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Register a new userInpainting Galactic Foreground Intensity and Polarization maps using Convolutional Neural Network
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Added by
Giuseppe Puglisi Ph.D.
Publication date
2020
fields
Physics
and research's language is
English
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Deep convolutional neural networks have been a popular tool for image generation and restoration. The performance of these networks is related to the capability of learning realistic features from a large dataset. In this work, we applied the problem of inpainting non-Gaussian signal, in the context of Galactic diffuse emissions at the millimetric and sub-millimetric regimes, specifically Synchrotron and Thermal Dust emission. Both of them are affected by contamination at small angular scales due to extra-galactic radio sources (the former) and to dusty star-forming galaxies (the latter). We consider the performances of a nearest-neighbors inpainting technique and compare it with two novels methodologies relying on generative Neural Networks. We show that the generative network is able to reproduce the statistical properties of the ground truth signal more consistently with high confidence level. The Python Inpainter for Cosmological and AStrophysical SOurces (PICASSO) is a package encoding a suite of inpainting methods described in this work and has been made publicly available.
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We present a novel application of partial convolutional neural networks (PCNN) that can inpaint masked images of the cosmic microwave background. The network can reconstruct both the maps and the power spectra to a few percent for circular and irregularly shaped masks covering up to ~10% of the image area. By performing a Kolmogorov-Smirnov test we show that the reconstructed maps and power spectra are indistinguishable from the input maps and power spectra at the 99.9% level. Moreover, we show that PCNNs can inpaint maps with regular and irregular masks to the same accuracy. This should be particularly beneficial to inpaint irregular masks for the CMB that come from astrophysical sources such as galactic foregrounds. The proof of concept application shown in this paper shows that PCNNs can be an important tool in data analysis pipelines in cosmology.
Active Galactic Nuclei (AGN) and star-forming galaxies are leading candidates for being the luminous sources that reionized our Universe. Next-generation 21cm surveys are promising to break degeneracies between a broad range of reionization models, hence revealing the nature of the source population. While many current efforts are focused on a measurement of the 21cm power spectrum, some surveys will also image the 21cm field during reionization. This provides further information with which to determine the nature of reionizing sources. We create a Convolutional Neural Network (CNN) that is efficiently able to distinguish between 21cm maps that are produced by AGN versus galaxies scenarios with an accuracy of 92-100%, depending on redshift and neutral fraction range. An exception to this is when our Universe is highly ionized, since the source models give near-identical 21cm maps in that case. When adding thermal noise from typical 21cm experiments, the classification accuracy depends strongly on the effectiveness of foreground removal. Our results show that if foregrounds can be removed reasonably well, SKA, HERA and LOFAR should be able to discriminate between source models with greater accuracy at a fixed redshift. Only future SKA 21cm surveys are promising to break the degeneracies in the power spectral analysis.
We present arcminute-resolution intensity and polarization maps of the Galactic center made with the Atacama Cosmology Telescope (ACT). The maps cover a 32 deg$^2$ field at 98, 150, and 224 GHz with $vert lvertle4^circ$, $vert bvertle2^circ$. We combine these data with Planck observations at similar frequencies to create coadded maps with increased sensitivity at large angular scales. With the coadded maps, we are able to resolve many known features of the Central Molecular Zone (CMZ) in both total intensity and polarization. We map the orientation of the plane-of-sky component of the Galactic magnetic field inferred from the polarization angle in the CMZ, finding significant changes in morphology in the three frequency bands as the underlying dominant emission mechanism changes from synchrotron to dust emission. Selected Galactic center sources, including Sgr A*, the Brick molecular cloud (G0.253+0.016), the Mouse pulsar wind nebula (G359.23-0.82), and the Tornado supernova remnant candidate (G357.7-0.1), are examined in detail. These data illustrate the potential for leveraging ground-based Cosmic Microwave Background polarization experiments for Galactic science.
We compute the cross correlation of the intensity and polarisation from the 5-year WMAP data in different sky-regions with respect to template maps for synchrotron, dust, and free-free emission. We derive the frequency dependence and polarisation fraction for all three components in 48 different sky regions of HEALPix (Nside=2) pixelisation. The anomalous emission associated with dust is clearly detected in intensity over the entire sky at the K (23 GHz) and Ka (33 GHz) WMAP bands, and is found to be the dominant foreground at low Galactic latitude, between b=-40 and b=+10. The synchrotron spectral index obtained from the K and Ka WMAP bands from an all-sky analysis is -3.32pm 0.12 for intensity and -3.01pm0.03 for the polarised intensity. The polarisation fraction of the synchrotron is constant in frequency and increases with latitude from ~5% near the Galactic plane up to ~40% in some regions at high latitude; the average value for |b|<20 is 8.6pm1.7 (stat) pm0.5 (sys) % while for |b|>20 it is 19.3pm0.8 (stat) pm 0.5 (sys) %. Anomalous dust and free-free emission appear to be relatively unpolarised...[Abridged]...the average polarisation fraction of dust-correlated emission at K-band is 3.2pm0.9 (stat) pm 1.5 (sys) %, or less than 5% at 95% confidence. When comparing real data with simulations, 8 regions show a detected polarisation above the 99th percentile of the distribution from simulations with no input foreground polarisation, 6 of which are detected at above 2sigma and display polarisation fractions between 2.6% and 7.2%, except for one anomalous region, which has 32pm12%. The dust polarisation values are consistent with the expectation from spinning-dust emission, but polarised dust emission from magnetic-dipole radiation cannot be ruled out. Free-free emission was found to be unpolarised with an upper limit of 3.4% at 95% confidence.
From 1,000 hydrodynamic simulations of the CAMELS project, each with a different value of the cosmological and astrophysical parameters, we generate 15,000 gas temperature maps. We use a state-of-the-art deep convolutional neural network to recover missing data from those maps. We mimic the missing data by applying regular and irregular binary masks that cover either $15%$ or $30%$ of the area of each map. We quantify the reliability of our results using two summary statistics: 1) the distance between the probability density functions (pdf), estimated using the Kolmogorov-Smirnov (KS) test, and 2) the 2D power spectrum. We find an excellent agreement between the model prediction and the unmasked maps when using the power spectrum: better than $1%$ for $k<20 h/$Mpc for any irregular mask. For regular masks, we observe a systematic offset of $sim5%$ when covering $15%$ of the maps while the results become unreliable when $30%$ of the data is missing. The observed KS-test p-values favor the null hypothesis that the reconstructed and the ground-truth maps are drawn from the same underlying distribution when irregular masks are used. For regular-shaped masks on the other hand, we find a strong evidence that the two distributions do not match each other. Finally, we use the model, trained on gas temperature maps, to perform inpainting on maps from completely different fields such as gas mass, gas pressure, and electron density and also for gas temperature maps from simulations run with other codes. We find that visually, our model is able to reconstruct the missing pixels from the maps of those fields with great accuracy, although its performance using summary statistics depends strongly on the considered field.
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