arXiv subjects

B. Sánchez

Publications and source records attributed to B. Sánchez.

8 recordsLinked to original sources

arXiv2026

The Dark Energy Survey Supernova Program: A Reanalysis Of Cosmology Results And Evidence For Evolving Dark Energy With An Updated Type Ia Supernova Calibration

We present improved cosmological constraints from a re-analysis of the Dark Energy Survey (DES) 5-year sample of Type Ia supernovae (DES-SN5YR). This re-analysis includes an improved photometric cross-calibration, recent white dwarf observations to cross-calibrate between DES and low redshift surveys, retraining the SALT3 light curve model and fixing a numerical approximation in the host galaxy colour law. Our fully recalibrated sample, which we call DES-Dovekie, comprises $\sim$1600 likely Type Ia SNe from DES and $\sim$200 low-redshift SNe from other surveys. With DES-Dovekie, we obtain $Ω_{\rm m} = 0.330 \pm 0.015$ in Flat $Λ$CDM which changes $Ω_{\rm m}$ by $-0.022$ compared to DES-SN5YR. Combining DES-Dovekie with CMB data from Planck, ACT and SPT and the DESI DR2 measurements in a Flat $w_0 w_a$CDM cosmology, we find $w_0 = -0.803 \pm 0.054$, $w_a = -0.72 \pm 0.21$. Our results hold a significance of $3.2σ$, reduced from $4.2σ$ for DES-SN5YR, to reject the null hypothesis that the data are compatible with the cosmological constant. This significance is equivalent to a Bayesian model preference odds of approximately 5:1 in favour of the Flat $w_0 w_a$CDM model. Using generally accepted thresholds for model preference, our updated data exhibits only a weak preference for evolving dark energy.

astro-ph.CO↗

arXiv2025

Photometric Redshift Estimation for Rubin Observatory Data Preview 1 with Redshift Assessment Infrastructure Layers (RAIL)

We present the first systematic analysis of photometric redshifts (photo-z) estimated from the Rubin Observatory Data Preview 1 (DP1) data taken with the Legacy Survey of Space and Time (LSST) Commissioning Camera. Employing the Redshift Assessment Infrastructure Layers (RAIL) framework, we apply eight photo-z algorithms to the DP1 photometry, using deep ugrizy coverage in the Extended Chandra Deep Field South (ECDFS) field and griz data in the Rubin_SV_38_7 field. In the ECDFS field, we construct a reference catalog from spectroscopic redshift (spec-z), grism redshift (grism-z), and multiband photo-z for training and validating photo-z. Performance metrics of the photo-z are evaluated using spec-zs from ECDFS and Dark Energy Spectroscopic Instrument Data Release 1 samples. Across the algorithms, we achieve per-galaxy photo-z scatter of $σ_{\rm NMAD} \sim 0.03$ and outlier fractions around 10% in the 6-band data, with performance degrading at faint magnitudes and z>1.2. The overall bias and scatter of our machine-learning based photo-zs satisfy the LSST Y1 requirement. We also use our photo-z to infer the ensemble redshift distribution n(z). We study the photo-z improvement by including near-infrared photometry from the Euclid mission, and find that Euclid photometry improves photo-z at z>1.2. Our results validate the RAIL pipeline for Rubin photo-z production and demonstrate promising initial performance.

astro-ph.IM↗

arXiv2022

Determining the aberrations in a nearly diffraction-limited spectrograph

We present a method to determine the static aberrations in a nearly diffraction-limited spectrograph introduced, for example, by alignment or manufacturing errors. We consider an instrument with two stages separated by a slit or image slicer located in the intermediate focal plane. In such a spectrograph, it is not trivial to distinguish aberrations in the first stage, before the slit, from those in the second, after the slit. However, our method achieves this. Measuring these aberrations separately opens the possibility of reducing them, by realignment or other means, and thereby improving the optical performance of the instrument. The method is based on fitting models to multiple images of a point source, with controlled displacements of the source perpendicular to the slit and controlled defocuses of the second stage or the detector. Fitting models to these images allows the determination of the aberrations in both stages. Our key discovery is that the displaced and defocused images provide additional information which allows us to break the ambiguity between the two stages. We present simulations that validate the performance of the method.

astro-ph.IM↗

arXiv2021

SNIa-Cosmology Analysis Results from Simulated LSST Images: from Difference Imaging to Constraints on Dark Energy

The Vera Rubin Observatory Legacy Survey of Space and Time (LSST) is expected to process ${\sim}10^6$ transient detections per night. For precision measurements of cosmological parameters and rates, it is critical to understand the detection efficiency, magnitude limits, artifact contamination levels, and biases in the selection and photometry. Here we rigorously test the LSST Difference Image Analysis (DIA) pipeline using simulated images from the Rubin Observatory LSST Dark Energy Science Collaboration (DESC) Data Challenge (DC2) simulation for the Wide-Fast-Deep (WFD) survey area. DC2 is the first large-scale (300 deg$^2$) image simulation of a transient survey that includes realistic cadence, variable observing conditions, and CCD image artifacts. We analyze ${\sim}$15 deg$^2$ of DC2 over a 5-year time-span in which artificial point-sources from Type Ia Supernovae (SNIa) light curves have been overlaid onto the images. We measure the detection efficiency as a function of Signal-to-Noise Ratio (SNR) and find a $50\%$ efficiency at $\rm{SNR}=5.8$. The magnitude limits for each filter are: $u=23.66$, $g=24.69$, $r=24.06$, $i=23.45$, $z=22.54$, $y=21.62$ $\rm{mag}$. The artifact contamination is $\sim90\%$ of detections, corresponding to $\sim1000$ artifacts/deg$^2$ in $g$ band, and falling to 300 per deg$^2$ in $y$ band. The photometry has biases $<1\%$ for magnitudes $19.5 < m <23$. Our DIA performance on simulated images is similar to that of the Dark Energy Survey pipeline applied to real images. We also characterize DC2 image properties to produce catalog-level simulations needed for distance bias corrections. We find good agreement between DC2 data and simulations for distributions of SNR, redshift, and fitted light-curve properties. Applying a realistic SNIa-cosmology analysis for redshifts $z<1$, we recover the input cosmology parameters to within statistical uncertainties.

astro-ph.CO↗

arXiv2019

Machine Learning on Difference Image Analysis: A comparison of methods for transient detection

We present a comparison of several Difference Image Analysis (DIA) techniques, in combination with Machine Learning (ML) algorithms, applied to the identification of optical transients associated with gravitational wave events. Each technique is assessed based on the scoring metrics of Precision, Recall, and their harmonic mean F1, measured on the DIA results as standalone techniques, and also in the results after the application of ML algorithms, on transient source injections over simulated and real data. This simulations cover a wide range of instrumental configurations, as well as a variety of scenarios of observation conditions, by exploring a multi dimensional set of relevant parameters, allowing us to extract general conclusions related to the identification of transient astrophysical events. The newest subtraction techniques, and particularly the methodology published in Zackay et al. (2016) are implemented in an Open Source Python package, named properimage, suitable for many other astronomical image analyses. This together with the ML libraries we describe, provides an effective transient detection software pipeline. Here we study the effects of the different ML techniques, and the relative feature importances for classification of transient candidates, and propose an optimal combined strategy. This constitutes the basic elements of pipelines that could be applied in searches of electromagnetic counterparts to GW sources.

astro-ph.IM↗

arXiv2019

The First Galaxy Cluster Discovered by the VISTA Variables in the Vía Láctea Survey

We report the first confirmed detection of the galaxy cluster VVV-J144321-611754 at very low latitudes (l = 315.836$^{\circ}$, b = -1.650$^{\circ}$) located in the tile d015 of the VISTA Variables in the Vía Láctea (VVV) survey. We defined the region of 30$\times$ 30 $arcmin^2$ centered in the brightest galaxy finding 25 galaxies. For these objects, extinction-corrected median colors of (H - K$_{s}$) = 0.34 $\pm$ 0.05 mag, (J - H) = 0.57 $\pm$ 0.08 mag and (J - K$_{s}$) = 0.87 $\pm$ 0.06 mag, and R$_{1/2}$ = 1.59 $\pm$ 0.16 $arcsec$; C = 3.01 $\pm$ 0.08; and Sersic index, n = 4.63 $\pm$ 0.39 were estimated. They were visually confirmed showing characteristics of early-type galaxies in the near-IR images. An automatic clustering analysis performed in the whole tile found that the concentration of galaxies VVV-J144321-611754 is a real, compact concentration of early-type galaxies. Assuming a typical galaxy cluster with low X-ray luminosity, the photometric redshift of the brightest galaxy is $z = $ 0.196 $\pm$ 0.025. Follow-up near-IR spectroscopy with FLAMINGOS-2 at the Gemini-South telescope revealed that the two brighter cluster galaxies have typical spectra of early-type galaxies and the estimated redshift for the brightest galaxy VVV-J144321.06-611753.9 is $z =$ 0.234$\pm$0.022 and for VVV-J144319.02-611746.1 is $z =$ 0.232$\pm$0.019. Finally, these galaxies clearly follow the cluster Red Sequence in the rest-frame near-IR color--magnitude diagram with the slope similar to galaxy cluster at redshift of 0.2. These results are consistent with the presence of a bona fide galaxy cluster beyond the Milky Way disk.

astro-ph.GA↗

arXiv2018

From FATS to feets: Further improvements to an astronomical feature extraction tool based on machine learning

Machine learning algorithms are highly useful for the classification of time series data in astronomy in this era of peta-scale public survey data releases. These methods can facilitate the discovery of new unknown events in most astrophysical areas, as well as improving the analysis of samples of known phenomena. Machine learning algorithms use features extracted from collected data as input predictive variables. A public tool called Feature Analysis for Time Series (FATS) has proved an excellent workhorse for feature extraction, particularly light curve classification for variable objects. In this study, we present a major improvement to FATS, which corrects inconvenient design choices, minor details, and documentation for the re-engineering process. This improvement comprises a new Python package called "feets", which is important for future code-refactoring for astronomical software tools.

astro-ph.IM↗

arXiv2017

Observations of the first electromagnetic counterpart to a gravitational wave source by the TOROS collaboration

We present the results of prompt optical follow-up of the electromagnetic counterpart of the gravitational-wave event GW170817 by the Transient Optical Robotic Observatory of the South Collaboration (TOROS). We detected highly significant dimming in the light curves of the counterpart (Delta g=0.17+-0.03 mag, Delta r=0.14+-0.02 mag, Delta i=0.10 +- 0.03 mag) over the course of only 80 minutes of observations obtained ~35 hr after the trigger with the T80-South telescope. A second epoch of observations, obtained ~59 hr after the event with the EABA 1.5m telescope, confirms the fast fading nature of the transient. The observed colors of the counterpart suggest that this event was a "blue kilonova" relatively free of lanthanides.

astro-ph.HE↗