We present the Dark Energy Spectroscopic Instrument (DESI) Strong Lensing Secondary Target Program. This is a spectroscopic follow-up program for strong gravitational lens candidates found in the DESI Legacy Imaging Surveys footprint. Spectroscopic redshifts for the lenses and lensed source are crucial for lens modeling to obtain physical parameters. The spectroscopic catalog in this paper consists of 73 candidate systems from the DESI Early Data Release (EDR). We have confirmed 20 strong lensing systems and determined four to not be lenses. For the remaining systems, more spectroscopic data from ongoing and future observations will be presented in future publications. We discuss the implications of our results for lens searches with neural networks in existing and future imaging surveys as well as for lens modeling. This Strong Lensing Secondary Target Program is part of the DESI Strong Lens Foundry project, and this is Paper II of a series on this project.

We investigate the stellar shape and size-mass relationship of X-ray selected Active Galactic Nuclei (AGN) host galaxies using the high-angular resolution and deep sensitivity in the near-infrared of the COSMOS-Web JWST survey field. We present the rest-frame 1-$\mu m$ size, stellar mass, Sersic index, axis-ratio, Gini-$M_{20}$ parameters of 690 moderate luminosity AGNs between redshift 0-3 and with stellar mass $\log M_s\sim 10.75$. We find that AGN host galaxies have an effective radius of 1-5 kpc, which is between star-forming (SFG) and quiescent galaxies (QGs) of the same stellar mass. AGN hosts have similar size-mass trends as SFG and QGs, being smaller at higher redshift for the same stellar mass. The slope of the size-mass relationship of AGN host galaxies is steeper than that of star-forming galaxies. Their rest-frame 1$\mu m$ stellar morphology indicates a significant spheroidal component. We observed a low merger fraction (6%) in our sample as well as substructures similar to disks, bars, and spiral arms in the residual images, which are in tension with evolutionary pathways that require major mergers. However, it may also be due to the different timescales between mergers and AGN activity.

We study the left-right symmetric extension of the Standard Model (LRSM), featuring a TeV-scale right-handed (RH) gauge boson $W_R$ and three RH neutrinos. This setup naturally realises the type-II seesaw mechanism for active neutrino masses. We identify the conditions that yield sufficient entropy dilution to reconcile the keV sterile neutrino dark matter energy density with observations while inducing an early matter domination (EMD) phase. These constrain the lightest active neutrino mass to 8.59 x 10^{-10} eV < m_{\nu_1} < 5.06 x 10^{-9} eV$. The resulting frequency-dependent suppression of the stochastic gravitational wave (GW) background is set by the mass and lifetime of the heavier RH neutrinos. Computing the signal-to-noise ratio (SNR) for future detectors, we find that a blue-tilted primordial tensor spectrum can boost the GW signal to detectable levels (SNR > 10) in experiments such as LISA, BBO, and DECIGO.

We use machine learning to measure the intracluster light (ICL) fractions of 177 galaxy groups and clusters identified from Hyper Suprime-Cam Subaru Strategic Program imaging to explore how the ICL varies with the properties of its host cluster. We study the variation in ICL fraction with host cluster redshift, halo mass, and magnitude gap to investigate how the ICL develops over time, in various cluster environments, and with cluster relaxation. We find that there is a decreasing correlation with redshift (Spearman correlation $r_S=-0.604$, p-value $=9\times10^{-10}$), however this can be plausibly accounted for by including the effects of cosmological surface brightness dimming and the passive aging of stellar populations. There is a weak negative correlation with halo mass ($r_S=-0.330$, p-value $=8\times 10^{-5}$) where ICL fractions are higher in lower halo mass groups than higher halo mass clusters. We also find that there is a marginal positive correlation with magnitude gap ($r_S=0.226$, p-value = 0.01), indicating that relaxed clusters are more likely to host higher ICL fractions. These results are consistent with a scenario where the dominant formation mechanism of the ICL is galaxy-galaxy interactions such as tidal stripping, and demonstrates the capability of the method to easily construct large samples and study large-scale trends in the ICL fraction.

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.

Stage-IV surveys will enable unprecedented tests of gravity on cosmological scales. However, assuming General Relativity in the analysis of large-scale structure could introduce systematic biases if gravity deviates from GR at these scales. Modified gravity theories, such as the Hu-Sawicki formulation of $f(R)$ gravity, offer an alternative explanation for cosmic acceleration without invoking a cosmological constant, while remaining consistent with Solar System tests through screening mechanisms. In this work, we quantify the cosmological parameter biases that arise when using a combination of galaxy clustering and weak-lensing data-vectors, the so-called 3x2pt analysis, from an $f(R)$ galaxy mock under the incorrect assumption of GR, using for the first time high-fidelity full-sky galaxy mock catalogues. We employ a pair of twin simulations: one with GR and one with Hu--Sawicki $f(R)$ gravity with $|f_{R0}| = 10^{-5}$. The mocks are built using an HOD method to populate the dark matter haloes with galaxies, calibrated against SDSS observations at low redshift. Using conservative scale cuts to minimise modelling uncertainties, we perform 3x2pt analyses and infer cosmological parameters through nested sampling, validating our pipeline with the GR mock. Our results show that when analysing the $f(R)$ galaxy mock assuming GR, the recovered cosmological parameters are very significantly biased, even when considering conservative scale cuts: the Figure of Bias reaches $\sim12\sigma$ for both $\{\Omega_{\rm m}, \sigma_8\}$ and $S_8$. These biases persist even when marginalising over the galaxy bias and baryonic feedback, demonstrating that nuisance parameters cannot absorb the effects of modified gravity. We conclude that incorrectly assuming GR in a universe governed by $f(R)$ gravity leads to severe and detectable biases in cosmological inference for Stage-IV surveys.

Euclid is expected to establish new state-of-the-art constraints on extensions beyond the standard LCDM cosmological model by measuring the positions and shapes of billions of galaxies. Specifically, its goal is to shed light on the nature of dark matter and dark energy. Achieving this requires developing and validating advanced statistical tools and theoretical prediction software capable of testing extensions of the LCDM model. In this work, we describe how the Euclid likelihood pipeline, Cosmology Likelihood for Observables in Euclid (CLOE), has been extended to accommodate alternative cosmological models and to refine the theoretical modelling of Euclid primary probes. In particular, we detail modifications made to CLOE to incorporate the magnification bias term into the spectroscopic two-point correlation function of galaxy clustering. Additionally, we explain the adaptations made to CLOE's implementation of Euclid primary photometric probes to account for massive neutrinos and modified gravity extensions. Finally, we present the validation of these CLOE modifications through dedicated forecasts on synthetic Euclid-like data by sampling the full posterior distribution and comparing with the results of previous literature. In conclusion, we have identified in this work several functionalities with regards to beyond-LCDM modelling that could be further improved within CLOE, and outline potential research directions to enhance pipeline efficiency and flexibility through novel inference and machine learning techniques.

We present reconstructed convergence maps, \textit{mass maps}, from the Dark Energy Survey (DES) third year (Y3) weak gravitational lensing data set. The mass maps are weighted projections of the density field (primarily dark matter) in the foreground of the observed galaxies. We use four reconstruction methods, each is a \textit{maximum a posteriori} estimate with a different model for the prior probability of the map: Kaiser-Squires, null B-mode prior, Gaussian prior, and a sparsity prior. All methods are implemented on the celestial sphere to accommodate the large sky coverage of the DES Y3 data. We compare the methods using realistic $\Lambda$CDM simulations with mock data that are closely matched to the DES Y3 data. We quantify the performance of the methods at the map level and then apply the reconstruction methods to the DES Y3 data, performing tests for systematic error effects. The maps are compared with optical foreground cosmic-web structures and are used to evaluate the lensing signal from cosmic-void profiles. The recovered dark matter map covers the largest sky fraction of any galaxy weak lensing map to date.

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.

A key component of the Dark Energy Spectroscopic Instrument (DESI) survey validation (SV) is a detailed visual inspection (VI) of the optical spectroscopic data to quantify key survey metrics. In this paper we present results from VI of the quasar survey using deep coadded SV spectra. We show that the majority (~70%) of the main-survey targets are spectroscopically confirmed as quasars, with ~16% galaxies, ~6% stars, and ~8% low-quality spectra lacking reliable features. A non-negligible fraction of the quasars are misidentified by the standard spectroscopic pipeline but we show that the majority can be recovered using post-pipeline "afterburner" quasar-identification approaches. We combine these "afterburners" with our standard pipeline to create a modified pipeline to improve the overall quasar yield. At the depth of the main DESI survey both pipelines achieve a good-redshift purity (reliable redshifts measured within 3000 km/s) of ~99%; however, the modified pipeline recovers ~94% of the visually inspected quasars, as compared to ~86% from the standard pipeline. We demonstrate that both pipelines achieve an median redshift precision and accuracy of ~100 km/s and ~70 km/s, respectively. We constructed composite spectra to investigate why some quasars are missed by the standard spectroscopic pipeline and find that they are more host-galaxy dominated (i.e., distant analogs of "Seyfert galaxies") and/or dust reddened than the standard-pipeline quasars. We also show example spectra to demonstrate the overall diversity of the DESI quasar sample and provide strong-lensing candidates where two targets contribute to a single spectrum.

We present a new statistical analysis that combines helioseismology (sound speed, surface helium and convective radius) and solar neutrino observations (the $^8$B and $^7$Be fluxes) to place upper limits to the properties of non standard weakly interacting particles. Our analysis includes theoretical and observational errors, accounts for tensions between input parameters of solar models and can be easily extended to include other observational constraints. We present two applications to test the method: the well studied case of axions and axion-like particles and the more novel case of low mass hidden photons. For axions we obtain an upper limit at $3\sigma$ for the axion-photon coupling constant of g_{a\gamma}\,&lt;\,4.1 \cdot 10^{-10} \rm{GeV^{-1}}. For hidden photons we obtain the most restrictive upper limit available accross a wide range of masses for the product of the kinetic mixing and mass of \chi m &lt; 1.8 \cdot 10^{-12} \rm{eV} at $3\sigma$. Both cases improve the previous solar constraints based on the Standard Solar Models showing the power of using a global statistical approach.

In this work we use Lagrangian perturbation theory to analyze the harmonic space galaxy clustering signal of Bright Galaxy Survey (BGS) and Luminous Red Galaxies (LRGs) targeted by the Dark Energy Spectroscopic Instrument (DESI), combined with the galaxy--galaxy lensing signal measured around these galaxies using Dark Energy Survey Year 3 source galaxies. The BGS and LRG galaxies are extremely well characterized by DESI spectroscopy and, as a result, lens galaxy redshift uncertainty and photometric systematics contribute negligibly to the error budget of our ``$2\times2$-point'' analysis. On the modeling side, this work represents the first application of the \texttt{spinosaurus} code, implementing an effective field theory model for galaxy intrinsic alignments, and we additionally introduce a new scheme (\texttt{MAIAR}) for marginalizing over the large uncertainties in the redshift evolution of the intrinsic alignment signal. Furthermore, this is the first application of a hybrid effective field theory (HEFT) model for galaxy bias based on the $\texttt{Aemulus}\, \nu$ simulations. Our main result is a measurement of the amplitude of the lensing signal, $S_8=\sigma_8 \left(\Omega_m/0.3\right)^{0.5} = 0.850^{+0.042}_{-0.050}$, consistent with values of this parameter derived from the primary CMB. This constraint is artificially improved by a factor of $51\%$ if we assume a more standard, but restrictive parameterization for the redshift evolution and sample dependence of the intrinsic alignment signal, and $63\%$ if we additionally assume the nonlinear alignment model. We show that when fixing the cosmological model to the best-fit values from Planck PR4 there is $> 5 \sigma$ evidence for a deviation of the evolution of the intrinsic alignment signal from the functional form that is usually assumed in cosmic shear and galaxy--galaxy lensing studies.

In the third APOKASC catalog, we present data for the complete sample of 15,808 evolved stars with APOGEE spectroscopic parameters and Kepler asteroseismology. We used ten independent asteroseismic analysis techniques and anchor our system on fundamental radii derived from Gaia $L$ and spectroscopic $T_{\rm eff}$. We provide evolutionary state, asteroseismic surface gravity, mass, radius, age, and the spectroscopic and asteroseismic measurements used to derive them for 12,418 stars. This includes 10,036 exceptionally precise measurements, with median fractional uncertainties in \nmax, \dnu, mass, radius and age of 0.6\%, 0.6\%, 3.8\%, 1.8\%, and 11.1\% respectively. We provide more limited data for 1,624 additional stars which either have lower quality data or are outside of our primary calibration domain. Using lower red giant branch (RGB) stars, we find a median age for the chemical thick disk of $9.14 \pm 0.05 ({\rm ran}) \pm 0.9 ({\rm sys})$ Gyr with an age dispersion of 1.1 Gyr, consistent with our error model. We calibrate our red clump (RC) mass loss to derive an age consistent with the lower RGB and provide asymptotic GB and RGB ages for luminous stars. We also find a sharp upper age boundary in the chemical thin disk. We find that scaling relations are precise and accurate on the lower RGB and RC, but they become more model dependent for more luminous giants and break down at the tip of the RGB. We recommend the usage of multiple methods, calibration to a fundamental scale, and the usage of stellar models to interpret frequency spacings.

We present a new shear calibration method based on machine learning. The method estimates the individual shear responses of the objects from the combination of several measured properties on the images using supervised learning. The supervised learning uses the true individual shear responses obtained from copies of the image simulations with different shear values. On simulated GREAT3data, we obtain a residual bias after the calibration compatible with 0 and beyond Euclid requirements for a signal-to-noise ratio > 20 within ~15 CPU hours of training using only ~10^5 objects. This efficient machine-learning approach can use a smaller data set because the method avoids the contribution from shape noise. The low dimensionality of the input data also leads to simple neural network architectures. We compare it to the recently described method Metacalibration, which shows similar performances. The different methods and systematics suggest that the two methods are very good complementary methods. Our method can therefore be applied without much effort to any survey such as Euclid or the Vera C. Rubin Observatory, with fewer than a million images to simulate to learn the calibration function.

We study the constraint on $f(R)$ gravity that can be obtained by photometric primary probes of the Euclid mission. Our focus is the dependence of the constraint on the theoretical modelling of the nonlinear matter power spectrum. In the Hu-Sawicki $f(R)$ gravity model, we consider four different predictions for the ratio between the power spectrum in $f(R)$ and that in $\Lambda$CDM: a fitting formula, the halo model reaction approach, ReACT and two emulators based on dark matter only $N$-body simulations, FORGE and e-Mantis. These predictions are added to the MontePython implementation to predict the angular power spectra for weak lensing (WL), photometric galaxy clustering and their cross-correlation. By running Markov Chain Monte Carlo, we compare constraints on parameters and investigate the bias of the recovered $f(R)$ parameter if the data are created by a different model. For the pessimistic setting of WL, one dimensional bias for the $f(R)$ parameter, $\log_{10}|f_{R0}|$, is found to be $0.5 \sigma$ when FORGE is used to create the synthetic data with $\log_{10}|f_{R0}| =-5.301$ and fitted by e-Mantis. The impact of baryonic physics on WL is studied by using a baryonification emulator BCemu. For the optimistic setting, the $f(R)$ parameter and two main baryon parameters are well constrained despite the degeneracies among these parameters. However, the difference in the nonlinear dark matter prediction can be compensated by the adjustment of baryon parameters, and the one-dimensional marginalised constraint on $\log_{10}|f_{R0}|$ is biased. This bias can be avoided in the pessimistic setting at the expense of weaker constraints. For the pessimistic setting, using the $\Lambda$CDM synthetic data for WL, we obtain the prior-independent upper limit of \log_{10}|f_{R0}|&lt; -5.6. Finally, we implement a method to include theoretical errors to avoid the bias.

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.

The Dark Energy Spectroscopic Instrument (DESI) survey will measure large-scale structures using quasars as direct tracers of dark matter in the redshift range 0.92.1. 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 Explorer (WISE). 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 16.599% purity for a nominal effective exposure time of ~1000s. With a 310 per sq. deg. target density, the main selection allows DESI to select more than 200 QSOs per sq. deg. (including 60 quasars with z>2.1), exceeding the project requirements by 20%. The redshift distribution of the selected quasars is in excellent agreement with quasar luminosity function predictions.

In this work, we extend the time-dependent conformable Schrödinger equation for a fractional dimensional system of N spatial coordinates to be used as an effective description of anisotropic and confined systems. A specific example is looked at in free particle conformable Schrödinger wave mechanics, particularly in N-Polar coordinates and N-Cartesian coordinates systems. The quantities of the conformable form are found to be in exact agreement with the corresponding traditional quantities when $\beta=1$

In this paper, we present the Discovery simulations: a new pair of high-resolution N-body simulations motivated by the DESI Y1 BAO cosmological constraints on dark energy. The Discovery simulations were run with identical initial conditions, and differ only in their cosmological parameters. The first simulation is based on a flat $\Lambda\mathrm{CDM}$ cosmology, while the second is based on a $w_0 w_a\mathrm{CDM}$ cosmology, with particular parameter values chosen based on the DESI analysis which includes constraints from BAO with CMB priors. Both simulations evolve $6720^3$ particles in a box with a side length of $L_\mathrm{box} = 1.5$ Gpc, leading to a mass resolution of $\sim4 \times 10^8$ $\mathrm{M}_{\odot}$ in each simulation. In this work we demonstrate the impact of the $w_0 w_a\mathrm{CDM}$ cosmology on the matter power spectrum, halo mass function, and halo mass accretion rate. We also populate halos with galaxies using a novel forward model for in-situ star formation, and examine the way in which changes to cosmology manifest as changes in star formation history. The Discovery simulations provide a testbed for alternative cosmological probes that may offer additional constraining power beyond BAO, such as higher-order summary statistics and observables in the nonlinear regime. Halo catalogs from the Discovery simulations are publicly available and can be downloaded from the HACC Simulation Data Portal.