-spectra (Figure 1 of the main paper, represented here by long-dashed lines), most importantly at the low-l modes. The dotted lines show a simple shot-noise spectrum scaled to high l to aid in discriminating the deviation at mid- and low-l scales.
Figure S23A (pdf) Figure S23A (png) Figure S23B (pdf) Figure S23B (png)		DGL scaling limits and the CIBER – DGL cross-correlations. The autospectra of 100 μm IRAS far-infrared maps scaled to the near-infrared (dashed lines), the crosscorrelation of the CIBER images with the scaled 100 μm maps (points), and fits of the Cl ∝ l−3 DGL fluctuation power spectra to the CIBER – DGL cross-power spectra for l < 3500 (shaded intervals denoting ±1σ about best fit) are shown. Panels show Bo¨otes A - NEP-2 (left) and Bootes B − ELAIS-N1 in the third flight (right). We marginally detect a cross-correlation in the NEP combinations, where DGL is brightest, but not in the Bootes B − ELAIS-N1 combination, where DGL is faint.
Figure S24A (pdf) Figure S24A (png) Figure S24B (pdf) Figure S24B (png)		Estimated fluctuations in astrophysical foregrounds. The CIBER auto- and cross-spectra for Bootes A − NEP-2 (left) and Bootes B − ELAIS-N1 (right) are shown for 1.1 μm×1.1 μm (blue), 1.6 μm×1.6 μm (red), and 1.1 μm×1.6 μm (purple). The colored regions show the fluctuations of galaxies below the CIBER masking flux threshold. The estimated foreground contributions from residual stars below the CIBER masking flux threshold are indicated by dashed lines, and the total foreground from residual point sources (the sum of unmasked galaxies and stars) is indicated by a solid line for each band and field combination. DGL fluctuations are estimated by fitting the CIBER – 100 μm cross-spectra to a simple l−3 model shown by the shaded regions denoting 1σ about the best fit (dotted lines). Finally, upper limits to ZL fluctuations are indicated for ISO, Spitzer, and AKARI (dashed lines).
Figure S25A (pdf) Figure S25A (png) Figure S25B (pdf) Figure S25B (png)		The spectral energy distribution of IHL stars and high-redshift galaxies. Left: We show here the rest-frame SED templates used to describe IHL with all SED models normalized at 2.2 μm. They are from models related to old stellar populations with ages at 0.2, 0.8, 3, 5 and 8 Gyr from the literature. These SED models have a broad peak around 1 μm. Thus the fluctuations measured at 1.1 and 1.6 μm directly probe this peak at z < 1, but at 3.6 μm, fluctuations arise from a somewhat wider range of redshifts out to z ∼ 2. In the future, a precise assessment of the correlation between 1.1 and 3.6 μm could constrain IHL evolution. The SEDs have a ratio of ∼ 7 from 1.6 μm to 3.6 μm. Right: Here we show the SED templates of PopII and PopIII stars. These stars are expected in z > 6 galaxies during the epoch of reionization. For the PopII case, we assume a constant escape fraction with fesc = 0.5 and show SEDs as a function of the redshift. For the case with PopIII stars, we fix the minimum redshift and show three cases with fesc = 0.1, 0.5 and 1.0. The case with fesc = 1.0 results in no nebular emission lines and shows simply a PopIII stellar spectrum. This case has the largest color ratio between 1.6 and 3.6 μm with a value of ∼ 3. Due to nebular and free-free emission, all other cases have a 1.6 to 3.6 μm color ratio that is <∼ 3.
Figure S26 (pdf) Figure S26 (png)		Color of the rms fluctuation amplitude at 1.6 μm to 3.6 μm and 1.1 μm to 1.6 μm at l = 3000. To account for the effects of masking, we plot the CIBER and Spitzer rms fluctuation ratios in a narrow 500 < l < 2000 bin (filled blue circle) and a broader 500 < l < 5000 bin (open blue circle), after subtracting the contribution of residual galaxies. The narrower l range point has larger uncertainties but is a more reliable estimate of the constant-δI component of the large angular scale power in Spitzer. The indicated 1σ errors are derived from the combination of statistical and foreground galaxy model uncertainties. The measured fluctuations color is much bluer than the color of the IGL background, estimated from measurements of deep galaxy counts, and indicated by the red hatched region. The green family of lines shows the color ratios of IHL as a function of redshift from z = 0 and z = 3 in δz = 1 steps for red stellar populations with stars aged 0.2, 0.8, 3, 5, and 8 Gyr. The orange lines show the expected color of EOR galaxies containing PopII and PopIII stars following models of Ref. 3. The DCBH model predicts very low intensity at 1.1 and 1.6 μm due to the redshifted Lyman cutoff, as shown by the left-going limit. The color of the measured fluctuations are bluer than any of the modeled components.
Figure S27A (pdf) Figure S27A (png) Figure S27B (pdf) Figure S27B (png)		dF/dz and total intensity for the IHL model. Here we show the IHL intensity as a function of redshift at 1.1, 1.6 and 3.6 μm (left) and the total integrated intensity (right), based on best-fit models to the CIBER and Spitzer fluctuations data. The range of predictions shown here captures the 68% confidence parameter uncertainties in the model fits to the data. The total IHL intensity at 1.1, 1.6 and 3.6 μm is 4.6 ± 0.8, 3.5 ± 0.7, and 0.7 ± 0.2 nW m−2 sr−1, respectively.

Publications

CIBER Astrophysical Journal Supplement Series Instrument Special Edition

The Cosmic Infrared Background Experiment (CIBER): A Sounding Rocket Payload to Study the near Infrared Extragalactic Background Light

Zemcov, M.; Arai, T.; Battle, J.; Bock, J.; Cooray, A.; Hristov, V.; Keating, B.; Kim, M. G.; Lee, D. H.; Levenson, L. R.; Mason, P.; Matsumoto, T.; Matsuura, S.; Nam, U. W.; Renbarger, T.; Sullivan, I.; Suzuki, K.; Tsumura, K.; Wada, T. | ApJS, 207, 31 | 2013 | ADS

The Cosmic Infrared Background Experiment (CIBER): The Wide-field Imagers

Bock, J.; Sullivan, I.; Arai, T.; Battle, J.; Cooray, A.; Hristov, V.; Keating, B.; Kim, M. G.; Lam, A. C.; Lee, D. H.; Levenson, L. R.; Mason, P.; Matsumoto, T.; Matsuura, S.; Mitchell-Wynne, K.; Nam, U. W.; Renbarger, T.; Smidt, J.; Suzuki, K.; Tsumura, K.; Wada, T.; Zemcov, M. | ApJS, 207, 32 | 2013 | ADS

The Cosmic Infrared Background Experiment (CIBER): The Low Resolution Spectrometer

Tsumura, K.; Arai, T.; Battle, J.; Bock, J.; Brown, S.; Cooray, A.; Hristov, V.; Keating, B.; Kim, M. G.; Lee, D. H.; Levenson, L. R.; Lykke, K.; Mason, P.; Matsumoto, T.; Matsuura, S.; Murata, K.; Nam, U. W.; Renbarger, T.; Smith, A.; Sullivan, I.; Suzuki, K.; Wada, T.; Zemcov, M. | ApJS, 207, 33 | 2013 | ADS

The Cosmic Infrared Background Experiment (CIBER): The Narrow-Band Spectrometer

Korngut, P. M.; Renbarger, T.; Arai, T.; Battle, J.; Bock, J.; Brown, S. W.; Cooray, A.; Hristov, V.; Keating, B.; Kim, M. G.; Lanz, A.; Lee, D. H.; Levenson, L. R.; Lykke, K. R.; Mason, P.; Matsumoto, T.; Matsuura, S.; Nam, U. W.; Shultz, B.; Smith, A. W.; Sullivan, I.; Tsumura, K.; Wada, T.; Zemcov, M. | ApJS, 207, 34 | 2013 | ADS

Other Publications

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Observations of the Near-infrared Spectrum of the Zodiacal Light with CIBER

Tsumura, K.; Battle, J.; Bock, J.; Cooray, A.; Hristov, V.; Keating, B.; Lee, D. H.; Levenson, L. R.; Mason, P.; Matsumoto, T.; Matsuura, S.; Nam, U. W.; Renbarger, T.; Sullivan, I.; Suzuki, K.; Wada, T.; Zemcov, M. | ApJ, 719, 394 | 2010 | ADS

Analysis of Dark Data of the PICNIC IR Arrays in the CIBER

Lee, D. H.; Kim, M. G.; Tsumura, K.; Zemcov, M.; Nam, U. W.; Bock, J.; Battle, J.; Hristov, V.; Renbarger, T.; Matsumoto, T.; Sullivan, I.; Levenson, L. R.; Mason, P.; Matsuura, S.; Kim, G. H. | JASS, 27, 4 | 2010 | ADS

The cosmic infrared background experiment

Bock, James; Battle, John; Cooray, Asantha; Kawada, Mitsunobu; Keating, Brian; Lange, Andrew; Lee, Dae-Hea; Matsumoto, Toshio; Matsuura, Shuji; Pak, Soojong; Renbarger, Tom; Sullivan, Ian; Tsumura, Kohji; Wada, Takehiko; Watabe, Toyoki | New Astronomy Reviews, 50, 215 | 2006 | ADS

Conference Proceedings

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Studying extragalactic background fluctuations with the Cosmic Infrared Background ExpeRiment 2 (CIBER-2)

Lanz, Alicia; Arai, Toshiaki; Battle, John; Bock, James; Cooray, Asantha; Hristov, Viktor; Korngut, Phillip; Lee, Dae Hee; Mason, Peter; Matsumoto, Toshio; Matsuura, Shuji; Morford, Tracy; Onishi, Yosuke; Shirahata, Mai; Tsumura, Kohji; Wada, Takehiko; Zemcov, Michael | SPIE, 9143E, 3NL | 2014 | ADS

Cosmic Infrared Background ExpeRiment (CIBER): A probe of Extragalactic Background Light from reionization

Cooray, Asantha; Bock, Jamie; Kawada, Mitsunobu; Keating, Brian; Lange, Andrew; Lee, Dae-Hee; Levenson, Louis; Matsumoto, Toshio; Matsuura, Shuji; Renbarger, Tom; Sullivan, Ian; Tsumura, Kohji; Wada, Takehiko; Zemcov, Michael | IAUS,294,482 | 2012 | ADS

Fluctuations In The Cosmic Infrared Background Using the Cosmic Infrared Background ExpeRiment (CIBER).

Smidt, Joseph; Arai, T.; Battle, J.; Bock, J. J.; Cooray, A.; Frazer, C.; Hristov, V.; Keating, B.; Kim, M.; Lee, D.; Mason, P.; Matsumoto, T.; Mitchell-Wynne, K.; Nam, U.; Renbarger, T.; Smith, A.; Sullivan, I.; Tsumura, K.; Wada, T.; Zemcov, M. | AAS, 21931202S | 2012 | ADS

Imaging the Spatial Fluctuations in Cosmic IR Background from Reionization with CIBER

Frazer, Chris; Bock, J.; Cooray, A.; Kawada, M.; Kim, M.; Lee, D.; Levenson, L.; Matsumoto, T.; Matsumuura, S.; Mitchell-Wynne, K.; Renbarger, T.; Smidt, J.; Sullivan, I.; Arai, T.; Tsumura, K.; Wada, T.; Zemcov, M. | AAS, 21724903F | 2011 | ADS

The Cosmic Infrared Background Experiment: Flight Characterization Of The Ciber Narrow Band Spectrometer.

Levenson, Louis R.; Battle, J.; Bock, J. J.; Cooray, A.; Hristov, V.; Keating, B.; Lee, D.; Mason, P.; Matsumoto, T.; Matsuura, S.; Nam, U. W.; Renbarger, T.; Sullivan, I.; Suzuki, K.; Wada, T.; Zemcov | AAS, 21724902L | 2011 | ADS

The cosmic infrared background experiment (CIBER): instrumentation and first results

Zemcov, M.; Battle, J.; Bock, J.; Cooray, A.; Hristov, V.; Keating, B.; Lee, D. H.; Levenson, L.; Mason, P.; Matsumoto, T.; Matsuura, S.; Nam, U. W.; Renbarger, T.; Sullivan, I.; Tsumura, K.; Wada, T. | SPIE, 7735E, 1WZ | 2010 | ADS

Measuring Light from the Epoch of Reionization with CIBER, the Cosmic Infrared Background Experiment

Zemcov, Michael; Arai, Toshiaki; Battle, John; Bock, James J.; Cooray, Asantha; Hristov, Viktor; Keating, Brian; Kim, Min-Gyu; Lee, Dae-Hee; Levenson, Louis; Mason, Peter; Matsumoto, Toshio; Matsuura, Shuji; Mitchell-Wynne, Ketron; Nam, Uk Won; Renbarger, Tom; Smidt, Joseph; Sullivan, Ian; Tsumura, Kohji; Wada, Takehiko | Proceedings of Science | 2010 | ADS

Cosmic Infrared Background ExpeRiment (CIBER): A Probe of Extragalactic Background Light from Reionization

Cooray, A.; Bock, J.; Kawada, M.; Keating, B.; Lange, A.; Lee, D.-H.; Levenson, L.; Matsumoto, T.; Matsuura, S.; Renbarger, T.; Sullivan, I.; Tsumura, K.; Wada, T.; Zemcov, M. | ASPC, 418, 535C | 2009 | ADS

PhD. Theses

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Spectral measurements of the diffuse near-infrared radiation with the CIBER rocket experiments.

Toshiaki Arai | University of Tokyo | 2014 |

Probing the High-Redshift Universe Using Fluctuations in the Cosmic Microwave and Infrared Backgrounds

Joseph Michael Smidt | University of California Irvine | 2012 | ADS

CIBER : a near-infrared probe of the epoch of reionization

Ian Sorensen Sullivan | Caltech | 2011 | ADS

CIBER Observations of the Near-Infrared Spectrum of the Zodiacal Light

Kohji Tsumura | The University of Tokyo | 2010 | U-Tokyo