We present the samples of galaxies and quasars used for DESI 2024 cosmological analyses, drawn from the DESI Data Release 1 (DR1). We describe the construction of large-scale structure (LSS) catalogs from these samples, which include matched sets of synthetic reference `randoms' and weights that account for variations in the observed density of the samples due to experimental design and varying instrument performance. We detail how we correct for variations in observational completeness, the input `target' densities due to imaging systematics, and the ability to confidently measure redshifts from DESI spectra. We then summarize how remaining uncertainties in the corrections can be translated to systematic uncertainties for particular analyses. We describe the weights added to maximize the signal-to-noise of DESI DR1 2-point clustering measurements. We detail measurement pipelines applied to the LSS catalogs that obtain 2-point clustering measurements in configuration and Fourier space. The resulting 2-point measurements depend on window functions and normalization constraints particular to each sample, and we present the corrections required to match models to the data. We compare the configuration- and Fourier-space 2-point clustering of the data samples to that recovered from simulations of DESI DR1 and find they are, generally, in statistical agreement to within 2\% in the inferred real-space over-density field. The LSS catalogs, 2-point measurements, and their covariance matrices will be released publicly with DESI DR1.

We perform a frequentist analysis using the standard profile likelihood method for clustering measurements from Data Release 1 of the Dark Energy Spectroscopic Instrument (DESI). While Bayesian inferences for Effective Field Theory models of galaxy clustering can be highly sensitive to the choice of priors for extended cosmological models, frequentist inferences are not susceptible to such effects. We compare Bayesian and frequentist constraints for the parameter set $\{\sigma_8, H_0, \Omega_{\rm{m}}, w_0, w_a\}$ when fitting to the full-shape of the power spectrum multipoles, the post-reconstruction Baryon Acoustic Oscillation (BAO) measurements, as well as external datasets from the CMB and type Ia supernovae measurements. Bayesian prior effects are very significant for the $w_0w_a$CDM model; while the $1 \sigma$ frequentist confidence intervals encompass the maximum a posteriori (MAP), the Bayesian credible intervals almost always exclude the maximum likelihood estimate (MLE) and the MAP - indicating strong prior volume projection effects - unless supernovae data are included. We observe limited prior effects for the $\Lambda$CDM model, due to the reduced number of parameters. When DESI full-shape and BAO data are jointly fit, we obtain the following $1\sigma$ frequentist confidence intervals for $\Lambda$CDM ($w_0w_a$CDM): $\sigma_8 = 0.867^{+0.048}_{-0.041} , \ H_0 = 68.91^{+0.80}_{-0.79} \ \rm{km \ s^{-1}Mpc^{-1}} , \ \Omega_{\rm{m}} = 0.3038\pm0.0110$ ($\sigma_8 = 0.793^{+0.069}_{-0.048} , \ H_0 = 64.9^{+4.8}_{-2.8} \ \rm{km \ s^{-1}Mpc^{-1}} , \ \Omega_{\rm{m}} = 0.369^{+0.029}_{-0.059}$ , $w_0 = -0.24^{+0.17}_{-0.64}$ , $w_a = -2.5^{+1.9}_{}$), corresponding to 0.7$\sigma$, 0.3$\sigma$, 0.7$\sigma$ (1.9$\sigma$, 3.4$\sigma$, 5.6$\sigma$, 5.5$\sigma$, 5.6$\sigma$) shifts between the MLE relative to the Bayesian posterior mean for $\Lambda$CDM ($w_0w_a$CDM) respectively.

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-alpha forest. We present a catalog 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/s 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.

We describe the photometric data set assembled from the full six years of observations by the Dark Energy Survey (DES) in support of static-sky cosmology analyses. DES Y6 Gold is a curated data set derived from DES Data Release 2 (DR2) that incorporates improved measurement, photometric calibration, object classification and value added information. Y6 Gold comprises nearly $5000~{\rm deg}^2$ of $grizY$ imaging in the south Galactic cap and includes 669 million objects with a depth of $i_{AB} \sim 23.4$ mag at S/N $\sim 10$ for extended objects and a top-of-the-atmosphere photometric uniformity < 2~{\rm mmag}. Y6 Gold augments DES DR2 with simultaneous fits to multi-epoch photometry for more robust galaxy shapes, colors, and photometric redshift estimates. Y6 Gold features improved morphological star-galaxy classification with efficiency $98.6\%$ and contamination $0.8\%$ for galaxies with 17.5 < i_{AB} < 22.5. Additionally, it includes per-object quality information, and accompanying maps of the footprint coverage, masked regions, imaging depth, survey conditions, and astrophysical foregrounds that are used for cosmology analyses. After quality selections, benchmark samples contain 448 million galaxies and 120 million stars. This paper will be complemented by online data access and documentation.

The Dark Energy Spectroscopic Instrument (DESI) data release 2 (DR2) galaxy and quasar clustering data represents a significant expansion of data from DR1, providing improved statistical precision in BAO constraints across multiple tracers, including bright galaxies (BGS), luminous red galaxies (LRGs), emission line galaxies (ELGs), and quasars (QSOs). In this paper, we validate the BAO analysis of DR2. We present the results of robustness tests on the blinded DR2 data and, after unblinding, consistency checks on the unblinded DR2 data. All results are compared to those obtained from a suite of mock catalogs that replicate the selection and clustering properties of the DR2 sample. We confirm the consistency of DR2 BAO measurements with DR1 while achieving a reduction in statistical uncertainties due to the increased survey volume and completeness. We assess the impact of analysis choices, including different data vectors (correlation function vs. power spectrum), modeling approaches and systematics treatments, and an assumption of the Gaussian likelihood, finding that our BAO constraints are stable across these variations and assumptions with a few minor refinements to the baseline setup of the DR1 BAO analysis. We summarize a series of pre-unblinding tests that confirmed the readiness of our analysis pipeline, the final systematic errors, and the DR2 BAO analysis baseline. The successful completion of these tests led to the unblinding of the DR2 BAO measurements, ultimately leading to the DESI DR2 cosmological analysis, with their implications for the expansion history of the Universe and the nature of dark energy presented in the DESI key paper.

We present Galaxy-Galaxy Lensing measurements obtained by cross-correlating spectroscopically observed galaxies from the first data release of the Dark Energy Spectroscopic Instrument (DESI) with source galaxies from the Hyper Suprime-Cam Subaru Strategic Survey, the Kilo-Degree Survey, the Sloan Digital Sky Survey, and the Dark Energy Survey. Specifically, we measure the excess surface mass density $\Delta\Sigma$ and tangential shear $\gamma_\mathrm{t}$ for the Bright Galaxy Sample and Luminous Red Galaxies measured within the first year of observations with DESI. To ensure robustness, we test the measurements for systematic biases, finding no significant trends related to the properties of the \acrshort{desi} lens galaxies. We identify a significant trend with the average redshift of source galaxies, however, this trend vanishes once we apply shifts to the Hyper Suprime-Cam Subaru Strategic Survey redshift distributions that are also favored by their fiducial cosmology analysis. Additionally, we compare the observed scatter in the measurements with the theoretical covariance and find excess scatter, driven primarily by small-scale measurements of $r\leq 1 \, \mathrm{Mpc}/h$; measurements on larger scales are consistent at the $2\,\sigma$ level. We further present the projected clustering measurements $w_p$ of the galaxy samples in the the first data release of DESI. These measurements, which will be made publicly available, serve as a foundation for forthcoming cosmological analyses.

We present three separate void catalogs created using a volume-limited sample of the DESI Year 1 Bright Galaxy Survey. We use the algorithms VoidFinder and V2 to construct void catalogs out to a redshift of z=0.24. We obtain 1,461 interior voids with VoidFinder, 420 with V2 using REVOLVER pruning, and 295 with V2 using VIDE pruning. Comparing our catalog with an overlapping SDSS void catalog, we find generally consistent void properties but significant differences in the void volume overlap, which we attribute to differences in the galaxy selection and survey masks. These catalogs are suitable for studying the variation in galaxy properties with cosmic environment and for cosmological studies.

The Dark Energy Spectroscopic Instrument (DESI) survey uses an automatic spectral classification pipeline to classify spectra. QuasarNET is a convolutional neural network used as part of this pipeline originally trained using data from the Baryon Oscillation Spectroscopic Survey (BOSS). In this paper we implement an active learning algorithm to optimally select spectra to use for training a new version of the QuasarNET weights file using only DESI data, specifically to improve classification accuracy. This active learning algorithm includes a novel outlier rejection step using a Self-Organizing Map to ensure we label spectra representative of the larger quasar sample observed in DESI. We perform two iterations of the active learning pipeline, assembling a final dataset of 5600 labeled spectra, a small subset of the approx 1.3 million quasar targets in DESI's Data Release 1. When splitting the spectra into training and validation subsets we meet or exceed the previously trained weights file in completeness and purity calculated on the validation dataset with less than one tenth of the amount of training data. The new weights also more consistently classify objects in the same way when used on unlabeled data compared to the old weights file. In the process of improving QuasarNET's classification accuracy we discovered a systemic error in QuasarNET's redshift estimation and used our findings to improve our understanding of QuasarNET's redshifts.

We implement Crossing Statistics to reconstruct in a model-agnostic manner the expansion history of the universe and properties of dark energy, using DESI Data Release 1 (DR1) BAO data in combination with one of three different supernova compilations (PantheonPlus, Union3, and DES-SN5YR) and Planck CMB observations. Our results hint towards an evolving and emergent dark energy behaviour, with negligible presence of dark energy at $z\gtrsim 1$, at varying significance depending on the data sets combined. In all these reconstructions, the cosmological constant lies outside the $95\%$ confidence intervals for some redshift ranges. This dark energy behaviour, reconstructed using Crossing Statistics, is in agreement with results from the conventional $w_0$--$w_a$ dark energy equation of state parametrization reported in the DESI Key cosmology paper. Our results add an extensive class of model-agnostic reconstructions with acceptable fits to the data, including models where cosmic acceleration slows down at low redshifts. We also report constraints on $H_0r_d$ from our model-agnostic analysis, independent of the pre-recombination physics.

We explore transformations of the Friedman-Lemaître-Robertson-Walker (FLRW) metric and cosmological parameters that align with observational data, aiming to gain insights into potential extensions of standard cosmological models. We modify the FLRW metric by introducing a scaling factor, $e^{2\Theta(a)}$ (the cosmological scaling function, CSF), which alters the standard relationship between cosmological redshift and the cosmic scale factor without affecting angular measurements or Cosmic Microwave Background (CMB) anisotropies. Using data from DESI Year 1, Pantheon+ supernovae, and the Planck CMB temperature power spectrum, we constrain both the CSF and cosmological parameters through a Markov Chain Monte Carlo approach. Our results indicate that the CSF model fits observational data with a lower Hubble constant (although it is compatible with the value given by Planck 2018 within 1$\sigma$) and is predominantly dark-matter-dominated. Additionally, the CSF model produces temperature and lensing power spectra similar to those predicted by the standard model, though with lower values in the CSF model at large scales. We have also checked that when fitting a CSF model without dark energy to the data, we obtain a more negative conformal function. This suggests that the CSF model may offer hints about missing elements and opens up a new avenue for exploring physical interpretations of cosmic acceleration.

In this paper, we investigate the possibility of selecting high-redshift Lyman-Break Galaxies (LBG) using current and future broadband wide photometric surveys, such as UNIONS or the Vera C. Rubin LSST. This work is conducted in the context of DESI-II, the next phase of DESI, which will start around 2029. We use deep imaging data from HSC and CLAUDS on the COSMOS and XMM-LSS fields. To predict the selection performance of LBGs with image quality similar to UNIONS, we degrade the $u, g, r, i$ and $z$ bands to UNIONS depth. The Random Forest algorithm is trained with the $u,g,r,i$ and $z$ bands to classify LBGs in the 2.5 < z < 3.5 range. We find that fixing a target density budget of $1,100$ deg$^{-2}$, the Random Forest approach gives a density of z>2 targets of $873$ deg$^{-2}$, and a density of $493$ deg$^{-2}$ of confirmed LBGs after spectroscopic confirmation with DESI. This UNIONS-like selection was tested in a dedicated spectroscopic observation campaign of 1,000 targets with DESI on the COSMOS field, providing a safe spectroscopic sample with a mean redshift of 3. This sample is used to derive forecasts for DESI-II, assuming a sky coverage of 5,000 deg$^2$. We predict uncertainties on Alcock-Paczynski parameters $\alpha_\perp$ and $\alpha_{\parallel}$ to be 0.7$\%$ and 1$\%$ for $2.6

We study the [OII] profiles of emission line galaxies (ELGs) from the Early Data Release of the Dark Energy Spectroscopic Instrument (DESI). To this end, we decompose and classify the shape of [OII] profiles with the first two eigenspectra derived from Principal Component Analysis. Our results show that DESI ELGs have diverse line profiles which can be categorized into three main types: (1) narrow lines with a median width of ~50 km/s, (2) broad lines with a median width of ~80 km/s, and (3) two-redshift systems with a median velocity separation of ~150 km/s, i.e., double-peak galaxies. To investigate the connections between the line profiles and galaxy properties, we utilize the information from the COSMOS dataset and compare the properties of ELGs, including star-formation rate (SFR) and galaxy morphology, with the average properties of reference star-forming galaxies with similar stellar mass, sizes, and redshifts. Our findings show that on average, DESI ELGs have higher SFR and more asymmetrical/disturbed morphology than the reference galaxies. Moreover, we uncover a relationship between the line profiles, the excess SFR and the excess asymmetry parameter, showing that DESI ELGs with broader [OII] line profiles have more disturbed morphology and higher SFR than the reference star-forming galaxies. Finally, we discuss possible physical mechanisms giving rise to the observed relationship and the implications of our findings on the galaxy clustering measurements, including the halo occupation distribution modeling of DESI ELGs and the observed excess velocity dispersion of the satellite ELGs.

Low metallicity stellar populations are very abundant in the Universe, either as the remnants of the past history of the Milky Way or similar spiral galaxies, or the young low metallicity stellar populations that are being observed in the local dwarf galaxies or in the high-z objects with low metal content recently found with JWST. Our goal is to develop new high-spectral-resolution models tailored for low-metallicity environments and apply them to analyse stellar population data, particularly in cases where a significant portion of the stellar content exhibits low metallicity. Methods. We used the state-of-the-art stellar population synthesis code HR-pyPopStar with available stellar libraries to create a new set of models focused on low metallicity stellar populations. We have compared the new spectral energy distributions with the previous models of HR-pyPopStar for solar metallicity. Once we verified that the spectra, except for the oldest ages that show some differences in the molecular bands of the TiO and G band, are similar, we reanalysed the high resolution data from the globular cluster M 15 by finding a better estimate of its age and metallicity. Finally, we analysed a subsample of mostly star-forming dwarf galaxies from the MaNGA survey we found similar stellar mass-mean stellar metallicity weighted by light to other studies that studied star forming dwarf galaxies and slightly higher mean stellar metallicity than the other works that analysed all types of dwarf galaxies at the same time, but are within error bars.

LiquidO is an innovative radiation detector concept. The core idea is to exploit stochastic light confinement in a highly scattering medium to self-segment the detector volume. In this paper, we demonstrate event-by-event muon tracking in a LiquidO opaque scintillator detector prototype. The detector consists of a 30 mm cubic scintillator volume instrumented with 64 wavelength-shifting fibres arranged in an 8$\times$8 grid with a 3.2 mm pitch and read out by silicon photomultipliers. A wax-based opaque scintillator with a scattering length of approximately 0.5 mm is used. The tracking performance of this LiquidO detector is characterised with cosmic-ray muons and the position resolution is demonstrated to be 450 $\mu$m per row of fibres. These results highlight the potential of LiquidO opaque scintillator detectors to achieve fine spatial resolution, enabling precise particle tracking and imaging.

This paper provides a comprehensive overview of how fitting of Baryon Acoustic Oscillations (BAO) is carried out within the upcoming Dark Energy Spectroscopic Instrument's (DESI) 2024 results using its DR1 dataset, and the associated systematic error budget from theory and modelling of the BAO. We derive new results showing how non-linearities in the clustering of galaxies can cause potential biases in measurements of the isotropic ($\alpha_{\mathrm{iso}}$) and anisotropic ($\alpha_{\mathrm{ap}}$) BAO distance scales, and how these can be effectively removed with an appropriate choice of reconstruction algorithm. We then demonstrate how theory leads to a clear choice for how to model the BAO and develop, implement and validate a new model for the remaining smooth-broadband (i.e., without BAO) component of the galaxy clustering. Finally, we explore the impact of all remaining modelling choices on the BAO constraints from DESI using a suite of high-precision simulations, arriving at a set of best-practices for DESI BAO fits, and an associated theory and modelling systematic error. Overall, our results demonstrate the remarkable robustness of the BAO to all our modelling choices and motivate a combined theory and modelling systematic error contribution to the post-reconstruction DESI BAO measurements of no more than $0.1\%$ ($0.2\%$) for its isotropic (anisotropic) distance measurements. We expect the theory and best-practices laid out to here to be applicable to other BAO experiments in the era of DESI and beyond.

We report cosmological results from the Dark Energy Spectroscopic Instrument (DESI) measurements of baryon acoustic oscillations (BAO) when combined with recent data from the Atacama Cosmology Telescope (ACT). By jointly analyzing ACT and Planck data and applying conservative cuts to overlapping multipole ranges, we assess how different Planck+ACT dataset combinations affect consistency with DESI. While ACT alone exhibits a tension with DESI exceeding 3$\sigma$ within the $\Lambda$CDM model, this discrepancy is reduced when ACT is analyzed in combination with Planck. For our baseline DESI DR2 BAO+Planck PR4+ACT likelihood combination, the preference for evolving dark energy over a cosmological constant is about 3$\sigma$, increasing to over 4$\sigma$ with the inclusion of Type Ia supernova data. While the dark energy results remain quite consistent across various combinations of Planck and ACT likelihoods with those obtained by the DESI collaboration, the constraints on neutrino mass are more sensitive, ranging from \sum m_\nu < 0.061 eV in our baseline analysis, to \sum m_\nu < 0.077 eV (95\% confidence level) in the CMB likelihood combination chosen by ACT when imposing the physical prior \sum m_\nu>0 eV.

The 2020 update of the European Strategy for Particle Physics emphasised the importance of an intensified and well-coordinated programme of accelerator R&D, supporting the design and delivery of future particle accelerators in a timely, affordable and sustainable way. This report sets out a roadmap for European accelerator R&D for the next five to ten years, covering five topical areas identified in the Strategy update. The R&D objectives include: improvement of the performance and cost-performance of magnet and radio-frequency acceleration systems; investigations of the potential of laser / plasma acceleration and energy-recovery linac techniques; and development of new concepts for muon beams and muon colliders. The goal of the roadmap is to document the collective view of the field on the next steps for the R&D programme, and to provide the evidence base to support subsequent decisions on prioritisation, resourcing and implementation.