scholarly journals The degeneracy between primordial non-Gaussianity and foregrounds in 21 cm intensity mapping experiments

2020 ◽  
Vol 499 (3) ◽  
pp. 4054-4067
Author(s):  
Steven Cunnington ◽  
Stefano Camera ◽  
Alkistis Pourtsidou
Keyword(s):  
Nuisance Parameter ◽  
Neutral Hydrogen ◽  
Real Data ◽  
Line Of Sight ◽  
Markov Chain Analysis ◽  
Significant Progress ◽  
Intensity Mapping ◽  
Chain Analysis ◽  
Parallel Text ◽  
Foreground Removal

ABSTRACT Potential evidence for primordial non-Gaussianity (PNG) is expected to lie in the largest scales mapped by cosmological surveys. Forthcoming 21 cm intensity mapping experiments will aim to probe these scales by surveying neutral hydrogen (H i) within galaxies. However, foreground signals dominate the 21 cm emission, meaning foreground cleaning is required to recover the cosmological signal. The effect this has is to damp the H i power spectrum on the largest scales, especially along the line of sight. Whilst there is agreement that this contamination is potentially problematic for probing PNG, it is yet to be fully explored and quantified. In this work, we carry out the first forecasts on fNL that incorporate simulated foreground maps that are removed using techniques employed in real data. Using an Monte Carlo Markov Chain analysis on an SKA1-MID-like survey, we demonstrate that foreground cleaned data recovers biased values [$f_{\rm NL}= -102.1_{-7.96}^{+8.39}$ (68 per cent CL)] on our fNL = 0 fiducial input. Introducing a model with fixed parameters for the foreground contamination allows us to recover unbiased results ($f_{\rm NL}= -2.94_{-11.9}^{+11.4}$). However, it is not clear that we will have sufficient understanding of foreground contamination to allow for such rigid models. Treating the main parameter $k_\parallel ^\text{FG}$ in our foreground model as a nuisance parameter and marginalizing over it, still recovers unbiased results but at the expense of larger errors ($f_{\rm NL}= 0.75^{+40.2}_{-44.5}$), which can only be reduced by imposing the Planck 2018 prior. Our results show that significant progress on understanding and controlling foreground removal effects is necessary for studying PNG with H i intensity mapping.

Attend, Correct And Focus: A Bidirectional Correct Attention Network For Image-Text Matching

2021 ◽  
Author(s):  
Yang Liu ◽  
Huaqiu Wang ◽  
Fanyang Meng ◽  
Mengyuan Liu ◽  
Hong Liu
Keyword(s):  
Attention Network ◽  
Text Matching

Fusion Layer Attention for Image-Text Matching

2021 ◽  
Author(s):  
Depeng Wang ◽  
Liejun Wang ◽  
Shiji Song ◽  
Gao Huang ◽  
Yuchen Guo ◽  
...  
Keyword(s):  
Text Matching

scholarly journals Bilingual text, matching using bilingual dictionary and statistics

1994 ◽  
Author(s):  
Takehito Utsuro ◽  
Hiroshi Ikeda ◽  
Masaya Yamane ◽  
Yuji Matsumoto ◽  
Makoto Nagao
Keyword(s):  
Bilingual Dictionary ◽  
Bilingual Text ◽  
Text Matching

Algorithm of Text Categorization Based on Cloud Computing

2013 ◽  
Vol 311 ◽  
pp. 158-163 ◽  
Author(s):  
Li Qin Huang ◽  
Li Qun Lin ◽  
Yan Huang Liu
Keyword(s):  
Cloud Computing ◽  
Text Categorization ◽  
Experimental Results ◽  
Support Vector ◽  
Computing Environment ◽  
Mapreduce Framework ◽  
Cloud Computing Environment ◽  
Environment Map ◽  
Vector Machines ◽  
Parallel Text

MapReduce framework of cloud computing has an effective way to achieve massive text categorization. In this paper a distributed parallel text training algorithm in cloud computing environment based on multi-class Support Vector Machines(SVM) is designed. In cloud computing environment Map tasks realize distributing various types of samples and Reduce tasks realize the specific SVM training. Experimental results show that the execution time of text training decreases with the number of Reduce tasks increasing. Also a parallel text classifying based on cloud computing is designed and implemented, which classify the unknown type texts. Experimental results show that the speed of text classifying increases with the number of Map tasks increasing.

scholarly journals News image-text matching with news knowledge graph

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Zhao Yumeng ◽  
Yun Jing ◽  
Gao Shuo ◽  
Liu Limin
Keyword(s):  
Knowledge Graph ◽  
Text Matching
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