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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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DOE OSTI2022

The influence of Hickson-like compact group environment on galaxy luminosities

Compact groups of galaxies are devised as extreme environments where interactions may drive galaxy evolution. In this work, we analysed whether the luminosities of galaxies inhabiting compact groups differ from those of galaxies in loose galaxy groups. We computed the luminosity functions of galaxy populations inhabiting a new sample of 1412 Hickson-like compact groups of galaxies identified in the Sloan Digital Sky Survey Data Release 16. We observed a characteristic absolute magnitude for galaxies in compact groups brighter than that observed in the field or loose galaxy systems. We also observed a deficiency of faint galaxies in compact groups in comparison with loose systems. Our analysis showed that the brightening is mainly due to galaxies inhabiting the more massive compact groups. In contrast to what is observed in loose systems, where only the luminosities of Red (and Early) galaxies show a dependency with group mass, luminosities of Red and Blue (also Early and Late) galaxies in compact groups are affected similarly as a function of group virial mass. When using Hubble types, we observed that elliptical galaxies in compact groups are the brightest galaxy population, and groups dominated by an elliptical galaxy also display the brightest luminosities in comparison with those dominated by spiral galaxies. Moreover, we show that the general luminosity trends can be reproduced using a mock catalogue obtained from a semi-analytical model of galaxy formation. These results suggest that the inner extreme environment in compact groups prompts a different evolutionary history for their galaxies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

The stellar parameters and elemental abundances from low-resolution spectra – I. 1.2 million giants from LAMOST DR8

As a typical data-driven method, deep learning becomes a natural choice for analysing astronomical data. In this study, we built a deep convolutional neural network (NN) to estimate basic stellar parameters $T\rm {_{eff}}$, log g , metallicity ([M/H] and [Fe/H]) and [α/M] along with nine individual elemental abundances ([C/Fe], [N/Fe], [O/Fe], [Mg/Fe], [Al/Fe], [Si/Fe], [Ca/Fe], [Mn/Fe], and [Ni/Fe]). The NN is trained using common stars between the APOGEE survey and the LAMOST survey. We used low-resolution spectra from LAMOST survey as input, and measurements from APOGEE as labels. For stellar spectra with the signal-to-noise ratio in g band larger than 10 in the test set, the mean absolute error (MAE) is 29 K for $T\rm {_{eff}}$, 0.07 dex for log g , 0.03 dex for both [Fe/H] and [M/H], and 0.02 dex for [α/M]. The MAE of most elements is between 0.02 and 0.04 dex. The trained NN was applied to 1210 145 giants, including sub-giants, from LAMOST DR8 within the range of stellar parameters 3500 K < $T\rm {_{eff}}$ < 5500 K, 0.0 dex < log g < 4.0 dex, −2.5 dex < [Fe/H] < 0.5 dex. The distribution of our results in the chemical spaces is highly consistent with APOGEE labels and stellar parameters show consistency with external high-resolution measurements from GALAH. The results in this study allow us to further studies based on LAMOST data and deepen our understanding of the accretion and evolution history of the Milky Way. The electronic version of the value added catalog is available at http://www.lamost.org/dr8/v1.1/doc/vac.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

On the inconsistency of [C/Fe] abundances and the fractions of carbon-enhanced metal-poor stars among various stellar surveys

Carbon-enhanced metal-poor (CEMP) stars are a unique resource for Galactic archaeology because they probe the properties of the First Stars, early chemical evolution, and binary interactions at very low metallicity. Comparing the fractions and properties of CEMP stars in different Galactic environments can provide us with unique insights into the formation and evolution of the Milky Way halo and its building blocks. In this work, we investigate whether directly comparing fractions of CEMP stars from different literature samples of very metal-poor [Fe/H] < -2.0 stars is valid. We compiled published CEMP fractions and samples of Galactic halo stars from the past 25 years, and find that they are not all consistent with each other. Focusing on giant stars, we find significant differences between various surveys when comparing their trends of [Fe/H] versus [C/Fe] and their distributions of CEMP stars. To test the role of the analysis pipelines for low-resolution spectroscopic samples, we re-analysed giant stars from various surveys with the sspp and ferre pipelines. We found systematic differences in [C/Fe] of ~0.1-0.4 dex, partly independent of degeneracies with the stellar atmospheric parameters. These systematics are likely due to the different pipeline approaches, different assumptions in the employed synthetic grids, and/or the comparison of different evolutionary phases. We conclude that current biases in (the analysis of) very metal-poor samples limit the conclusions one can draw from comparing different surveys. We provide some recommendations and suggestions that will hopefully aid the community to unlock the full potential of CEMP stars for Galactic archaeology.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Non-universal stellar initial mass functions: large uncertainties in star formation rates at z ≈ 2–4 and other astrophysical probes

ABSTRACT We explore the assumption, widely used in many astrophysical calculations, that the stellar initial mass function (IMF) is universal across all galaxies. By considering both a canonical broken-power-law IMF and a non-universal IMF, we are able to compare the effect of different IMFs on multiple observables and derived quantities in astrophysics. Specifically, we consider a non-universal IMF that varies as a function of the local star formation rate, and explore the effects on the star formation rate density (SFRD), the extragalactic background light, the supernova (both core-collapse and thermonuclear) rates, and the diffuse supernova neutrino background. Our most interesting result is that our adopted varying IMF leads to much greater uncertainty on the SFRD at $z \approx 2-4$ than is usually assumed. Indeed, we find an SFRD (inferred using observed galaxy luminosity distributions) that is a factor of $\gtrsim 3$ lower than canonical results obtained using a universal IMF. Secondly, the non-universal IMF we explore implies a reduction in the supernova core-collapse rate of a factor of $\sim 2$, compared against a universal IMF. The other potential tracers are only slightly affected by changes to the properties of the IMF. We find that currently available data do not provide a clear preference for universal or non-universal IMF. However, improvements to measurements of the star formation rate and core-collapse supernova rate at redshifts $z \gtrsim 2$ may offer the best prospects for discernment.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

The miniJPAS survey quasar selection – II. Machine learning classification with photometric measurements and uncertainties

Astrophysical surveys rely heavily on the classification of sources as stars, galaxies, or quasars from multiband photometry. Surveys in narrow-band filters allow for greater discriminatory power, but the variety of different types and redshifts of the objects present a challenge to standard template-based methods. In this work, which is part of a larger effort that aims at building a catalogue of quasars from the miniJPAS survey, we present a machine learning-based method that employs convolutional neural networks (CNNs) to classify point-like sources including the information in the measurement errors. We validate our methods using data from the miniJPAS survey, a proof-of-concept project of the Javalambre Physics of the Accelerating Universe Astrophysical Survey (J-PAS) collaboration covering ∼1 deg 2 of the northern sky using the 56 narrow-band filters of the J-PAS survey. Due to the scarcity of real data, we trained our algorithms using mocks that were purpose-built to reproduce the distributions of different types of objects that we expect to find in the miniJPAS survey, as well as the properties of the real observations in terms of signal and noise. We compare the performance of the CNNs with other well-established machine learning classification methods based on decision trees, finding that the CNNs improve the classification when the measurement errors are provided as inputs. The predicted distribution of objects in miniJPAS is consistent with the putative luminosity functions of stars, quasars, and unresolved galaxies. Our results are a proof of concept for the idea that the J-PAS survey will be able to detect unprecedented numbers of quasars with high confidence.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

The miniJPAS survey quasar selection – I. Mock catalogues for classification

In this series of papers, we employ several machine learning (ML) methods to classify the point-like sources from the miniJPAS catalogue, and identify quasar candidates. Since no representative sample of spectroscopically confirmed sources exists at present to train these ML algorithms, we rely on mock catalogues. In this first paper, we develop a pipeline to compute synthetic photometry of quasars, galaxies, and stars using spectra of objects targeted as quasars in the Sloan Digital Sky Survey . To match the same depths and signal-to-noise ratio distributions in all bands expected for miniJPAS point sources in the range 17.5 ≤ r < 24, we augment our sample of available spectra by shifting the original r-band magnitude distributions towards the faint end, ensure that the relative incidence rates of the different objects are distributed according to their respective luminosity functions, and perform a thorough modelling of the noise distribution in each filter, by sampling the flux variance either from Gaussian realizations with given widths, or from combinations of Gaussian functions. Finally, we also add in the mocks the patterns of non-detections which are present in all real observations. Although the mock catalogues presented in this work are a first step towards simulated data sets that match the properties of the miniJPAS observations, these mocks can be adapted to serve the purposes of other photometric surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Photometric identification of compact galaxies, stars, and quasars using multiple neural networks

We present MargNet, a deep learning-based classifier for identifying stars, quasars, and compact galaxies using photometric parameters and images from the Sloan Digital Sky Survey Data Release 16 catalogue. MargNet consists of a combination of convolutional neural network and artificial neural network architectures. Using a carefully curated data set consisting of 240 000 compact objects and an additional 150 000 faint objects, the machine learns classification directly from the data, minimizing the need for human intervention. MargNet is the first classifier focusing exclusively on compact galaxies and performs better than other methods to classify compact galaxies from stars and quasars, even at fainter magnitudes. This model and feature engineering in such deep learning architectures will provide greater success in identifying objects in the ongoing and upcoming surveys, such as Dark Energy Survey and images from the Vera C. Rubin Observatory.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

A measurement of the distance to the Galactic centre using the kinematics of bar stars

The distance to the Galactic centre R 0 is a fundamental parameter for understanding the Milky Way, because all observations of our Galaxy are made from our heliocentric reference point. The uncertainty in R 0 limits our knowledge of many aspects of the Milky Way, including its total mass and the relative mass of its major components, and any orbital parameters of stars employed in chemo-dynamical analyses. While measurements of R 0 have been improving over a century, measurements in the past few years from a variety of methods still find a wide range of R 0 being somewhere within 8.0 to $8.5\, \mathrm{kpc}$. The most precise measurements to date have to assume that Sgr A* is at rest at the Galactic centre, which may not be the case. In this paper, we use maps of the kinematics of stars in the Galactic bar derived from APOGEE DR17 and Gaia EDR3 data augmented with spectrophotometric distances from the astroNN neural-network method. These maps clearly display the minimum in the rotational velocity v T and the quadrupolar signature in radial velocity v R expected for stars orbiting in a bar. From the minimum in v T , we measure $R_0 = 8.23\pm 0.12\, \mathrm{kpc}$. We validate our measurement using realistic N-body simulations of the Milky Way. We further measure the pattern speed of the bar to be $\Omega _\mathrm{bar} = 40.08\pm 1.78\, \mathrm{km\, s}^{-1}\,\mathrm{kpc}^{-1}$. Because the bar forms out of the disc, its centre is manifestly the barycentre of the bar+disc system and our measurement is therefore one of the most robust and accurate measurements of R 0 to date.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Photometric calibration in u -band using blue halo stars

ABSTRACT We develop a method to calibrate u-band photometry based on the observed colour of blue Galactic halo stars. The Galactic halo stars belong to an old stellar population of the Milky Way and have relatively low metallicity. The ‘blue tip’ of the halo population – the main sequence turn-off (MSTO) stars – is known to have a relatively uniform intrinsic edge u-g colour with only slow spatial variation. In SDSS data, the observed variation is correlated with Galactic Latitude, which we attribute to contamination by higher metallicity disc stars and fit with an empirical curve. This curve can then be used to calibrate u-band imaging if g-band imaging of matching depth is available. Our approach can be applied to single-field observations at |b| > 30°, and removes the need for standard star observations or overlap with calibrated u-band imaging. We include in our method the calibration of g-band data with ATLAS-Refcat2. We test our approach on stars in KiDS DR 4, ATLAS DR 4, and DECam imaging from the NOIRLab Source Catalog (NSC DR2), and compare our calibration with SDSS. For this process, we use synthetic magnitudes to derive the colour equations between these data sets, in order to improve zero-point accuracy. We find an improvement for all data sets, reaching a zero-point precision of 0.016 mag for KiDS (compared to the original 0.033 mag), 0.020 mag for ATLAS (originally 0.027 mag), and 0.016 mag for DECam (originally 0.041 mag). Thus, this method alone reaches the goal of 0.02 mag photometric precision in u-band for the Rubin Observatory’s Legacy Survey of Space and Time (LSST).

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Model-independent constraints on Ωm and H ( z ) from the link between geometry and growth

ABSTRACT We constrain the expansion history of the Universe and the cosmological matter density fraction in a model-independent way by exclusively making use of the relationship between background and perturbations under a minimal set of assumptions. We do so by employing a Gaussian process to model the expansion history of the Universe from present time to the recombination era. The expansion history and the cosmological matter density are then constrained using recent measurements from cosmic chronometers, Type-Ia supernovae, baryon acoustic oscillations, and redshift-space distortion data. Our results show that the evolution in the reconstructed expansion history is compatible with the Planck 2018 prediction at all redshifts. The current data considered in this study can constrain a Gaussian process on H(z) to an average $9.4 {{\ \rm per\ cent}}$ precision across redshift. We find Ωm = 0.224 ± 0.066, lower but statistically compatible with the Planck 2018 cosmology. Finally, the combination of future DESI measurements with the CMB measurement considered in this work holds the promise of $8 {{\ \rm per\ cent}}$ average constraints on a model-independent expansion history as well as a five-fold tighter Ωm constraint using the methodology developed in this work.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Deep learning methods for obtaining photometric redshift estimations from images

ABSTRACT Knowing the redshift of galaxies is one of the first requirements of many cosmological experiments, and as it is impossible to perform spectroscopy for every galaxy being observed, photometric redshift (photo-z) estimations are still of particular interest. Here, we investigate different deep learning methods for obtaining photo-z estimates directly from images, comparing these with ‘traditional’ machine learning algorithms which make use of magnitudes retrieved through photometry. As well as testing a convolutional neural network (CNN) and inception-module CNN, we introduce a novel mixed-input model that allows for both images and magnitude data to be used in the same model as a way of further improving the estimated redshifts. We also perform benchmarking as a way of demonstrating the performance and scalability of the different algorithms. The data used in the study comes entirely from the Sloan Digital Sky Survey (SDSS) from which 1 million galaxies were used, each having 5-filtre (ugriz) images with complete photometry and a spectroscopic redshift which was taken as the ground truth. The mixed-input inception CNN achieved a mean squared error (MSE) =0.009, which was a significant improvement ($30{{\ \rm per\ cent}}$) over the traditional random forest (RF), and the model performed even better at lower redshifts achieving a MSE = 0.0007 (a $50{{\ \rm per\ cent}}$ improvement over the RF) in the range of z < 0.3. This method could be hugely beneficial to upcoming surveys, such as Euclid and the Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST), which will require vast numbers of photo-z estimates produced as quickly and accurately as possible.

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 OSTI2023

What drives the variance of galaxy spectra?

We present a study aimed at understanding the physical phenomena underlying the formation and evolution of galaxies following a data-driven analysis of spectroscopic data based on the variance in a carefully selected sample. We apply principal component analysis (PCA) independently to three subsets of continuum-subtracted optical spectra, segregated into their nebular emission activity as quiescent, star-forming, and active galactic nuclei (AGNs). We emphasize that the variance of the input data in this work only relates to the absorption lines in the photospheres of the stellar populations. The sample is taken from the Sloan Digital Sky Survey (SDSS) in the stellar velocity dispersion range 100–150 km s −1 , to minimize the ‘blurring’ effect of the stellar motion. We restrict the analysis to the first three principal components (PCs) and find that PCA segregates the three types with the highest variance mapping SSP-equivalent age, along with an inextricable degeneracy with metallicity, even when all three PCs are included. Spectral fitting shows that stellar age dominates PC1, whereas PC2 and PC3 have a mixed dependence of age and metallicity. The trends support – independently of any model fitting – the hypothesis of an evolutionary sequence from star formation to AGN to quiescence. As a further test of the consistency of the analysis, we apply the same methodology in different spectral windows, finding similar trends, but the variance is maximal in the blue wavelength range, roughly around the 4000 Å break.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Data-driven selection and spectral classification of white dwarf stars

The next generation of spectroscopic surveys is expected to provide spectra for hundreds of thousands of white dwarf (WD) candidates in the upcoming years. Currently, spectroscopic classification of white dwarfs is mostly done by visual inspection, requiring substantial amounts of expert attention. We propose a data-driven pipeline for fast, automatic selection, and spectroscopic classification of WD candidates, trained using spectroscopically confirmed objects with available Gaia astrometry, photometry, and Sloan Digital Sky Survey (SDSS) spectra with signal-to-noise ratios ≥9. The pipeline selects WD candidates with improved accuracy and completeness over existing algorithms, classifies their primary spectroscopic type with ≥ 90 % accuracy, and spectroscopically detects main sequence companions with similar performance. We apply our pipeline to the Gaia Data Release 3 cross-matched with the SDSS Data Release 17 (DR17), identifying 424 096 high-confidence WD candidates and providing the first catalogue of automated and quantifiable classification for 36 523 WD spectra. Both the catalogue and pipeline are made available online. Such a tool will prove particularly useful for the undergoing SDSS-V survey, allowing for rapid classification of thousands of spectra at every data release.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Robust clustering of the local Milky Way stellar kinematic substructures with Gaia eDR3

Understanding local stellar kinematic substructures in the solar neighbourhood helps build a complete picture of the formation of the Milky Way, as well as an empirical phase space distribution of dark matter that would inform detection experiments. We apply the clustering algorithm HDBSCAN on the Gaia early third data release to identify a list of stable clusters in velocity space and action-angle space by taking into account the measurement uncertainties and studying the stability of the clustering results. We find 1405 (497) stars in 23 (6) robust clusters in velocity space (action-angle space) that are consistently not associated with noise. We discuss the kinematic properties of these structures and study whether many of the small clusters belong to a similar larger cluster based on their chemical abundances. They are attributed to the known structures: the Gaia Sausage-Enceladus, the Helmi Stream, and globular cluster NGC 3201 are found in both spaces, while NGC 104 and the thick disc (Sequoia) are identified in velocity space (action-angle space). Although we do not identify any new structures, we find that the HDBSCAN member selection of already known structures is unstable to input kinematics of the stars when resampled within their uncertainties. We therefore present the stable subset of local kinematic structures, which are consistently identified by the clustering algorithm, and emphasize the need to take into account error propagation during both the manual and automated identification of stellar structures, both for existing ones as well as future discoveries.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Measurements of the z > 5 Lyman-α forest flux autocorrelation functions from the extended XQR-30 data set

We present the first observational measurements of the Lyman-α (Ly α) forest flux autocorrelation functions in ten redshift bins from 5.1 ≤ z ≤ 6.0. We use a sample of 35 quasar sightlines at z > 5.7 from the extended XQR-30 data set; these data have signal-to-noise ratios of >20 per spectral pixel. We carefully account for systematic errors in continuum reconstruction, instrumentation, and contamination by damped Ly α systems. With these measurements, we introduce software tools to generate autocorrelation function measurements from any simulation. Our measurements of the smallest bin of the autocorrelation function increase with redshift when normalizing by the mean flux, $\langle{F}\rangle$. This increase may come from decreasing $\langle{F}\rangle$ or increasing mean free path of hydrogen-ionizing photons, λmfp. Recent work has shown that the autocorrelation function from simulations at z > 5 is sensitive to λmfp, a quantity that contains vital information on the ending of reionization. For an initial comparison, we show our autocorrelation measurements with simulation models for recently measured λmfp values and find good agreements. Further work in modelling and understanding the covariance matrices of the data is necessary to get robust measurements of λmfp from this data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Detection of the large-scale tidal field with galaxy multiplet alignment in the DESI Y1 spectroscopic survey

We explore correlations between the orientations of small galaxy groups, or ‘multiplets’, and the large-scale gravitational tidal field. Using data from the Dark Energy Spectroscopic Instrument (DESI) Y1 survey, we detect the intrinsic alignment (IA) of multiplets to the galaxy-traced matter field out to separations of $100\,h^{-1}$ Mpc. Unlike traditional IA measurements of individual galaxies, this estimator is not limited by imaging of galaxy shapes and allows for direct IA detection beyond redshift $z=1$. Multiplet alignment is a form of higher order clustering, for which the scale-dependence traces the underlying tidal field and amplitude is a result of small-scale ($\lt 1h^{-1}$ Mpc) dynamics. Within samples of bright galaxies, luminous red galaxies (LRG) and emission-line galaxies, we find similar scale-dependence regardless of intrinsic luminosity or colour. This is promising for measuring tidal alignment in galaxy samples that typically display no IA. DESI’s LRG mock galaxy catalogues created from the A BACUS S UMMIT N -body simulations produce a similar alignment signal, though with a 33 per cent lower amplitude at all scales. An analytic model using a non-linear power spectrum (NLA) only matches the signal down to 20 $h^{-1}$ Mpc. Our detection demonstrates that galaxy clustering in the non-linear regime of structure formation preserves an interpretable memory of the large-scale tidal field. Multiplet alignment complements traditional two-point measurements by retaining directional information imprinted by tidal forces, and contains additional line-of-sight information compared to weak lensing. This is a more effective estimator than the alignment of individual galaxies in dense, blue, or faint galaxy samples.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Redshift-dependent RSD bias from intrinsic alignment with DESI Year 1 spectra

ABSTRACT We estimate the redshift-dependent, anisotropic clustering signal in the Dark Energy Spectroscopic Instrument (DESI) Year 1 Survey created by tidal alignments of Luminous Red Galaxies (LRGs) and a selection-induced galaxy orientation bias. To this end, we measured the correlation between LRG shapes and the tidal field with DESI’s Year 1 redshifts, as traced by LRGs and Emission-Line Galaxies. We also estimate the galaxy orientation bias of LRGs caused by DESI’s aperture-based selection, and find it to increase by a factor of seven between redshifts 0.4−1.1 due to redder, fainter galaxies falling closer to DESI’s imaging selection cuts. These effects combine to dampen measurements of the quadrupole of the correlation function (ξ2) caused by structure growth on scales of 10–80 h−1 Mpc by about 0.15 per cent for low redshifts (0.4 < z < 0.6) and 0.8 per cent for high (0.8 < z < 1.1), a significant fraction of DESI’s error budget. We provide estimates of the ξ2 signal created by intrinsic alignments that can be used to correct this effect, which is necessary to meet DESI’s forecasted precision on measuring the growth rate of structure. While imaging quality varies across DESI’s footprint, we find no significant difference in this effect between imaging regions in the Legacy Imaging Survey.

79 ASTRONOMY AND ASTROPHYSICS↗