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Constraining Primordial Non-Gaussianity by Combining Photometric Galaxy and 21cm Intensity Mapping Surveys


Core Concepts
Combining photometric galaxy surveys and 21cm intensity mapping surveys can significantly improve the precision of constraints on primordial non-Gaussianity.
Abstract

The paper focuses on constraining primordial non-Gaussianity, specifically the local-type non-Gaussianity parameter fNL, by employing a multi-tracer analysis that combines different cosmological probes. The authors consider combining photometric galaxy surveys (similar to DES and LSST) with 21cm intensity mapping surveys in both single-dish (MeerKAT-like and SKA-like) and interferometric (HIRAX-like and PUMA-like) modes.

The key highlights are:

  1. The multi-tracer approach can effectively mitigate the impact of cosmic variance and maximize the non-Gaussian signal, leading to tighter constraints on fNL compared to using a single tracer.

  2. Combining photometric galaxy surveys with 21cm interferometric surveys can improve the precision on fNL by up to 23% compared to using the photometric surveys alone. The improvement is up to 16% when combining with 21cm single-dish surveys.

  3. The authors investigate the impact of varying the foreground filter parameter, redshift range, and sky area on the derived fNL constraint. They find that the constraint is highly sensitive to both the redshift range and sky area, while the foreground filter parameter shows a negligible effect.

  4. The multi-tracer analysis is more effective at mitigating the loss of long modes due to foreground removal, especially when the overlapping sky area and redshift range are larger.

  5. Compared to photometric surveys, 21cm intensity mapping surveys provide stronger constraints on the spectral index ns and the amplitude of primordial fluctuations As, despite offering weaker constraints on fNL.

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Stats
The paper presents the following key figures and statistics: The Gaussian clustering bias for galaxies and HI intensity maps, as a function of redshift (Figures 2 and 3). The physical baseline density for the HIRAX-like and PUMA-like interferometric surveys (Figure 4). The marginalised 68% confidence level errors on fNL for various survey combinations (Tables 3 and 4). The impact of varying the foreground filter parameter, redshift range, and sky area on the fNL constraint for the H256 ⊗ L multi-tracer (Table 5).
Quotes
None.

Deeper Inquiries

How would the inclusion of wide-angle effects and other theoretical improvements impact the forecasted constraints on primordial non-Gaussianity from the multi-tracer analysis?

The inclusion of wide-angle effects in the analysis of primordial non-Gaussianity (PNG) is crucial for improving the accuracy of cosmological constraints derived from multi-tracer techniques. Wide-angle effects account for the geometric distortions that arise when observing large-scale structures over significant angular extents. These effects can lead to biases in the estimation of power spectra, particularly in the context of redshift-space distortions and the angular clustering of galaxies. By incorporating wide-angle corrections, the analysis can more accurately reflect the true underlying cosmological signals, thereby enhancing the precision of the constraints on the local-type non-Gaussianity parameter ( f_{NL} ). Moreover, other theoretical improvements, such as refining the models for foreground contamination and noise characteristics, can further enhance the robustness of the Fisher forecasts. For instance, better modeling of the thermal noise associated with 21 cm intensity mapping and the implementation of advanced foreground-avoidance techniques can mitigate the impact of systematic errors. These improvements would likely lead to tighter constraints on ( f_{NL} ), potentially achieving the goal of ( \sigma(f_{NL}) < 1 ), which is essential for distinguishing between various inflationary models. Overall, integrating wide-angle effects and refining theoretical models would provide a more comprehensive understanding of the primordial Universe and improve the reliability of the multi-tracer analysis.

What are the potential limitations or caveats of the Fisher forecast approach used in this study, and how could more sophisticated analysis techniques further enhance the precision of the ( f_{NL} ) constraints?

The Fisher forecast approach, while a powerful tool for estimating the precision of cosmological parameters, has several limitations and caveats. One significant limitation is that it assumes Gaussianity of the likelihood function, which may not hold true in the presence of non-Gaussian signals such as those from primordial non-Gaussianity. This assumption can lead to over-optimistic estimates of the uncertainties in the ( f_{NL} ) parameter. Additionally, the Fisher matrix does not account for potential correlations between different parameters, which can further skew the results if these correlations are significant. Another caveat is the neglect of non-overlapping information from multi-tracer pairs, which can result in an underestimation of the precision. The Fisher forecast also typically does not incorporate the full complexity of the data, such as the effects of systematic errors, foreground contamination, and the intricacies of the survey geometry. To enhance the precision of ( f_{NL} ) constraints, more sophisticated analysis techniques could be employed. For instance, Markov Chain Monte Carlo (MCMC) methods could be utilized to explore the parameter space more thoroughly, allowing for the inclusion of non-Gaussian likelihoods and the full covariance structure of the data. Additionally, machine learning techniques could be applied to optimize the extraction of cosmological signals from complex datasets, improving the robustness of the parameter estimates. By addressing the limitations of the Fisher forecast and employing advanced statistical methods, researchers can achieve more accurate and reliable constraints on primordial non-Gaussianity.

Given the complementary strengths of photometric galaxy surveys and 21cm intensity mapping in constraining different cosmological parameters, how could the synergies between these probes be leveraged to obtain a more comprehensive understanding of the early Universe and structure formation?

The complementary strengths of photometric galaxy surveys and 21 cm intensity mapping provide a unique opportunity to leverage synergies that can significantly enhance our understanding of the early Universe and structure formation. Photometric galaxy surveys, such as those conducted by DES and LSST, excel in providing high-resolution spatial information and can effectively trace the distribution of galaxies across different redshifts. They are particularly adept at measuring the clustering of galaxies, which is sensitive to the underlying dark matter distribution and can provide insights into the growth of structure. On the other hand, 21 cm intensity mapping surveys, like those from MeerKAT and SKA, offer a broader view of the Universe by mapping the distribution of neutral hydrogen (HI) over large volumes. This technique is sensitive to the large-scale structure and can capture the cosmic web's evolution, including the effects of primordial non-Gaussianity. The ability of 21 cm surveys to probe the dark ages and the epoch of reionization adds a crucial dimension to our understanding of the early Universe. By combining these two approaches in a multi-tracer analysis, researchers can mitigate cosmic variance and enhance the precision of cosmological parameter estimates, including ( f_{NL} ). The joint analysis allows for the cross-calibration of biases and the extraction of complementary information from both datasets. For instance, while photometric surveys can provide detailed redshift information, 21 cm intensity mapping can fill in the gaps in large-scale structure measurements, particularly in under-sampled regions of the sky. Furthermore, the integration of these datasets can lead to improved constraints on cosmological models, helping to distinguish between different inflationary scenarios and providing a clearer picture of the mechanisms driving structure formation. Overall, leveraging the synergies between photometric galaxy surveys and 21 cm intensity mapping can lead to a more comprehensive understanding of the Universe's evolution from the primordial era to the present day.
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