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NASA NTRS1981

Determination of planetary photometric functions

The determination and removal of the photometric properties of the atmosphere and surfaces of pale planetary objects are of signal importance in the production of planetary mosaics and are themselves important scientific endeavors. The work done at the JPL Image Processing Laboratory in this area in support of the Voyager project is reviewed. Topics discussed include data-base generation, parameter determination, and photometric function removal. The importance of determining the photometric function for a planet in mosaicking together sets of images is stressed. It is noted that techniques have been developed and software has been written to allow scientists to easily determine the parameters to several photometric functions.

Mosher, J.↗
NASA NTRS1994

The photometric method of extrasolar planet detection revisited

We investigate the geometry concerning the photometric method of extrasolar planet detection, i.e., the detection of dimunition of a parent star's brightness during a planetary transit. Under the assumption that planetary orbital inclinations can be defined by a Gaussian with a sigma of 10 deg centered on the parent star's equatorial plane, Monte Carlo simulations suggest that for a given star observed at an inclination of exactly 90 deg, the probability of at least one Earth-sized or larger planet being suitably placed for transits is approximately 4%. This probability drops to 3% for a star observed at an inclination of 80 deg, and is still approximately 0.5% for a star observed at an inclination of 60 deg. If one can select 100 stars with a pre-determined inclination equal or greater than 80 deg, the probability of at least one planet being suitably configured for transits is 95%. The majority of transit events are due to planets in small-a orbits similar to the Earth and Venus; thus, the photometric method in principle is the method best suited for the detection of Earthlike planets. The photometric method also allows for testing whether or not planets can exist within binary systems. This can ge done by selecting binary systems observed at high orbital inclinations, both eclipsing binaries and wider visual binaries. For a 'real-world' example, we look at the alpha Centauri system (i = 79.2 deg). If we assume that the equatorial planes of both components coincide with the system's orbital plane, Monte Carlo simulations suggest that the probability of at least one planet (of either component) being suitably configured for transits is approximately 8%. In conclusion, we present a non-exhaustive list of solar-type stars, both single and within binary systems, which exhibit a high equatorial inclination. These objects may be considered as preliminary candidates for planetary searches via the photometric method.

Hale, Alan↗
NASA NTRS1997

Infrared Imaging, Spectroscopic, and Photometric Studies of Comets

We have continued our program of infrared (IR) photometric, imaging, spectroscopic, and polarimetric temporal observations of comets to study the properties of comet dust and comet nuclei. During the first two years we digitized our IR data base on P/Halley and other recent comets to facilitate further analysis and comparison with other data bases, and found compelling evidence for the emission of a burst of small grains from P/Halley's nucleus at perihelion. We reported imaging and photometric observations of Comets Austin 1990 V and Swift-Tuttle 1992. The Swift-Tuttle 1992t observations included IR photometry, several 7-14 micron long-slit spectra of the coma and a time-sequence of more than 150 10 micron broadband images of the coma. An analysis of near-IR images of the inner coma of P/Halley obtained on three consecutive nights in 1986 March showed sunwardjets. We completed our analysis of IR imaging spectrosco-photometric data on comets. We also obtained observations of Comets Hyakutake 1996 B2 and Hale/Bopp 1995 01. We obtained infrared imaging, photometric, spectroscopic and polarimetric temporal observations of bright comets using a network of five telescopes, with emphasis on simultaneous observations of comets at many wavelengths with different instruments. Our program offers several unique advantages: 1) rapid observational response to new comets with dedicated infrared telescopes; 2) observations within a few degrees of the sun when comets are near perihelion and 3) access to advanced infrared array imagers and spectrometers. In particular, reduction, analysis, publication and archiving of our Jupiter/sl-9 and Comet Hyakutake infrared data received special emphasis. Instrumentation development included installation of the latest version of the innovative FORTH telescope control and a data acquisition system that enables us to control three telescopes remotely by telephone from anywhere in the world for comet observations in broad daylight. We have acquired more than 3000 256x256 images totaling nearly two gigabytes of data detailing the near-IR development of the impact sites of the S-L9 fragments on Jupiter. These data were obtained using the University of Rochester Imaging IR Camera at the cassegrain focus of the 92" at WIRO. The WIRO data set covers 8 days and is, to our knowledge, one of the most extensive observational records of the S-L/Jupiter encounter obtained by any ground-based telescope. This program benefitted from the compilation during these last few months of an upgrade to the data acquisition program at WIRO with support of this NASA contract.

Gehrz, Robert D.↗
NASA NTRS1998

The Dependence of Signal-To-Noise Ratio (S/N) Between Star Brightness and Background on the Filter Used in Images Taken by the Vulcan Photometric Planet Search Camera

The Vulcan Photometric Planet Search is the ground-based counterpart of Kepler Mission Proposal. The Kepler Proposal calls for the launch of telescope to look intently at a small patch of sky for four year. The mission is designed to look for extra-solar planets that transit sun-like stars. The Kepler Mission should be able to detect Earth-size planets. This goal requires an instrument and software capable of detecting photometric changes of several parts per hundred thousand in the flux of a star. The goal also requires the continuous monitoring of about a hundred thousand stars. The Kepler Mission is a NASA Discovery Class proposal similar in cost to the Lunar Prospector. The Vulcan Search is also a NASA project but based at Lick Observatory. A small wide-field telescope monitors various star fields successively during the year. Dozens of images, each containing tens of thousands of stars, are taken any night that weather permits. The images are then monitored for photometric changes of the order of one part in a thousand. These changes would reveal the transit of an inner-orbit Jupiter-size planet similar to those discovered recently in spectroscopic searches. In order to achieve a one part in one thousand photometric precision even the choice of a filter used in taking an exposure can be critical. The ultimate purpose of an filter is to increase the signal-to-noise ratio (S/N) of one's observation. Ideally, filters reduce the sky glow cause by street lights and, thereby, make the star images more distinct. The higher the S/N, the higher is the chance to observe a transit signal that indicates the presence of a new planet. It is, therefore, important to select the filter that maximizes the S/N.

Mena-Werth, Jose↗
NASA NTRS2011

The Herschel Multi-Tiered Extragalactic Survey: SPIRE-mm Photometric Redshifts

We investigate the potential of submm-mm and submm-mm-radio photometric red-shifts using a sample of mm-selected sources as seen at 250, 350 and 500 micrometers by the SPIRE instrument on Herschel. From a sample of 63 previously identified mm-sources with reliable radio identifications in the GOODS-N and Lockman Hole North fields 46 (73 per cent) are found to have detections in at least one SPIRE band. We explore the observed submm/mm colour evolution with redshift, finding that the colours of mm-sources are adequately described by a modified blackbody with constant optical depth Tau = (nu/nu(0))beta where beta = +1.8 and nu(0) = c/100 micrometers. We find a tight correlation between dust temperature and IR luminosity. Using a single model of the dust temperature and IR luminosity relation we derive photometric redshift estimates for the 46 SPIRE detected mm-sources. Testing against the 22 sources with known spectroscopic, or good quality optical/near-IR photometric, redshifts we find submm/mm photometric redshifts offer a redshift accuracy of |delta z|/(1+z) = 0.16 (less than |delta z| greater than = 0.51). Including constraints from the radio-far IR correlation the accuracy is improved to |delta z|/(1 + z) = 0.15 (less than |delta z| greater than = 0.45). We estimate the redshift distribution of mm-selected sources finding a significant excess at z greater than 3 when compared to 850 micrometer selected samples.

Roseboom, I. G.↗
NASA NTRS2012

The Herschel Multi-Tiered Extragalactic Survey: SPIRE-mm Photometric Redshifts

We investigate the potential of submm-mm and submm-mm-radio photometric redshifts using a sample of mm-selected sources as seen at 250, 350 and 500 micron by the SPIRE instrument on Herschel. From a sample of 63 previously identified mm sources with reliable radio identifications in the Great Observatories Origins Deep Survey North and Lockman Hole North fields, 46 (73 per cent) are found to have detections in at least one SPIRE band. We explore the observed submm/mm color evolution with redshift, finding that the colors of mm sources are adequately described by a modified blackbody with constant optical depth Tau = (Nu/nu(sub 0))(exp Beta), where Beta = +1.8 and nu(sub 0) = c/100 micron. We find a tight correlation between dust temperature and IR luminosity. Using a single model of the dust temperature and IR luminosity relation, we derive photometric redshift estimates for the 46 SPIRE-detected mm sources. Testing against the 22 sources with known spectroscopic or good quality optical/near-IR photometric redshifts, we find submm/mm photometric redshifts offer a redshift accuracy of (absolute value of Delta sub (z))/(1 + z) = 0.16 (absolute value of Delta sub (z)) = 0.51). Including constraints from the radio-far-IR correlation, the accuracy is improved to (absolute value of Delta sub (z))/(1 + z) = 0.14 (((absolute value of Delta sub (z))) = 0.45). We estimate the redshift distribution of mm-selected sources finding a significant excess at Z > 3 when compared to approx 8S0 micron selected samples.

Roseboom, I. G.↗
DOE OSTI2021

Core-collapse contamination in photometric samples of Type Ia Supernovae

This is an exciting time for cosmology with type Ia supernovae (SNe Ia). The recentlyconcluded Dark Energy Survey SN programme (DES-SN) has obtained the largest anddeepest high-redshift cosmological SN Ia sample, and the Vera Rubin Observatory isexpected to observe at least one order of magnitude more SNe Ia in the next decade.In both these experiments, only a limited fraction (.10 per cent) of the SNe can bespectroscopically classified. This leaves us with large ‘photometric’ SN samples, withthe potential for significant contamination by core-collapse SNe that may bias SN Iacosmological measurements. This thesis demonstrates how this contamination can bemodelled and accounted for in current and future cosmological analyses.First, I present state-of-the-art simulations of the SN universe. These are designedto accurately model the population of SNe Ia, peculiar SNe Ia and core-collapse SNe, aswell as their host galaxies. To improve the diversity and quality of the simulated corecollapseSNe, I build a new library of core-collapse SN templates using spectroscopicand photometric (optical and near-ultraviolet) data of 67 core-collapse SNe from theliterature. I account for our incomplete knowledge of core-collapse SN properties bygenerating a set of SN simulations (rather than a single one), each exploring differentmodelling choices and template libraries. I then characterise selection effects in theDES-SN survey and incorporate them in the simulations, thus obtaining a series ofDES-like simulated SN samples that can be compared to the observed DES-SN data.The agreement between the simulations and data is excellent across many observed SNproperties, including Hubble residuals. These simulations are the first to reproduce theobserved photometric SN and host galaxy properties in high-redshift surveys with no fine-tuning of the input parameters.I use my simulation framework to train and test the performance of SuperNNova,a photometric SN classifier based on recurrent neural networks. I explore differenttraining and validation strategies and show that, across all the DES-SN simulationstested, SuperNNova reduces core-collapse SN contamination to 0.8–3.5 per cent. Ithen show that biases due to contamination on the equation-of-state of dark energy,w, are < 0:008 when using our reference SuperNNova model. This compares to anexpected statistical uncertainty on w from the DES-SN sample of 0:039, thus showing that contamination is not a limiting systematic for the cosmological analysis of theDES-SN sample.The results presented in this thesis are the foundation of the DES SN Ia cosmologicalanalysis; they also provide important implications for the future of SN cosmology,as they demonstrate that contamination is not expected to significantly degrade thecosmological figure of merit of the Rubin SN Ia analysis.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Towards Precision Photometric Type ia Supernova Cosmology With Machine Learning

The revolutionary discovery of dark energy and accelerating cosmic expansion was made with just 42 type Ia supernovae (SNe Ia) in 1999. Since then, large synoptic surveys, e.g., Dark Energy Survey (DES), have observed thousands more SNe Ia and the upcoming Rubin Legacy Survey of Space and Time (LSST) and Roman Space Telescope promise to deliver millions in the next decade. This unprecedented data volume can produce the required precision to unambiguously test concordance cosmology which could represent a monumental shift in our understanding of dark energy and its role in cosmic history. However, extracting a pure SN Ia sample with accurate redshifts for such a large dataset will be a challenge. Specifically, spectroscopic classification will not be possible for the vast majority of discovered objects, and only ~25% will have spectroscopic redshifts. This thesis presents a series of observational and methodological studies designed to address the questions associated with this new era of photometric SN Ia cosmology. First, we present a machine learning method for photometric classification of SNe, Supernova Classification with a COnvolutional Neural Network (SCONE). Photometric classification enables SNe with no spectroscopic information to be confidently categorized, and is a critical component of current and future analysis pipelines. SCONE achieves >99% accuracy distinguishing simulated SNe Ia from non-Ia SNe, and has been integrated into DES, LSST, and Roman analysis pipelines. We also demonstrate the efficacy of SCONE on early-time photometric classification, which will be vital for optimal allocation of spectroscopic resources. We show that SCONE can distinguish between 6 SN types with 75% accuracy on the night of initial discovery, comparable to results in the literature for full-phase SNe. Next, we study current methods for estimating SN Ia redshifts and propose a machine learning alternative that uses SN photometry alone to extract redshift information. Most SNe Ia inherit redshift information from their host galaxy, but the process of matching SNe to the correct host galaxy can be challenging. We systematically analyze the impact of incorrect redshifts from host galaxy mismatch on 5 years of DES SN data, and conclude that improved host matching or redshift estimation methods can reduce our systematic errors by ~10%. In response to this finding, we present a SN photometry-only method for estimating redshifts independent of host galaxy information, Photo-zSNthesis. We show that Photo-zSNthesis redshift estimates are accurate to within 2% across the full redshift range of LSST, a first in the literature. Finally, we focus on the robustness of machine learning (ML) algorithms for real-world and scientific applications. ML models generalize poorly beyond their training set and often experience severe performance degradation when deployed on new data. We demonstrate a general method for improving robustness that achieves new state-of-the-art results on astronomical object classification, wildlife identification, and tumor detection.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Studying baryon acoustic oscillations using photometric redshifts from the DESI Legacy Imaging survey DR9

Context. The Dark Energy Spectroscopic Instrument (DESI) Legacy Imaging Survey DR9 (DR9 hereafter), with its extensive dataset of galaxy locations and photometric redshifts, presents an opportunity to study baryon acoustic oscillations (BAOs) in the region covered by the ongoing spectroscopic survey with DESI. Aims. We aim to investigate differences between different parts of the DR9 footprint. Furthermore, we want to measure the BAO scale for luminous red galaxies within them. Our selected redshift range of 0.6–0.8 corresponds to the bin in which a tension between DESI Y1 and eBOSS was found. Methods. We calculated the anisotropic two-point correlation function in a modified binning scheme to detect the BAOs in DR9 data. We then used template fits based on simulations to measure the BAO scale in the imaging data. Results. Our analysis reveals the expected correlation function shape in most of the footprint areas, showing a BAO scale consistent with Planck’s observations. Aside from identified mask-related data issues in the southern region of the South Galactic Cap, we find a notable variance between the different footprints. Conclusions. We find that this variance is consistent with the difference between the DESI Y1 and eBOSS data, and it supports the argument that that tension is caused by sample variance. Additionally, we also uncovered systematic biases not previously accounted for in photometric BAO studies. We emphasize the necessity of adjusting for the systematic shift in the BAO scale associated with typical photometric redshift uncertainties to ensure accurate measurements.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

J-PLUS: Systematic impact of metallicity on photometric calibration with the stellar locus

Here, we present the photometric calibration of the twelve optical passbands for the Javalambre Photometric Local Universe Survey (J-PLUS) second data release (DR2), comprising 1088 pointings of two square degrees, and study the systematic impact of metallicity on the stellar locus technique. The [Fe/H] metallicity from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) for 146 184 high-quality calibration stars, defined with signal-to-noise ratio larger than ten in J-PLUS passbands and larger than three in Gaia parallax, was used to compute the metallicity-dependent stellar locus (ZSL). The initial homogenization of J-PLUS photometry, performed with a unique stellar locus, was refined by including the metallicity effect in colors via the ZSL. The variation of the average metallicity along the Milky Way produces a systematic offset in J-PLUS calibration. This effect is well above 1% for the bluer passbands and amounts 0.07, 0.07, 0.05, 0.03, and 0.02 mag in u, J0378, J0395, J0410, and J0430, respectively. We modeled this effect with the Milky Way location of the J-PLUS pointing, also providing an updated calibration for those observations without LAMOST information. The estimated accuracy in the calibration after including the metallicity effect is at 1% for the bluer J-PLUS passbands and below for the rest. Photometric calibration with the stellar locus technique is prone to significant systematic bias in the Milky Way for passbands bluer than λ = 4500 Å. The calibration method for J-PLUS DR2 reaches 1–2% precision and 1% accuracy for 12 optical filters within an area of 2176 square degrees.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Photometric cross-calibration of the SDSS Stripe 82 Standard Stars catalogue with Gaia EDR3, and comparison with Pan-STARRS1, DES, CFIS, and GALEX catalogues

ABSTRACT We extend the SDSS Stripe 82 Standard Stars catalogue with post-2007 SDSS imaging data. This improved version lists averaged SDSS ugriz photometry for nearly a million stars brighter than r ∼ 22 mag. With 2–3 times more measurements per star, random errors are 1.4–1.7 times smaller than in the original catalogue, and about three times smaller than for individual SDSS runs. Random errors in the new catalogue are $\lesssim$0.01 mag for stars brighter than 20.0, 21.0, 21.0, 20.5, and 19.0 mag in u, g, r, i, and z-bands, respectively. We achieve this error threshold by using the Gaia Early Data Release 3 (EDR3) Gmag photometry to derive grey photometric zeropoint corrections, as functions of RA and Declination, for the SDSS catalogue, and use the Gaia BP–RP colour to derive corrections in the ugiz bands, relative to the r-band. The quality of the recalibrated photometry, tested against Pan-STARRS1, DES, CFIS, and GALEX surveys, indicates spatial variations of photometric zeropoints ≦ 0.01 mag (rms), with typical values of 3–7 millimag in the RA, and 1–2 millimag in the Declination directions, except for $\lesssim$6 millimag scatter in the u-band. We also report a few minor photometric problems with other surveys considered here, including a magnitude-dependent ∼0.01 mag bias between 16 ≤ GGaia ≤ 20 in the Gaia EDR3. Our new, publicly available catalogue offers robust calibration of ugriz photometry below 1 per cent level, and will be helpful during the commissioning of the Vera C. Rubin Observatory Legacy Survey of Space and Time.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Photometric redshift estimation with convolutional neural networks and galaxy images: Case study of resolving biases in data-driven methods

Deep-learning models have been increasingly exploited in astrophysical studies, but these data-driven algorithms are prone to producing biased outputs that are detrimental for subsequent analyses. In this work, we investigate two main forms of biases: class-dependent residuals, and mode collapse. We do this in a case study, in which we estimate photometric redshift as a classification problem using convolutional neural networks (CNNs) trained with galaxy images and associated spectroscopic redshifts. We focus on point estimates and propose a set of consecutive steps for resolving the two biases based on CNN models, involving representation learning with multichannel outputs, balancing the training data, and leveraging soft labels. The residuals can be viewed as a function of spectroscopic redshift or photometric redshift, and the biases with respect to these two definitions are incompatible and should be treated individually. We suggest that a prerequisite for resolving biases in photometric space is resolving biases in spectroscopic space. Experiments show that our methods can better control biases than benchmark methods, and they are robust in various implementing and training conditions with high-quality data. Our methods hold promises for future cosmological surveys that require a good constraint of biases, and they may be applied to regression problems and other studies that make use of data-driven models. Nonetheless, the bias-variance tradeoff and the requirement of sufficient statistics suggest that we need better methods and optimized data usage strategies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

The Impact of Bias Row Noise to Photometric Accuracy: Case Study Based on a Scientific CMOS Detector

Abstract We tested a new model of CMOS detector manufactured by the Gpixel Inc, for potential space astronomical application. In laboratory, we obtain some bias images under the typical application environment. In these bias images, clear random row noise pattern is observed. The row noise also contains some characteristic spatial frequencies. We quantitatively estimated the impact of this feature to photometric measurements, by making simulated images. We compared different bias noise types under strict parameter control. The result shows the row noise will significantly deteriorate the photometric accuracy. It effectively increases the readout noise by a factor of 2–10. However, if it is properly removed, the image quality and photometric accuracy will be significantly improved.

Astronomy & Astrophysics↗
DOE OSTI2021

SDSS-IV DR17: final release of MaNGA PyMorph photometric and deep-learning morphological catalogues

ABSTRACT We present the MaNGA PyMorph photometric Value Added Catalogue (MPP-VAC-DR17) and the MaNGA Deep Learning Morphological VAC (MDLM-VAC-DR17) for the final data release of the MaNGA survey, which is part of the SDSS Data Release 17 (DR17). The MPP-VAC-DR17 provides photometric parameters from Sérsic and Sérsic+Exponential fits to the two-dimensional surface brightness profiles of the MaNGA DR17 galaxy sample in the g, r, and i bands (e.g. total fluxes, half-light radii, bulge-disc fractions, ellipticities, position angles, etc.). The MDLM-VAC-DR17 provides deep-learning-based morphological classifications for the same galaxies. The MDLM-VAC-DR17 includes a number of morphological properties, for example, a T-Type, a finer separation between elliptical and S0, as well as the identification of edge-on and barred galaxies. While the MPP-VAC-DR17 simply extends the MaNGA PyMorph photometric VAC published in the SDSS Data Release 15 (MPP-VAC-DR15) to now include galaxies that were added to make the final DR17, the MDLM-VAC-DR17 implements some changes and improvements compared to the previous release (MDLM-VAC-DR15): Namely, the low end of the T-Types is better recovered in this new version. The catalogue also includes a separation between early or late type, which classifies the two populations in a complementary way to the T-Type, especially at the intermediate types (−1 < T-Type < 2), where the T-Type values show a large scatter. In addition, k-fold-based uncertainties on the classifications are also provided. To ensure robustness and reliability, we have also visually inspected all the images. We describe the content of the catalogues and show some interesting ways in which they can be combined.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

A composite likelihood approach for inference under photometric redshift uncertainty

ABSTRACT Obtaining accurately calibrated redshift distributions of photometric samples is one of the great challenges in photometric surveys like LSST, Euclid, HSC, KiDS, and DES. We present an inference methodology that combines the redshift information from the galaxy photometry with constraints from two-point functions, utilizing cross-correlations with spatially overlapping spectroscopic samples, and illustrate the approach on CosmoDC2 simulations. Our likelihood framework is designed to integrate directly into a typical large-scale structure and weak lensing analysis based on two-point functions. We discuss efficient and accurate inference techniques that allow us to scale the method to the large samples of galaxies to be expected in LSST. We consider statistical challenges like the parametrization of redshift systematics, discuss and evaluate techniques to regularize the sample redshift distributions, and investigate techniques that can help to detect and calibrate sources of systematic error using posterior predictive checks. We evaluate and forecast photometric redshift performance using data from the CosmoDC2 simulations, within which we mimic a DESI-like spectroscopic calibration sample for cross-correlations. Using a combination of spatial cross-correlations and photometry, we show that we can provide calibration of the mean of the sample redshift distribution to an accuracy of at least 0.002(1 + z), consistent with the LSST-Y1 science requirements for weak lensing and large-scale structure probes.

(cosmology:) large-scale structure of Universe↗
DOE OSTI2021

QSO photometric redshifts using machine learning and neural networks

ABSTRACT The scientific value of the next generation of large continuum surveys would be greatly increased if the redshifts of the newly detected sources could be rapidly and reliably estimated. Given the observational expense of obtaining spectroscopic redshifts for the large number of new detections expected, there has been substantial recent work on using machine learning techniques to obtain photometric redshifts. Here, we compare the accuracy of the predicted photometric redshifts obtained from deep learning (DL) with the k-nearest neighbour (kNN) and the decision tree regression (DTR) algorithms. We find using a combination of near-infrared, visible, and ultraviolet magnitudes, trained upon a sample of Sloan Digital Sky Survey quasi-stellar objects, that the kNN and DL algorithms produce the best self-validation result with a standard deviation of σΔz = 0.24 (σΔz(norm) = 0.11). Testing on various subsamples, we find that the DL algorithm generally has lower values of σΔz, in addition to exhibiting a better performance in other measures. Our DL method, which uses an easy to implement off-the-shelf algorithm with neither filtering nor removal of outliers, performs similarly to other, more complex, algorithms, resulting in an accuracy of Δz < 0.1 up to z ∼ 2.5. Applying the DL algorithm trained on our 70 000 strong sample to other independent (radio-selected) data sets, we find σΔz ≤ 0.36 (σΔz(norm) ≤ 0.17) over a wide range of radio flux densities. This indicates much potential in using this method to determine photometric redshifts of quasars detected with the Square Kilometre Array.

Curran, S. J.↗
DOE OSTI2022

Clusters of galaxies up to z = 1.5 identified from photometric data of the Dark Energy Survey and unWISE

ABSTRACT Using photometric data from the Dark Energy Survey and the Wide-field Infrared Survey Explorer, we estimate photometric redshifts for 105 million galaxies using the nearest-neighbour algorithm. From such a large data base, 151 244 clusters of galaxies are identified in the redshift range of 0.1 < z ≲ 1.5 based on the overdensity of the total stellar mass of galaxies within a given photometric redshift slice, among which 76 826 clusters are newly identified and 30 477 clusters have a redshift z > 1. We cross-match these clusters with those in the catalogues identified from the X-ray surveys and the Sunyaev–Zel’dovich (SZ) effect by the Planck, South Pole Telescope and Atacama Cosmology Telescope surveys, and get the redshifts for 45 X-ray clusters and 56 SZ clusters. More than 95 per cent SZ clusters in the sky region have counterparts in our catalogue. We find multiple optical clusters in the line of sight towards about 15 per cent of SZ clusters.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Photometric redshifts from SDSS images with an interpretable deep capsule network

ABSTRACT Studies of cosmology, galaxy evolution, and astronomical transients with current and next-generation wide-field imaging surveys like the Rubin Observatory Legacy Survey of Space and Time are all critically dependent on estimates of photometric redshifts. Capsule networks are a new type of neural network architecture that is better suited for identifying morphological features of the input images than traditional convolutional neural networks. We use a deep capsule network trained on ugriz images, spectroscopic redshifts, and Galaxy Zoo spiral/elliptical classifications of ∼400 000 Sloan Digital Sky Survey galaxies to do photometric redshift estimation. We achieve a photometric redshift prediction accuracy and a fraction of catastrophic outliers that are comparable to or better than current methods for SDSS main galaxy sample-like data sets (r ≤ 17.8 and zspec ≤ 0.4) while requiring less data and fewer trainable parameters. Furthermore, the decision-making of our capsule network is much more easily interpretable as capsules act as a low-dimensional encoding of the image. When the capsules are projected on a two-dimensional manifold, they form a single redshift sequence with the fraction of spirals in a region exhibiting a gradient roughly perpendicular to the redshift sequence. We perturb encodings of real galaxy images in this low-dimensional space to create synthetic galaxy images that demonstrate the image properties (e.g. size, orientation, and surface brightness) encoded by each dimension. We also measure correlations between galaxy properties (e.g. magnitudes, colours, and stellar mass) and each capsule dimension. We publicly release our code, estimated redshifts, and additional catalogues at https://biprateep.github.io/encapZulate-1.

79 ASTRONOMY AND ASTROPHYSICS↗