116 research outputs found
A framework to measure the properties of intergalactic metal systems with two-point flux statistics
The abundance, temperature, and clustering of metals in the intergalactic
medium are important parameters for understanding their cosmic evolution and
quantifying their impact on cosmological analysis with the Ly forest.
The properties of these systems are typically measured from individual quasar
spectra redward of the quasar's Ly emission line, yet that approach
may provide biased results due to selection effects. We present an alternative
approach to measure these properties in an unbiased manner with the two-point
statistics commonly employed to quantify large-scale structure. Our model
treats the observed flux of a large sample of quasar spectra as a continuous
field and describes the one-dimensional, two-point statistics of this field
with three parameters per ion: the abundance (column density distribution),
temperature (Doppler parameter) and clustering (cloud-cloud correlation
function). We demonstrate this approach on multiple ions (e.g., C IV, Si IV, Mg
II) with early data from the Dark Energy Spectroscopic Instrument (DESI) and
high-resolution spectra from the literature. Our initial results show some
evidence that the C IV abundance is higher than previous measurements and
evidence for abundance evolution over time. The first full year of DESI
observations will have over an order of magnitude more quasar spectra than this
study. In a future paper we will use those data to measure the growth of
clustering and its impact on the Ly forest, as well as test other DESI
analysis infrastructure such as the pipeline noise estimates and the resolution
matrix.Comment: 15 pages, 14 figure
Mock data sets for the Eboss and DESI Lyman- forest surveys
{We present a publicly-available code to generate mock Lyman- (\lya)
forest data sets. The code is based on the Fluctuating Gunn-Peterson
Approximation (FGPA) applied to Gaussian random fields and on the use of fast
Fourier transforms (FFT). The output includes spectra of lya transmitted flux
fraction, , a quasar catalog, and a catalog of high-column-density systems.
While these three elements have realistic correlations, additional code is then
used to generate realistic quasar spectra, to add absorption by
high-column-density systems and metals, and to simulate instrumental effects.
Redshift space distortions (RSD) are implemented by including the large-scale
velocity-gradient field in the FGPA resulting in a correlation function of
that can be accurately predicted. One hundred realizations have been produced
over the 14,000 deg Dark Energy Spectroscopy Instrument (DESI) survey
footprint with 100 quasars per deg, and they are being used for the
Extended Baryon Oscillation Survey (eBOSS) and DESI surveys. The analysis of
these realizations shows that the correlation of follows the prediction
within the accuracy of eBOSS survey. The most time-consuming part of the
production occurs before application of the FGPA, and the existing pre-FGPA
forests can be used to easily produce new mock sets with modified
redshift-dependent bias parameters or observational conditions.Comment: to be submitted ot JCA
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
Broad absorption line quasars in the Dark Energy Spectroscopic Instrument Early Data Release
Broad absorption line (BAL) quasars are characterized by gas clouds that absorb flux at the wavelength of common quasar spectral features, although blueshifted by velocities that can exceed 0.1c. BAL features are interesting as signatures of significant feedback, yet they can also compromise cosmological studies with quasars by distorting the shape of the most prominent quasar emission lines, impacting redshift accuracy and measurements of the matter density distribution traced by the Lyman α forest. We present a catalogue of BAL quasars discovered in the Dark Energy Spectroscopic Instrument (DESI) survey Early Data Release, which were observed as part of DESI Survey Validation, as well as the first two months of the main survey. We describe our method to automatically identify BAL quasars in DESI data, the quantities we measure for each BAL, and investigate the completeness and purity of this method with mock DESI observations. We mask the wavelengths of the BAL features and re-evaluate each BAL quasar redshift, finding new redshifts which are 243 km sâ1 smaller on average for the BAL quasar sample. These new, more accurate redshifts are important to obtain the best measurements of quasar clustering, especially at small scales. Finally, we present some spectra of rarer classes of BALs that illustrate the potential of DESI data to identify such populations for further study
Target Selection and Validation of DESI Quasars
The Dark Energy Spectroscopic Instrument (DESI) survey will measure large-scale structures using quasars as direct tracers of dark matter in the redshift range 0.9 2.1. We present several methods to select candidate quasars for DESI, using input photometric imaging in three optical bands (g, r, z) from the DESI Legacy Imaging Surveys and two infrared bands (W1, W2) from the Wide-field Infrared Survey Explorer. These methods were extensively tested during the Survey Validation of DESI. In this paper, we report on the results obtained with the different methods and present the selection we optimized for the DESI main survey. The final quasar target selection is based on a random forest algorithm and selects quasars in the magnitude range of 16.5 2.1), exceeding the project requirements by 20%. The redshift distribution of the selected quasars is in excellent agreement with quasar luminosity function predictions
Optimal 1D Lyâα forest power spectrum estimation â III. DESI early data
The 1D power spectrum P1D of the Lyâα forest provides important information about cosmological and astrophysical parameters, including constraints on warm dark matter models, the sum of the masses of the three neutrino species, and the thermal state of the intergalactic medium. We present the first measurement of P1D with the quadratic maximum likelihood estimator (QMLE) from the Dark Energy Spectroscopic Instrument (DESI) survey early data sample. This early sample of 54 600 quasars is already comparable in size to the largest previous studies, and we conduct a thorough investigation of numerous instrumental and analysis systematic errors to evaluate their impact on DESI data with QMLE. We demonstrate the excellent performance of the spectroscopic pipeline noise estimation and the impressive accuracy of the spectrograph resolution matrix with 2D image simulations of raw DESI images that we processed with the DESI spectroscopic pipeline. We also study metal line contamination and noise calibration systematics with quasar spectra on the red side of the Lyâα emission line. In a companion paper, we present a similar analysis based on the Fast Fourier Transform estimate of the power spectrum. We conclude with a comparison of these two approaches and discuss the key sources of systematic error that we need to address with the upcoming DESI Year 1 analysis
The Dark Energy Spectroscopic Instrument: one-dimensional power spectrum from first Ly α forest samples with Fast Fourier Transform
We present the one-dimensional Ly α forest power spectrum measurement using the first data provided by the Dark Energy Spectroscopic Instrument (DESI). The data sample comprises 26 330 quasar spectra, at redshift z > 2.1, contained in the DESI Early Data Release and the first 2 months of the main survey. We employ a Fast Fourier Transform (FFT) estimator and compare the resulting power spectrum to an alternative likelihood-based method in a companion paper. We investigate methodological and instrumental contaminants associated with the new DESI instrument, applying techniques similar to previous Sloan Digital Sky Survey (SDSS) measurements. We use synthetic data based on lognormal approximation to validate and correct our measurement. We compare our resulting power spectrum with previous SDSS and high-resolution measurements. With relatively small number statistics, we successfully perform the FFT measurement, which is already competitive in terms of the scale range. At the end of the DESI survey, we expect a five times larger Ly α forest sample than SDSS, providing an unprecedented precise one-dimensional power spectrum measurement
3D Correlations in the Lyman- Forest from Early DESI Data
We present the first measurements of Lyman- (Ly) forest
correlations using early data from the Dark Energy Spectroscopic Instrument
(DESI). We measure the auto-correlation of Ly absorption using 88,509
quasars at , and its cross-correlation with quasars using a further
147,899 tracer quasars at . Then, we fit these correlations using
a 13-parameter model based on linear perturbation theory and find that it
provides a good description of the data across a broad range of scales. We
detect the BAO peak with a signal-to-noise ratio of , and show that
our measurements of the auto- and cross-correlations are fully-consistent with
previous measurements by the Extended Baryon Oscillation Spectroscopic Survey
(eBOSS). Even though we only use here a small fraction of the final DESI
dataset, our uncertainties are only a factor of 1.7 larger than those from the
final eBOSS measurement. We validate the existing analysis methods of
Ly correlations in preparation for making a robust measurement of the
BAO scale with the first year of DESI data
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
Validation of the DESI 2024 Ly forest BAO analysis using synthetic datasets
The first year of data from the Dark Energy Spectroscopic Instrument (DESI)
contains the largest set of Lyman- (Ly) forest spectra ever
observed. This data, collected in the DESI Data Release 1 (DR1) sample, has
been used to measure the Baryon Acoustic Oscillation (BAO) feature at redshift
. In this work, we use a set of 150 synthetic realizations of DESI DR1
to validate the DESI 2024 Ly forest BAO measurement. The synthetic data
sets are based on Gaussian random fields using the log-normal approximation. We
produce realistic synthetic DESI spectra that include all major contaminants
affecting the Ly forest. The synthetic data sets span a redshift range
, and are analysed using the same framework and pipeline used for
the DESI 2024 Ly forest BAO measurement. To measure BAO, we use both
the Ly auto-correlation and its cross-correlation with quasar
positions. We use the mean of correlation functions from the set of DESI DR1
realizations to show that our model is able to recover unbiased measurements of
the BAO position. We also fit each mock individually and study the population
of BAO fits in order to validate BAO uncertainties and test our method for
estimating the covariance matrix of the Ly forest correlation
functions. Finally, we discuss the implications of our results and identify the
needs for the next generation of Ly forest synthetic data sets, with
the top priority being to simulate the effect of BAO broadening due to
non-linear evolution.Comment: Supporting publication of DESI 2024 IV: Baryon Acoustic Oscillations
from the Lyman Alpha Fores
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