Davide Bianchi

We present the largest statistical spectroscopic sample of radio quasars at 4.7 ≤ z ≤ 6.7 to date. These have been selected from a dedicated Dark Energy Spectroscopic Instrument target program and provide a robust sample to study the influence of radio jets on early quasar evolution. Using low-frequency radio data from the LOFAR Two Metre Sky Survey Data Release 3 at 144 MHz, we identify 83 high- z radio quasars, 26 of which are classified as radio-loud (5%–15% of 646 confirmed high- z quasars, defined as R = F _5GHz / F _4400Å > 10). With radio luminosities of ∼10 ²⁵⁻²⁸ W Hz ⁻¹ , the radio emission in all 83 quasars is expected to be jet dominated rather than powered by star formation. Analysis of their rest-frame ultraviolet spectra reveals broadly similar composite spectral properties and C iv velocity shifts compared to radio-nondetected quasars. The radio-detected and radio-loud quasars exhibit slightly smaller Ly α and C iv equivalent widths on average; however, we find no significant evidence (∼1.3 σ ) for a higher fraction of weak-line quasars (∼10% ± 4% versus ∼4% ± 1% for radio-nondetected quasars). We further find that the broad absorption line (BAL) quasars are predominantly radio-quiet, and no correlation is observed between absorption index and radio-loudness, suggesting that radio emission is not produced by the BAL outflows themselves and is a physically distinct phenomenon. Our results demonstrate that powerful radio jets were already well established by z ∼ 5.

We present a multi-redshift Baryon Acoustic Oscillations (BAO) analysis of the DESI Data Release 2 (DR2) Lyman- α (Ly α ) forest, splitting the forest auto-correlation and its cross-correlation with quasars into three redshift bins. We obtain BAO measurements at effective redshifts z _eff = 2.13, 2.40, and 2.81 with ∼ 2.0–2.5% precision per bin in the radial and transverse directions, corresponding to ∼ 1.1–1.2% precision for the isotropic BAO measurement. Using the same data products and modeling framework as the DESI DR2 Ly α BAO analysis, we validate the pipeline on 400 synthetic datasets and find unbiased BAO recovery with well-calibrated uncertainties. The measurements show an increase in the isotropic dilation parameter D _V / r _d from 30.26 ± 0.39 to 32.22 ± 0.47 and in the Alcock-Paczyński parameter D _M / D _H from 3.96 ± 0.15 to 5.63 ^+0.22 _-0.24 . The Hubble distance D _H / r _d decreases from 9.40 ± 0.20 to 7.22 ± 0.17, providing a direct measurement of the expansion history consistent with ΛCDM and the expected matter-dominated scaling, with H ( z ) ∝ (1 + z ) ⁿ giving n = 1.34 ± 0.16. The redshift split also provides a self-consistent measurement of clustering evolution: the Ly α forest bias evolves as (1 + z ) ^γ with γ _α = 3.05 ± 0.16, the RSD parameter has a redshift evolution described by γ _β = -0.97 ± 0.26, and the quasar bias evolves with γ _Q = 1.56 ± 0.23, consistent with independent quasar clustering measurements. Combining these three-bin BAO measurements with DESI DR2 galaxy and quasar BAO measurements yields cosmological constraints consistent with the single-bin Ly α BAO analysis in flat ΛCDM and w ₀ w _a CDM and improves curvature constraints by ∼ 12% in ΛCDM+Ω _K .

We present and validate a blinding method for the Lyman- α (Ly α ) forest analysis based on a modification of the Alcock-Paczynski test. In order to hide the background expansion history, the method employs a geometrical shift of each quasar (QSO) forest in wavelength space, once the quasar continuum has been fitted and the fluctuation field is extracted. The redshift positions for the QSO sample are also changed in a consistent manner. We show that the method remains effective when applied to real data, where contamination from metals and Lyman- β is intrinsically mixed with the Lyman- α forest. This limitation is primarily visible in the 1D correlation function, where other blinding strategies can mitigate the effect. To assess its effectiveness, the prescription is tested against a series of datasets of increasing complexity: from idealized low-noise mocks, to realistic DESI year one synthetic datasets, and finally to data from DESI first data release (DR1), using both the auto (Ly α ×Ly α ) and cross (Ly α × QSO) correlations. We find that the method robustly shifts the BAO peak position from the 3D correlation functions to the expected value for cosmology changes of around 5% in the matter content, without altering the shape of the posteriors in the model parameters. In conclusion, this catalog-level blinding strategy is a viable method for cosmological inference with the Lyman- α forest, particularly if a cross-analysis with other tracers using the same blinding strategy is pursued.

Binary stars are common in the Galaxy, and understanding how stellar binarity influences the formation and evolution of planetary systems is an active area of research. In this study, we use metal-enriched white dwarfs in wide binaries as tracers of long-lived planetary systems. With Data Release 1 from the Dark Energy Spectroscopic Instrument (DESI), we find that the fraction of cool metal-enriched white dwarfs in wide binaries is 9.8% ± 2.1%, significantly lower (4.7 σ ) than the 20.5% ± 0.9% in a control sample of single systems. Furthermore, we identify a tentative dependence of metal enrichment on projected separation and white dwarf effective temperature, where enrichment fraction decreases at smaller separations and lower temperatures. These findings indicate that, compared to single stars, binary systems either start with smaller initial planetary reservoirs due to suppressed planetesimal formation or undergo more rapid depletion of planetary material during the initial part of the white dwarf stage.

We present an automatic method based on machine learning convolutional neural network (CNN) architecture to detect Ly α emitters (LAEs) hidden in the Data Release 1 spectroscopic data set of the Dark Energy Spectroscopic Instrument (DESI). Those LAEs mostly have incorrect redshift estimations because the current DESI pipeline is not designed to detect and measure the redshifts of galaxies at z > 2. To uncover those sources, we first visually inspect 4134 DESI spectra and construct a sample, consisting of both LAEs and non-LAEs, for training and testing the CNN-based model to (1) detect LAEs in DESI spectra and (2) determine their Ly α redshifts. The final model yields 95.2% purity and 95.9% completeness for detecting LAEs. We apply this model to approximately 2 × 10 ⁶ spectra of sources targeted as emission-line galaxies and detect 19,685 LAEs from z ∼ 2 to 3.5 within 12 minutes with a single GPU, illustrating the high efficiency of this model for identifying LAEs. The detected LAEs are mostly at the bright end of the luminosity function with Ly α luminosity L _{Ly α} ≳ 10 ⁴³ erg s ⁻¹ . The high signal-to-noise composite spectrum of the detected LAEs further shows various spectral features, including P-Cygni profiles of metal lines and Mg ii emission lines, possible indicators of Lyman continuum escape fraction, revealing the rich astrophysical information in this LAE sample. Finally, this sample can be used to train and validate the pipelines for redshift determination of LAEs for the preparation of the DESI-II survey.

Context. The Tully-Fisher (TF) relation is an empirical tool for estimating distances to spiral galaxies. Dark Energy Spectroscopic Instrument (DESI) Peculiar Velocity (PV) Survey uses tractor photometric position angles (PAs) to place its fibers along the semi-major axis of the galaxy and infer maximum rotational velocities for ≈53 000 spiral galaxies. Systematic errors may arise if the photometric PA differs from the kinematic PA measured from velocity fields. Aims. We aim to quantify the systematic uncertainty in DESI-TF distance estimates introduced by the use of photometric PAs and assess the impact of photometric–kinematic PA misalignment on derived distances. Methods. We analyzed a curated sample of 215 nearby galaxies from the PISCO and AMUSING surveys. For consistency with the DESI-PV survey, we estimated the maximum rotational velocity at 0.4 R ₂₆ . We estimated the TF distance using three different sets of parameters. We obtained kinematic parameters using PaFit from Cappellari Software . For the global photometric parameters, we relied on data available from the Siena Galaxy Atlas SGA-2020 drawn from tractor photometry and we also used HostPhot photometry as an additional photometric approach. Results. Approximately 28% of our sample exhibit a photometric-kinematic PA misalignment >10°. The median bias in distance is ≈ + 1.98Mpc with a skewed residual distribution dominated by outliers. The overall standard deviation ( σ ) for the estimated distance is 19.35 Mpc. The misaligned galaxies show twice the dispersion compared to aligned galaxies. The mean percentage error (MPE) is 2.8% ± 1.96 (SE) throughout the sample. Conclusions. Kinematic constraints from integral field spectroscopy (IFS) reveal a low positive median bias «1.98 Mpc but high galaxy-to-galaxy scatter in DESI-TF distances, particularly for misaligned galaxies. Because DESI-TF targets lack kinematic PA measurements, we recommend a global fractional uncertainty of ≈3.5% for DESI-TF distances. When kinematic information is available, aligned galaxies with photometric-kinematic PA offsets <10° are consistent with a ≈1% uncertainty, while strongly misaligned or kinematically incomplete systems warrant an upper threshold of ≈10%.

We present constraints on cosmic structure growth from the analysis of galaxy clustering and galaxy–galaxy lensing with galaxies from the Dark Energy Spectroscopic Instrument (DESI) Data Release 1. Our analysis includes four samples drawn from the Bright Galaxy Survey (BGS) and the luminous red galaxy (LRG) target classes. Projected galaxy clustering measurements from DESI are supplemented with lensing data from the Dark Energy Survey (DES), the Kilo-Degree Survey (KiDS), and the Hyper Suprime-Cam (HSC) survey around the same targets. Our method relies on a simulation-based modeling framework using the AbacusSummit simulations and a complex halo occupation distribution model that incorporates assembly bias. We analyze scales down to 0.4 h ⁻¹ Mpc for clustering and 2.5 h ⁻¹ Mpc for lensing, leading to stringent constraints on S 8 = σ 8 Ω m / 0.3 and Ω _m when fixing other cosmological parameters to those preferred by the cosmic microwave background. We find S 8 = 0.79 7 − 0.024 + 0.023 and Ω _m = 0.292 ± 0.011 when using lensing measurements from DES and KiDS. Similarly, for HSC, we find S 8 = 0.79 1 − 0.021 + 0.020 and Ω _m = 0.300 ± 0.009 when assuming the best-fit photometric redshift offset suggested by the HSC collaboration. Overall, our results are in good agreement with other results in the literature while continuing to highlight the constraining power of nonlinear scales.

DESI observational data for the GD-1 and C-19 streams are compared to stream simulations in an evolving multihalo potential of a Milky Way–like galaxy based on a cosmological Milky Way–like simulation. The number of subhalos decreases with time, and the subhalo-stream encounter velocities rise as the galaxy and its disk build up their mass. The streams develop from star clusters inserted at ≃1 Gyr after the Big Bang and evolved for 13 Gyr to their current orbital positions. The measured velocity widths of the streams are compared to the matched simulations. Streams in a cold dark matter (CDM) subhalo population provide the best match to the velocity width, on average, with considerable scatter. Streams younger than ≃12 Gyr in CDM subhalos are insufficiently hot. Streams in the same potentials but with populations of warm dark matter (WDM) 5.5 keV subhalos are not, on average, heated to the observed velocity widths, although some of the realizations do reach the observed levels. The stream density power spectrum measured along the length of the DESI GD-1 sample agrees with the CDM stream simulations, with 1.3–2.3 times more power than WDM 7 and 5.5 keV simulations. The simulations show that modeling specific streams from the time of the formation of their progenitor clusters is both feasible and necessary to reproduce their stream-averaged kinematic properties.