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We explore the science prospects of a 14,000 deg$^2$ Kinematic Lensing (KL) survey with the Dark Energy Spectroscopic Instrument (DESI) and overlapping imaging surveys. KL infers the cosmic shear signal by jointly forward modeling the observed photometric image and velocity field of a disk galaxy. The latter can be constrained by placing multiple DESI fibers along the galaxy's major and minor axis, a concept similar to the DESI Peculiar Velocity Survey. We study multiple subset galaxy samples of the DESI Legacy Survey Data Release 9 catalog and quantify the residual shape noise, $\sigma_\epsilon$, of each sample as a function of cuts in $r$-band magnitude using mock observations. We conduct simulated likelihood analyses for these galaxy samples and find that a DESI-KL program can place highly interesting constraints on structure formation at very low redshifts, i.e. $\sigma_8(z<0.15)$. We conclude that if the $S_8$ tension consolidates as a phenomenon, a DESI-KL survey can provide unique insights into this phenomenon in the very late-time Universe. Given the different footprints of DESI and Rubin Observatory's Legacy Survey of Space and Time (LSST), lensing results from both surveys are highly complementary and can be combined into a joint lensing survey. We further note that DESI-KL benefits multiple additional science cases, e.g. studies of modified gravity models when combined with peculiar velocity surveys, and dark matter studies that are based on galaxy-galaxy lensing of dwarf galaxies.

For the joint analysis of second-order weak lensing and galaxy clustering statistics, so-called $3{\times}2$ analyses, the selection and characterization of optimal galaxy samples is a major area of research. One promising choice is to use the same galaxy sample as lenses and sources, which reduces the systematics parameter space that describes uncertainties related to galaxy samples. Such a "lens-equal-source" analysis significantly improves self-calibration of photo-z systematics leading to improved cosmological constraints. With the aim to enable a lens-equal-source analysis on small scales we investigate the halo-galaxy connection of DES-Y3 source galaxies. We develop a technique to construct mock source galaxy populations by matching COSMOS/UltraVISTA photometry onto UniverseMachine galaxies. These mocks predict a source halo occupation distribution (HOD) that exhibits significant redshift evolution, non-trivial central incompleteness and galaxy assembly bias. We produce multiple realizations of mock source galaxies drawn from the UniverseMachine posterior with added uncertainties in measured DES photometry and galaxy shapes. We fit a modified HOD formalism to these realizations to produce priors on the galaxy-halo connection for cosmological analyses. We additionally train an emulator that predicts this HOD to $\sim2\%$ accuracy from redshift $z = 0.1 - 1.3$ that models the dependence of this HOD on 1) observational uncertainties in galaxy size and photometry, and 2) uncertainties in the UniverseMachine predictions., Comment: 15 pages, 7 figures, 1 table. Submitted to The Astrophysical Journal

We train neural networks to quickly generate redshift-space galaxy power spectrum covariances from a given parameter set (cosmology and galaxy bias). This covariance emulator utilizes a combination of traditional fully-connected network layers and transformer architecture to accurately predict covariance matrices for the high redshift, north galactic cap sample of the BOSS DR12 galaxy catalog. We run simulated likelihood analyses with emulated and brute-force computed covariances, and we quantify the network's performance via two different metrics: 1) difference in $\chi^2$ and 2) likelihood contours for simulated BOSS DR 12 analyses. We find that the emulator returns excellent results over a large parameter range. We then use our emulator to perform a re-analysis of the BOSS HighZ NGC galaxy power spectrum, and find that varying covariance with cosmology along with the model vector produces $\Omega_m = 0.276^{+0.013}_{-0.015}$, $H_0 = 70.2\pm 1.9$ km/s/Mpc, and $\sigma_8 = 0.674^{+0.058}_{-0.077}$. These constraints represent an average $0.46\sigma$ shift in best-fit values and a $5\%$ increase in constraining power compared to fixing the covariance matrix ($\Omega_m = 0.293\pm 0.017$, $H_0 = 70.3\pm 2.0$ km/s/Mpc, $\sigma_8 = 0.702^{+0.063}_{-0.075}$). This work demonstrates that emulators for more complex cosmological quantities than second-order statistics can be trained over a wide parameter range at sufficiently high accuracy to be implemented in realistic likelihood analyses., Comment: 11 pages, 8 figures, to be submitted to Physical Review D

SPHEREx, the Spectro-Photometer for the History of the Universe, Epoch of Reionization, and ices Explorer, is a NASA MIDEX mission planned for launch in 2024. SPHEREx will carry out the first all-sky spectral survey at wavelengths between 0.75 micron and 5 micron with spectral resolving power ~40 between 0.75 and 3.8 micron and ~120 between 3.8 and 5 micron At the end of its two-year mission, SPHEREx will provide 0.75-to-5 micron spectra of each 6.2"x6.2" pixel on the sky - 14 billion spectra in all. This paper updates an earlier description of SPHEREx presenting changes made during the mission's Preliminary Design Phase, including a discussion of instrument integration and test and a summary of the data processing, analysis, and distribution plans.

Kinematic lensing (KL) is a new weak lensing technique that reduces shape noise for disk galaxies by including spectroscopically measured galaxy kinematics in addition to photometrically measured galaxy shapes. Since KL utilizes the Tully-Fisher relation, any correlation of this relation with the local environment may bias the cosmological interpretation. For the first time, we explore such a Tully-Fisher environmental dependence (TED) effect as a potential astrophysical systematic for KL. Our derivation of the TED systematic can be described in a similar analytical form as intrinsic alignment for traditional weak lensing. We demonstrate analytically that TED only impacts KL if intrinsic aligment for disk galaxies is non-zero. We further use IllustrisTNG simulations to quantify the TED effect. Our two-point correlation measurements do not yield any additional coherent signals that would indicate a systematic bias on KL, within the uncertainties set by the simulation volume., Comment: 12 pages, 5 figures, to be submitted to PRD

Validating modeling choices through simulated analyses and quantifying the impact of different systematic effects will form a major computational bottleneck in the preparation for 3$\times$2 analysis with Stage-IV surveys such as Vera Rubin Observatory's Legacy Survey of Space and Time (LSST). We can significantly reduce the computational requirements by using machine learning based emulators, which allow us to run fast inference while maintaining the full realism of the data analysis pipeline. In this paper, we use such an emulator to run simulated 3$\times$2 (cosmic shear, galaxy-galaxy lensing, and galaxy clustering) analyses for mock LSST-Y1/Y3/Y6/Y10 surveys and study the impact of various systematic effects (galaxy bias, intrinsic alignment, baryonic physics, shear calibration and photo-$z$ uncertainties). Closely following the DESC Science Requirement Document (with several updates) our main findings are: {\it a)} The largest contribution to the `systematic error budget' of LSST 3$\times$2 analysis comes from galaxy bias uncertainties, while the contribution of baryonic and shear calibration uncertainties are significantly less important. {\it b)} Tighter constraints on intrinsic alignment and photo-$z$ parameters can improve cosmological constraints noticeably, which illustrates synergies of LSST and spectroscopic surveys. {\it c)} The scale cuts adopted in the DESC SRD may be too conservative and pushing to smaller scales can increase cosmological information significantly. {\it d)} We investigate the impact of photo-$z$ outliers on 3$\times$2 pt analysis and find that we need to determine the outlier fraction to within $5-10\%$ accuracy to ensure robust cosmological analysis. We caution that these findings depend on analysis choices (parameterizations, priors, scale cuts) and can change for different settings., Comment: 16 pages, 10 Figures, To be submitted to PRD. Comments welcome

The next generation of cosmological surveys is expected to generate unprecedented high-quality data, consequently increasing the already substantial computational costs of Bayesian statistical methods. This will pose a significant challenge to analyzing theoretical models of cosmology. Additionally, new mitigation techniques of baryonic effects, intrinsic alignment, and other systematic effects will inevitably introduce more parameters, slowing down the convergence of Bayesian analyses. In this scenario, machine-learning-based accelerators are a promising solution, capable of reducing the computational costs and execution time of such tools by order of thousands. Yet, they have not been able to provide accurate predictions over the wide prior ranges in parameter space adopted by Stage III/IV collaborations in studies employing real-space two-point correlation functions. This paper offers a leap in this direction by carefully investigating the modern transformer-based neural network (NN) architectures in realistic simulated Rubin Observatory year one cosmic shear $\Lambda$CDM inferences. Building on the framework introduced in Part I, we generalize the transformer block and incorporate additional layer types to develop a more versatile architecture. We present a scalable method to efficiently generate an extensive training dataset that significantly exceeds the scope of prior volumes considered in Part I, while still meeting strict accuracy standards. Through our meticulous architecture comparison and comprehensive hyperparameter optimization, we establish that the attention-based architecture performs an order of magnitude better in accuracy than widely adopted NN designs. Finally, we test and apply our emulators to calibrate tension metrics.

We present a new class of machine-learning emulators that accurately model the cosmic shear, galaxy-galaxy lensing, and galaxy clustering real space correlation functions in the context of Rubin Observatory year one simulated data. To illustrate its capabilities in forecasting models beyond the standard $\Lambda$CDM, we forecast how well LSST Year 1 data will be able to probe the consistency between geometry $\Omega^{\rm geo}_\mathrm{m}$ and growth $\Omega^{\rm growth}_\mathrm{m}$ dark matter densities in the so-called split $\Lambda$CDM parameterization. When trained with a few million samples, our emulator shows uniform accuracy across a wide range in an 18-dimensional parameter space. We provide a detailed comparison of three neural network designs, illustrating the importance of adopting state-of-the-art Transformer blocks. Our study also details their performance when computing Bayesian evidence for cosmic shear on three fiducial cosmologies. The transformers-based emulator is always accurate within PolyChord's precision. As an application, we use our emulator to study the degeneracies between dark energy models and growth geometry split parameterizations. We find that the growth-geometry split remains to be a meaningful test of the smooth dark energy assumption., Comment: 16 pages, 6 figures