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Line intensity mapping is emerging as a novel method that can measure the collective intensity fluctuations of atomic/molecular line emission from distant galaxies. Several observational programs with various wavelengths are ongoing and…

Astrophysics of Galaxies · Physics 2021-12-15 Kana Moriwaki , Naoki Yoshida

Line intensity mapping (LIM) is an emerging technique with a unique potential to probe a wide range of scales and redshifts. Realizing the full potential of LIM, however, relies on accurate modeling of the signal. We introduce an extended…

Cosmology and Nongalactic Astrophysics · Physics 2022-02-23 Azadeh Moradinezhad Dizgah , Farnik Nikakhtar , Garrett K. Keating , Emanuele Castorina

The number density and correlation function of galaxies are two key quantities to characterize the distribution of the observed galaxy population. High-$z$ spectroscopic surveys, which usually involve complex target selection and are…

Astrophysics of Galaxies · Physics 2024-02-05 Jiacheng Meng , Cheng Li , Houjun Mo , Yangyao Chen , Kai Wang

Blind cleaning methods are currently the preferred strategy for handling foreground contamination in single-dish HI intensity mapping surveys. Despite the increasing sophistication of blind techniques, some signal loss will be inevitable…

It is crucial to distinguish mislabeled samples for dealing with noisy labels. Previous methods such as Coteaching and JoCoR introduce two different networks to select clean samples out of the noisy ones and only use these clean ones to…

Computer Vision and Pattern Recognition · Computer Science 2021-03-26 Rumeng Yi , Yaping Huang

Recent studies on learning with noisy labels have shown remarkable performance by exploiting a small clean dataset. In particular, model agnostic meta-learning-based label correction methods further improve performance by correcting noisy…

Machine Learning · Computer Science 2022-07-13 Seong Min Kye , Kwanghee Choi , Joonyoung Yi , Buru Chang

Learning with noisy labels is an important and challenging task for training accurate deep neural networks. Some commonly-used loss functions, such as Cross Entropy (CE), suffer from severe overfitting to noisy labels. Robust loss functions…

Machine Learning · Computer Science 2021-08-03 Xiong Zhou , Xianming Liu , Chenyang Wang , Deming Zhai , Junjun Jiang , Xiangyang Ji

In supervised classification tasks, models are trained to predict a label for each data point. In real-world datasets, these labels are often noisy due to annotation errors. While the impact of label noise on the performance of deep…

Machine Learning · Computer Science 2025-10-09 Ali Hussaini Umar , Franky Kevin Nando Tezoh , Jean Barbier , Santiago Acevedo , Alessandro Laio

We present a method for automated classification of galaxies with low signal-to-noise (S/N) spectra typical of redshift surveys. We develop spectral simulations based on the parameters for the 2dF Galaxy Redshift Survey, and with these…

Astrophysics · Physics 2015-06-24 S. R. Folkes , O. Lahav , S. J. Maddox

The topology and dynamics of the solar chromosphere are greatly affected by the presence of magnetic fields. The magnetic field can be inferred by analyzing polarimetric observations of spectral lines. Polarimetric signals induced by…

Solar and Stellar Astrophysics · Physics 2019-09-11 C. J. Díaz Baso , J. de la Cruz Rodríguez , S. Danilovic

Current radio interferometers output multi-petabyte-scale volumes of data per year making the storage, transfer, and processing of this data a sizeable challenge. This challenge is expected to grow with the next-generation telescopes such…

Cosmology and Nongalactic Astrophysics · Physics 2024-12-18 J. K. Chege , L. V. E. Koopmans , A. R. Offringa , B. K. Gehlot , S. A. Brackenhoff , E. Ceccotti , S. Ghosh , C. Höfer , F. G. Mertens , M. Mevius , S. Munshi

Context: JWST has enabled transmission spectroscopy at unprecedented precision, but stellar heterogeneities (spots and faculae) remain a dominant contamination source that can bias atmospheric retrievals if uncorrected. Aims: We present a…

Earth and Planetary Astrophysics · Physics 2026-02-13 David S. Duque-Castaño , Lauren Flor-Torres , Jorge I. Zuluaga

An accurate theoretical template of the redshift-space galaxy power spectrum, if applicable out to nonlinear scales, enables us to extract more stringent and robust constraints on cosmological parameters from the measured galaxy clustering.…

Cosmology and Nongalactic Astrophysics · Physics 2020-10-20 Yosuke Kobayashi , Takahiro Nishimichi , Masahiro Takada , Ryuichi Takahashi , Ken Osato

Galaxy power spectrum and bispectrum signals are distorted by peculiar velocities and other relativistic effects arising from a perturbed spacetime background. In addition, study of correlation functions of tracers in Fourier space is often…

Cosmology and Nongalactic Astrophysics · Physics 2023-04-27 Milad Noorikuhani , Roman Scoccimarro

Cross-correlations between the galaxy number density in a lensing source sample and that in an overlapping spectroscopic sample can in principle be used to calibrate the lensing source redshift distribution. In this paper, we study in…

Cosmology and Nongalactic Astrophysics · Physics 2015-06-16 Roland de Putter , Olivier Doré , Sudeep Das

The cosmological power spectrum of the coherent matter flow is measured exploiting an improved prescription for the apparent anisotropic clustering pattern in redshift space. New statistical analysis is presented to provide an optimal…

Cosmology and Nongalactic Astrophysics · Physics 2013-08-22 Yong-Seon Song , Takahiro Nishimichi , Atsushi Taruya , Issha Kayo

We explore how to mitigate the clustering distortions in Lyman-$\alpha$ emitters (LAEs) samples caused by the miss-identification of the Lyman-$\alpha$ (Ly$\alpha$) wavelength in their Ly$\alpha$ line profiles. We use the Ly$\alpha$ line…

Astrophysics of Galaxies · Physics 2020-10-28 Siddhartha Gurung-Lopez , Shun Saito , Carlton M. Baugh , Silvia Bonoli , Cedric G. Lacey , Alvaro A. Orsi

The identification of an emission line is unambiguous when multiple spectral features are clearly visible in the same spectrum. However, in many cases, only one line is detected, making it difficult to correctly determine the redshift. We…

Label noise in training data can significantly degrade a model's generalization performance for supervised learning tasks. Here we focus on the problem that noisy labels are primarily mislabeled samples, which tend to be concentrated near…

Machine Learning · Computer Science 2021-03-16 Hao-Chiang Shao , Hsin-Chieh Wang , Weng-Tai Su , Chia-Wen Lin

We present the first cosmological constraints from analyzing higher-order galaxy clustering on non-linear scales. We use ${\rm S{\scriptsize IM}BIG}$, a forward modeling framework for galaxy clustering analyses that employs simulation-based…