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The current standard cosmological model is constructed within the framework of general relativity with a cosmological constant $\Lambda$, which is often associated with dark energy, and phenomenologically explains the accelerated cosmic expansion. Understanding the nature of dark energy is one of the most appealing questions in achieving a self-consistent physical model at cosmological scales. Modification of general relativity could potentially provide a more natural and physical solution to the accelerated expansion. The growth of the cosmic structure is sensitive in constraining gravity models. In this paper, we aim to provide a concise introductory review of modified gravity models from an observational point of view. We will discuss various mainstream cosmological observables, and their potential advantages and limitations as probes of gravity models., Comment: 72 pages, 19 figures, Invited review for Universe, Special Issue "Cosmological Constant"

Modern in-orbit satellites and other available remote sensing tools have generated a huge availability of public data waiting to be exploited in different formats hosted on different servers. In this context, ETL formalism becomes relevant for the integration and analysis of the combined information from all these sources. Throughout this work, we present the theoretical and practical foundations to build a modular analysis infrastructure that allows the creation of ETLs to download, transform and integrate data coming from different instruments in different formats. Part of this work is already implemented in a Python library which is intended to be integrated into already available workflow management tools based on acyclic-directed graphs which also have different adapters to impact the combined data in different warehouses., Comment: 8 pages, 3 figures. Submitted to SAIV 2023 - Simposio Argentino de Im\'agenes y Visi\'on

In this paper we present the development of a dataset consisting of 91 Multi-band Cloud and Moisture Product Full-Disk (MCMIPF) from the Advanced Baseline Imager (ABI) on board GOES-16 geostationary satellite with 91 temporally and spatially corresponding CLDCLASS products from the CloudSat polar satellite. The products are diurnal, corresponding to the months of January and February 2019 and were chosen such that the products from both satellites can be co-located over South America. The CLDCLASS product provides the cloud type observed for each of the orbit's steps and the GOES-16 multiband images contain pixels that can be co-located with these data. We develop an algorithm that returns a product in the form of a table that provides pixels from multiband images labelled with the type of cloud observed in them. These labelled data conformed in this particular structure are very useful to perform supervised learning. This was corroborated by training a simple linear artificial neural network based on the work of Gorooh et al. (2020), which gave good results, especially for the classification of deep convective clouds., Comment: 6 pages, 7 figures. Submitted to 2022 IEEE Biennial Congress of Argentina (ARGENCON). Presented in 2022 IEEE Biennial Congress of Argentina (ARGENCON), San Juan, Argentina

The one-dimensional power spectrum $P_{\mathrm{1D}}$ of the Ly$\alpha$ 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 $P_{\mathrm{1D}}$ 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 two-dimensional 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 Ly$\alpha$ 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 implications for the upcoming DESI Year 1 analysis., Comment: 23 pages, 20 figures. To be published in MNRAS

We present and validate the catalog of Lyman-$\alpha$ forest fluctuations for 3D analyses using the Early Data Release (EDR) from the Dark Energy Spectroscopic Instrument (DESI) survey. We used 88,511 quasars collected from DESI Survey Validation (SV) data and the first two months of the main survey (M2). We present several improvements to the method used to extract the Lyman-$\alpha$ absorption fluctuations performed in previous analyses from the Sloan Digital Sky Survey (SDSS). In particular, we modify the weighting scheme and show that it can improve the precision of the correlation function measurement by more than 20%. This catalog can be downloaded from https://data.desi.lbl.gov/public/edr/vac/edr/lya/fuji/v0.3 and it will be used in the near future for the first DESI measurements of the 3D correlations in the Lyman-$\alpha$ forest.

We present the one-dimensional Lyman-$\alpha$ 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 two 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 to the new DESI instrument, applying techniques similar to previous Sloan Digital Sky Survey (SDSS) measurements. We use synthetic data based on log-normal 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 Lyman-$\alpha$ forest sample than SDSS, providing an unprecedented precise one-dimensional power spectrum measurement., Comment: 23 pages, 23 figures, Journal version

The Dark Energy Spectroscopic Instrument (DESI) completed its five-month Survey Validation in May 2021. Spectra of stellar and extragalactic targets from Survey Validation constitute the first major data sample from the DESI survey. This paper describes the public release of those spectra, the catalogs of derived properties, and the intermediate data products. In total, the public release includes good-quality spectral information from 466,447 objects targeted as part of the Milky Way Survey, 428,758 as part of the Bright Galaxy Survey, 227,318 as part of the Luminous Red Galaxy sample, 437,664 as part of the Emission Line Galaxy sample, and 76,079 as part of the Quasar sample. In addition, the release includes spectral information from 137,148 objects that expand the scope beyond the primary samples as part of a series of secondary programs. Here, we describe the spectral data, data quality, data products, Large-Scale Structure science catalogs, access to the data, and references that provide relevant background to using these spectra., Comment: 43 pages, 7 figures, 17 tables, accepted for publication in the Astronomical Journal

The Dark Energy Spectroscopic Instrument (DESI) was designed to conduct a survey covering 14,000 deg$^2$ over five years to constrain the cosmic expansion history through precise measurements of Baryon Acoustic Oscillations (BAO). The scientific program for DESI was evaluated during a five month Survey Validation (SV) campaign before beginning full operations. This program produced deep spectra of tens of thousands of objects from each of the stellar (MWS), bright galaxy (BGS), luminous red galaxy (LRG), emission line galaxy (ELG), and quasar target classes. These SV spectra were used to optimize redshift distributions, characterize exposure times, determine calibration procedures, and assess observational overheads for the five-year program. In this paper, we present the final target selection algorithms, redshift distributions, and projected cosmology constraints resulting from those studies. We also present a `One-Percent survey' conducted at the conclusion of Survey Validation covering 140 deg$^2$ using the final target selection algorithms with exposures of a depth typical of the main survey. The Survey Validation indicates that DESI will be able to complete the full 14,000 deg$^2$ program with spectroscopically-confirmed targets from the MWS, BGS, LRG, ELG, and quasar programs with total sample sizes of 7.2, 13.8, 7.46, 15.7, and 2.87 million, respectively. These samples will allow exploration of the Milky Way halo, clustering on all scales, and BAO measurements with a statistical precision of 0.28% over the redshift interval $z<1.1$, 0.39% over the redshift interval $1.1

A fairly reliable trend in deep reinforcement learning is that the performance scales with the number of parameters, provided a complimentary scaling in amount of training data. As the appetite for large models increases, it is imperative to address, sooner than later, the potential problem of running out of high-quality demonstrations. In this case, instead of collecting only new data via costly human demonstrations or risking a simulation-to-real transfer with uncertain effects, it would be beneficial to leverage vast amounts of readily-available low-quality data. Since classical control algorithms such as behavior cloning or temporal difference learning cannot be used on reward-free or action-free data out-of-the-box, this solution warrants novel training paradigms for continuous control. We propose a simple algorithm called Diffused Value Function (DVF), which learns a joint multi-step model of the environment-robot interaction dynamics using a diffusion model. This model can be efficiently learned from state sequences (i.e., without access to reward functions nor actions), and subsequently used to estimate the value of each action out-of-the-box. We show how DVF can be used to efficiently capture the state visitation measure for multiple controllers, and show promising qualitative and quantitative results on challenging robotics benchmarks.

Data analysis from upcoming large galaxy redshift surveys, such as Euclid and DESI will significantly improve constraints on cosmological parameters. To optimally extract the information from these galaxy surveys, it is important to control with a high level of confidence the uncertainty and bias arising from the estimation of the covariance that affects the inference of cosmological parameters. In this work, we are addressing two different but closely related issues: (i) the sampling noise present in a covariance matrix estimated from a finite set of simulations and (ii) the impact on cosmological constraints of the non-Gaussian contribution to the covariance matrix of the power spectrum. We focus on the parameter estimation obtained from fitting the matter power spectrum in real space, using the DEMNUni N-body simulations. Regarding the first issue, we adopt two different approaches to reduce the sampling noise in the precision matrix that propagates in the parameter space: on the one hand using an alternative estimator of the covariance matrix based on a non-linear shrinkage, NERCOME; and on the other hand employing a method of fast generation of approximate mock catalogs, COVMOS. We find that NERCOME can significantly reduce the noise induced on the posterior distribution of parameters, but at the cost of a systematic overestimation of the error bars on the cosmological parameters. We show that using a COVMOS covariance matrix estimated from a large number of realisations (10~000) results in unbiased cosmological constraints. Regarding the second issue, we quantify the impact on cosmological constraints of the non-Gaussian part of the power spectrum covariance purely coming from non-linear clustering. We find that when this term is neglected, both the errors and central values of the estimated parameters are affected for a scale cut $\kmax > 0.2\ \invMpc$., Comment: 21 pages, 2 appendices, 20 figures. Submitted to A&A

We present Manifold Diffusion Fields (MDF), an approach that unlocks learning of diffusion models of data in general non-Euclidean geometries. Leveraging insights from spectral geometry analysis, we define an intrinsic coordinate system on the manifold via the eigen-functions of the Laplace-Beltrami Operator. MDF represents functions using an explicit parametrization formed by a set of multiple input-output pairs. Our approach allows to sample continuous functions on manifolds and is invariant with respect to rigid and isometric transformations of the manifold. In addition, we show that MDF generalizes to the case where the training set contains functions on different manifolds. Empirical results on multiple datasets and manifolds including challenging scientific problems like weather prediction or molecular conformation show that MDF can capture distributions of such functions with better diversity and fidelity than previous approaches., Comment: ICLR24 paper

The clustering of data is one of the most important and challenging topics in data science. The minimum sum-of-squares clustering (MSSC) problem asks to cluster the data points into $k$ clusters such that the sum of squared distances between the data points and their cluster centers (centroids) is minimized. This problem is NP-hard, but there exist exact solvers that can solve such problem to optimality for small or medium size instances. In this paper, we use a branch-and-bound solver based on semidefinite programming relaxations called SOS-SDP to compute the optimum solutions of the MSSC problem for various $k$ and for multiple datasets, with real and artificial data, for which the data provider has provided ground truth clustering. Next, we use several extrinsic and intrinsic measures to evaluate how the optimum clustering and ground truth clustering matches, and how well these clusterings perform with respect to the criteria underlying the intrinsic measures. Our calculations show that the ground truth clusterings are generally far from the optimum solution to the MSSC problem. Moreover, the intrinsic measures evaluated on the ground truth clusterings are generally significantly worse compared to the optimum clusterings. However, when the ground truth clustering is in the form of convex sets, e.g., ellipsoids, that are well separated from each other, the ground truth clustering comes very close to the optimum clustering., Comment: 23 pages; 2 figures, 5 tables

In this article, we obtain the exact solutions for bound states of tilted anisotropic Dirac materials under the action of external electric and magnetic fields with translational symmetry. In order to solve the eigenvalue equation that arises from the effective Hamiltonian of these materials, we describe an algorithm that allow us to decouple the differential equations that are obtained for the spinor components., Comment: 15 pages, 4 figures

We obtain a complete description of the integer group determinants for SmallGroup(16,8), the semidihedral group of order 16. While this paper was in preparation, a complete descriptions for this group was independently obtained by Yuka Yamaguchi and Naoya Yamaguchi in [21] (and the other remaining group of order 16 in [20]). We offer our version here for anyone interested in a slightly different approach.

Using the recently derived higher spin gravitational Compton amplitude from low-energy analytically continued ($a/Gm\gg1$) solutions of the Teukolsky equation for the scattering of a gravitational wave off the Kerr black hole, observables for non-radiating super-extremal Kerr binary systems at second post-Minkowskian (PM) order and up to sixth order in spin are computed. The relevant 2PM amplitude is obtained from the triangle-leading singularity in conjunction with a generalization of the holomorphic classical limit for massive particles with spin oriented in generic directions. Explicit results for the 2PM eikonal phase written for both Covariant and Canonical spin supplementary conditions -- CovSSC and CanSSC respectively -- as well as the 2PM linear impulses and individual spin kicks in the CanSSC are presented. The observables reported in this letter are expressed in terms of generic contact deformations of the gravitational Compton amplitude, which can then be specialized to Teukolsky solutions. In the latter case, the resulting 2PM observables break the newly proposed spin-shift symmetry of the 2PM amplitude starting at the fifth order in spin. Aligned spin checks as well as the high energy behavior of the computed observables are discussed., Comment: v2: Typos and transcription errors in the last row of Table 1 fixed v3: Version to appear in PRD. Typos fixed, ancillary files updated for the exotic contributions to the observables and eikonal phase

We use the PyNeb 1.1.16 Python package to evaluate the atomic datasets available for the spectral modeling of [Fe II] and [Fe III], which list level energies, A-values, and effective collision strengths. Most datasets are reconstructed from the sources, and new ones are incorporated to be compared with observed and measured benchmarks. For [Fe III], we arrive at conclusive results that allow us to select the default datasets, while for [Fe II], the conspicuous temperature dependency on the collisional data becomes a deterrent. This dependency is mainly due to the singularly low critical density of the $\mathrm{3d^7\ a\,^4F_{9/2}}$ metastable level that strongly depends on both the radiative and collisional data, although the level populating by fluorescence pumping from the stellar continuum cannot be ruled out. A new version of PyNeb (1.1.17) is released containing the evaluated datasets., Comment: 16 Tables, 16 Figures. Atoms, Vol. 11, page 63