Darth Fader: Using wavelets to obtain accurate redshifts of spectra at very low signal-to-noise
arXiv:1309.3579 · doi:10.1051/0004-6361/201219857
Abstract
We present the DARTH FADER algorithm, a new wavelet-based method for estimating redshifts of galaxy spectra in spectral surveys that is particularly adept in the very low SNR regime. We use a standard cross-correlation method to estimate the redshifts of galaxies, using a template set built using a PCA analysis on a set of simulated, noise-free spectra. Darth Fader employs wavelet filtering to both estimate the continuum & to extract prominent line features in each galaxy spectrum. A simple selection criterion based on the number of features present in the spectrum is then used to clean the catalogue: galaxies with fewer than six total features are removed as we are unlikely to obtain a reliable redshift estimate. Applying our wavelet-based cleaning algorithm to a simulated testing set, we successfully build a clean catalogue including extremely low signal-to-noise data (SNR=2.0), for which we are able to obtain a 5.1% catastrophic failure rate in the redshift estimates (compared with 34.5% prior to cleaning). We also show that for a catalogue with uniformly mixed SNRs between 1.0 & 20.0, with realistic pixel-dependent noise, it is possible to obtain redshifts with a catastrophic failure rate of 3.3% after cleaning (as compared to 22.7% before cleaning). Whilst we do not test this algorithm exhaustively on real data, we present a proof of concept of the applicability of this method to real data, showing that the wavelet filtering techniques perform well when applied to some typical spectra from the SDSS archive. The Darth Fader algorithm provides a robust method for extracting spectral features from very noisy spectra. The resulting clean catalogue gives an extremely low rate of catastrophic failures, even when the spectra have a very low SNR. For very large sky surveys, this technique may offer a significant boost in the number of faint galaxies with accurately determined redshifts.
arXiv comment22 pages, 15 figures. Accepted for publication in Astronomy & Astrophysics
References in corpus (13)
- Stellar population synthesis at the resolution of 2003
- Spectroscopic Target Selection in the Sloan Digital Sky Survey: The Main Galaxy Sample
- The Eighth Data Release of the Sloan Digital Sky Survey: First Data from SDSS-III
- COSMOS Photometric Redshifts with 30-bands for 2-deg2
- Spectral Classification and Redshift Measurement for the SDSS-III Baryon Oscillation Spectroscopic Survey
- RVSAO 2.0: Digital Redshifts and Radial Velocities
- Controlling the False Discovery Rate in Astrophysical Data Analysis
- A new source detection algorithm using FDR
- EZ: A Tool for Automatic Redshift Measurement
- THE CANADA-FRANCE REDSHIFT SURVEY II: Spectroscopic Program; Data for the 0000-00 and 1000+25 Fields
- Automatic Redshift Determination by use of Principal Component Analysis --- I: Fundamentals
- The Star Formation History of the Virgo early-type galaxy NGC4435: the Spitzer Mid Infrared view
- Sunyaev-Zel'dovich clusters reconstruction in multiband bolometer camera surveys
Cited by in corpus (10)
- Convolutional Neural Networks for Spectroscopic Redshift Estimation on Euclid Data
- Inversion of Stokes Profiles with Systematic Effects
- TFAW: wavelet-based signal reconstruction to reduce photometric noise in time-domain surveys
- Representation learning for automated spectroscopic redshift estimation
- Automated reliability assessment for spectroscopic redshift measurements
- CIELO-RGS: a catalogue of soft X-ray ionized emission lines
- A novel stellar spectrum denoising method based on deep Bayesian modeling
- Automated quasar continuum estimation using neural networks: a comparative study of deep-learning architectures
- Redshift determination through weighted phase correlation: a linearithmic implementation
- Applying Vision Transformers on Spectral Analysis of Astronomical Objects