Blind foreground subtraction for intensity mapping experiments

Alonso, David; Bull, Philip; Ferreira, Pedro G.; Santos, Mário G.

doi:10.1093/mnras/stu2474

Cited by 138 publications

(195 citation statements)

References 94 publications

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“…Determination of contaminated modes in the data themselves has been exploited in GBT data (Chang et al 2010;Masui et al 2013;Switzer et al 2013), GMRT (Paciga et al 2013), and most recently in PAPER (e.g., Ali et al 2015). Blind methods have been considered for SKA (Wolz et al 2014;Alonso et al 2015) and BINGO (Bigot-Sazy et al 2015). Switzer & Liu (2014) develop a similar method for monopole signals.…”

Section: Introductionmentioning

confidence: 99%

Interpreting the Unresolved Intensity of Cosmologically Redshifted Line Radiation

Switzer

Chang

Masui

et al. 2015

ApJ

107

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Intensity mapping experiments survey the spectrum of diffuse line radiation rather than detect individual objects at high signal-to-noise ratio. Spectral maps of unresolved atomic and molecular line radiation contain threedimensional information about the density and environments of emitting gasand efficiently probe cosmological volumes out to high redshift. Intensity mapping survey volumes also contain all other sources of radiation at the frequencies of interest. Continuum foregrounds are typically~10 2 -10 3 times brighter than the cosmological signal. The instrumental response to bright foregrounds will produce new spectral degrees of freedom that are not known in advance, nor necessarily spectrally smooth. The intrinsic spectra of foregrounds may also not be wellknown in advance. We describe a general class of quadratic estimators to analyze data from single-dish intensity mapping experimentsand determine contaminated spectral modes from the data themselves. The key attribute of foregrounds is not that they are spectrally smooth, but instead that they have fewer bright spectral degrees of freedom than the cosmological signal. Spurious correlations between the signal and foregrounds produce additional bias. Compensation for signal attenuation must estimate and correct this bias. A successful intensity mapping experiment will control instrumental systematics that spread variance into new modes, and it must observe a large enough volume that contaminant modes can be determined independently from the signal on scales of interest.

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Section: Introductionmentioning

confidence: 99%

Interpreting the Unresolved Intensity of Cosmologically Redshifted Line Radiation

Switzer

Chang

Masui

et al. 2015

ApJ

107

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“…Parametric methods use a model to describe some physical properties of the foregrounds. Others, such as Principal Component Analysis (PCA) (Masui et al 2013;Switzer et al 2013;Alonso et al 2015;Bigot-Sazy et al 2015), Independent Component Analysis (ICA) (Alonso et al 2015), FASTICA (Wolz et al 2014), inverse variance (Liu & Tegmark 2011), and quadratic estimators (Switzer et al 2015), use only the observed data to recover the HI signal and therefore do not assume a specific parametric model for the foregrounds.…”

Section: Introductionmentioning

confidence: 99%

Extracting H i cosmological signal with generalized needlet internal linear combination

Olivari¹,

Remazeilles²,

Dickinson³

2015

Mon. Not. R. Astron. Soc.

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HI intensity mapping is a new observational technique to map fluctuations in the large-scale structure of matter using the 21 cm emission line of atomic hydrogen (HI). Sensitive HI intensity mapping experiments have the potential to detect Baryon Acoustic Oscillations (BAO) at low redshifts (z 1) in order to constrain the properties of dark energy. Observations of the HI signal will be contaminated by instrumental noise and, more significantly, by astrophysical foregrounds, such as Galactic synchrotron emission, which is at least four orders of magnitude brighter than the HI signal. Foreground cleaning is recognised as one of the key challenges for future radio astronomy surveys. We study the ability of the Generalized Needlet Internal Linear Combination (GNILC) method to subtract radio foregrounds and to recover the cosmological HI signal for a general HI intensity mapping experiment. The GNILC method is a new technique that uses both frequency and spatial information to separate the components of the observed data. Our results show that the method is robust to the complexity of the foregrounds. For simulated radio observations including HI emission, Galactic synchrotron, Galactic free-free, radio sources and 0.05 mK thermal noise, we find that the GNILC method can reconstruct the HI power spectrum for multipoles 30 < < 150 with 6% accuracy on 50% of the sky for a redshift z ∼ 0.25.

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“…Some other schemes, such as weighting maps with inverse noise variance to build the frequency covariance used in PCA (Alonso et al 2015), could mitigate such noise contamination.…”

Section: D Power Spectrum Resultsmentioning

confidence: 99%

“…Therefore, we recommend using three components in the reconstructed foreground model for our specific simulation parameters in Table 1. It is worth noting that, as mentioned by Alonso et al (2015), the optimal number of ICs in fact strongly depends on the spectral smoothness of true foregrounds, characterized by a frequency correlation length ξ as defined in Equation (51). In fact, a longer coherence length implies a smoother frequency spectrum for a physical foreground component.…”

Section: Determination Of the Number Of Icsmentioning

confidence: 99%

BAYESIAN SEMI-BLIND COMPONENT SEPARATION FOR FOREGROUND REMOVAL IN INTERFEROMETRIC 21 cm OBSERVATIONS

Zhang

Bunn

Karakci

et al. 2016

ApJS

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In this paper, we present a new Bayesian semi-blind approach for foreground removal in observations of the 21 cm signal measured by interferometers. The technique, which we call H I Expectation-Maximization Independent Component Analysis (HIEMICA), is an extension of the Independent Component Analysis technique developed for two-dimensional (2D) cosmic microwave background maps to three-dimensional (3D) 21 cm cosmological signals measured by interferometers. This technique provides a fully Bayesian inference of power spectra and maps and separates the foregrounds from the signal based on the diversity of their power spectra. Relying only on the statistical independence of the components, this approach can jointly estimate the 3D power spectrum of the 21 cm signal, as well as the 2D angular power spectrum and the frequency dependence of each foreground component, without any prior assumptions about the foregrounds. This approach has been tested extensively by applying it to mock data from interferometric 21 cm intensity mapping observations under idealized assumptions of instrumental effects. We also discuss the impact when the noise properties are not known completely. As a first step toward solving the 21 cm power spectrum analysis problem, we compare the semi-blind HIEMICA technique to the commonly used Principal Component Analysis. Under the same idealized circumstances, the proposed technique provides significantly improved recovery of the power spectrum. This technique can be applied in a straightforward manner to all 21 cm interferometric observations, including epoch of reionization measurements, and can be extended to single-dish observations as well.

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Blind foreground subtraction for intensity mapping experiments

Cited by 138 publications

References 94 publications

Interpreting the Unresolved Intensity of Cosmologically Redshifted Line Radiation

Interpreting the Unresolved Intensity of Cosmologically Redshifted Line Radiation

Extracting H i cosmological signal with generalized needlet internal linear combination

BAYESIAN SEMI-BLIND COMPONENT SEPARATION FOR FOREGROUND REMOVAL IN INTERFEROMETRIC 21 cm OBSERVATIONS

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