Paula S. Soares
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Previous Organizations / Institutes
The organizations / institutes from the scholar's other articles in the past ten years
Research Subjects
Paula S. Soares has a total of 25 co-authors. Here are the top co-authors by number of shared publications:
- Steven Cunnington
Institute of Cosmology & Gravitation University of Portsmouth Dennis Sciama Building Burnaby Road PO1 3FX Portsmouth UK
5 shared publications - Alkistis Pourtsidou
Institute for Astronomy, University of Edinburgh, Royal Observatory, Blackford Hill
4 shared publications Chris Blake
Centre for Astrophysics & Supercomputing, Swinburne University of Technology , PO Box 218, Hawthorn, VIC 3122, Australia
4 shared publicationsAlkistis Pourtsidou
Institute for Astronomy, The University of Edinburgh, Royal Observatory , Edinburgh EH9 3HJ, UK
2 shared publicationsBrandon Engelbrecht
Department of Physics and Astronomy, University of the Western Cape , Robert Sobukwe Road, Cape Town, 7535, South Africa
2 shared publications- Jingying Wang
Shanghai Astronomical Observatory, Chinese Academy of Sciences , 80 Nandan Road, Shanghai 200030
2 shared publications
Latest Publications
- Top Cited PapersOpen Access
- 31 October 2022
- Published by Oxford University Press (OUP) in Monthly Notices of the Royal Astronomical Society
- Vol. 518 (4) , 6262-6272
H i intensity mapping with MeerKAT: power spectrum detection in cross-correlation with WiggleZ galaxies
We present a detection of correlated clustering between MeerKAT radio intensity maps and galaxies from the WiggleZ Dark Energy Survey. We find a 7.7σ detection of the cross-correlation power spectrum, the amplitude of which is proportional to the product of the |${\rm H}\, \small {\rm I}$| density fraction (|$\Omega _{{\rm H}\, \small {\rm I}}$|), |${\rm H}\, \small {\rm I}$| bias (|$b_{{\rm H}\, \small {\rm I}}$|) and the cross-correlation coefficient (r). We therefore obtain the constraint |$\Omega _{{\rm H}\, \small {\rm I}}b_{{\rm H}\, \small {\rm I}}r\, {=}\, [0.86\, {\pm }\, 0.10\, ({\rm stat})\, {\pm }\, 0.12\, ({\rm sys})]\, {\times }\, 10^{-3}$|, at an effective scale of keff ∼ 0.13 h Mpc−1. The intensity maps were obtained from a pilot survey with the MeerKAT telescope, a 64-dish pathfinder array to the SKA Observatory (SKAO). The data were collected from 10.5 hours of observations using MeerKAT’s L-band receivers over six nights covering the 11hr field of WiggleZ, in the frequency range 1015–973 MHz (0.400 < z < 0.459 in redshift). This detection is the first practical demonstration of the multi-dish auto-correlation intensity mapping technique for cosmology. This marks an important milestone in the roadmap for the cosmology science case with the full SKAO.- 31 October 2022
- Published by Oxford University Press (OUP) in Monthly Notices of the Royal Astronomical Society
- Vol. 518 (4) , 6262-6272
Top Cited PapersOpen AccessH i intensity mapping with MeerKAT: power spectrum detection in cross-correlation with WiggleZ galaxies
We present a detection of correlated clustering between MeerKAT radio intensity maps and galaxies from the WiggleZ Dark Energy Survey. We find a 7.7σ detection of the cross-correlation power spectrum, the amplitude of which is proportional to the product of the |${\rm H}\, \small {\rm I}$| density fraction (|$\Omega _{{\rm H}\, \small {\rm I}}$|), |${\rm H}\, \small {\rm I}$| bias (|$b_{{\rm H}\, \small {\rm I}}$|) and the cross-correlation coefficient (r). We therefore obtain the constraint |$\Omega _{{\rm H}\, \small {\rm I}}b_{{\rm H}\, \small {\rm I}}r\, {=}\, [0.86\, {\pm }\, 0.10\, ({\rm stat})\, {\pm }\, 0.12\, ({\rm sys})]\, {\times }\, 10^{-3}$|, at an effective scale of keff ∼ 0.13 h Mpc−1. The intensity maps were obtained from a pilot survey with the MeerKAT telescope, a 64-dish pathfinder array to the SKA Observatory (SKAO). The data were collected from 10.5 hours of observations using MeerKAT’s L-band receivers over six nights covering the 11hr field of WiggleZ, in the frequency range 1015–973 MHz (0.400 < z < 0.459 in redshift). This detection is the first practical demonstration of the multi-dish auto-correlation intensity mapping technique for cosmology. This marks an important milestone in the roadmap for the cosmology science case with the full SKAO. - 13 September 2021
- Published by Oxford University Press (OUP) in Monthly Notices of the Royal Astronomical Society
- Vol. 510 (4) , 5872-5890
Gaussian Process Regression for foreground removal in H i Intensity Mapping experiments
We apply for the first time Gaussian Process Regression (GPR) as a foreground removal technique in the context of single-dish, low redshift H i intensity mapping, and present an open-source python toolkit for doing so. We use MeerKAT and SKA1-MID-like simulations of 21 cm foregrounds (including polarization leakage), H i cosmological signal, and instrumental noise. We find that it is possible to use GPR as a foreground removal technique in this context, and that it is better suited in some cases to recover the H i power spectrum than principal component analysis (PCA), especially on small scales. GPR is especially good at recovering the radial power spectrum, outperforming PCA when considering the full bandwidth of our data. Both methods are worse at recovering the transverse power spectrum, since they rely on frequency-only covariance information. When halving our data along frequency, we find that GPR performs better in the low-frequency range, where foregrounds are brighter. It performs worse than PCA when frequency channels are missing, to emulate RFI flagging. We conclude that GPR is an excellent foreground removal option for the case of single-dish, low-redshift H i intensity mapping in the absence of missing frequency channels. Our python toolkit gpr4im and the data used in this analysis are publicly available on GitHub.
- 13 September 2021
- Published by Oxford University Press (OUP) in Monthly Notices of the Royal Astronomical Society
- Vol. 510 (4) , 5872-5890
Gaussian Process Regression for foreground removal in H i Intensity Mapping experiments
We apply for the first time Gaussian Process Regression (GPR) as a foreground removal technique in the context of single-dish, low redshift H i intensity mapping, and present an open-source python toolkit for doing so. We use MeerKAT and SKA1-MID-like simulations of 21 cm foregrounds (including polarization leakage), H i cosmological signal, and instrumental noise. We find that it is possible to use GPR as a foreground removal technique in this context, and that it is better suited in some cases to recover the H i power spectrum than principal component analysis (PCA), especially on small scales. GPR is especially good at recovering the radial power spectrum, outperforming PCA when considering the full bandwidth of our data. Both methods are worse at recovering the transverse power spectrum, since they rely on frequency-only covariance information. When halving our data along frequency, we find that GPR performs better in the low-frequency range, where foregrounds are brighter. It performs worse than PCA when frequency channels are missing, to emulate RFI flagging. We conclude that GPR is an excellent foreground removal option for the case of single-dish, low-redshift H i intensity mapping in the absence of missing frequency channels. Our python toolkit gpr4im and the data used in this analysis are publicly available on GitHub.
- 17 May 2021
- Published by Oxford University Press (OUP) in Monthly Notices of the Royal Astronomical Society
- Vol. 505 (3) , 3698-3721
H iintensity mapping with MeerKAT: calibration pipeline for multidish autocorrelation observations
While most purpose-built 21-cm intensity mapping experiments are close-packed interferometer arrays, general-purpose dish arrays should also be capable of measuring the cosmological 21-cm signal. This can be achieved most efficiently if the array is used as a collection of scanning autocorrelation dishes rather than as an interferometer. As a first step towards demonstrating the feasibility of this observing strategy, we show that we are able to successfully calibrate dual-polarization autocorrelation data from 64 MeerKAT dishes in the L band (856–1712 MHz, 4096 channels), with 10.5 h of data retained from six nights of observing. We describe our calibration pipeline, which is based on multilevel radio frequency interference flagging, periodic noise diode injection to stabilize gain drifts, and an absolute calibration based on a multicomponent sky model. We show that it is sufficiently accurate to recover maps of diffuse celestial emission and point sources over a 10° × 30° patch of the sky overlapping with the WiggleZ 11-h field. The reconstructed maps have a good level of consistency between per-dish maps and external data sets, with the estimated thermal noise limited to 1.4 × the theoretical noise level (∼2 mK). The residual maps have rms amplitudes below 0.1 K, corresponding to |$\lt 1{{\ \rm per\ cent}}$| of the model temperature. The reconstructed Galactic H i intensity map shows excellent agreement with the Effelsberg–Bonn H i Survey, and the flux of the radio galaxy 4C + 03.18 is recovered to within 3.6 per cent, which demonstrates that the autocorrelation can be successfully calibrated to give the zero-spacing flux and potentially help in the imaging of MeerKAT interferometric data. Our results provide a positive indication towards the feasibility of using MeerKAT and the future Square Kilometre Array to measure the H i intensity mapping signal and probe cosmology on degree scales and above.
- 17 May 2021
- Published by Oxford University Press (OUP) in Monthly Notices of the Royal Astronomical Society
- Vol. 505 (3) , 3698-3721
H iintensity mapping with MeerKAT: calibration pipeline for multidish autocorrelation observations
While most purpose-built 21-cm intensity mapping experiments are close-packed interferometer arrays, general-purpose dish arrays should also be capable of measuring the cosmological 21-cm signal. This can be achieved most efficiently if the array is used as a collection of scanning autocorrelation dishes rather than as an interferometer. As a first step towards demonstrating the feasibility of this observing strategy, we show that we are able to successfully calibrate dual-polarization autocorrelation data from 64 MeerKAT dishes in the L band (856–1712 MHz, 4096 channels), with 10.5 h of data retained from six nights of observing. We describe our calibration pipeline, which is based on multilevel radio frequency interference flagging, periodic noise diode injection to stabilize gain drifts, and an absolute calibration based on a multicomponent sky model. We show that it is sufficiently accurate to recover maps of diffuse celestial emission and point sources over a 10° × 30° patch of the sky overlapping with the WiggleZ 11-h field. The reconstructed maps have a good level of consistency between per-dish maps and external data sets, with the estimated thermal noise limited to 1.4 × the theoretical noise level (∼2 mK). The residual maps have rms amplitudes below 0.1 K, corresponding to |$\lt 1{{\ \rm per\ cent}}$| of the model temperature. The reconstructed Galactic H i intensity map shows excellent agreement with the Effelsberg–Bonn H i Survey, and the flux of the radio galaxy 4C + 03.18 is recovered to within 3.6 per cent, which demonstrates that the autocorrelation can be successfully calibrated to give the zero-spacing flux and potentially help in the imaging of MeerKAT interferometric data. Our results provide a positive indication towards the feasibility of using MeerKAT and the future Square Kilometre Array to measure the H i intensity mapping signal and probe cosmology on degree scales and above.
- 7 January 2021
- Published by Oxford University Press (OUP) in Monthly Notices of the Royal Astronomical Society
- Vol. 502 (2) , 2549-2564
Power spectrum multipole expansion for H i intensity mapping experiments: unbiased parameter estimation
We assess the performance of the multipole expansion formalism in the case of single-dish H i intensity mapping, including instrumental and foreground removal effects. This formalism is used to provide Markov chain Monte Carlo forecasts for a range of H i and cosmological parameters, including redshift space distortions and the Alcock–Paczynski effect. We first determine the range of validity of our power spectrum modelling by fitting to simulation data, concentrating on the monopole, quadrupole, and hexadecapole contributions. We then show that foreground subtraction effects can lead to severe biases in the determination of cosmological parameters, in particular the parameters relating to the transverse Baryon Acoustic Oscillations (BAO) rescaling, the growth rate, and the H i bias (α⊥, $\overline{T}_\rm{H{\small I}}f\sigma _8$, and $\overline{T}_{\mathrm {H}\,{\small I}}b_{\mathrm {H}\,{\small I}}\sigma _8$, respectively). We attempt to account for these biases by constructing a two-parameter foreground modelling prescription, and find that our prescription leads to unbiased parameter estimation at the expense of increasing the estimated uncertainties on cosmological parameters. In addition, we confirm that instrumental and foreground removal effects significantly impact the theoretical covariance matrix, and cause the covariance between different multipoles to become non-negligible. Finally, we show the effect of including higher order multipoles in our analysis, and how these can be used to investigate the presence of instrumental and systematic effects in H i intensity mapping data.- 7 January 2021
- Published by Oxford University Press (OUP) in Monthly Notices of the Royal Astronomical Society
- Vol. 502 (2) , 2549-2564
Power spectrum multipole expansion for H i intensity mapping experiments: unbiased parameter estimation
We assess the performance of the multipole expansion formalism in the case of single-dish H i intensity mapping, including instrumental and foreground removal effects. This formalism is used to provide Markov chain Monte Carlo forecasts for a range of H i and cosmological parameters, including redshift space distortions and the Alcock–Paczynski effect. We first determine the range of validity of our power spectrum modelling by fitting to simulation data, concentrating on the monopole, quadrupole, and hexadecapole contributions. We then show that foreground subtraction effects can lead to severe biases in the determination of cosmological parameters, in particular the parameters relating to the transverse Baryon Acoustic Oscillations (BAO) rescaling, the growth rate, and the H i bias (α⊥, $\overline{T}_\rm{H{\small I}}f\sigma _8$, and $\overline{T}_{\mathrm {H}\,{\small I}}b_{\mathrm {H}\,{\small I}}\sigma _8$, respectively). We attempt to account for these biases by constructing a two-parameter foreground modelling prescription, and find that our prescription leads to unbiased parameter estimation at the expense of increasing the estimated uncertainties on cosmological parameters. In addition, we confirm that instrumental and foreground removal effects significantly impact the theoretical covariance matrix, and cause the covariance between different multipoles to become non-negligible. Finally, we show the effect of including higher order multipoles in our analysis, and how these can be used to investigate the presence of instrumental and systematic effects in H i intensity mapping data.- Open Access
Power spectrum multipole expansion for HI intensity mapping experiments: unbiased parameter estimation
We assess the performance of the multipole expansion formalism in the case of single-dish HI intensity mapping, including instrumental and foreground removal effects. This formalism is used to provide MCMC forecasts for a range of HI and cosmological parameters, including redshift space distortions and the Alcock-Paczynski effect. We first determine the range of validity of our power spectrum modelling by fitting to simulation data, concentrating on the monopole, quadrupole, and hexadecapole contributions. We then show that foreground subtraction effects can lead to severe biases in the determination of cosmological parameters, in particular the parameters relating to the transverse BAO rescaling, the growth rate and the HI bias ($\alpha_\perp$, $\overline{T}_\text{HI} f\sigma_8$, and $\overline{T}_\text{HI} b_\text{HI} \sigma_8$, respectively). We attempt to account for these biases by constructing a 2-parameter foreground modelling prescription, and find that our prescription leads to unbiased parameter estimation at the expense of increasing the estimated uncertainties on cosmological parameters. In addition, we confirm that instrumental and foreground removal effects significantly impact the theoretical covariance matrix, and cause the covariance between different multipoles to become non-negligible. Finally, we show the effect of including higher-order multipoles in our analysis, and how these can be used to investigate the presence of instrumental and systematic effects in HI intensity mapping data.Open AccessPower spectrum multipole expansion for HI intensity mapping experiments: unbiased parameter estimation
We assess the performance of the multipole expansion formalism in the case of single-dish HI intensity mapping, including instrumental and foreground removal effects. This formalism is used to provide MCMC forecasts for a range of HI and cosmological parameters, including redshift space distortions and the Alcock-Paczynski effect. We first determine the range of validity of our power spectrum modelling by fitting to simulation data, concentrating on the monopole, quadrupole, and hexadecapole contributions. We then show that foreground subtraction effects can lead to severe biases in the determination of cosmological parameters, in particular the parameters relating to the transverse BAO rescaling, the growth rate and the HI bias ($\alpha_\perp$, $\overline{T}_\text{HI} f\sigma_8$, and $\overline{T}_\text{HI} b_\text{HI} \sigma_8$, respectively). We attempt to account for these biases by constructing a 2-parameter foreground modelling prescription, and find that our prescription leads to unbiased parameter estimation at the expense of increasing the estimated uncertainties on cosmological parameters. In addition, we confirm that instrumental and foreground removal effects significantly impact the theoretical covariance matrix, and cause the covariance between different multipoles to become non-negligible. Finally, we show the effect of including higher-order multipoles in our analysis, and how these can be used to investigate the presence of instrumental and systematic effects in HI intensity mapping data. - 5 October 2020
- Published by Oxford University Press (OUP) in Monthly Notices of the Royal Astronomical Society
- Vol. 501 (1) , 411-418
The cosmic abundance of cold gas in the local Universe
We determine the cosmic abundance of molecular hydrogen (H2) in the local Universe from the xCOLD GASS survey. To constrain the H2 mass function at low masses and correct for the effect of the lower stellar mass limit of $10^9 \, \mathrm{M}_{\odot }$ in the xCOLD GASS survey, we use an empirical approach based on an observed scaling relation between star formation rate and gas mass. We also constrain the H i and H i+H2 mass functions using the xGASS survey and compare them to the H i mass function from the ALFALFA survey. We find the cosmic abundance of molecular gas in the local Universe to be $\Omega _{\mathrm{H_2}} = (5.34 \pm 0.47) \times 10^{-5} h^{-1}$. Molecular gas accounts for $19.6\pm 3.9{{\ \rm per\ cent}}$ of the total abundance of cold gas, $\mathrm{\Omega _{H\,{\small I}+H_{2}}} = (4.66 \pm 0.70) \times 10^{-4}\, h_{70}^{-1}$. Galaxies with stellar masses in excess of 109$\, \mathrm{M}_{\odot }$ account for 89 per cent of the molecular gas in the local Universe, while in comparison such galaxies only contain 73 per cent of the cold atomic gas as traced by the H i 21cm line. The xCOLD GASS CO, molecular gas and cold gas mass functions, and $\Omega _{\mathrm{H_2}}$ measurements provide constraints for models of galaxy evolution and help to anchor blind molecular gas surveys attempting to determine the abundance of molecular gas at high redshifts.- 5 October 2020
- Published by Oxford University Press (OUP) in Monthly Notices of the Royal Astronomical Society
- Vol. 501 (1) , 411-418
The cosmic abundance of cold gas in the local Universe
We determine the cosmic abundance of molecular hydrogen (H2) in the local Universe from the xCOLD GASS survey. To constrain the H2 mass function at low masses and correct for the effect of the lower stellar mass limit of $10^9 \, \mathrm{M}_{\odot }$ in the xCOLD GASS survey, we use an empirical approach based on an observed scaling relation between star formation rate and gas mass. We also constrain the H i and H i+H2 mass functions using the xGASS survey and compare them to the H i mass function from the ALFALFA survey. We find the cosmic abundance of molecular gas in the local Universe to be $\Omega _{\mathrm{H_2}} = (5.34 \pm 0.47) \times 10^{-5} h^{-1}$. Molecular gas accounts for $19.6\pm 3.9{{\ \rm per\ cent}}$ of the total abundance of cold gas, $\mathrm{\Omega _{H\,{\small I}+H_{2}}} = (4.66 \pm 0.70) \times 10^{-4}\, h_{70}^{-1}$. Galaxies with stellar masses in excess of 109$\, \mathrm{M}_{\odot }$ account for 89 per cent of the molecular gas in the local Universe, while in comparison such galaxies only contain 73 per cent of the cold atomic gas as traced by the H i 21cm line. The xCOLD GASS CO, molecular gas and cold gas mass functions, and $\Omega _{\mathrm{H_2}}$ measurements provide constraints for models of galaxy evolution and help to anchor blind molecular gas surveys attempting to determine the abundance of molecular gas at high redshifts.- 3 June 2020
- Published by Oxford University Press (OUP) in Monthly Notices of the Royal Astronomical Society
- Vol. 496 (1) , 415-433
Multipole expansion for H i intensity mapping experiments: simulations and modelling
We present a framework and an open-source python toolkit to analyse the two-point statistics of 3D fluctuations in the context of H i intensity maps using the multipole expansion formalism. We include simulations of the cosmological H i signal using N-body and lognormal methods, foregrounds and their removal, as well as instrumental effects. Using these simulations and analytical modelling, we investigate the impact of foreground cleaning and the instrumental beam on the power spectrum multipoles as well as on the Fourier space clustering wedges. We find that both the instrumental beam and the foreground removal can produce a quadrupole (and a hexadecapole) signal, and demonstrate the importance of controlling and accurately modelling these effects for precision radio cosmology. We conclude that these effects can be modelled with reasonable accuracy using our multipole expansion technique. We also perform a Markov Chain Monte Carlo (MCMC) analysis to showcase the effect of foreground cleaning on the estimation of the H i abundance and bias parameters. The accompanying python toolkit is available at https://github.com/IntensityTools/MultipoleExpansion, and includes an interactive suite of examples to aid new users.
- 3 June 2020
- Published by Oxford University Press (OUP) in Monthly Notices of the Royal Astronomical Society
- Vol. 496 (1) , 415-433
Multipole expansion for H i intensity mapping experiments: simulations and modelling
We present a framework and an open-source python toolkit to analyse the two-point statistics of 3D fluctuations in the context of H i intensity maps using the multipole expansion formalism. We include simulations of the cosmological H i signal using N-body and lognormal methods, foregrounds and their removal, as well as instrumental effects. Using these simulations and analytical modelling, we investigate the impact of foreground cleaning and the instrumental beam on the power spectrum multipoles as well as on the Fourier space clustering wedges. We find that both the instrumental beam and the foreground removal can produce a quadrupole (and a hexadecapole) signal, and demonstrate the importance of controlling and accurately modelling these effects for precision radio cosmology. We conclude that these effects can be modelled with reasonable accuracy using our multipole expansion technique. We also perform a Markov Chain Monte Carlo (MCMC) analysis to showcase the effect of foreground cleaning on the estimation of the H i abundance and bias parameters. The accompanying python toolkit is available at https://github.com/IntensityTools/MultipoleExpansion, and includes an interactive suite of examples to aid new users.