Photometric Redshifts with Copula Entropy

• URL: http://arxiv.org/abs/2310.16633v1
• Date: Wed, 25 Oct 2023 13:33:40 GMT
• Title: Photometric Redshifts with Copula Entropy
• Authors: Jian Ma
• Abstract summary: Copula entropy (CE) is used to measure the correlations between photometric measurements and redshifts. The accuracy of photometric redshifts is improved with the selected measurements.
• Score: 1.7125489646780319
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper we propose to apply copula entropy (CE) to photometric redshifts. CE is used to measure the correlations between photometric measurements and redshifts and then the measurements associated with high CEs are selected for predicting redshifts. We verified the proposed method on the SDSS quasar data. Experimental results show that the accuracy of photometric redshifts is improved with the selected measurements compared to the results with all the measurements used in the experiments, especially for the samples with high redshifts. The measurements selected with CE include luminosity magnitude, the brightness in ultraviolet band with standard deviation, and the brightness of the other four bands. Since CE is a rigorously defined mathematical concept, the models such derived is interpretable.

Related papers

• Determination of galaxy photometric redshifts using Conditional Generative Adversarial Networks (CGANs) [0.0]
  We present a new algorithmic approach for determining photometric redshifts of galaxies using Conditional Generative Adversarial Networks (CGANs) Proposed CGAN implementation, approaches photometric redshift determination as a probabilistic regression, where instead of determining a single value for the estimated redshift of the galaxy, a full probability density is computed.
  arXiv  Detail & Related papers  (2025-01-11T12:42:07Z)
• CLAP. I. Resolving miscalibration for deep learning-based galaxy photometric redshift estimation [3.611102630303458]
  We develop a novel method called the Contrastive Learning and Adaptive KNN for Photometric Redshift (CLAP) It leverages supervised contrastive learning (SCL) and k-nearest neighbours (KNN) to construct and calibrate raw probability density estimates. The harmonic mean is adopted to combine an ensemble of estimates from multiple realisations for improving accuracy.
  arXiv  Detail & Related papers  (2024-10-25T08:46:55Z)
• MixLight: Borrowing the Best of both Spherical Harmonics and Gaussian Models [69.39388799906409]
  Existing works estimate illumination by generating illumination maps or regressing illumination parameters. This paper presents MixLight, a joint model that utilizes the complementary characteristics of SH and SG to achieve a more complete illumination representation.
  arXiv  Detail & Related papers  (2024-04-19T10:17:10Z)
• High-dimensional quantum correlation measurements with an adaptively gated hybrid single-photon camera [58.720142291102135]
  We propose an adaptively-gated hybrid intensified camera (HIC) that combines a high spatial resolution sensor and a high temporal resolution detector. With a spatial resolution of nearly 9 megapixels and nanosecond temporal resolution, this system allows for the realization of previously infeasible quantum optics experiments.
  arXiv  Detail & Related papers  (2023-05-25T16:59:27Z)
• Photo-zSNthesis: Converting Type Ia Supernova Lightcurves to Redshift Estimates via Deep Learning [0.0]
  Photo-zSNthesis is a convolutional neural network-based method for predicting full redshift probability distributions. We show a 61x improvement in prediction bias Delta z> on PLAsTiCC simulations and 5x improvement on real SDSS data.
  arXiv  Detail & Related papers  (2023-05-19T17:59:00Z)
• Spectral Properties of Transverse Laguerre-Gauss Modes in Parametric Down-Conversion [0.0]
  We study the spectral dependence of the transverse Laguerre-Gauss modes in parametric downconversion. We show how the spectral and spatial coupling can be harnessed to tune the purity of the well-known orbital angular momentum entanglement. This work has implications for efficient collection of entangled photons in a transverse single mode, quantum imaging, and engineering pure states for high-dimensional quantum information processing.
  arXiv  Detail & Related papers  (2022-09-05T11:37:31Z)
• Estimating the concentration of chiral media with bright squeezed light [77.34726150561087]
  We quantify the performance of Gaussian probes in estimating the concentration of chiral analytes. Four-fold precision enhancement is achievable using state-of-the-art squeezing levels and intensity measurements.
  arXiv  Detail & Related papers  (2022-08-21T17:18:10Z)
• Weak Thermal State Quadrature-Noise Shadow Imaging [0.0]
  We demonstrate the possibility to create an image of an opaque object using a few-photon thermal optical field. We show that such thermal QSI scheme has an advantage over the classical differential imaging when the effect of dark counts is considered.
  arXiv  Detail & Related papers  (2022-02-04T16:57:15Z)
• Remote Phase Sensing by Coherent Single Photon Addition [58.720142291102135]
  We propose a remote phase sensing scheme inspired by the high sensitivity of the entanglement produced by coherent multimode photon addition on the phase set in the remote heralding apparatus. We derive the optimal observable to perform remote phase estimation from heralded quadrature measurements.
  arXiv  Detail & Related papers  (2021-08-26T14:52:29Z)
• Leveraging Spatial and Photometric Context for Calibrated Non-Lambertian Photometric Stereo [61.6260594326246]
  We introduce an efficient fully-convolutional architecture that can leverage both spatial and photometric context simultaneously. Using separable 4D convolutions and 2D heat-maps reduces the size and makes more efficient.
  arXiv  Detail & Related papers  (2021-03-22T18:06:58Z)
• Parameterized Temperature Scaling for Boosting the Expressive Power in Post-Hoc Uncertainty Calibration [57.568461777747515]
  We introduce a novel calibration method, Parametrized Temperature Scaling (PTS) We demonstrate that the performance of accuracy-preserving state-of-the-art post-hoc calibrators is limited by their intrinsic expressive power. We show with extensive experiments that our novel accuracy-preserving approach consistently outperforms existing algorithms across a large number of model architectures, datasets and metrics.
  arXiv  Detail & Related papers  (2021-02-24T10:18:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.