Browsing by Autor "C. Allende Prieto"

Filter results by typing the first few letters
Now showing 1 - 2 of 2
  • Thumbnail Image
    Item type: Item ,
    DESI DR2 results. I. Baryon acoustic oscillations from the Lyman alpha forest
    (American Physical Society, 2025) M. Abdul Karim; J. Aguilar; S. Ahlen; C. Allende Prieto; O. Alves; A. Anand; U. Andrade; E. Armengaud; A. Aviles; S. Bailey
    We present the baryon acoustic oscillation (BAO) measurements with the Lyman-<a:math xmlns:a="http://www.w3.org/1998/Math/MathML" display="inline"><a:mi>α</a:mi></a:math> (<c:math xmlns:c="http://www.w3.org/1998/Math/MathML" display="inline"><c:mi>Ly</c:mi><c:mi>α</c:mi></c:math>) forest from the second data release (DR2) of the Dark Energy Spectroscopic Instrument (DESI) survey. Our BAO measurements include both the autocorrelation of the <e:math xmlns:e="http://www.w3.org/1998/Math/MathML" display="inline"><e:mi>Ly</e:mi><e:mi>α</e:mi></e:math> forest absorption observed in the spectra of high-redshift quasars and the cross-correlation of the absorption with the quasar positions. The total sample size is approximately a factor of 2 larger than the DR1 dataset, with forest measurements in over 820,000 quasar spectra and the positions of over 1.2 million quasars. We describe several significant improvements to our analysis in this paper, and two supporting papers describe improvements to the synthetic datasets that we use for validation and how we identify damped <g:math xmlns:g="http://www.w3.org/1998/Math/MathML" display="inline"><g:mi>Ly</g:mi><g:mi>α</g:mi></g:math> absorbers. Our main result is that we have measured the BAO scale with a statistical precision of 1.1% along and 1.3% transverse to the line of sight, for a combined precision of 0.65% on the isotropic BAO scale at <i:math xmlns:i="http://www.w3.org/1998/Math/MathML" display="inline"><i:msub><i:mi>z</i:mi><i:mi>eff</i:mi></i:msub><i:mo>=</i:mo><i:mn>2.33</i:mn></i:math>. This excellent precision, combined with recent theoretical studies of the BAO shift due to nonlinear growth, motivated us to include a systematic error term in <k:math xmlns:k="http://www.w3.org/1998/Math/MathML" display="inline"><k:mi>Ly</k:mi><k:mi>α</k:mi></k:math> BAO analysis for the first time. We measure the ratios <m:math xmlns:m="http://www.w3.org/1998/Math/MathML" display="inline"><m:msub><m:mi>D</m:mi><m:mi>H</m:mi></m:msub><m:mo stretchy="false">(</m:mo><m:msub><m:mi>z</m:mi><m:mi>eff</m:mi></m:msub><m:mo stretchy="false">)</m:mo><m:mo>/</m:mo><m:msub><m:mi>r</m:mi><m:mi>d</m:mi></m:msub><m:mo>=</m:mo><m:mn>8.632</m:mn><m:mo>±</m:mo><m:mn>0.098</m:mn><m:mo>±</m:mo><m:mn>0.026</m:mn></m:math> and <q:math xmlns:q="http://www.w3.org/1998/Math/MathML" display="inline"><q:msub><q:mi>D</q:mi><q:mi>M</q:mi></q:msub><q:mo stretchy="false">(</q:mo><q:msub><q:mi>z</q:mi><q:mi>eff</q:mi></q:msub><q:mo stretchy="false">)</q:mo><q:mo>/</q:mo><q:msub><q:mi>r</q:mi><q:mi>d</q:mi></q:msub><q:mo>=</q:mo><q:mn>38.99</q:mn><q:mo>±</q:mo><q:mn>0.52</q:mn><q:mo>±</q:mo><q:mn>0.12</q:mn></q:math>, where <u:math xmlns:u="http://www.w3.org/1998/Math/MathML" display="inline"><u:msub><u:mi>D</u:mi><u:mi>H</u:mi></u:msub><u:mo>=</u:mo><u:mi>c</u:mi><u:mo>/</u:mo><u:mi>H</u:mi><u:mo stretchy="false">(</u:mo><u:mi>z</u:mi><u:mo stretchy="false">)</u:mo></u:math> is the Hubble distance, <y:math xmlns:y="http://www.w3.org/1998/Math/MathML" display="inline"><y:msub><y:mi>D</y:mi><y:mi>M</y:mi></y:msub></y:math> is the transverse comoving distance, <ab:math xmlns:ab="http://www.w3.org/1998/Math/MathML" display="inline"><ab:msub><ab:mi>r</ab:mi><ab:mi>d</ab:mi></ab:msub></ab:math> is the sound horizon at the drag epoch, and we quote both the statistical and the theoretical systematic uncertainty. The companion paper presents the BAO measurements at lower redshifts from the same dataset and the cosmological interpretation.
  • Thumbnail Image
    Item type: Item ,
    Identifying Anomalous DESI Galaxy Spectra with a Variational Autoencoder
    (Oxford University Press, 2026) C. Nicolaou; Rowina S Nathan; O Lahav; A. Palmese; A. Saintonge; J. Aguilar; S. P. Ahlen; C. Allende Prieto; S. Bailey; S. BenZvi
    ABSTRACT The tens of millions of spectra being captured by the Dark Energy Spectroscopic Instrument (DESI) provide tremendous discovery potential. In this work we show how Machine Learning, in particular Variational Autoencoders (VAE), can detect anomalies in a sample of approximately 200 000 DESI spectra comprising galaxies, quasars and stars. We demonstrate that the VAE can compress the dimensionality of a spectrum by a factor of 100, while still retaining enough information to accurately reconstruct spectral features. We detect anomalous spectra as those with high reconstruction error and those which are isolated in the VAE latent representation. The anomalies identified fall into two categories: spectra with artefacts and spectra with unique physical features. Awareness of the former could improve the DESI spectroscopic pipeline; whilst the latter could help us discover new and unusual objects. To further curate the list of outliers identified, we use the Astronomaly package which employs Active Learning to provide personalized outlier recommendations for visual inspection. In this work we also explore the VAE latent space, finding that different object classes and subclasses are separated despite being unlabelled. We inject controlled synthetic anomalies and analyse their locations in the latent space to illustrate how the VAE responds to atypical spectral features; and we demonstrate the interpretability of this latent space by identifying tracks within it that correspond to various spectral characteristics. In upcoming work we hope to apply the methods presented here to search for both systematics and astrophysically interesting objects in much larger datasets of DESI spectra.