Relevant bibliographies by topics / Photometric gaussian mixtures

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Academic literature on the topic 'Photometric gaussian mixtures'

Author: Grafiati
Published: 1 June 2024
Last updated: 31 July 2025

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Journal articles on the topic "Photometric gaussian mixtures"

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Crombez, Nathan, El Mustapha Mouaddib, Guillaume Caron, and Francois Chaumette. "Visual Servoing With Photometric Gaussian Mixtures as Dense Features." IEEE Transactions on Robotics 35, no. 1 (2019): 49–63. http://dx.doi.org/10.1109/tro.2018.2876765.

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Hatfield, P. W., I. A. Almosallam, M. J. Jarvis, et al. "Augmenting machine learning photometric redshifts with Gaussian mixture models." Monthly Notices of the Royal Astronomical Society 498, no. 4 (2020): 5498–510. http://dx.doi.org/10.1093/mnras/staa2741.

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ABSTRACT Wide-area imaging surveys are one of the key ways of advancing our understanding of cosmology, galaxy formation physics, and the large-scale structure of the Universe in the coming years. These surveys typically require calculating redshifts for huge numbers (hundreds of millions to billions) of galaxies – almost all of which must be derived from photometry rather than spectroscopy. In this paper, we investigate how using statistical models to understand the populations that make up the colour–magnitude distribution of galaxies can be combined with machine learning photometric redshif
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Jones, Daniel M., and Alan F. Heavens. "Gaussian mixture models for blended photometric redshifts." Monthly Notices of the Royal Astronomical Society 490, no. 3 (2019): 3966–86. http://dx.doi.org/10.1093/mnras/stz2687.

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ABSTRACT Future cosmological galaxy surveys such as the Large Synoptic Survey Telescope (LSST) will photometrically observe very large numbers of galaxies. Without spectroscopy, the redshifts required for the analysis of these data will need to be inferred using photometric redshift techniques that are scalable to large sample sizes. The high number density of sources will also mean that around half are blended. We present a Bayesian photometric redshift method for blended sources that uses Gaussian mixture models to learn the joint flux–redshift distribution from a set of unblended training g
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Ansari, Zoe, Adriano Agnello, and Christa Gall. "Mixture models for photometric redshifts." Astronomy & Astrophysics 650 (June 2021): A90. http://dx.doi.org/10.1051/0004-6361/202039675.

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Context. Determining photometric redshifts (photo-zs) of extragalactic sources to a high accuracy is paramount to measure distances in wide-field cosmological experiments. With only photometric information at hand, photo-zs are prone to systematic uncertainties in the intervening extinction and the unknown underlying spectral-energy distribution of different astrophysical sources, leading to degeneracies in the modern machine learning algorithm that impacts the level of accuracy for photo-z estimates. Aims. Here, we aim to resolve these model degeneracies and obtain a clear separation between
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Wagenveld, J. D., A. Saxena, K. J. Duncan, H. J. A. Röttgering, and M. Zhang. "Revealing new high-redshift quasar populations through Gaussian mixture model selection." Astronomy & Astrophysics 660 (April 2022): A22. http://dx.doi.org/10.1051/0004-6361/202142445.

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We present a novel method for identifying candidate high-redshift quasars (HzQs; z ≳ 5.5) –which are unique probes of supermassive black hole growth in the early Universe– from large-area optical and infrared photometric surveys. Using Gaussian mixture models to construct likelihoods and incorporating informed priors based on population statistics, our method uses a Bayesian framework to assign posterior probabilities that differentiate between HzQs and contaminating sources. We additionally include deep radio data to obtain informed priors. Using existing HzQ data in the literature, we set a
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D’Isanto, A., and K. L. Polsterer. "Photometric redshift estimation via deep learning." Astronomy & Astrophysics 609 (January 2018): A111. http://dx.doi.org/10.1051/0004-6361/201731326.

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Context. The need to analyze the available large synoptic multi-band surveys drives the development of new data-analysis methods. Photometric redshift estimation is one field of application where such new methods improved the results, substantially. Up to now, the vast majority of applied redshift estimation methods have utilized photometric features. Aims. We aim to develop a method to derive probabilistic photometric redshift directly from multi-band imaging data, rendering pre-classification of objects and feature extraction obsolete. Methods. A modified version of a deep convolutional netw
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Duncan, Kenneth J. "All-purpose, all-sky photometric redshifts for the Legacy Imaging Surveys Data Release 8." Monthly Notices of the Royal Astronomical Society 512, no. 3 (2022): 3662–83. http://dx.doi.org/10.1093/mnras/stac608.

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ABSTRACT In this paper, we present photometric redshift (photo-z) estimates for the Dark Energy Spectroscopic Instrument (DESI) Legacy Imaging Surveys, currently the most sensitive optical survey covering the majority of the extragalactic sky. Our photo-z methodology is based on a machine-learning approach, using sparse Gaussian processes augmented with Gaussian mixture models (GMMs) that allow regions of parameter space to be identified and trained separately in a purely data-driven way. The same GMMs are also used to calculate cost-sensitive learning weights that mitigate biases in the spect
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Dinesh, Kadam, R. Madane Amol, Kutty Krishnan, and V. Bonde S. "Rain Streaks Elimination Using Image Processing Algorithms." Signal & Image Processing: An International Journal (SIPIJ) 10, no. 3 (2019): 21–32. https://doi.org/10.5281/zenodo.3351005.

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The paper addresses the problem of rain streak removal from videos. While, Rain streak removal from scene is important but a lot of research in this area, robust and real time algorithms is unavailable in the market. Difficulties in the rain streak removal algorithm arises due to less visibility, less illumination, and availability of moving camera and objects. The challenge that plagues rain streak recovery algorithm is detecting rain streaks and replacing them with original values to recover the scene. In this paper, we discuss the use of photometric and chromatic properties for rain detecti
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Johnston, Harry, Nora Elisa Chisari, Shahab Joudaki, et al. "6 × 2 pt: Forecasting gains from joint weak lensing and galaxy clustering analyses with spectroscopic-photometric galaxy cross-correlations." Astronomy & Astrophysics 699 (July 2025): A127. https://doi.org/10.1051/0004-6361/202452466.

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Accurate knowledge of galaxy redshift distributions is crucial in the inference of cosmological parameters from large-scale structure data. We explore the potential for enhanced self-calibration of photometric galaxy redshift distributions, n(z), through the joint analysis of up to six two-point functions. Our 3 × 2 pt configuration comprises photometric shear, spectroscopic galaxy clustering, and spectroscopic-photometric galaxy-galaxy lensing (GGL). We expand this to include spectroscopic-photometric cross-clustering, photometric GGL, and photometric auto-clustering, using the photometric sh
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Jang, J. K., Sukyoung K. Yi, Yohan Dubois, et al. "Translators of Galaxy Morphology Indicators between Observation and Simulation." Astrophysical Journal 950, no. 1 (2023): 4. http://dx.doi.org/10.3847/1538-4357/accd68.

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Abstract Based on the recent advancements in numerical simulations of galaxy formation, we anticipate the achievement of realistic models of galaxies in the near future. Morphology is the most basic and fundamental property of galaxies, yet observations and simulations still use different methods to determine galaxy morphology, making it difficult to compare them. We hereby perform a test on the recent NewHorizon simulation, which has spatial and mass resolutions that are remarkably high for a large-volume simulation, to resolve the situation. We generate mock images for the simulated galaxies
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Dissertations / Theses on the topic "Photometric gaussian mixtures"

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Guerbas, Seif Eddine. "Modélisation adaptée des images omnidirectionnelles pour agrandir le domaine de convergence de l'asservissement visuel virtuel direct." Electronic Thesis or Diss., Amiens, 2022. http://www.theses.fr/2022AMIE0026.

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La vision omnidirectionnelle capture dans toutes les directions une scène en temps réel grâce à un champ de vision plus étendu que celui offert par une caméra conventionnelle. Au sein de l'environnement, relier les caractéristiques visuelles contenues dans les images de la caméra à ses mouvements est une problématique centrale pour l'asservissement visuel. Les approches directes se caractérisent cependant par un domaine de convergence limité. La thèse que nous présentons a pour premier objectif d'étendre significativement ce dernier dans le cadre de l'asservissement visuel virtuel en représent
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Conference papers on the topic "Photometric gaussian mixtures"

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Schulte, Sinta, Antoine N. André, Nathan Crombez, and Guillaume Caron. "On the impact of the camera field-of-view to Direct Visual Servoing robot trajectories when using the Photometric Gaussian Mixtures as dense feature." In 2025 IEEE/SICE International Symposium on System Integration (SII). IEEE, 2025. https://doi.org/10.1109/sii59315.2025.10871062.

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Crombez, Nathan, Guillaume Caron, and El Mustapha Mouaddib. "Photometric Gaussian mixtures based visual servoing." In 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2015. http://dx.doi.org/10.1109/iros.2015.7354154.

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