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

Nature Astronomy volume 6, pages 607–617 (2022)Cite this article

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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.

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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.

References

  1. Pritchard, J. R. & Loeb, A. 21 cm cosmology in the 21st century. Rep. Prog. Phys. 75, 086901 (2012).

    Article  ADS  Google Scholar 

  2. Fialkov, A., Barkana, R. & Visbal, E. The observable signature of late heating of the Universe during cosmic reionization. Nature 506, 197–199 (2014).

    Article  ADS  Google Scholar 

  3. Furlanetto, S. R., Oh, S. P. & Briggs, F. H. Cosmology at low frequencies: the 21 cm transition and the high-redshift Universe. Phys. Rep. 433, 181–301 (2006).

    Article  ADS  Google Scholar 

  4. Pritchard, J. R. & Loeb, A. Constraining the unexplored period between the dark ages and reionization with observations of the global 21 cm signal. Phys. Rev. D 82, 023006 (2010).

    Article  ADS  Google Scholar 

  5. Bowman, J. D., Rogers, A. E. E., Monsalve, R. A., Mozdzen, T. J. & Mahesh, N. An absorption profile centred at 78 megahertz in the sky-averaged spectrum. Nature 555, 67–70 (2018).

    Article  ADS  Google Scholar 

  6. Fialkov, A. & Barkana, R. Signature of excess radio background in the 21-cm global signal and power spectrum. Mon. Not. R. Astron. Soc. 486, 1763–1773 (2019).

    Article  ADS  Google Scholar 

  7. McGaugh, S. S. Predictions for the sky-averaged depth of the 21 cm absorption signal at high redshift in cosmologies with and without nonbaryonic cold dark matter. Phys. Rev. Lett. 121, 081305 (2018).

    Article  ADS  Google Scholar 

  8. Barkana, R. Possible interaction between baryons and dark-matter particles revealed by the first stars. Nature 555, 71–74 (2018).

    Article  ADS  Google Scholar 

  9. Muñoz, J. B. & Loeb, A. A small amount of mini-charged dark matter could cool the baryons in the early universe. Nature 557, 684–686 (2018).

    Article  ADS  Google Scholar 

  10. Barkana, R., Outmezguine, N. J., Redigolo, D. & Volansky, T. Strong constraints on light dark matter interpretation of the EDGES signal. Phys. Rev. D 98, 103005 (2018).

  11. Ewall-Wice, A. et al. Modeling the radio background from the first black holes at cosmic dawn: implications for the 21 cm absorption amplitude. Astrophys. J. 868, 63 (2018).

    Article  ADS  Google Scholar 

  12. Hills, R., Kulkarni, G., Meerburg, P. D. & Puchwein, E. Concerns about modelling of the EDGES data. Nature 564, E32–E34 (2018).

    Article  ADS  Google Scholar 

  13. Singh, S. & Subrahmanyan, R. The redshifted 21 cm signal in the EDGES Low-band spectrum. Astrophys. J. 880, 26 (2019).

    Article  ADS  Google Scholar 

  14. Bradley, R. F., Tauscher, K., Rapetti, D. & Burns, J. O. A ground plane artifact that induces an absorption profile in averaged spectra from global 21 cm measurements, with possible application to EDGES. Astrophys. J. 874, 153 (2019).

    Article  ADS  Google Scholar 

  15. Sims, P. H. & Pober, J. C. Testing for calibration systematics in the EDGES low-band data using Bayesian model selection. Mon. Not. R. Astron. Soc. 492, 22–38 (2019).

    Article  ADS  Google Scholar 

  16. Nambissan T., J. et al. SARAS 3 CD/EoR radiometer: design and performance of the receiver. Exp. Astron. 51, 193–234 (2021).

  17. Girish, B. S. et al. SARAS CD/EoR radiometer: design and performance of the digital correlation spectrometer. J. Astron. Instrum. 9, 2050006 (2020).

    Article  Google Scholar 

  18. Raghunathan, A. et al. A floating octave bandwidth cone–disk antenna for detection of cosmic dawn. IEEE Trans. Antennas Propag. 69, 6209–6217 (2021).

    Article  ADS  Google Scholar 

  19. CODE ionosphere products. http://ftp.aiub.unibe.ch/CODE/

  20. Thompson, A. R., Moran, J. M., Swenson, J. & George, W. Interferometry and Synthesis in Radio Astronomy 3rd edn (Springer, Cham, 2017).

  21. Vedantham, H. K. et al. Chromatic effects in the 21 cm global signal from the cosmic dawn. Mon. Not. R. Astron. Soc. 437, 1056–1069 (2014).

    Article  ADS  Google Scholar 

  22. Sathyanarayana Rao, M., Subrahmanyan, R., Udaya Shankar, N. & Chluba, J. Modeling the radio foreground for detection of CMB spectral distortions from the cosmic dawn and the epoch of reionization. Astrophys. J. 840, 33 (2017).

    Article  ADS  Google Scholar 

  23. de Oliveira-Costa, A. et al. A model of diffuse Galactic radio emission from 10 MHz to 100 GHz. Mon. Not. R. Astron. Soc. 388, 247–260 (2008).

    Article  ADS  Google Scholar 

  24. Zheng, H. et al. An improved model of diffuse galactic radio emission from 10 MHz to 5 THz. Mon. Not. R. Astron. Soc. 464, 3486–3497 (2016).

    Article  ADS  Google Scholar 

  25. Foreman-Mackey, D., Hogg, D. W., Lang, D. & Goodman, J. emcee: the MCMC hammer. Publ. Astron. Soc. Pac. 125, 306–312 (2013).

    Article  ADS  Google Scholar 

  26. Monsalve, R. A., Rogers, A. E. E., Bowman, J. D. & Mozdzen, T. J. Results from EDGES High-band. I. Constraints on phenomenological models for the global 21 cm signal. Astrophys. J. 847, 64 (2017).

    Article  ADS  Google Scholar 

  27. Singh, S. et al. SARAS 2 constraints on global 21 cm signals from the epoch of reionization. Astrophys. J. 858, 54 (2018).

    Article  ADS  Google Scholar 

  28. EDGES data releases. http://loco.lab.asu.edu/edges/edges-data-release/

  29. Schwarz, G. Estimating the dimension of a model. Ann. Stat. 6, 461–464 (1978).

    Article  MathSciNet  Google Scholar 

  30. Spinelli, M., Bernardi, G. & Santos, M. G. On the contamination of the global 21-cm signal from polarized foregrounds. Mon. Not. R. Astron. Soc. 489, 4007–4015 (2019).

    ADS  Google Scholar 

  31. Burns, J. O. et al. A space-based observational strategy for characterizing the first stars and galaxies using the redshifted 21 cm global spectrum. Astrophys. J. 844, 33 (2017).

    Article  ADS  Google Scholar 

  32. Patra, N., Subrahmanyan, R., Raghunathan, A. & Udaya Shankar, N. SARAS: a precision system for measurement of the cosmic radio background and signatures from the epoch of reionization. Exp. Astron. 36, 319–370 (2013).

    Article  ADS  Google Scholar 

  33. Patra, N., Subrahmanyan, R., Sethi, S., Shankar, N. U. & Raghunathan, A. SARAS measurement of the radio background at long wavelengths. Astrophys. J. 801, 138 (2015).

    Article  ADS  Google Scholar 

  34. Landecker, T. L. & Wielebinski, R. The galactic metre wave radiation: a two-frequency survey between declinations +25° and −25° and the preparation of a map of the whole sky. Aust. J. Phys. 23, 957–958 (1970).

    Article  Google Scholar 

  35. Singh, S. et al. SARAS 2: a spectral radiometer for probing cosmic dawn and the epoch of reionization through detection of the global 21-cm signal. Exp. Astron. 45, 269–314 (2018).

    Article  ADS  Google Scholar 

  36. Singh, S. et al. First results on the epoch of reionization from first light with SARAS 2. Astrophys. J. Lett. 845, L12 (2017).

    Article  ADS  Google Scholar 

  37. Sathyanarayana Rao, M., Subrahmanyan, R., Udaya Shankar, N. & Chluba, J. GMOSS: all-sky model of spectral radio brightness based on physical components and associated radiative processes. Astron. J. 153, 26 (2017).

    ADS  Google Scholar 

  38. Bevins, H. T. J. et al. maxsmooth: rapid maximally smooth function fitting with applications in Global 21-cm cosmology. Mon. Not. R. Astron. Soc. 502, 4405–4425 (2021).

    Article  ADS  Google Scholar 

  39. Harris, F. J. On the use of windows for harmonic analysis with the discrete Fourier transform. IEEE Proc. 66, 51–83 (1978).

    Article  ADS  Google Scholar 

  40. Hampel, F. R. The influence curve and its role in robust estimation. J. Am. Stat. Assoc. 69, 383–393 (1974).

    Article  MathSciNet  Google Scholar 

  41. Narula, S. C. & Korhonen, P. J. Multivariate multiple linear regression based on the minimum sum of absolute errors criterion. Eur. J. Oper. Res. 73, 70–75 (1994).

    Article  Google Scholar 

  42. Handley, W. J., Hobson, M. P. & Lasenby, A. N. POLYCHORD: next-generation nested sampling. Mon. Not. R. Astron. Soc. 453, 4384–4398 (2015).

    Article  ADS  Google Scholar 

  43. Kass, R. E. & Raftery, A. E. Bayes factors. J. Am. Stat. Assoc. 90, 773–795 (1995).

    Article  MathSciNet  Google Scholar 

Download references

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

  1. Raman Research Institute, Bangalore, India

    Saurabh Singh, Jishnu Nambissan T., Ravi Subrahmanyan, N. Udaya Shankar, B. S. Girish, A. Raghunathan, R. Somashekar, K. S. Srivani & Mayuri Sathyanarayana Rao

  2. Department of Physics, McGill University, Montreal, Canada

    Saurabh Singh

  3. McGill Space Institute, McGill University, Montreal, Canada

    Saurabh Singh

  4. International Centre for Radio Astronomy Research, Curtin University, Perth, WA, Australia

    Jishnu Nambissan T.

  5. Space & Astronomy, CSIRO, Perth, WA, Australia

    Ravi Subrahmanyan

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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.

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Correspondence to Saurabh Singh.

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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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