A Bayesian method for point source polarisation estimation

Herranz, D.; Argüeso, F.; Toffolatti, L.; Manjón-García, Alberto; López‐Caniego, M.

doi:10.1051/0004-6361/202039741

Cited by 2 publications

(2 citation statements)

References 37 publications

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“…As a comparison with other methods for polarisation flux density estimations, the Bayesian method by Herranz et al (2021) provides reliable results down to 180 mJy when applied to realistic simulations with the QUIJOTE experiment characteristics. However, both the frequency analysed, namely 11 GHz, and the instrument sensitivity, 105 µK s 1/2 , are different from our case (217 GHz with a ∼650 µK s 1/2 sensitivity).…”

Section: Polarisation Flux Density Estimationmentioning

confidence: 99%

“…Several methods have been developed in recent years for the detection and flux density estimation of the polarisation of ERSs: Argüeso et al (2009) developed Filtered Fusion (FF), which was applied by López-Caniego et al (2009) to both WMAP and Planck data. More recently, Herranz et al (2021) developed a Bayesian estimator (BFF), reaching reliable polarised estimations down to 0.01 Jy in realistic simulations of the QUIJOTE experiment (Rubiño-Martín et al 2012). Furthermore, Diego-Palazuelos et al (2021) proposed a method based on steerable wavelets, which is reliable down to 3.38 and 5.76 mJy for regions of faint galactic emission in simulations of the 30 and 155 GHz bands, respectively, of the PICO experiment (Hanany et al 2019).…”

Section: Introductionmentioning

confidence: 99%

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Constraining the polarisation flux density and angle of point sources by training a convolutional neural network

Casas¹,

Bonavera²,

González‐Nuevo³

et al. 2023

A&A

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Context. Constraining the polarisation properties of extragalactic point sources is a relevant task not only because they are one of the main contaminants for primordial cosmic microwave background B-mode detection if the tensor-to-scalar ratio is lower than r = 0.001, but also for a better understanding of the properties of radio-loud active galactic nuclei. Aims. We develop and train a machine learning model based on a convolutional neural network to learn how to estimate the polarisation flux density and angle of point sources embedded in cosmic microwave background images knowing only their positions. Methods. To train the neural network, we used realistic simulations of patches of 32×32 pixels in area at the 217 GHz Planck channel with injected point sources at their centres. The patches also contain a realistic background composed of the cosmic microwave background signal, the Galactic thermal dust, and instrumental noise. We split our analysis into three parts: firstly, we studied the comparison between true and estimated polarisation flux densities for P, Q, and U simulations. Secondly, we analysed the comparison between true and estimated polarisation angles. Finally, we studied the performance of our model with the 217 GHz Planck map and compared our results against the detected sources of the Second Planck Catalogue of Compact Sources (PCCS2). Results. We find that our model can be used to reliably constrain the polarisation flux density of sources above the 80 mJy level. For this limit, we obtain relative errors of lower than 30% in most of the flux density levels. Training the same network with Q and U maps, the reliability limit is above ±250 mJy when determining the polarisation angle of both Q and U sources. Above that cut, the network can constrain angles with a 1σ uncertainty of ±29 • and ±32 • for Q and U sources, respectively. We test this neural network against real data from the 217 GHz Planck channel, obtaining similar results to the PCCS2 for some sources; although we also find discrepancies in the 300-400 mJy flux density range with respect to the Planck catalogue. Conclusions. Based on these results, our model appears to be a promising tool for estimating the polarisation flux densities and angles of point sources above 80 mJy in any catalogue with very small computational time requirements.

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Section: Polarisation Flux Density Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Constraining the polarisation flux density and angle of point sources by training a convolutional neural network

Casas¹,

Bonavera²,

González‐Nuevo³

et al. 2023

A&A

View full text Add to dashboard Cite

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Multi-frequency point source detection with fully convolutional networks: Performance in realistic microwave sky simulations

Casas

González‐Nuevo

Bonavera

et al. 2022

A&A

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Context. Point source (PS) detection is an important issue for future cosmic microwave background (CMB) experiments since they are one of the main contaminants to the recovery of CMB signal on small scales. Improving its multi-frequency detection would allow us to take into account valuable information otherwise neglected when extracting PS using a channel-by-channel approach. Aims. We aim to develop an artificial intelligence method based on fully convolutional neural networks to detect PS in multi-frequency realistic simulations and compare its performance against one of the most popular multi-frequency PS detection methods, the matrix filters. The frequencies used in our analysis are 143, 217, and 353 GHz, and we imposed a Galactic cut of 30 • . Methods. We produced multi-frequency realistic simulations of the sky by adding contaminating signals to the PS maps as the CMB, the cosmic infrared background, the Galactic thermal emission, the thermal Sunyaev-Zel'dovich effect, and the instrumental and PS shot noises. These simulations were used to train two neural networks called flat and spectral MultiPoSeIDoNs. The first one considers PS with a flat spectrum, and the second one is more realistic and general because it takes into account the spectral behaviour of the PS. Then, we compared the performance on reliability, completeness, and flux density estimation accuracy for both MultiPoSeIDoNs and the matrix filters. Results. Using a flux detection limit of 60 mJy, MultiPoSeIDoN successfully recovered PS reaching the 90% completeness level at 58 mJy for the flat case, and at 79, 71, and 60 mJy for the spectral case at 143, 217, and 353 GHz, respectively. The matrix filters reach the 90% completeness level at 84, 79, and 123 mJy. To reduce the number of spurious sources, we used a safer 4σ flux density detection limit for the matrix filters, the same as was used in the Planck catalogues, obtaining the 90% of completeness level at 113, 92, and 398 mJy. In all cases, MultiPoSeIDoN obtains a much lower number of spurious sources with respect to the filtering method. The recovering of the flux density of the detections, attending to the results on photometry, is better for the neural networks, which have a relative error of 10% above 100 mJy for the three frequencies, while the filter obtains a 10% relative error above 150 mJy for 143 and 217 GHz, and above 200 mJy for 353 GHz. Conclusions. Based on the results, neural networks are the perfect candidates to substitute filtering methods to detect multi-frequency PS in future CMB experiments. Moreover, we show that a multi-frequency approach can detect sources with higher accuracy than single-frequency approaches also based on neural networks.

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A Bayesian method for point source polarisation estimation

Cited by 2 publications

References 37 publications

Constraining the polarisation flux density and angle of point sources by training a convolutional neural network

Constraining the polarisation flux density and angle of point sources by training a convolutional neural network

Multi-frequency point source detection with fully convolutional networks: Performance in realistic microwave sky simulations

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