اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدPartially Constrained Internal Linear Combination: a method for low-noise CMB foreground mitigation
129
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
Internal Linear Combination (ILC) methods are some of the most widely used multi-frequency cleaning techniques employed in CMB data analysis. These methods reduce foregrounds by minimizing the total variance in the coadded map (subject to a signal-preservation constraint), although often significant foreground residuals or biases remain. A modification to the ILC method is the constrained ILC (cILC), which explicitly nulls certain foreground components; however, this foreground nulling often comes at a high price for ground-based CMB datasets, with the map noise increasing significantly on small scales. In this paper we explore a new method, the partially constrained ILC (pcILC), which allows us to optimize the tradeoff between foreground bias and variance in ILC methods. In particular, this method allows us to minimize the variance subject to an inequality constraint requiring that the constrained foregrounds are reduced by at least a fixed factor, which can be chosen based on the foreground sensitivity of the intended application. We test our method on simulated sky maps for a Simons Observatory-like experiment; we find that for cleaning thermal Sunyaev-Zeldovich (tSZ) contamination at $ell in [3000,4800]$, if a small tSZ residual of 20% of the standard ILC residual can be tolerated, the variance of the CMB temperature map is reduced by at least 50% over the cILC value. We also demonstrate an application of this method to reduce noise in CMB lensing reconstruction.
قيم البحث
اقرأ أيضاً
Measuring weak lensing cosmic magnification signal is very challenging due to the overwhelming intrinsic clustering in the observed galaxy distribution. In this paper, we modify the Internal Linear Combination (ILC) method to reconstruct the lensing
signal with an extra constraint to suppress the intrinsic clustering. To quantify the performance, we construct a realistic galaxy catalogue for the LSST-like photometric survey, covering 20000 $deg^{2}$ with mean source redshift at $z_{s}sim 1$. We find that the reconstruction performance depends on the width of the photo-z bin we choose. Due to the correlation between the lensing signal and the source galaxy distribution, the derived signal has smaller systematic bias but larger statistical uncertainty for a narrower photo-z bin. We conclude that the lensing signal reconstruction with the Modified ILC method is unbiased with a statistical uncertainty $<5%$ for bin width $Delta z^{P} = 0.2$.
Foreground contamination is the fundamental hindrance to the cosmic microwave background (CMB) signals and its separation from it represents a fundamental question in Cosmology. One of the most popular algorithm used to disentangle foregrounds from t
he CMB signals is the internal linear combination method (ILC). In its original version, this technique is applied directly to the observed maps. In recent literature, however, it is suggested that in the harmonic (Fourier) domain it is possible to obtain better results since a separation can be attempted where the various Fourier frequencies are given different weights. This is seen as a useful characteristic in the case of noisy data. Here, we argue that the benefits of using such an approach are overestimated. Better results can be obtained if a classic procedure is adopted where data are filtered before the separation is carried out.
We seek to remove foreground contaminants from 21cm intensity mapping observations. We demonstrate that a deep convolutional neural network (CNN) with a UNet architecture and three-dimensional convolutions, trained on simulated observations, can effe
ctively separate frequency and spatial patterns of the cosmic neutral hydrogen (HI) signal from foregrounds in the presence of noise. Cleaned maps recover cosmological clustering statistics within 10% at all relevant angular scales and frequencies. This amounts to a reduction in prediction variance of over an order of magnitude on small angular scales ($ell > 300$), and improved accuracy for small radial scales ($k_{parallel} > 0.17 rm h Mpc^{-1})$ compared to standard Principal Component Analysis (PCA) methods. We estimate posterior confidence intervals for the networks prediction by training an ensemble of UNets. Our approach demonstrates the feasibility of analyzing 21cm intensity maps, as opposed to derived summary statistics, for upcoming radio experiments, as long as the simulated foreground model is sufficiently realistic. We provide the code used for this analysis on Github https://github.com/tlmakinen/deep21 as well as a browser-based tutorial for the experiment and UNet model via the accompanying http://bit.ly/deep21-colab Colab notebook.
We test for foreground residuals in the foreground cleaned Planck Cosmic Microwave Background (CMB) maps outside and inside U73 mask commonly used for cosmological analysis. The aim of this paper is to introduce a new method to validate masks by look
ing at the differences in cleaned maps obtained by different component separation methods. By analyzing the power spectrum as well as the mean, variance and skewness of needlet coefficients on bands outside and inside the U73 mask we first confirm that the pixels already masked by U73 are highly contaminated and cannot be used for cosmological analysis. We further find that the U73 mask needs extension in order to reduce large scale foreground residuals to a level of less than $20%$ of the standard deviation of CMB fluctuations within the bands closest to the galactic equator. We also find 276 point sources in the cleaned foreground maps which are currently not masked by the U73 mask. Our final publicly available extended mask leaves $65.9%$ of the sky for cosmological analysis. Note that this extended mask may be important for analyses on local sky patches; in full sky analyses the additional residuals near the galactic equator may average out.
Precise polarisation measurements of the cosmic microwave background (CMB) require accurate knowledge of the instrument orientation relative to the sky frame used to define the cosmological Stokes parameters. Suitable celestial calibration sources th
at could be used to measure the polarimeter orientation angle are limited, so current experiments commonly `self-calibrate. The self-calibration method exploits the theoretical fact that the $EB$ and $TB$ cross-spectra of the CMB vanish in the standard cosmological model, so any detected $EB$ and $TB$ signals must be due to systematic errors. However, this assumption neglects the fact that polarized Galactic foregrounds in a given portion of the sky may have non-zero $EB$ and $TB$ cross-spectra. If these foreground signals remain in the observations, then they will bias the self-calibrated telescope polarisation angle and produce a spurious $B$-mode signal. In this paper we estimate the foreground-induced bias for various instrument configurations and then expand the self-calibration formalism to account for polarized foreground signals. Assuming the $EB$ correlation signal for dust is in the range constrained by angular power spectrum measurements from Planck at 353 GHz (scaled down to 150 GHz), then the bias is negligible for high angular resolution experiments, which have access to CMB-dominated high $ell$ modes with which to self-calibrate. Low-resolution experiments observing particularly dusty sky patches can have a bias as large as $0.5^circ$. A miscalibration of this magnitude generates a spurious $BB$ signal corresponding to a tensor-to-scalar ratio of approximately $rsim2times10^{-3}$, within the targeted range of planned experiments.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
