arXiv subjects

Eusebio Sánchez

Publications and source records attributed to Eusebio Sánchez.

8 recordsLinked to original sources

arXiv2025

Nearby stellar substructures in the Galactic halo from DESI Milky Way Survey Year 1 Data Release

We report five nearby ($d_{\mathrm{helio}} < 5$ kpc) stellar substructures in the Galactic halo from a subset of 138,661 stars in the Dark Energy Spectroscopic Instrument (DESI) Milky Way Survey Year 1 Data Release. With an unsupervised clustering algorithm, HDBSCAN*, these substructures are independently identified in Integrals of Motion ($E_{\mathrm{tot}}$, $L_{\mathrm z}$, $\log{J_r}$, $\log{J_z}$) space and Galactocentric cylindrical velocity space ($V_{R}$, $V_ϕ$, $V_{z}$). We associate all identified clusters with known nearby substructures (Helmi streams, M18-Cand10/MMH-1, Sequoia, Antaeus, and ED-2) previously reported in various studies. With metallicities precisely measured by DESI, we confirm that the Helmi streams, M18-Cand10, and ED-2 are chemically distinct from local halo stars. We have characterised the chemodynamic properties of each dynamic group, including their metallicity dispersions, to associate them with their progenitor types (globular cluster or dwarf galaxy). Our approach for searching substructures with HDBSCAN* reliably detects real substructures in the Galactic halo, suggesting that applying the same method can lead to the discovery of new substructures in future DESI data. With more stars from future DESI data releases and improved astrometry from the upcoming Gaia Data Release 4, we will have a more detailed blueprint of the Galactic halo, offering a significant improvement in our understanding of the formation and evolutionary history of the Milky Way Galaxy.

astro-ph.GA↗

arXiv2023

GTC Follow-up Observations of Very Metal-Poor Star Candidates from DESI

The observations from the Dark Energy Spectroscopic Instrument (DESI) will significantly increase the numbers of known extremely metal-poor stars by a factor of ~ 10, improving the sample statistics to study the early chemical evolution of the Milky Way and the nature of the first stars. In this paper we report high signal-to-noise follow-up observations of 9 metal-poor stars identified during the DESI commissioning with the Optical System for Imaging and low-Intermediate-Resolution Integrated Spectroscopy (OSIRIS) instrument on the 10.4m Gran Telescopio Canarias (GTC). The analysis of the data using a well-vetted methodology confirms the quality of the DESI spectra and the performance of the pipelines developed for the data reduction and analysis of DESI data.

astro-ph.SR↗

arXiv2023

The PAU survey: Estimating galaxy photometry with deep learning

With the dramatic rise in high-quality galaxy data expected from Euclid and Vera C. Rubin Observatory, there will be increasing demand for fast high-precision methods for measuring galaxy fluxes. These will be essential for inferring the redshifts of the galaxies. In this paper, we introduce Lumos, a deep learning method to measure photometry from galaxy images. Lumos builds on BKGnet, an algorithm to predict the background and its associated error, and predicts the background-subtracted flux probability density function. We have developed Lumos for data from the Physics of the Accelerating Universe Survey (PAUS), an imaging survey using a 40 narrow-band filter camera (PAUCam). PAUCam images are affected by scattered light, displaying a background noise pattern that can be predicted and corrected for. On average, Lumos increases the SNR of the observations by a factor of 2 compared to an aperture photometry algorithm. It also incorporates other advantages like robustness towards distorting artefacts, e.g. cosmic rays or scattered light, the ability of deblending and less sensitivity to uncertainties in the galaxy profile parameters used to infer the photometry. Indeed, the number of flagged photometry outlier observations is reduced from 10% to 2%, comparing to aperture photometry. Furthermore, with Lumos photometry, the photo-z scatter is reduced by ~10% with the Deepz machine learning photo-z code and the photo-z outlier rate by 20%. The photo-z improvement is lower than expected from the SNR increment, however currently the photometric calibration and outliers in the photometry seem to be its limiting factor.

astro-ph.CO↗

arXiv2021

The PAU Survey: narrowband photometric redshifts using Gaussian processes

We study the performance of the hybrid template-machine-learning photometric redshift (photo-$z$) algorithm Delight, which uses Gaussian processes, on a subset of the early data release of the Physics of the Accelerating Universe Survey (PAUS). We calibrate the fluxes of the $40$ PAUS narrow bands with $6$ broadband fluxes ($uBVriz$) in the COSMOS field using three different methods, including a new method which utilises the correlation between the apparent size and overall flux of the galaxy. We use a rich set of empirically derived galaxy spectral templates as guides to train the Gaussian process, and we show that our results are competitive with other standard photometric redshift algorithms. Delight achieves a photo-$z$ $68$th percentile error of $σ_{68}=0.0081(1+z)$ without any quality cut for galaxies with $i_\mathrm{auto}<22.5$ as compared to $0.0089(1+z)$ and $0.0202(1+z)$ for the BPz and ANNz2 codes, respectively. Delight is also shown to produce more accurate probability distribution functions for individual redshift estimates than BPz and ANNz2. Common photo-$z$ outliers of Delight and BCNz2 (previously applied to PAUS) are found to be primarily caused by outliers in the narrowband fluxes, with a small number of cases potentially indicating spectroscopic redshift failures in the reference sample. In the process, we introduce performance metrics derived from the results of BCNz2 and Delight, allowing us to achieve a photo-$z$ quality of $σ_{68}<0.0035(1+z)$ at a magnitude of $i_\mathrm{auto}<22.5$ while keeping $50$ per cent objects of the galaxy sample.

astro-ph.CO↗

arXiv2020

The PAU Survey: Background light estimation with deep learning techniques

In any imaging survey, measuring accurately the astronomical background light is crucial to obtain good photometry. This paper introduces BKGnet, a deep neural network to predict the background and its associated error. BKGnet has been developed for data from the Physics of the Accelerating Universe Survey (PAUS), an imaging survey using a 40 narrow-band filter camera (PAUCam). Images obtained with PAUCam are affected by scattered light: an optical effect consisting of light multiply that deposits energy in specific detector regions contaminating the science measurements. Fortunately, scattered light is not a random effect, but it can be predicted and corrected for. We have found that BKGnet background predictions are very robust to distorting effects, while still being statistically accurate. On average, the use of BKGnet improves the photometric flux measurements by 7% and up to 20% at the bright end. BKGnet also removes a systematic trend in the background error estimation with magnitude in the i-band that is present with the current PAU data management method. With BKGnet, we reduce the photometric redshift outlier rate

astro-ph.IM↗

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↗

arXiv2016

DNF - Galaxy photometric redshift by Directional Neighbourhood Fitting

Wide field images taken in several photometric bands allow simultaneous measurement of redshifts for thousands of galaxies. A variety of algorithms to make this measurement have appeared in the last few years, the majority of which can be classified as either template or training based methods. Among the latter, nearest neighbour estimatorsstand out as one of the most successful, in terms of both precision and the quality of error estimation. In this paper we describe the Directional Neighbourhood Fitting (DNF) algorithm based on the following: a new neighbourhood metric (Directional Neighbourhood), a photo-z estimation strategy (Neighbourhood Fitting) and a method for generating the photo-z probability distribution function. We compare DNF with other well-known empirical photometric redshift tools using different public datasets (Sloan Digital Sky Survey, VIMOS VLT Deep Survey and Photo-z Accuracy Testing). DNF achieves high-quality results with reliable error.

astro-ph.CO↗

arXiv2015

Homogeneity and isotropy in the 2MASS Photometric Redshift catalogue

Using the 2MASS Photometric Redshift catalogue we perform a number of statistical tests aimed at detecting possible departures from statistical homogeneity and isotropy in the large-scale structure of the Universe. Making use of the angular homogeneity index, an observable proposed in a previous publication, as well as studying the scaling of the angular clustering and number counts with magnitude limit, we place constraints on the fractal nature of the galaxy distribution. We find that the statistical properties of our sample are in excellent agreement with the standard cosmological model, and that it reaches the homogeneous regime significantly faster than a class of fractal models with dimensions $D<2.75$. As part of our search for systematic effects, we also study the presence of hemispherical asymmetries in our data, finding no significant deviation beyond those allowed by the concordance model.

astro-ph.CO↗