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On the detection of a cosmic dawn signal in the radio background

Abstract

The astrophysics of the cosmic dawn, when star formation commenced in the first collapsed objects, is predicted to be revealed by spectral and spatial signatures in the cosmic radio background at long wavelengths. The sky-averaged redshifted 21 cm absorption line of neutral hydrogen is a probe of the cosmic dawn. The line profile is determined by the evolving thermal state of the gas, radiation background, Lyman α radiation from stars scattering off cold primordial gas, and relative populations of the hyperfine spin levels in neutral hydrogen atoms. We report a radiometer measurement of the spectrum of the radio sky in the 55–85 MHz band, which shows that the profile found by Bowman et al. in data taken with the Experiment to Detect the Global Epoch of Reionization Signature (EDGES) low-band instrument is not of astrophysical origin; their best-fitting profile is rejected with 95.3% confidence. The profile was interpreted to be a signature of the cosmic dawn; however, its amplitude was substantially higher than that predicted by standard cosmological models. Our non-detection bears out earlier concerns and suggests that the profile found by Bowman et al. is not evidence for new astrophysics or non-standard cosmology.

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Fig. 1: Spectrum of the radio sky.
Fig. 2: Comparison of residuals.
Fig. 3: MCMC modelling of the SARAS 3 spectrum.
Fig. 4: One-dimensional distribution of s.
Fig. 5: Comparison of residuals from modelling of spectra from SARAS 3 and EDGES low-band instruments.

Data availability

The processed spectrum that supports the findings of this study are available from the corresponding authors on reasonable request.

Code availability

The dataset is stored and preprocessed in Miriad (https://www.atnf.csiro.au/computing/software/miriad/). Later stages of analysis are done in Python, extensively using publicly available routines in SciPy (https://scipy.org/), NumPy (https://numpy.org/) and matplotlib (https://matplotlib.org/). MCMC analysis was done using the emcee package (https://github.com/dfm/emcee), and Bayesian evidence was computed using PolyChord (https://github.com/PolyChord/PolyChordLite). Other processing and analysis steps were performed using custom code, which is available from the corresponding author upon reasonable request.

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Acknowledgements

We thank the staff at the Gauribidanur Field Station for assistance with system tests and measurements, and the Mechanical and Electronics Engineering Groups at the Raman Research Institute for building, assembling and deploying SARAS 3. S.S. thanks J. Mirocha and A. Liu for useful discussions on global signal modelling and acknowledges support from a McGill Astrophysics postdoctoral fellowship. J.N.T. acknowledges a PhD bursary from the Australian Research Council (ARC) Future Fellowship under grant FT180100196.

Author information

Authors and Affiliations

Authors

Contributions

R. Subrahmanyan led the experiment and supervised all stages of this work. N.U.S. contributed to all activities of the work and provided expert feedback. S.S. performed the data selection, modelling and signal inference. J.N.T. formulated analogue receiver calibration and characterization, and contributed to data analysis. B.S.G., K.S.S., R. Somashekar and A.R. engineered various subsystems of the experiment, and participated in system testing and deployment. M.S.R. provided the physically motivated sky model and formulated MS functions used for qualifying the system.

Corresponding author

Correspondence to Saurabh Singh.

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The authors declare no competing interests.

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Nature Astronomy thanks the anonymous reviewers for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 The architecture of the SARAS 3 instrument.

The building blocks and signal flow within the instrument is shown from the antenna to the digital spectrometer.

Extended Data Fig. 2 Laboratory verification of the receiver.

Panel (a) shows the calibrated spectrum measured in the laboratory, in a long duration test, with the antenna replaced by an antenna simulator that approximates its reflection coefficient. A maximally smooth function fit to the spectrum is shown using a red dashed line; the data, with deviations from the fit magnified by factor 50, is shown as a blue continuous line. Panel (b) shows the residuals smoothed to successively lower resolutions, using a Hann function, from the native 61 kHz to 3.906 MHz. The FWHM of the smoothing functions and the r.m.s. values of the residuals on smoothing is in the legend. For clarity, the traces have been offset vertically and magnified by the ratio of r.m.s. value after smoothing to the r.m.s. value at native resolution.

Extended Data Fig. 3 Antenna reflection efficiency.

Panel (a) shows the fit of a maximally smooth function to the reflection efficiency, as a red dashed line. Overlaid is the measurement, as a continuous blue line, with the difference between the fit and measurement magnified by factor 1000 for clarity. Panel (b) shows the difference between the measurement and MS fit.

Extended Data Fig. 4 Antenna total and radiation efficiencies.

Panel (a) shows the total and radiation efficiencies of the instrument as measured on water in the Sharavati backwaters, where most of the observations were made. Maximally smooth function fit to the total efficiency is overlaid on the measurement and the difference between the best-fit MS function and measurement is shown in panel (b).

Extended Data Fig. 5 Antenna temperature versus LST.

Measured sky brightness temperature versus local sidereal time (LST), at a set of frequencies across the science band. The brightness temperature at any time is the global radio sky viewed by the antenna beam pattern of the instrument.

Extended Data Fig. 6 Systematic calibration errors.

Panel (a) shows mock sky spectra constructed using erroneous calibrations for efficiencies, water thermal emission and receiver noise, with errors reflecting the uncertainties in their derivations. Panels (b), (c), (d), (e) and (f) show, in red, the systematic residuals that might result due to calibration errors and using 3rd, 4th, 5th, 6th or 7th order polynomials to represent the foreground. For comparison we also show, in black, the residual expected if the global sky spectrum contained the profile found by Bowman et al.

Extended Data Fig. 7 Model selection.

Panel (a) shows the logarithm of Bayesian evidence versus the order of the polynomial: in black symbols is shown the case where the sky spectrum measured by SARAS 3 is modelled as a polynomial plus the profile found by Bowman et al. with a free scaling factor, and in red symbols is shown the case where the model is a plain polynomial. Higher the evidence, more preferred is the model. Panel (b) shows Bayesian Information Criterion: here models with lower values are preferred. Panel (c) shows the r.m.s. value of the residuals smoothed to 1.4 MHz resolution: the spectrum is consistent with measurement noise for 6th and higher orders; panel (d) shows exclusively the r.m.s. values for order 6 and above.

Extended Data Fig. 8 Breakdown of systematic calibration errors.

Mock spectra of the global sky are generated using GSM and GMOSS, using the SARAS 3 beam, and processed to impress systematic errors in calibrations for total efficiency (panels a, b & c), water thermal emission (panels d, e & f) and receiver noise (panels g, h & i). Calibrations with errors that span the expected distributions are shown in the first column; our best-estimate calibrations are also shown using black dashed lines. Mock spectra with these calibration errors are shown in the second column. Residuals from fitting and subtracting out 6th-order polynomials from the mock spectra are shown in the last column.

Extended Data Fig. 9 Deriving confidence of rejection for the profile found by Bowman et al.

Panel (a) shows flattened Gaussian profiles with parameters in the ranges charted by Bowman et al.5; the relative density in profile space displays their relative likelihood. The dashed orange line shows the best-fitting profile; the vertical lines and shaded domain demarcate the band used in our analysis. Lower panels show the result of MCMC analysis using the SARAS 3 data that determines the distribution of scale factors associated with each of these profiles: (b) shows distribution of scale factors for the whole class and (c) shows the distribution of the mean scale factor corresponding to each of the profiles. The shaded region in panel (b) spans 5th − 95th percentile values, and dash–dotted black line represents the mean. The dashed black lines represent 1σ confidence intervals. The profile set in panel (a) as a whole is rejected with 90.4% confidence.

Extended Data Fig. 10 Rejection confidence in the space of profile parameters.

Scatter plots corresponding to the different parameters are shown in separate panels, colour coded to show the significance with which the different parameter spaces are rejected by our MCMC analysis using the SARAS 3 data. The parameters describing the signal are the same as in Bowman et al.: amplitude (in mK), central frequency f0 (in MHz), full width at half maximum (FWHM) of the profile (in MHz) and the flattening parameter τ.

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Singh, S., Nambissan T., J., Subrahmanyan, R. et al. On the detection of a cosmic dawn signal in the radio background. Nat Astron 6, 607–617 (2022). https://doi.org/10.1038/s41550-022-01610-5

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