arXiv subjects

Juan de Vicente

Publications and source records attributed to Juan de Vicente.

7 recordsLinked to original sources

arXiv2026

Opportunities in AI/ML for the Rubin LSST Dark Energy Science Collaboration

The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) will produce unprecedented volumes of heterogeneous astronomical data (images, catalogs, and alerts) that challenge traditional analysis pipelines. The LSST Dark Energy Science Collaboration (DESC) aims to derive robust constraints on dark energy and dark matter from these data, requiring methods that are statistically powerful, scalable, and operationally reliable. Artificial intelligence and machine learning (AI/ML) are already embedded across DESC science workflows, from photometric redshifts and transient classification to weak lensing inference and cosmological simulations. Yet their utility for precision cosmology hinges on trustworthy uncertainty quantification, robustness to covariate shift and model misspecification, and reproducible integration within scientific pipelines. This white paper surveys the current landscape of AI/ML across DESC's primary cosmological probes and cross-cutting analyses, revealing that the same core methodologies and fundamental challenges recur across disparate science cases. Since progress on these cross-cutting challenges would benefit multiple probes simultaneously, we identify key methodological research priorities, including Bayesian inference at scale, physics-informed methods, validation frameworks, and active learning for discovery. With an eye on emerging techniques, we also explore the potential of the latest foundation model methodologies and LLM-driven agentic AI systems to reshape DESC workflows, provided their deployment is coupled with rigorous evaluation and governance. Finally, we discuss critical software, computing, data infrastructure, and human capital requirements for the successful deployment of these new methodologies, and consider associated risks and opportunities for broader coordination with external actors.

astro-ph.IM↗

arXiv2019

The Physics of the Accelerating Universe Camera

The PAU (Physics of the Accelerating Universe) Survey goal is to obtain photometric redshifts (photo-z) and Spectral Energy Distribution (SED) of astronomical objects with a resolution roughly one order of magnitude better than current broad band photometric surveys. To accomplish this, a new large field of view camera (PAUCam) has been designed, built, commissioned and is now operated at the William Herschel Telescope (WHT). With the current WHT Prime Focus corrector, the camera covers ~1-degree diameter Field of View (FoV), of which, only the inner ~40 arcmin diameter are unvignetted. The focal plane consists of a mosaic of 18 2k$x4k Hamamatsu fully depleted CCDs, with high quantum efficiency up to 1 micrometers in wavelength. To maximize the detector coverage within the FoV, filters are placed in front of the CCDs inside the camera cryostat (made out of carbon fiber) using a challenging movable tray system. The camera uses a set of 40 narrow band filters ranging from ~4500 to ~8500 Angstroms complemented with six standard broad-band filters, ugrizY. The PAU Survey aims to cover roughly 100 square degrees over fields with existing deep photometry and galaxy shapes to obtain accurate photometric redshifts for galaxies down to i_AB~22.5, detecting also galaxies down to i_AB~24 with less precision in redshift. With this data set we will be able to measure intrinsic alignments, galaxy clustering and perform galaxy evolution studies in a new range of densities and redshifts. Here, we describe the PAU camera, its first commissioning results and performance.

astro-ph.IM↗

arXiv2018

Astrometry and Occultation predictions to Trans-Neptunian and Centaur Objects observed within the Dark Energy Survey

Transneptunian objects (TNOs) are a source of invaluable information to access the history and evolution of the outer solar system. However, observing these faint objects is a difficult task. As a consequence, important properties such as size and albedo are known for only a small fraction of them. Now, with the results from deep sky surveys and the Gaia space mission, a new exciting era is within reach as accurate predictions of stellar occultations by numerous distant small solar system bodies become available. From them, diameters with kilometer accuracies can be determined. Albedos, in turn, can be obtained from diameters and absolute magnitudes. We use observations from the Dark Energy Survey (DES) from November 2012 until February 2016, amounting to 4292847 CCD frames. We searched them for all known small solar system bodies and recovered a total of 202 TNOs and Centaurs, 63 of which have been discovered by the DES collaboration until the date of this writing. Their positions were determined using the Gaia Data Release 2 as reference and their orbits were refined. Stellar occultations were then predicted using these refined orbits plus stellar positions from Gaia. These predictions are maintained, and updated, in a dedicated web service. The techniques developed here are also part of an ambitious preparation to use the data from the Large Synoptic Survey Telescope (LSST), that expects to obtain accurate positions and multifilter photometry for tens of thousands of TNOs.

astro-ph.EP↗

arXiv2018

The PAU Survey: star-galaxy classification with multi narrow-band data

Classification of stars and galaxies is a well-known astronomical problem that has been treated using different approaches, most of them relying on morphological information. In this paper, we tackle this issue using the low-resolution spectra from narrow band photometry, provided by the PAUS (Physics of the Accelerating Universe) survey. We find that, with the photometric fluxes from the 40 narrow band filters and without including morphological information, it is possible to separate stars and galaxies to very high precision, 98.4% purity with a completeness of 98.8% for objects brighter than I = 22.5. This precision is obtained with a Convolutional Neural Network as a classification algorithm, applied to the objects' spectra. We have also applied the method to the ALHAMBRA photometric survey and we provide an updated classification for its Gold sample.

astro-ph.IM↗

arXiv2018

Locally C-Nash groups

We introduce the category of locally C-Nash groups, basic examples of such groups are complex algebraic groups. We prove that if two complex algebraic groups are locally C-Nash isomorphic then they also are biregularly isomorphic. We also show that both, abelian locally Nash and abelian locally C-Nash groups, can be characterized via meromorphic maps admitting an algebraic addition theorem; we give an invariant of such groups associated to the groups of periods of a chart at the identity. Finally, we prove that the category of simply connected abelian locally C-Nash groups coincides with that of universal coverings of the abelian complex irreducible algebraic groups (a complex version of a result of Hrushovski and Pillay).

math.CV↗

arXiv2018

DES Science Portal: Computing Photometric Redshifts

A significant challenge facing photometric surveys for cosmological purposes is the need to produce reliable redshift estimates. The estimation of photometric redshifts (photo-zs) has been consolidated as the standard strategy to bypass the high production costs and incompleteness of spectroscopic redshift samples. Training-based photo-z methods require the preparation of a high-quality list of spectroscopic redshifts, which needs to be constantly updated. The photo-z training, validation, and estimation must be performed in a consistent and reproducible way in order to accomplish the scientific requirements. To meet this purpose, we developed an integrated web-based data interface that not only provides the framework to carry out the above steps in a systematic way, enabling the ease testing and comparison of different algorithms, but also addresses the processing requirements by parallelizing the calculation in a transparent way for the user. This framework called the Science Portal (hereafter Portal) was developed in the context the Dark Energy Survey (DES) to facilitate scientific analysis. In this paper, we show how the Portal can provide a reliable environment to access vast data sets, provide validation algorithms and metrics, even in the case of multiple photo-zs methods. It is possible to maintain the provenance between the steps of a chain of workflows while ensuring reproducibility of the results. We illustrate how the Portal can be used to provide photo-z estimates using the DES first year (Y1A1) data. While the DES collaboration is still developing techniques to obtain more precise photo-zs, having a structured framework like the one presented here is critical for the systematic vetting of DES algorithmic improvements and the consistent production of photo-zs in the future DES releases.

astro-ph.GA↗

arXiv2017

Two-dimensional locally $\mathbb{K}$-Nash groups

We give a classification of connected abelian locally (real) Nash groups of dimension two. We first consider Painlevé's description of meromorphic maps admitting an Algebraic Addition Theorem and analyse the algebraic dependence of such maps. We then give a classification of connected abelian locally complex Nash groups of dimension two, from which we deduce the corresponding real classification.

math.AG↗