Related papers: Template-Fitting Meets Deep Learning: Redshift Est…
Miscalibration - a mismatch between a model's confidence and its correctness - of Deep Neural Networks (DNNs) makes their predictions hard to rely on. Ideally, we want networks to be accurate, calibrated and confident. We show that, as…
This paper presents a comprehensive study of quasar photometric classification and redshift estimation using machine learning techniques. We cross-matched photometric data from the Dark Energy Survey Data Release 2 (DES DR2) with…
Accurate weak lensing mass estimates of clusters are needed in order to calibrate mass proxies for the cosmological exploitation of galaxy cluster surveys. Such measurements require accurate knowledge of the redshift distribution of the…
This article presents an innovative study in exploring, evaluating, and implementing deep learning architectures for the calibration of multi-modal sensor systems. The focus behind this is to leverage the use of sensor fusion to achieve…
Photometric redshifts are a key tool to extract as much information as possible from planned cosmic shear experiments. In this work we aim to test the performances that can be achieved with observations in the near-infrared from space and…
In astronomy, neural networks are often trained on simulation data with the prospect of being used on telescope observations. Unfortunately, training a model on simulation data and then applying it to instrument data leads to a substantial…
Deep neural networks have been very successful in image estimation applications such as compressive-sensing and image restoration, as a means to estimate images from partial, blurry, or otherwise degraded measurements. These networks are…
We apply instance-based machine learning in the form of a k-nearest neighbor algorithm to the task of estimating photometric redshifts for 55,746 objects spectroscopically classified as quasars in the Fifth Data Release of the Sloan Digital…
Modern deep neural networks (DNNs) are highly accurate on many recognition tasks for overhead (e.g., satellite) imagery. However, visual domain shifts (e.g., statistical changes due to geography, sensor, or atmospheric conditions) remain a…
Landslide monitoring is essential for understanding geohazards and mitigating associated risks. Existing point cloud-based methods, however, typically rely on either geometric or radiometric information and often yield sparse or non-3D…
We present a method, PhotoWeb, for estimating photometric redshifts of individual galaxies, and their equivalent distance, with megaparsec and even sub-megaparsec accuracy using the Cosmic Web as a constraint over photo-z estimates.…
Advances in optical astrometry allow us to infer the non-radial kinematic structure of the Universe directly from observations. Here I use a supervised machine learning neural network method to predict 1.57 million redshifts based on…
The exponential growth in the number of complex datasets every year requires more enhancement in machine learning methods to provide robust and accurate data classification. Lately, deep learning approaches have achieved surpassing results…
We present the methodology and data behind the photometric redshift database of the Sloan Digital Sky Survey Data Release 12 (SDSS DR12). We adopt a hybrid technique, empirically estimating the redshift via local regression on a…
Neural networks often obtain sub-optimal representations during training, which degrade robustness as well as classification performances. This is a severe problem in applying deep learning to bio-medical domains, since models are…
How do the neural networks distinguish two images? It is of critical importance to understand the matching mechanism of deep models for developing reliable intelligent systems for many risky visual applications such as surveillance and…
Purpose: The fusion of transrectal ultrasound (TRUS) and magnetic resonance (MR) images for guiding targeted prostate biopsy has significantly improved the biopsy yield of aggressive cancers. A key component of MR-TRUS fusion is image…
We investigate the expected accuracy of redshifts that can be obtained using low-resolution spectroscopic (medium-band) data from the 7-Dimensional Sky Survey (7DS). By leveraging 40 densely sampled filters with widths of full width at half…
Template estimation plays a crucial role in computational anatomy since it provides reference frames for performing statistical analysis of the underlying anatomical population variability. While building models for template estimation,…
We present photometric redshifts for 1,031 X-ray sources in the X-ATLAS field, using the machine learning technique TPZ (Carrasco Kind & Brunner 2013). X-ATLAS covers 7.1 deg2 observed with the XMM-Newton within the Science Demonstration…