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DOE OSTI2021

Photometric redshifts for the S-PLUS Survey: Is machine learning up to the task?

The Southern Photometric Local Universe Survey (S-PLUS) is a novel project that aims to map the Southern Hemisphere using a twelve filter system, comprising five broad-band SDSS-like filters and seven narrow-band filters optimized for important stellar features in the local universe. In this paper we use the photometry and morphological information from the first S-PLUS data release (S-PLUS DR1) cross-matched to unWISE data and spectroscopic redshifts from Sloan Digital Sky Survey DR15. We explore three different machine learning methods (Gaussian Processes with GPz and two Deep Learning models made with TensorFlow) and compare them with the currently used template-fitting method in the S-PLUS DR1 to address whether machine learning methods can take advantage of the twelve filter system for photometric redshift prediction. Using tests for accuracy for both single-point estimates such as the calculation of the scatter, bias, and outlier fraction, and probability distribution functions (PDFs) such as the Probability Integral Transform (PIT), the Continuous Ranked Probability Score (CRPS) and the Odds distribution, we conclude that a deep-learning method using a combination of a Bayesian Neural Network and a Mixture Density Network offers the most accurate photometric redshifts for the current test sample. In conclusion, it achieves single-point photometric redshifts with scatter (σ NMAD ) of 0.023, normalized bias of -0.001, and outlier fraction of 0.64% for galaxies with r_auto magnitudes between 16 and 21.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

The Close AGN Reference Survey (CARS)

Active galactic nuclei (AGN) are thought to be responsible for the suppression of star formation in massive ~10 10 M ⊙ galaxies. While this process is a key feature in numerical simulations of galaxy formation, it has not been unambiguously confirmed in observational studies yet. The characterization of the star formation rate (SFR) in AGN host galaxies is challenging as AGN light contaminates most SFR tracers. Furthermore, the various SFR tracers are sensitive to different timescales of star formation from approximately a few to 100 Myr. We aim to obtain and compare SFR estimates from different tracers for AGN host galaxies in the Close AGN Reference Survey (CARS) to provide new observational insights into the recent SFR history of those systems. We constructed integrated panchromatic spectral energy distributions to measure the far infrared (FIR) luminosity as a tracer for the recent (< 100 Myr) SFR. In addition we used the integral-field unit observation of the CARS targets to employ the Hα luminosity decontaminated by AGN excitation as a proxy for the current (< 5 Myr) SFR. We find that significant differences in specific SFR of the AGN host galaxies as compared with the larger galaxy population disappear once cold gas mass, in addition to stellar mass, is used to predict the SFR for a specific AGN host. Only a tentative trend with the inclination of the host galaxy remains, such that SFR appears slightly lower than expected when the galaxies of unobscured AGN appear more edge-on along our line-of-sight, particular for dust-insensitive FIR-based SFRs. We identify individual galaxies with a significant difference in their SFR which can be related to a recent enhancement or decline in their SFR history that might be related to various processes including interactions, gas consumption, outflows, and AGN feedback. AGN can be present in various stages of galaxy evolution which makes it difficult to relate the SFR solely to the impact of the AGN. Our study shows that stellar mass alone is an insufficient parameter to estimate the expected SFR of an AGN host galaxy compared to the underlying non-AGN galaxy population. We do not find any strong evidence for a global positive or negative AGN feedback in the CARS sample. However, there is tentative evidence that (1) the relative orientation of the AGN engine with respect to the host galaxies might alter the efficiency of AGN feedback and that (2) the recent SFH is an additional tool to identify rapid changes in galaxy growth driven by the AGN or other processes.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

The edges of galaxies: Tracing the limits of star formation

The outskirts of galaxies have been studied from multiple perspectives for the past few decades. However, it is still unknown if all galaxies have clear-cut edges similar to everyday objects. We address this question by developing physically motivated criteria to define the edges of galaxies. Based on the gas density threshold required for star formation, we define the edge of a galaxy as the outermost radial location associated with a significant drop in either past or ongoing in situ star formation. We explore ~1000 low-inclination galaxies with a wide range in morphology (dwarfs to ellipticals) and stellar mass (10 7 M ⊙ < M * < 10 12 M ⊙ ). The location of the edges of these galaxies (R edge ) were visually identified as the outermost cutoff or truncation in their radial profiles using deep multi-band optical imaging from the IAC Stripe82 Legacy Project. We find this characteristic feature at the following mean stellar mass density, which varies with galaxy morphology: 2.9 ± 0.10 M ⊙ pc -2 for ellipticals, 1.1 ± 0.04 M ⊙ pc -2 for spirals, and 0.6 ± 0.03 M ⊙ pc -2 for present-day star-forming dwarfs. Additionally, we find that R edge depends on its age (colour) where bluer galaxies have larger R edge at a fixed stellar mass. The resulting stellar mass–size plane using R edge as a physically motivated galaxy size measure has a very narrow intrinsic scatter (≲0.06 dex). These results highlight the importance of new deep imaging surveys to explore the growth of galaxies and trace the limits of star formation in their outskirts.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Asteroids seen by JWST-MIRI: Radiometric size, distance, and orbit constraints

Infrared measurements of asteroids are crucial for the determination of physical and thermal properties of individual objects, and for understanding the small-body populations in the solar system as a whole. However, standard radiometric methods can only be applied if the orbit of an object is known, hence its position at the time of the observation. With JWST-MIRI observations the situation will change and many unknown, often very small, solar system objects will be detected. Later orbit determinations are difficult due to the faintness of the objects and the lack of dedicated follow-up concepts. We present MIRI observations of the outer-belt asteroid (10920) 1998 BC1 and an unknown object, detected in all nine MIRI bands in close apparent proximity to (10920). We developed a new method called STM-ORBIT to interpret the multi-band measurements without knowing the object’s true location. The power of the new technique is that it determines the most-likely heliocentric and observer-centric distance and phase angle ranges, allowing us to make a radiometric size estimate. The application to the MIRI fluxes of (10920) was used to validate the method. It leads to a confirmation of the known radiometric size-albedo solution, and puts constraints on the asteroid’s location and orbit in agreement with its true orbit. To back up the validation of the method, we obtained additional ground-based light curve observations of (10920), combined with Gaia data, which indicate a very elongated object (a/b ≥ 1.5), with a spin-pole at (λ, β) ecl = (178°, +81°), with an estimated error of about 20°, and a rotation period of 4.861191 ± 0.000015 h. A thermophysical study of all available JWST-MIRI and WISE measurements leads to a size of 14.5–16.5 km (diameter of an equal-volume sphere), a geometric albedo pV between 0.05 and 0.10, and a thermal inertia in the range 9–35 (best value 15) J m -2 s -0.5 K -1 . For the newly discovered MIRI object, the STM-ORBIT method revealed a size of 100–230 m. The new asteroid must be on a low-inclination orbit (0.7° < i < 2.0°) and it was located in the inner main-belt region during JWST observations. A beaming parameter η larger than 1.0 would push the size even below 100 meters, a main-belt regime that has escaped IR detections so far. This kind of MIRI observations can therefore contribute to formation and evolution studies via classical size-frequency studies, which are currently limited to objects larger than about one kilometer in size. We estimate that MIRI frames with pointings close to the ecliptic and short integration times of only a few seconds will always include a few asteroids; most of them will be unknown objects.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Candidate strongly lensed type Ia supernovae in the Zwicky Transient Facility archive

Gravitationally lensed type Ia supernovae (glSNe Ia) are unique astronomical tools that can be used to study cosmological parameters, distributions of dark matter, the astrophysics of the supernovae, and the intervening lensing galaxies themselves. A small number of highly magnified glSNe Ia have been discovered by ground-based telescopes such as the Zwicky Transient Facility (ZTF), but simulations predict that a fainter, undetected population may also exist. We present a systematic search for glSNe Ia in the ZTF archive of alerts distributed from June 1 2019 to September 1 2022. Using the AMPEL platform, we developed a pipeline that distinguishes candidate glSNe Ia from other variable sources. Initial cuts were applied to the ZTF alert photometry (with constraints on the peak absolute magnitude and the distance to a catalogue-matched galaxy, as examples) before forced photometry was obtained for the remaining candidates. Additional cuts were applied to refine the candidates based on their light curve colours, lens galaxy colours, and the resulting parameters from fits to the SALT2 SN Ia template. The candidates were also cross-matched with the DESI spectroscopic catalogue. Seven transients were identified that passed all the cuts and had an associated galaxy DESI redshift, which we present as glSN Ia candidates. Although superluminous supernovae (SLSNe) cannot be fully rejected as contaminants, two events, ZTF19abpjicm and ZTF22aahmovu, are significantly different from typical SLSNe and their light curves can be modelled as two-image glSN Ia systems. From this two-image modelling, we estimate time delays of 22 ± 3 and 34 ± 1 days for the two events, respectively, which suggests that we have uncovered a population of glSNe Ia with longer time delays. The pipeline is efficient and sensitive enough to parse full alert streams. It is currently being applied to the live ZTF alert stream to identify and follow-up future candidates while active. This pipeline could be the foundation for glSNe Ia searches in future surveys, such as the Rubin Observatory Legacy Survey of Space and Time.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Photometric redshift estimation of BASS DR3 quasars by machine learning

ABSTRACT Correlating Beijing–Arizona Sky Survey (BASS) data release 3 (DR3) catalogue with the ALLWISE data base, the data from optical and infrared information are obtained. The quasars from Sloan Digital Sky Survey are taken as training and test samples while those from LAMOST are considered as external test sample. We propose two schemes to construct the redshift estimation models with XGBoost, CatBoost, and Random Forest. One scheme (namely one-step model) is to predict photometric redshifts directly based on the optimal models created by these three algorithms; the other scheme (namely two-step model) is to first classify the data into low- and high-redshift data sets, and then predict photometric redshifts of these two data sets separately. For one-step model, the performance of these three algorithms on photometric redshift estimation is compared with different training samples, and CatBoost is superior to XGBoost and Random Forest. For two-step model, the performances of these three algorithms on the classification of low and high redshift subsamples are compared, and CatBoost still shows the best performance. Therefore, CatBoost is regarded as the core algorithm of classification and regression in two-step model. In contrast to one-step model, two-step model is optimal when predicting photometric redshift of quasars, especially for high-redshift quasars. Finally, the two models are applied to predict photometric redshifts of all quasar candidates of BASS DR3. The number of high-redshift quasar candidates is 3938 (redshift ≥3.5) and 121 (redshift ≥4.5) by two-step model. The predicted result will be helpful for quasar research and follow-up observation of high-redshift quasars.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

On the physical size of the Milky Way globular cluster NGC 7089 (M2)

ABSTRACT We study the outer regions of the Milky Way globular cluster NGC 7089 based on new Dark Energy Camera observations. The resulting background-cleaned stellar density profile reveals the existence of an extended envelope. We confirm previous results that cluster stars are found out up to ∼1° from the cluster’s centre, which is nearly three times the value of the most robust tidal radii estimations. We also used results from direct N-body simulations in order to compare with the observations. We found a fairly good agreement between the observed and numerically generated stellar density profiles. Because of the existence of gaps and substructures along globular cluster tidal tails, we closely examined the structure of the outer cluster region beyond the Jacobi radius. We extended the analysis to a sample of 35 globular clusters, 20 of them with observed tidal tails. We found that if the stellar density profile follows a power law ∝ r−α, the α slope correlates with the globular cluster present mass, in the sense that, the more massive the globular cluster, the smaller the α value. This trend is not found in globular clusters without observed tidal tails. The origin of such a phenomenon could be related, among other reasons, to the proposed so-called potential escapers or to the formation of globular clusters within dark matter minihaloes.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Empirically Driven multiwavelength K-c orrections at low redshift

K -corrections – a necessary ingredient for converting between flux in observed bands to flux in rest-frame bands – are critical for comparing galaxies at differing redshifts. These corrections often rely on fits to empirical or theoretical spectral energy distribution (SED) templates of galaxies. However, templates can only produce reliable K-corrections in regimes where SED models are robust. For instance, the templates utilized in some popular software packages are not well-constrained in some bands (e.g. WISE W4 in KCORRECT ), which results in ill-behaved K -corrections. We address this shortcoming by developing an empirically driven approach to K -corrections that limits the dependence on SED templates. We perform a polynomial fit for the K -correction as a function of a galaxy’s rest-frame colour determined in a pair of well-constrained bands (e.g. 0 (g − r)) and redshift, exploiting the fact that galaxy SEDs can be approximated as a one-parameter family at low redshift. For bands well-constrained by SED templates, our empirically driven K -corrections yield results comparable to the SED fitting methods used by KCORRECT and the GSWLC-M2 catalogue (the updated medium-deep GALEX–SDSS–WISE Legacy Catalogue). However, our method dramatically outperforms Kcorrect derived K -corrections for WISE W4 . Our method is also robust to incorrect template assumptions outside of the optical bands and enforces that the K -correction must be zero at z = 0. Our K -corrected photometry and code are publicly available.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Impact of survey spatial variability on galaxy redshift distributions and the cosmological 3 × 2-point statistics for the Rubin Legacy Survey of Space and Time (LSST)

We investigate the impact of spatial survey non-uniformity on the galaxy redshift distributions for forthcoming data releases of the Rubin Observatory Legacy Survey of Space and Time (LSST). Specifically, we construct a mock photometry data set degraded by the Rubin OpSim observing conditions, and estimate photometric redshifts of the sample using a template-fitting photo-z estimator, BPZ, and a machine learning method, FlexZBoost. We select the Gold sample, defined as $i\lt 25.3$ for 10 yr LSST data, with an adjusted magnitude cut for each year and divide it into five tomographic redshift bins for the weak lensing lens and source samples. We quantify the change in the number of objects, mean redshift, and width of each tomographic bin as a function of the coadd i-band depth for 1-yr (Y1), 3-yr (Y3), and 5-yr (Y5) data. In particular, Y3 and Y5 have large non-uniformity due to the rolling cadence of LSST, hence provide a worst-case scenario of the impact from non-uniformity. We find that these quantities typically increase with depth, and the variation can be $10\!-\!40~{{\rm per\ cent}}$ at extreme depth values. Using Y3 as an example, we propagate the variable depth effect to the weak lensing $3\times 2$ pt analysis, and assess the impact on cosmological parameters via a Fisher forecast. We find that galaxy clustering is most susceptible to variable depth, and non-uniformity needs to be mitigated below 3 per cent to recover unbiased cosmological constraints. There is little impact on galaxy–shear and shear–shear power spectra, given the expected LSST Y3 noise.

cosmology↗
DOE OSTI2021

Science Validation for Dark Energy Research with Optical Imaging Surveys

The Universe has been expanding at an accelerating rate over the past several billion years, as though an unknown form of dark energy permeates all of space. The ultimate scientific goal of the proposed research is to distinguish between different physical mechanisms that could account for this observed accelerated expansion, for example, the zero-point energy of the vacuum, a dynamical form of energy that varies in time and/or space, or a modification to our theory of gravity. Wide- field optical imaging surveys of the night sky can test these competing models by measuring both the cosmic expansion history and the growth of large-scale structure. To this end, the Dark Energy Survey (DES) has cataloged several hundred million galaxies and thousands of supernovae. The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST), will enlarge the census to billions of galaxies and hundreds of thousands of supernovae. A critical question for these dark energy experiments is whether systematic uncertainties can continue to be controlled at a level to keep pace with the statistical precision offered by such enormous datasets. The immediate research objectives of this project were (1) to prepare and validate input datasets that are the foundation of cosmological analyses with DES, and (2) to prepare for value-added characterization of Rubin Observatory commissioning data to inform early operations and accelerate the realization of dark energy science from LSST data products. For DES, we assembled and curated cosmology-ready data releases that include value-added components such as enhanced photometric and astrometric calibrations, alternative source extraction algorithms, maps of the survey coverage and survey conditions, object classifications, object quality selections, galaxy shapes, and photometric redshifts. We used the galaxy clustering technique to validate the photometric redshift distributions of various galaxy samples to be used as lenses in combined studies of galaxy clustering and weak gravitational lensing. The galaxy clustering redshift analysis was enhanced by use of a larger sample of reference galaxies from the eBOSS spectroscopic survey that extends to higher redshifts. For LSST, we prepared for science validation studies of commissioning data aimed at dark energy science capability that extend beyond the normative system-level tests to be done by the Rubin Observatory Construction Project. We identified a set of proposed survey strategies and candidate target fields that could be observed during the commissioning period to enhance science validation activities related to studies of dark energy.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
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 OSTI2021

Dark Energy Survey Year 3 Results: Galaxy mock catalogs for BAO analysis

The calibration and validation of scientific analysis in simulations is a fundamental tool to ensure unbiased and robust results in observational cosmology. In particular, mock galaxy catalogs are a crucial resource to achieve these goals in the measurement of baryon acoustic oscillation (BAO) in the clustering of galaxies. Here we present a set of 1952 galaxy mock catalogs designed to mimic the Dark Energy Survey Year 3 BAO sample over its full photometric redshift range 0.6 < zphoto < 1.1. The mocks are based upon 488 ICE-COLA fast N-body simulations of full-sky light cones and were created by populating halos with galaxies, using a hybrid halo occupation distribution – halo abundance matching model. This model has ten free parameters, which were determined, for the first time, using an automatic likelihood minimization procedure. We also introduced a novel technique to assign photometric redshift for simulated galaxies, following a two-dimensional probability distribution with VIMOS Public Extragalactic Redshift Survey data. The calibration was designed to match the observed abundance of galaxies as a function of photometric redshift, the distribution of photometric redshift errors, and the clustering amplitude on scales smaller than those used for BAO measurements. An exhaustive analysis was done to ensure that the mocks reproduce the input properties. Finally, mocks were tested by comparing the angular correlation function w(θ), angular power spectrum Cℓ, and projected clustering ξp(r⊥) to theoretical predictions and data. The impact of volume replication in the estimate of the covariance is also investigated. The success in accurately reproducing the photometric redshift uncertainties and the galaxy clustering as a function of redshift render this mock creation pipeline as a benchmark for future analyses of photometric galaxy surveys.Key words: catalogs / large-scale structure of Universe / galaxies: distances and redshifts / Galaxy: halo / methods: numerical

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

AutoEnRichness: A hybrid empirical and analytical approach for estimating the richness of galaxy clusters

ABSTRACT We introduce AutoEnRichness, a hybrid approach that combines empirical and analytical strategies to determine the richness of galaxy clusters (in the redshift range of 0.1 ≤ z ≤ 0.35) using photometry data from the Sloan Digital Sky Survey Data Release 16, where cluster richness can be used as a proxy for cluster mass. In order to reliably estimate cluster richness, it is vital that the background subtraction is as accurate as possible when distinguishing cluster and field galaxies to mitigate severe contamination. AutoEnRichness is comprised of a multistage machine learning algorithm that performs background subtraction of interloping field galaxies along the cluster line of sight and a conventional luminosity distribution fitting approach that estimates cluster richness based only on the number of galaxies within a magnitude range and search area. In this proof-of-concept study, we obtain a balanced accuracy of 83.20 per cent when distinguishing between cluster and field galaxies as well as a median absolute percentage error of 33.50 per cent between our estimated cluster richnesses and known cluster richnesses within r200. In the future, we aim for AutoEnRichness to be applied on upcoming large-scale optical surveys, such as the Legacy Survey of Space and Time and Euclid, to estimate the richness of a large sample of galaxy groups and clusters from across the halo mass function. This would advance our overall understanding of galaxy evolution within overdense environments as well as enable cosmological parameters to be further constrained.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Stellar Mass and Stellar Mass-to-light Ratio–Color Relations for Low Surface Brightness Galaxies

We estimate the stellar mass for a sample of low surface brightness galaxies (LSBGs) by fitting their multiband spectral energy distributions (SEDs) to the stellar population synthesis model. The derived stellar masses (log M {sub *}/M {sub ⊙}) span from 7.1 to 11.1, with a mean of log M {sub *}/M {sub ⊙} = 8.5, which is lower than that for normal galaxies. The stellar mass-to-light ratio (γ*) in each band varies little with the absolute magnitude but increases with higher M {sub *}. This trend of γ* with M {sub *} is even stronger in bluer bands. In addition, the γ* for our LSBGs slightly declines from the r band to the longer-wavelength bands. The log γ{sub ∗}{sup j} (j = g, r, i, and z) have relatively tight relations with optical colors of g − r and g − i. Compared with several representative γ*–color relations (MLCRs) from the literature, our MLCRs based on LSBG data are consistently among those literature MLCRs previously defined on diverse galaxy samples, and the existing minor differences between the MLCRs are caused by the differences in the SED model ingredients (including initial mass function, star formation history, and stellar population model), line fitting techniques, galaxy samples, and photometric zero-point, rather than the galaxy surface brightness itself, which distinguishes LSBGs from high surface brightness galaxies. Our LSBGs would be very likely to follow those representative MLCRs previously defined in diverse galaxy populations, if those main ingredients were taken into account.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Spectroscopy of QUBRICS quasar candidates: 1672 new redshifts and a golden sample for the Sandage test of the redshift drift

ABSTRACT The QUBRICS (QUasars as BRIght beacons for Cosmology in the Southern hemisphere) survey aims at constructing a sample of the brightest quasars with $z \lower.5ex\hbox{$\,\, \buildrel\gt \over \sim \,\,$}2.5$, observable with facilities in the Southern Hemisphere. QUBRICS makes use of the available optical and IR wide-field surveys in the South and of Machine Learning techniques to produce thousands of bright quasar candidates of which only a few hundred have been confirmed with follow-up spectroscopy. Taking advantage of the recent Gaia Data Release 3, which contains 220 million low-resolution spectra, and of a newly developed spectral energy distribution fitting technique, designed to combine the photometric information with the Gaia spectroscopy, it has been possible to measure 1672 new secure redshifts of QUBRICS candidates, with a typical uncertainty of σz = 0.02. This significant progress of QUBRICS brings it closer to (one of) its primary goals: providing a sample of bright quasars at redshift 2.5 < z < 5 to perform the Sandage test of the cosmological redshift drift. A Golden Sample of seven quasars is presented that makes it possible to carry out this experiment in about 1500 h of observation in 25 yr, using the ANDES spectrograph at the 39m ELT, a significant improvement with respect to previous estimates.

Cristiani, Stefano (ORCID:0000000221155234)↗
DOE OSTI2022

The Gaia EDR3 view of Johnson-Kron-Cousins standard stars: the curated Landolt and Stetson collections

In the era of large surveys and space missions, it is necessary to rely on large samples of well-characterized stars for inter-calibrating and comparing measurements from different surveys and catalogues. Among the most employed photometric systems, the Johnson-Kron-Cousins has been used for decades and for a large amount of important datasets. Our goal is to profit from the Gaia EDR3 data, Gaia official cross-match algorithm, and Gaia -derived literature catalogues, to provide a well-characterized and clean sample of secondary standards in the Johnson-Kron-Cousins system, as well as a set of transformations between the main photometric systems and the Johnson-Kron-Cousins one. Using Gaia as a reference, as well as data from reddening maps, spectroscopic surveys, and variable stars monitoring surveys, we curated and characterized the widely used Landolt and Stetson collections of more than 200 000 secondary standards, employing classical as well as machine learning techniques. In particular, our atmospheric parameters agree significantly better with spectroscopic ones, compared to other machine learning catalogues. We also cross-matched the curated collections with the major photometric surveys to provide a comprehensive set of reliable measurements in the most widely adopted photometric systems. We provide a curated catalogue of secondary standards in the Johnson-Kron-Cousins system that are well-measured and as free as possible from variable and multiple sources. We characterize the collection in terms of astrophysical parameters, distance, reddening, and radial velocity. We provide a table with the magnitudes of the secondary standards in the most widely used photometric systems (ugriz, grizy, Gaia , HIPPARCOS, Tycho , 2MASS). We finally provide a set of 167 polynomial transformations, valid for dwarfs and giants, metal-poor and metal-rich stars, to transform UBVRI magnitudes in the above photometric systems and vice-versa.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

QSO photometric redshifts from SDSS, WISE, and GALEX colours

ABSTRACT Machine learning techniques, specifically the k-nearest neighbour algorithm applied to optical band colours, have had some success in predicting photometric redshifts of quasi-stellar objects (QSOs): Although the mean of differences between the spectroscopic and photometric redshifts, Δ$z$, is close to zero, the distribution of these differences remains wide and distinctly non-Gaussian. As per our previous empirical estimate of photometric redshifts, we find that the predictions can be significantly improved by adding colours from other wavebands, namely the near-infrared and ultraviolet. Self-testing this, by using half of the 33 643 strong QSO sample to train the algorithm, results in a significantly narrower spread in Δ$z$ for the remaining half of the sample. Using the whole QSO sample to train the algorithm, the same set of magnitudes return a similar spread in Δ$z$ for a sample of radio sources (quasars). Although the matching coincidence is relatively low (739 of the 3663 sources having photometry in the relevant bands), this is still significantly larger than from the empirical method (2 per cent) and thus may provide a method with which to obtain redshifts for the vast number of continuum radio sources expected to be detected with the next generation of large radio telescopes.

Curran, S. J.↗
DOE OSTI2022

XXL-HSC: An updated catalogue of high-redshift ( z ≥ 3.5) X-ray AGN in the XMM-XXL northern field

X-rays offer a reliable method to identify active galactic nuclei (AGNs). However, in the high-redshift Universe, X-ray AGNs are poorly sampled due to their relatively low space density and the small areas covered by X-ray surveys. In addition to wide-area X-ray surveys, it is important to have deep optical data in order to locate the optical counterparts and determine their redshifts. Here, in this work, we built a high-redshift (z ≥ 3.5) X-ray-selected AGN sample in the XMM-XXL northern field using the most updated [0.5–2 keV] catalogue along with a plethora of new spectroscopic and multi-wavelength catalogues, including the deep optical Subaru Hyper Suprime-Cam (HSC) data, reaching magnitude limits i ~ 26 mag. We selected all the spectroscopically confirmed AGN and complement this sample with high-redshift candidates that are HSC g- and r-band dropouts. To confirm the dropouts, we derived their photometric redshifts using spectral energy distribution techniques. We obtained a sample of 54 high-z sources (28 with spec-z), the largest in this field so far (almost three times larger than in previous studies), and we estimated the possible contamination and completeness. We calculated the number counts (log N-log S) in different redshift bins and compared our results with previous studies and models. We provide the strongest high-redshift AGN constraints yet at bright fluxes (f 0.5 – 2 keV > 10 –15 erg s –1 cm –2 ). The samples of z ≥ 3.5, z ≥ 4, and z ≥ 5 are in agreement with an exponential decline model similar to that witnessed at optical wavelengths. Our work emphasises the importance of using wide-area X-ray surveys with deep optical data to uncover high-redshift AGNs.

79 ASTRONOMY AND ASTROPHYSICS↗