75 research outputs found
Self-consistent redshift estimation using correlation functions without a spectroscopic reference sample
We present a new method to estimate redshift distributions and galaxy-dark
matter bias parameters using correlation functions in a fully data driven and
self-consistent manner. Unlike other machine learning, template, or correlation
redshift methods, this approach does not require a reference sample with known
redshifts. By measuring the projected cross- and auto- correlations of
different galaxy sub-samples, e.g., as chosen by simple cells in
color-magnitude space, we are able to estimate the galaxy-dark matter bias
model parameters, and the shape of the redshift distributions of each
sub-sample. This method fully marginalises over a flexible parameterisation of
the redshift distribution and galaxy-dark matter bias parameters of sub-samples
of galaxies, and thus provides a general Bayesian framework to incorporate
redshift uncertainty into the cosmological analysis in a data-driven,
consistent, and reproducible manner. This result is improved by an order of
magnitude by including cross-correlations with the CMB and with galaxy-galaxy
lensing.
We showcase how this method could be applied to real galaxies. By using
idealised data vectors, in which all galaxy-dark matter model parameters and
redshift distributions are known, this method is demonstrated to recover
unbiased estimates on important quantities, such as the offset
between the mean of the true and estimated redshift distribution and the 68\%
and 95\% and 99.5\% widths of the redshift distribution to an accuracy required
by current and future surveys.Comment: 20pages, 11 figures, text revised for clarification, version accepted
by journal, conclusions unchange
Feature importance for machine learning redshifts applied to SDSS galaxies
We present an analysis of importance feature selection applied to photometric
redshift estimation using the machine learning architecture Decision Trees with
the ensemble learning routine Adaboost (hereafter RDF). We select a list of 85
easily measured (or derived) photometric quantities (or `features') and
spectroscopic redshifts for almost two million galaxies from the Sloan Digital
Sky Survey Data Release 10. After identifying which features have the most
predictive power, we use standard artificial Neural Networks (aNN) to show that
the addition of these features, in combination with the standard magnitudes and
colours, improves the machine learning redshift estimate by 18% and decreases
the catastrophic outlier rate by 32%. We further compare the redshift estimate
using RDF with those from two different aNNs, and with photometric redshifts
available from the SDSS. We find that the RDF requires orders of magnitude less
computation time than the aNNs to obtain a machine learning redshift while
reducing both the catastrophic outlier rate by up to 43%, and the redshift
error by up to 25%. When compared to the SDSS photometric redshifts, the RDF
machine learning redshifts both decreases the standard deviation of residuals
scaled by 1/(1+z) by 36% from 0.066 to 0.041, and decreases the fraction of
catastrophic outliers by 57% from 2.32% to 0.99%.Comment: 10 pages, 4 figures, updated to match version accepted in MNRA
Tuning target selection algorithms to improve galaxy redshift estimates
We showcase machine learning (ML) inspired target selection algorithms to
determine which of all potential targets should be selected first for
spectroscopic follow up. Efficient target selection can improve the ML redshift
uncertainties as calculated on an independent sample, while requiring less
targets to be observed. We compare the ML targeting algorithms with the Sloan
Digital Sky Survey (SDSS) target order, and with a random targeting algorithm.
The ML inspired algorithms are constructed iteratively by estimating which of
the remaining target galaxies will be most difficult for the machine learning
methods to accurately estimate redshifts using the previously observed data.
This is performed by predicting the expected redshift error and redshift offset
(or bias) of all of the remaining target galaxies. We find that the predicted
values of bias and error are accurate to better than 10-30% of the true values,
even with only limited training sample sizes. We construct a hypothetical
follow-up survey and find that some of the ML targeting algorithms are able to
obtain the same redshift predictive power with 2-3 times less observing time,
as compared to that of the SDSS, or random, target selection algorithms. The
reduction in the required follow up resources could allow for a change to the
follow-up strategy, for example by obtaining deeper spectroscopy, which could
improve ML redshift estimates for deeper test data.Comment: 16 pages, 9 figures, updated to match MNRAS accepted version. Minor
text changes, results unchange
Anomaly detection for machine learning redshifts applied to SDSS galaxies
We present an analysis of anomaly detection for machine learning redshift
estimation. Anomaly detection allows the removal of poor training examples,
which can adversely influence redshift estimates. Anomalous training examples
may be photometric galaxies with incorrect spectroscopic redshifts, or galaxies
with one or more poorly measured photometric quantity. We select 2.5 million
'clean' SDSS DR12 galaxies with reliable spectroscopic redshifts, and 6730
'anomalous' galaxies with spectroscopic redshift measurements which are flagged
as unreliable. We contaminate the clean base galaxy sample with galaxies with
unreliable redshifts and attempt to recover the contaminating galaxies using
the Elliptical Envelope technique. We then train four machine learning
architectures for redshift analysis on both the contaminated sample and on the
preprocessed 'anomaly-removed' sample and measure redshift statistics on a
clean validation sample generated without any preprocessing. We find an
improvement on all measured statistics of up to 80% when training on the
anomaly removed sample as compared with training on the contaminated sample for
each of the machine learning routines explored. We further describe a method to
estimate the contamination fraction of a base data sample.Comment: 13 pages, 8 figures, 1 table, minor text updates to macth MNRAS
accepted versio
Stacking for machine learning redshifts applied to SDSS galaxies
We present an analysis of a general machine learning technique called
'stacking' for the estimation of photometric redshifts. Stacking techniques can
feed the photometric redshift estimate, as output by a base algorithm, back
into the same algorithm as an additional input feature in a subsequent learning
round. We shown how all tested base algorithms benefit from at least one
additional stacking round (or layer). To demonstrate the benefit of stacking,
we apply the method to both unsupervised machine learning techniques based on
self-organising maps (SOMs), and supervised machine learning methods based on
decision trees. We explore a range of stacking architectures, such as the
number of layers and the number of base learners per layer. Finally we explore
the effectiveness of stacking even when using a successful algorithm such as
AdaBoost. We observe a significant improvement of between 1.9% and 21% on all
computed metrics when stacking is applied to weak learners (such as SOMs and
decision trees). When applied to strong learning algorithms (such as AdaBoost)
the ratio of improvement shrinks, but still remains positive and is between
0.4% and 2.5% for the explored metrics and comes at almost no additional
computational cost.Comment: 13 pages, 3 tables, 7 figures version accepted by MNRAS, minor text
updates. Results and conclusions unchange
Calibrating Long Period Variables as Standard Candles with Machine Learning
Variable stars with well-calibrated period-luminosity relationships provide
accurate distance measurements to nearby galaxies and are therefore a vital
tool for cosmology and astrophysics. While these measurements typically rely on
samples of Cepheid and RR-Lyrae stars, abundant populations of luminous
variable stars with longer periods of days remain largely unused.
We apply machine learning to derive a mapping between lightcurve features of
these variable stars and their magnitude to extend the traditional
period-luminosity (PL) relation commonly used for Cepheid samples. Using
photometric data for long period variable stars in the Large Magellanic cloud
(LMC), we demonstrate that our predictions produce residual errors comparable
to those obtained on the corresponding Cepheid population. We show that our
model generalizes well to other samples by performing a blind test on
photometric data from the Small Magellanic Cloud (SMC). Our predictions on the
SMC again show small residual errors and biases, comparable to results that
employ PL relations fitted on Cepheid samples. The residual biases are
complementary between the long period variable and Cepheid fits, which provides
exciting prospects to better control sources of systematic error in
cosmological distance measurements. We finally show that the proposed
methodology can be used to optimize samples of variable stars as standard
candles independent of any prior variable star classification.Comment: 14 pages, 10 figures, 1 table, updated to match the version accepted
by the MNRA
Photometric Redshift Uncertainties in Weak Gravitational Lensing Shear Analysis: Models and Marginalization
Recovering credible cosmological parameter constraints in a weak lensing
shear analysis requires an accurate model that can be used to marginalize over
nuisance parameters describing potential sources of systematic uncertainty,
such as the uncertainties on the sample redshift distribution . Due to
the challenge of running Markov Chain Monte-Carlo (MCMC) in the high
dimensional parameter spaces in which the uncertainties may be
parameterized, it is common practice to simplify the parameterization or
combine MCMC chains that each have a fixed resampled from the
uncertainties. In this work, we propose a statistically-principled Bayesian
resampling approach for marginalizing over the uncertainty using
multiple MCMC chains. We self-consistently compare the new method to existing
ones from the literature in the context of a forecasted cosmic shear analysis
for the HSC three-year shape catalog, and find that these methods recover
similar cosmological parameter constraints, implying that using the most
computationally efficient of the approaches is appropriate. However, we find
that for datasets with the constraining power of the full HSC survey dataset
(and, by implication, those upcoming surveys with even tighter constraints),
the choice of method for marginalizing over uncertainty among the
several methods from the literature may significantly impact the statistical
uncertainties on cosmological parameters, and a careful model selection is
needed to ensure credible parameter intervals.Comment: 15 pages, 8 figures, submitted to mnra
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