Evolution of oxygen isotopic composition in the inner solar nebula

Changes in the chemical and isotopic composition of the solar nebula with time are reflected in the properties of different constituents that are preserved in chondritic meteorites. CR carbonaceous chondrites are among the most primitive of all chondrite types and must have preserved solar nebula records largely unchanged. We have analyzed the oxygen and magnesium isotopes in a range of the CR constituents of different formation temperatures and ages, including refractory inclusions and chondrules of various types. The results provide new constraints on the time variation of the oxygen isotopic composition of the inner (<5 AU) solar nebula - the region where refractory inclusions and chondrules most likely formed. A chronology based on the decay of short-lived 26Al (t1/2 ~ 0.73 Ma) indicates that the inner solar nebula gas was 16O-rich when refractory inclusions formed, but less than 0.8 Ma later, gas in the inner solar nebula became 16O-poor and this state persisted at least until CR chondrules formed ~1-2 Myr later. We suggest that the inner solar nebula became 16O-poor because meter-size icy bodies, which were enriched in 17,18O due to isotopic self-shielding during the ultraviolet photo dissociation of CO in the protosolar molecular cloud or protoplanetary disk, agglomerated outside the snowline, drifted rapidly towards the Sun, and evaporated at the snowline. This led to significant enrichment in 16O-depleted water, which then spread through the inner solar system. Astronomical studies of the spatial and/or temporal variations of water abundance in protoplanetary disks may clarify these processes.Comment: 27 pages, 5 figure

Angular clustering properties of the DESI QSO target selection using DR9 Legacy Imaging Surveys

The quasar target selection for the upcoming survey of the Dark Energy Spectroscopic Instrument (DESI) will be fixed for the next 5 yr. The aim of this work is to validate the quasar selection by studying the impact of imaging systematics as well as stellar and galactic contaminants, and to develop a procedure to mitigate them. Density fluctuations of quasar targets are found to be related to photometric properties such as seeing and depth of the Data Release 9 of the DESI Legacy Imaging Surveys. To model this complex relation, we explore machine learning algorithms (random forest and multilayer perceptron) as an alternative to the standard linear regression. Splitting the footprint of the Legacy Imaging Surveys into three regions according to photometric properties, we perform an independent analysis in each region, validating our method using extended Baryon Oscillation Spectroscopic Survey (eBOSS) EZ-mocks. The mitigation procedure is tested by comparing the angular correlation of the corrected target selection on each photometric region to the angular correlation function obtained using quasars from the Sloan Digital Sky Survey (SDSS) Data Release 16. With our procedure, we recover a similar level of correlation between DESI quasar targets and SDSS quasars in two-thirds of the total footprint and we show that the excess of correlation in the remaining area is due to a stellar contamination that should be removed with DESI spectroscopic data. We derive the Limber parameters in our three imaging regions and compare them to previous measurements from SDSS and the 2dF QSO Redshift Survey

Angular clustering properties of the DESI QSO target selection using DR9 Legacy Imaging Surveys

The quasar target selection for the upcoming survey of the Dark Energy Spectroscopic Instrument (DESI) will be fixed for the next 5 yr. The aim of this work is to validate the quasar selection by studying the impact of imaging systematics as well as stellar and galactic contaminants, and to develop a procedure to mitigate them. Density fluctuations of quasar targets are found to be related to photometric properties such as seeing and depth of the Data Release 9 of the DESI Legacy Imaging Surveys. To model this complex relation, we explore machine learning algorithms (random forest and multilayer perceptron) as an alternative to the standard linear regression. Splitting the footprint of the Legacy Imaging Surveys into three regions according to photometric properties, we perform an independent analysis in each region, validating our method using extended Baryon Oscillation Spectroscopic Survey (eBOSS) EZ-mocks. The mitigation procedure is tested by comparing the angular correlation of the corrected target selection on each photometric region to the angular correlation function obtained using quasars from the Sloan Digital Sky Survey (SDSS) Data Release 16. With our procedure, we recover a similar level of correlation between DESI quasar targets and SDSS quasars in two-thirds of the total footprint and we show that the excess of correlation in the remaining area is due to a stellar contamination that should be removed with DESI spectroscopic data. We derive the Limber parameters in our three imaging regions and compare them to previous measurements from SDSS and the 2dF QSO Redshift Survey.This research is supported by the Director, Office of Science, Office of High Energy Physics of the U.S. Department of Energy under contract no. DE-AC02-05CH11231, and by the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility under the same contract; additional support for DESI is provided by the U.S. National Science Foundation, Division of Astronomical Sciences under contract no. AST-0950945 to the NSF’s National Optical–Infrared Astronomy Research Laboratory; the Science and Technology Facilities Council of the United Kingdom; the Gordon and Betty Moore Foundation; the Heising-Simons Foundation; the French Alternative Energies and Atomic Energy Commission (CEA); the National Council of Science and Technology, Mexico; the Ministry of Economy of Spain, and by the DESI Member Institutions. ADM was supported by the U.S. Department of Energy, Office of Science, Office of High Energy Physics, under Award Number DE-SC0019022

Impact and mitigation of spectroscopic systematics on DESI DR1 clustering measurements

The large scale structure catalogs within DESI Data Release 1 (DR1) use nearly 6 million galaxies and quasars as tracers of the large-scale structure of the universe to measure the expansion history with baryon acoustic oscillations and the growth of structure with redshift-space distortions. In order to take advantage of DESI's unprecedented statistical power, we must ensure that the galaxy clustering measurements are unaffected by non-cosmological density fluctuations. One source of spurious fluctuations comes from variation in galaxy density with spectroscopic observing conditions, lowering the redshift efficiency (and thus galaxy density) in certain areas of the sky. We measure the uniformity of the redshift success rate for DESI luminous red galaxies (LRG), bright galaxies (BGS) and quasars (QSO), complementing the detailed discussion of emission line galaxy (ELG) systematics in a companion paper (Yu et al., 2024). We find small but significant fluctuations of up to 3% in redshift success rate with the effective spectroscopic signal-to-noise, and create and describe weights that remove these fluctuations. We also describe the process to identify and remove data from certain poorly performing fibers from DESI DR1, and measure the stability of the redshift success rate with time. Finally, we find small but significant correlations of redshift success rate with position on the focal plane, survey speed, and number of exposures required, and show the impact of weights correcting these trends on the power spectrum multipoles and on cosmological parameters from BAO and RSD fits. These corrections change the best-fit parameters by $<15\%$ of their statistical errors, and thus contribute negligibly to the overall DESI error budget.Comment: 53 pages, 41 figures. Supporting paper for DESI DR1 cosmological measurement

Constraining primordial non-Gaussianity from the large scale structure two-point and three-point correlation functions

Surveys of cosmological large-scale structure (LSS) are sensitive to the presence of local primordial non-Gaussianity (PNG), and may be used to constrain models of inflation. Local PNG, characterized by fNL, the amplitude of the quadratic correction to the potential of a Gaussian random field, is traditionally measured from LSS two-point and three-point clustering via the power spectrum and bi-spectrum. We propose a framework to measure fNL using the configuration space two-point correlation function (2pcf) monopole and three-point correlation function (3pcf) monopole of survey tracers. Our model estimates the effect of the scale-dependent bias induced by the presence of PNG on the 2pcf and 3pcf from the clustering of simulated dark matter halos. We describe how this effect may be scaled to an arbitrary tracer of the cosmological matter density. The 2pcf and 3pcf of this tracer are measured to constrain the value of fNL. Using simulations of luminous red galaxies observed by the Dark Energy Spectroscopic Instrument (DESI), we demonstrate the accuracy and constraining power of our model, and forecast the ability to constrainfNL to a precision of sigma(fNL) = 22 with one year of DESI survey data

Oxygen isotope heterogeneity of the mantle beneath the Canary Islands : insights from olivine phenocrysts

Author Posting. © The Author(s), 2010. This is the author's version of the work. It is posted here by permission of Springer for personal use, not for redistribution. The definitive version was published in Contributions to Mineralogy and Petrology 162 (2011): 349-363, doi:10.1007/s00410-010-0600-5.A relatively narrow range of oxygen isotopic ratios (δ18O = 5.05.4‰) is preserved in olivine of mantle xenoliths, mid-ocean ridge (MORB) and most ocean island basalts (OIB). The values in excess of this range are generally attributed either to the presence of a recycled component in the Earth’s mantle or to shallow level contamination processes. A viable way forward to trace source heterogeneity is to find a link between chemical (elemental and isotopic) composition of the earlier crystallized mineral phases (olivine) and the composition of their parental magmas, then using them to reconstruct the composition of source region. The Canary hotspot is one of a few that contains ~1-2 Ga old recycled ocean crust that can be traced to the core-mantle boundary using seismic tomography and whose origin is attributed to the mixing of at least three main isotopically distinct mantle components i.e., HIMU, DMM and EM. This work reports ion microprobe and single crystal laser fluorination oxygen isotope data of 148 olivine grains also analyzed for major and minor elements in the same spot. The olivines are from 20 samples resembling the most primitive shield stage picrite through alkali basalt to basanite series erupted on Gran Canaria, Tenerife, La Gomera, La Palma and El Hierro, Canary Islands, for which shallow level contamination processes were not recognized. A broad range of δ18Oolivine values from 4.6 to 6.1‰ was obtained and explained by stable, long-term oxygen isotope heterogeneity of crystal cumulates present under different volcanoes. These cumulates are thought to have crystallized from mantle derived magmas uncontaminated at crustal depth, representing oxygen isotope heterogeneity of source region. A relationship between Ni×FeO/MgO and δ18Oolivine values found in one basanitic lava erupted on El Hierro, the westernmost island of the Canary Archipelago, was used to estimate oxygen isotope compositions of partial melts presumably originated from peridotite (HIMU-type component inherited its radiogenic isotope composition from ancient, ~12 Ga, recycled ocean crust) and pyroxenite (young, <1 Ga, recycled oceanic crust preserved as eclogite with depleted MORB-type isotopic signature) components of the Canary plume. The model calculations yield 5.2 and 5.9±0.3‰ for peridotite and pyroxenite derived melts, respectively, which appeared to correspond closely to the worldwide HIMU-type OIB and upper limit N-MORB δ18O values. This difference together with the broad range of δ18O variations found in the Canarian olivines cannot be explained by thermodynamic effects of oxygen isotopic fractionation and are believed to represent true variations in the mantle, due to oceanic crust and continental lithosphere recycling.This work was supported by the CNRS “poste rouge” grant to AG, the NSF EAR-CAREER-0844772 grant to IB and the CRPG-CNRS and at its initial stage by the DFG (grant SCHM 250/64) and the Alexander von Humboldt Foundation (Wolfgang Paul Award to A.V. Sobolev who provided access to the electron microprobe at the Max Planck Institute, Mainz, Germany)

Validating the Galaxy and Quasar Catalog-Level Blinding Scheme for the DESI 2024 analysis

In the era of precision cosmology, ensuring the integrity of data analysis through blinding techniques is paramount -- a challenge particularly relevant for the Dark Energy Spectroscopic Instrument (DESI). DESI represents a monumental effort to map the cosmic web, with the goal to measure the redshifts of tens of millions of galaxies and quasars. Given the data volume and the impact of the findings, the potential for confirmation bias poses a significant challenge. To address this, we implement and validate a comprehensive blind analysis strategy for DESI Data Release 1 (DR1), tailored to the specific observables DESI is most sensitive to: Baryonic Acoustic Oscillations (BAO), Redshift-Space Distortion (RSD) and primordial non-Gaussianities (PNG). We carry out the blinding at the catalog level, implementing shifts in the redshifts of the observed galaxies to blind for BAO and RSD signals and weights to blind for PNG through a scale-dependent bias. We validate the blinding technique on mocks, as well as on data by applying a second blinding layer to perform a battery of sanity checks. We find that the blinding strategy alters the data vector in a controlled way such that the BAO and RSD analysis choices do not need any modification before and after unblinding. The successful validation of the blinding strategy paves the way for the unblinded DESI DR1 analysis, alongside future blind analyses with DESI and other surveys.Comment: Supporting publication of "DESI 2024 II: Sample definitions, characteristics, and two-point clustering statistics", "DESI 2024 III: Baryon Acoustic Oscillations from Galaxies and Quasars", and "DESI 2024 V: Analysis of the full shape of two-point clustering statistics from galaxies and quasars". (v2 - update DESI references

Performance of the Quasar Spectral Templates for the Dark Energy Spectroscopic Instrument

Millions of quasar spectra will be collected by the Dark Energy Spectroscopic Instrument (DESI), leading to a fourfold increase in the number of known quasars. High-accuracy quasar classification is essential to tighten constraints on cosmological parameters measured at the highest redshifts DESI observes (z > 2.0). We present spectral templates for identification and redshift estimation of quasars in the DESI Year 1 data release. The quasar templates are comprised of two quasar eigenspectra sets, trained on spectra from the Sloan Digital Sky Survey. The sets are specialized to reconstruct quasar spectral variation observed over separate yet overlapping redshift ranges and, together, are capable of identifying DESI quasars from 0.05 < z < 7.0. The new quasar templates show significant improvement over the previous DESI quasar templates regarding catastrophic failure rates, redshift precision and accuracy, quasar completeness, and the contamination fraction in the final quasar sample

The Spectroscopic Data Processing Pipeline for the Dark Energy Spectroscopic Instrument

We describe the spectroscopic data processing pipeline of the Dark Energy Spectroscopic Instrument (DESI), which is conducting a redshift survey of about 40 million galaxies and quasars using a purpose-built instrument on the 4-m Mayall Telescope at Kitt Peak National Observatory. The main goal of DESI is to measure with unprecedented precision the expansion history of the Universe with the Baryon Acoustic Oscillation technique and the growth rate of structure with Redshift Space Distortions. Ten spectrographs with three cameras each disperse the light from 5000 fibers onto 30 CCDs, covering the near UV to near infrared (3600 to 9800 Angstrom) with a spectral resolution ranging from 2000 to 5000. The DESI data pipeline generates wavelength- and flux-calibrated spectra of all the targets, along with spectroscopic classifications and redshift measurements. Fully processed data from each night are typically available to the DESI collaboration the following morning. We give details about the pipeline's algorithms, and provide performance results on the stability of the optics, the quality of the sky background subtraction, and the precision and accuracy of the instrumental calibration. This pipeline has been used to process the DESI Survey Validation data set, and has exceeded the project's requirements for redshift performance, with high efficiency and a purity greater than 99 percent for all target classes.Comment: AJ, revised version, 55 pages, 55 figures, 4 table