Cosmic Ray Rejection Research Articles

Likelihood-based Jump Detection and Cosmic Ray Rejection for Detectors Read Out Up-the-ramp

This paper implements likelihood-based jump detection for detectors read out up-the-ramp, using the entire set of reads to compute likelihoods. The approach compares the χ 2 value of a fit with and without a jump for every possible jump location. I show that this approach can be substantially more sensitive than one that only uses the difference between sequential groups of reads, especially for long ramps and for jumps that occur in the middle of a group of reads. It can also be implemented for a computational cost that is linear in the number of resultants. I provide and describe a pure Python implementation that can process a 10-resultant ramp on a 4096 × 4096 detector in ≈20 s, including iterative cosmic ray detection and removal, on a single core of a 2020 Macbook Air. This Python implementation, together with tests and a tutorial notebook, are available at https://github.com/t-brandt/fitramp. I also provide tests and demonstrations of the full ramp fitting and cosmic ray rejection approach on data from the JWST.

A New Cosmic-Ray Rejection Routine for HST WFC3/UVIS via Label-free Training of deepCR

deepCR is a deep-learning-based cosmic-ray (CR) rejection framework originally presented by Zhang & Bloom. The original approach requires a dedicated training set that consists of multiple frames of the same fields, enabling automatic CR labeling through comparison with their median coadds. Here, we present a novel training approach that circumvents the need for a dedicated training set, but instead utilizes dark frames and the science images requiring CR removal themselves. During training, CRs present in dark frames are added to the science images, which the network is then trained to identify. In turn, the trained deepCR model can then be applied to identify CRs originally present in the science images. Using this approach, we present a new deepCR model trained on a diverse set of Hubble Space Telescope images taken from resolved galaxies in the Local Group, which is universally applicable across all WFC3/UVIS filters. We introduce a robust approach to determining the threshold for generating binary cosmic-ray masks from predictions from deepCR probability maps. When applied to the Panchromatic Hubble Andromeda Southern Treasury survey, our new deepCR model added ∼7% of good-quality stars that exhibit distinct features in their color–magnitude diagrams.

Cosmic Ray Rejection in Small Angle X-Ray Scattering

Cosmic Ray Rejection in Small Angle X-Ray Scattering

Cosmic Ray rejection with attention augmented deep learning

Cosmic Ray (CR) hits are the major contaminants in astronomical imaging and spectroscopic observations involving solid-state detectors. Correctly identifying and masking them is a crucial part of the image processing pipeline, since it may otherwise lead to spurious detections. For this purpose, we have developed and tested a novel Deep Learning based framework for the automatic detection of CR hits from astronomical imaging data from two different imagers: Dark Energy Camera (DECam) and Las Cumbres Observatory Global Telescope (LCOGT). We considered two baseline models namely deepCR and Cosmic-CoNN, which are the current state-of-the-art learning based algorithms that were trained using Hubble Space Telescope (HST) ACS/WFC and LCOGT Network images respectively. We have experimented with the idea of augmenting the baseline models using Attention Gates (AGs) to improve the CR detection performance. We have trained our models on DECam data and demonstrate a consistent marginal improvement by adding AGs in True Positive Rate (TPR) at 0.01% False Positive Rate (FPR) and Precision at 95% TPR over the aforementioned baseline models for the DECam dataset. We demonstrate that the proposed AG augmented models provide significant gain in TPR at 0.01% FPR when tested on previously unseen LCO test data having images from three distinct telescope classes. Furthermore, we demonstrate that the proposed baseline models with and without attention augmentation outperform state-of-the-art models such as Astro-SCRAPPY, Maximask (that is trained natively on DECam data) and pre-trained ground-based Cosmic-CoNN. This study demonstrates that the AG module augmentation enables us to get a better deepCR and Cosmic-CoNN models and to improve their generalization capability on unseen data.

DeepCR on ACS/WFC: Cosmic-Ray Rejection for HST ACS/WFC Photometry

Abstract deepCR is a deep-learning-based cosmic-ray rejection algorithm previously demonstrated to be superior to state-of-the-art LACosmic on Hubble Space Telescope (HST) Advanced Camera for Surveys (ACS)/WFC F606W imaging data. In this research note, we present a new deepCR model for use on all filters of HST ACS/WFC. We train and test the model with ACS/WFC F435W, F606W, and F814W images, covering the entire spectral range of the ACS optical channel. The global model demonstrates near 100% detection rates of CRs in extragalactic fields and globular clusters and 91% in resolved galaxy fields. We further confirm the global applicability of the model by comparing its performance against single-filter models that were trained simultaneously and by testing the global model on data from another filter which was not previously used for training.

DeepCR: Cosmic Ray Rejection with Deep Learning

Abstract Cosmic ray (CR) identification and replacement are critical components of imaging and spectroscopic reduction pipelines involving solid-state detectors. We present deepCR, a deep-learning-based framework for CR identification and subsequent image inpainting based on the predicted CR mask. To demonstrate the effectiveness of this framework, we train and evaluate models on Hubble Space Telescope (HST) ACS/WFC images of sparse extragalactic fields, globular clusters, and resolved galaxies. We demonstrate that at a false-positive rate of 0.5%, deepCR achieves close to 100% detection rates in both extragalactic and globular cluster fields, and 91% in resolved galaxy fields, which is a significant improvement over the current state-of-the-art method LACosmic. Compared with a multicore CPU implementation of LACosmic, deepCR CR mask predictions run up to 6.5 times faster on a CPU and 90 times faster on a single GPU. For image inpainting, the mean squared errors of deepCR predictions are 20 times lower in globular cluster fields, 5 times lower in resolved galaxy fields, and 2.5 times lower in extragalactic fields, compared with the best performing nonneural technique tested. We present our framework and the trained models as an open-source Python project , with a simple-to-use API. To facilitate reproducibility of the results we also provide a benchmarking codebase .

DeepCR: Cosmic Ray Rejection with Deep Learning

Cosmic ray (CR) identification and replacement are critical components of imaging and spectroscopic reduction pipelines involving solid-state detectors. We present deepCR, a deep learning based framework for CR identification and subsequent image inpainting based on the predicted CR mask. To demonstrate the effectiveness of this framework, we train and evaluate models on Hubble Space Telescope ACS/WFC images of sparse extragalactic fields, globular clusters, and resolved galaxies. We demonstrate that at a false positive rate of 0.5%, deepCR achieves close to 100% detection rates in both extragalactic and globular cluster fields, and 91% in resolved galaxy fields, which is a significant improvement over the current state-of-the-art method LACosmic. Compared to a multicore CPU implementation of LACosmic, deepCR CR mask predictions run up to 6.5 times faster on CPU and 90 times faster on a single GPU. For image inpainting, the mean squared errors of deepCR predictions are 20 times lower in globular cluster fields, 5 times lower in resolved galaxy fields, and 2.5 times lower in extragalactic fields, compared to the best performing non-neural technique tested. We present our framework and the trained models as an open-source Python project, with a simple-to-use API. To facilitate reproducibility of the results we also provide a benchmarking codebase.

A Semi-Autonomous Method to Detect Cosmic Rays in Raman Hyperspectral Data Sets

Cosmic rays can degrade Raman hyperspectral images by introducing high-intensity noise to spectra, obfuscating the results of downstream analyses. We describe a novel method to detect cosmic rays in deep ultraviolet Raman hyperspectral data sets adapted from existing cosmic ray removal methods applied to astronomical images. This method identifies cosmic rays as outliers in the distribution of intensity values in each wavelength channel. In some cases, this algorithm fails to identify cosmic rays in data sets with high inter-spectral variance, uncorrected baseline drift, or few spectra. However, this method effectively identifies cosmic rays in spatially uncorrelated hyperspectral data sets more effectively than other cosmic ray rejection methods and can potentially be employed in commercial and robotic Raman systems to identify cosmic rays semi-autonomously.

A kilo-pixel imaging system for future space based far-infrared observatories using microwave kinetic inductance detectors

Future astrophysics and cosmic microwave background space missions operating in the far-infrared to millimetre part of the spectrum will require very large arrays of ultra-sensitive detectors in combination with high multiplexing factors and efficient low-noise and low-power readout systems. We have developed a demonstrator system suitable for such applications. The system combines a 961 pixel imaging array based upon Microwave Kinetic Inductance Detectors (MKIDs) with a readout system capable of reading out all pixels simultaneously with only one readout cable pair and a single cryogenic amplifier. We evaluate, in a representative environment, the system performance in terms of sensitivity, dynamic range, optical efficiency, cosmic ray rejection, pixel-pixel crosstalk and overall yield at at an observation centre frequency of 850 GHz and 20% fractional bandwidth. The overall system has an excellent sensitivity, with an average detector sensitivity NEPdet=3x10^-19 W/rt(Hz) measured using a thermal calibration source. At a loading power per pixel of 50fW we demonstrate white, photon noise limited detector noise down to 300 mHz. The dynamic range would allow the detection of 1 Jy bright sources within the field of view without tuning the readout of the detectors. The expected dead time due to cosmic ray interactions, when operated in an L2 or a similar far-Earth orbit, is found to be <4%. Additionally, the achieved pixel yield is 83% and the crosstalk between the pixels is <-30dB. This demonstrates that MKID technology can provide multiplexing ratios on the order of a 1000 with state-of-the-art single pixel performance, and that the technology is now mature enough to be considered for future space based observatories and experiments.

ARCONS: A 2024 Pixel Optical through Near-IR Cryogenic Imaging Spectrophotometer

We present the design, construction, and commissioning results of ARCONS, the Array Camera for Optical to Near-IR Spectrophotometry. ARCONS is the first ground-based instrument in the optical through near-IR wavelength range based on microwave kinetic inductance detectors (MKIDs). MKIDs are revolutionary cryogenic detectors, capable of detecting single photons and measuring their energy without filters or gratings, similar to an X-ray microcalorimeter. MKIDs are nearly ideal, noiseless photon detectors, as they do not suffer from read noise or dark current and have nearly perfect cosmic ray rejection. ARCONS is an integral field spectrograph (IFS) containing a lens-coupled 2024 pixel MKID array yielding a 20″ × 20″ field of view and has been deployed on the Palomar 200 inch and Lick 120 inch telescopes for 24 nights of observing. We present initial results showing that ARCONS and its MKID arrays are now a fully operational and powerful tool for astronomical observations.

A study of the performance parameters of the High Altitude Gamma Ray (HAGAR) telescope system at Ladakh in India

We present results of Monte Carlo simulations for the High Altitude Gamma Ray (HAGAR) telescope array which detects very high energy gamma rays from astronomical sources. This telescope array, located at Hanle at an altitude of 4270m in the Ladakh region of the Himalayas in India, is the highest altitude atmospheric Cherenkov experiment in the world. Taking advantage of the high altitude, this experiment could achieve relatively low energy threshold with a modest mirror area coverage. To understand the performance parameters of this telescope system, we have simulated large samples of extensive air showers initiated by gamma rays and various species of cosmic rays, using the CORSIKA package. Cherenkov photons produced in the atmosphere are sampled at ground level. These photons are then passed through the detector simulation program, which takes into account various design details and the data acquisition system of HAGAR. Night sky photons are also considered in the detector simulation program as performance of the telescope depends strongly on the level of night sky background (NSB) at the observation site. We have estimated various performance parameters like energy threshold and effective area for vertically incident showers as well as inclined showers. Details of these parameters, results obtained from simulations and comparison with the observed data are presented. It is shown that the energy threshold of the HAGAR telescope system is about 208GeV, a factor of ∼4 less than for a similar set up at about 1000m altitude, and it is able to detect Crab like sources at 5σ significance in 17h of observation without imposing additional criteria, like gamma-hadron separation, for further rejection of cosmic rays.

CRBLASTER: A Parallel-Processing Computational Framework for Embarrassingly Parallel Image-Analysis Algorithms

ABSTRACTThe development of parallel-processing image-analysis codes is generally a challenging task that requires complicated choreography of interprocessor communications. If, however, the image-analysis algorithm is embarrassingly parallel, then the development of a parallel-processing implementation of that algorithm can be a much easier task to accomplish because, by definition, there is little need for communication between the compute processes. I describe the design, implementation, and performance of a parallel-processing image-analysis application, called crblaster, which does cosmic-ray rejection of CCD images using the embarrassingly parallel l.a.cosmic algorithm. crblaster is written in C using the high-performance computing industry standard Message Passing Interface (MPI) library. crblaster uses a two-dimensional image partitioning algorithm that partitions an input image into N rectangular subimages of nearly equal area; the subimages include sufficient additional pixels along common image partition edges such that the need for communication between computer processes is eliminated. The code has been designed to be used by research scientists who are familiar with C as a parallel-processing computational framework that enables the easy development of parallel-processing image-analysis programs based on embarrassingly parallel algorithms. The crblaster source code is freely available at the official application Web site at the National Optical Astronomy Observatory. Removing cosmic rays from a single 800 × 800 pixel Hubble Space Telescope WFPC2 image takes 44 s with the IRAF script lacos_im.cl running on a single core of an Apple Mac Pro computer with two 2.8 GHz quad-core Intel Xeon processors. crblaster is 7.4 times faster when processing the same image on a single core on the same machine. Processing the same image with crblaster simultaneously on all eight cores of the same machine takes 0.875 s—which is a speedup factor of 50.3 times faster than the IRAF script. A detailed analysis is presented of the performance of crblaster, using between 1 and 57 processors on a low-power Tilera 700 MHz 64 core TILE64 processor.

Cosmic-ray rejection for single spectroscopic CCD images by means of template matching

This article describes a novel algorithm for cosmic-ray rejection in single spectroscopic CCD images. This algorithm is based on a variation of template matching. It focuses on identifying those pixels belong to spectra, while other conventional algorithms tried to locate the cosmic-ray hits directly. The main principle is applying template matching to find suspicious blocks, which is followed by surface patching to locate the legitimate pixels accurately. Therefore, the rest pixels are the ones corrupted by cosmic-ray hits. Meanwhile, almost all the parameters are automatically extracted from the images. Examples of its performance are given for both simulated and observed images. It shows an advantage of significantly low false alarm rate with relatively high detection rate (© 2009 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)

Integrated real‐time Raman system for clinical in vivo skin analysis

Raman spectroscopy is a non-invasive optical technique that can probe the molecular structure and conformation of biochemical constituents. The probability of Raman scattering is exceedingly low ( approximately 10(-10)), and consequently up to now the practical application of Raman spectroscopy to clinical medicine has been limited by either the weak spectral signal or by the long data acquisition times. Recent advances in Raman hardware and probe design have reduced spectral acquisition times, paving the way for clinical applications. We present an integrated real-time Raman spectroscopy system for skin evaluation and characterization, which combines customized hardware features and software implementation. Real-time data acquisition and processing includes CCD dark-noise subtraction, wavelength calibration, spectral response calibration, intensity calibration, signal saturation detection, cosmic ray rejection, fluorescence background removal, and composition modeling. Real-time in vivo Raman measurement of volar forearm skin is presented to illustrate the methods and modeling. The system design implemented full-chip vertical hardware binning to improve the signal-to-noise ratio by 16-fold. The total time for a single in vivo measurement with analysis can be reduced to 100 ms with this implementation. Human skin was well modeled using the base Raman spectra. In vivo real-time Raman can be a very promising research and practical technique for skin evaluation.

A MOST open-field data reduction software and its applications to BRITE

Die Serie Communications in Asteroseismology (CoAst) stellt eine international gefragte Plattform für wissenschaftliche Publikationen der neuesten Forschungsergebnisse auf dem Gebiet der Asteroseismologie dar. Forschungsgegenstand der Asteroseismologie sind veränderliche und pulsierende Sterne. Neben den halbjährlich erscheinenden regulären Ausgaben mit Beiträgen aus der laufenden Forschung werden auch Sonderausgaben für Konferenzberichte bzw. Programmhandbücher herausgegeben. Diese Sonderausgabe von CoAst widmet sich der technischen Planung sowie dem wissenschaftlichen Hintergrund des ersten Österreichischen Satellitenpaares: UniBrite/BriteAustria. In den Konferenzbeiträgen zum First BRIght Target Explorer Workshop werden Fragen zum optimalen Satellitendesign, der Flugbahn, den Bodenstationen sowie Startmöglichkeiten erörtert. In weiterer Folge werden verschiede Klassen variabler Sterne als mögliche Zielobjekte und Beobachtungsstrategien für die photometrischen Beobachtungen besprochen. Contents: W. Weiss: BRITE-Constellation: Astrophysical Concept; O. Koudelka: BRITE-Austria/TUG Sat1: Project Overview; C. Grant and R. Zee: From MOST to BRITE: With a Stopover at CanX-2; M. Unterberger: BRITE-Austria/TUG Sat1: System Design and Simulation Results; R. Kuschnig: BRITE-Constellation: Science Operations Concept; A. Kaiser, S. Mochnacki and W. W. Weiss: BRITE-Constellation: Simulation of Photometric Performance; C. Lhotka and B. Funk: BRITE Orbits - Visiblity and Feature Plots; A. Kaiser and R. Kuschnig: BRITE-Constellation: Observation Planning; O. Koudelka: BRITE-Austria/TUG Sat1: Ground Station Technology; B. Josseck: BRITE-Austria/TUG Sat1: Launch Opportunities; D. Huber and P. Reegen: A MOST open-field data reduction sofware and its applications to BRITE; A. Kaiser, K. Zwintz and W. W. Weiss: BRITE-Constellation: Input Catalogue; M. Breger: The BRITE satellite and Delta Scuti Stars: The Magnificent Seven; T. Lüftinger and W. W. Weiss: CP Stars - probing stellar surface structure with BRITE Constellation; M. Gruberbauer, R. Neuteufel and W. W. Weiss: Observing Ƴ Doradus Stars with BRITE - an Outlook; K. Zwintz: β Pictoris: Planets and Pulsations; K. G. Strassmeier: Stellar Activity with BRITE: the "Aurigae" field; T. Kallinger: Exploring solar-type pulsation with BRITE; J. Daszyńska-Daszkiewicz: Identifying pulsation modes from two-passband photometry; E. A. Dorfi and A. Stökl: Theoretical Aspects of Massive Stars; G. Handler: β Cephei and Slowly Pulsating B stars as targets for BRITE-Constellation; R. Dvorak and Á. Bazsó: The search for extrasolar planets with BRITE; E. Paunzen: Cluster and Association Members; T. Lebzelter and J. Hron: BRITE stars on the AGB

Gaia: how to map a billion stars with a billion pixels

AbstractGaia, ESA's ambitious star-mapper mission due for launch late-2011, will provide multi-epoch micro-arcsecond astrometric and milli-magnitude photometric data for the brightest one billion objects in the sky, down to at least magnitude 20. Spectroscopic data will simultaneously be collected for the subset of the brightest 100 million stars, down to about magnitude 17. This massive data volume will allow astronomers to reconstruct the structure, evolution and formation history of the Milky Way. It will also revolutionize studies of the solar system and stellar physics and will contribute to diverse research areas, ranging from extra-solar planets to general relativity.Underlying Gaia's scientific harvest will lie in a Catalogue, built on the fundamental space-based measurements. During the 5-year nominal operational lifetime, Gaia's payload, with its CCD mosaic containing 1 billion pixels, will autonomously detect all objects of interest and observe them throughout their passage of the focal plane. This paper discusses the workings of the Gaia instrument, details its payload, and discusses in depth how the scientific measurements will be collected. It addresses issues like maintenance of the scanning law, on-board data processing, the detection and confirmation of objects (single and multiple stars), the detection and rejection of cosmic rays and solar protons, the fundamental science measurements themselves composed of windows of CCD samples (pixels), and special strategies employed to maximize the science return for moving (i.e., solar-system) objects. The paper also explains how an on-board priority scheme will ensure catalogue completeness down to the faintest magnitudes possible, despite the limited ground-station availability and the enormous data volume that will be sent to the ground.

Hubble Space Telescope Advanced Camera for Surveys Mosaic of the Prototypical Starburst Galaxy M8211Our processed FITS mosaics and related information can be downloaded from http://archive.stsci.edu/prepds/m82.

ABSTRACTIn 2006 March, the Hubble Heritage Team obtained a large, four‐filter (B, V, I, and Hα), 6 point mosaic data set of the prototypical starburst galaxy NGC 3034 (M82) with the Advanced Camera for Surveys on board the Hubble Space Telescope (HST). The resulting color composite Heritage image was released in 2006 April to celebrate HST’s 16th anniversary. Cycle 15 HST proposers were encouraged to submit General Observer and Archival Research proposals to complement and/or analyze this unique data set. Since our M82 mosaics represent a significant investment of expert processing beyond the standard archival products, we also released our drizzle‐combined FITS data as a High‐Level Science Product via the Multimission Archive at STScI in 2006 December. This paper documents the key aspects of the observing program and image processing: calibration, image registration and combination (drizzling), and the rejection of cosmic rays and detector artifacts.

Parallel MOPEX: Computing Mosaics of Large‐Area Spitzer Surveys on a Cluster Computer

The Spitzer Science Center′s MOPEX software is a part of the Spitzer Space Telescope′s operational pipeline that enables detection of cosmic ray collisions with the detector array, masking of the corrupted pixels due to these collisions, subsequent mosaicking of image fields, and extraction of point sources to create catalogs of celestial objects. This paper reports on our experiences in parallelizing the parts of MOPEX related to cosmic ray rejection and mosaicking on a 1,024‐processor cluster computer at NASA′s Jet Propulsion Laboratory. The architecture and performance of the new Parallel MOPEX software are described. This work was done in order to rapidly mosaic the IRAC shallow survey data, covering a region of the sky observed with one of Spitzer′s infrared instruments for the study of galaxy clusters, large‐scale structure, and brown dwarfs.

Evaluation of Cosmic Ray Rejection Algorithms on Single-Shot Exposures

AbstractTo maximise data output from single-shot astronomical images, the rejection of cosmic rays is important. We present the results of a benchmark trial comparing various cosmic ray rejection algorithms. The procedures assess relative performances and characteristics of the processes in cosmic ray detection, rates of false detections of true objects, and the quality of image cleaning and reconstruction. The cosmic ray rejection algorithms developed by Rhoads (2000, PASP, 112, 703), van Dokkum (2001, PASP, 113, 1420), Pych (2004, PASP, 116, 148), and the IRAF task XZAP by Dickinson are tested using both simulated and real data. It is found that detection efficiency is independent of the density of cosmic rays in an image, being more strongly affected by the density of real objects in the field. As expected, spurious detections and alterations to real data in the cleaning process are also significantly increased by high object densities. We find the Rhoads' linear filtering method to produce the best performance in the detection of cosmic ray events; however, the popular van Dokkum algorithm exhibits the highest overall performance in terms of detection and cleaning.

Cosmic‐Ray Rejection by Linear Filtering of Single Images

ABSTRACTWe present a convolution‐based algorithm for finding cosmic rays in single well‐sampled astronomical images. The spatial filter used is the point‐spread function (approximated by a Gaussian) minus a scaled delta function, and cosmic rays are identified by thresholding the filtered image. This filter searches for features with significant power at spatial frequencies too high for legitimate objects. Noise properties of the filtered image are readily calculated, which allows us to compute the probability of rejecting a pixel not contaminated by a cosmic ray (the false alarm probability). We demonstrate that the false alarm probability for a pixel containing object flux will never exceed the corresponding probability for a blank‐sky pixel, provided we choose the convolution kernel appropriately. This allows confident rejection of cosmic rays superposed on real objects. Identification of multiple‐pixel cosmic‐ray hits can be enhanced by running the algorithm iteratively, replacing flagged pixels with the background level at each iteration.