4,599 research outputs found
Stellar formation rates in galaxies using Machine Learning models
Global Stellar Formation Rates or SFRs are crucial to constrain theories of
galaxy formation and evolution. SFR's are usually estimated via spectroscopic
observations which require too much previous telescope time and therefore
cannot match the needs of modern precision cosmology. We therefore propose a
novel method to estimate SFRs for large samples of galaxies using a variety of
supervised ML models.Comment: ESANN 2018 - Proceedings, ISBN-13 978287587048
Comparison of Observed Galaxy Properties with Semianalytic Model Predictions using Machine Learning
With current and upcoming experiments such as WFIRST, Euclid and LSST, we can
observe up to billions of galaxies. While such surveys cannot obtain spectra
for all observed galaxies, they produce galaxy magnitudes in color filters.
This data set behaves like a high-dimensional nonlinear surface, an excellent
target for machine learning. In this work, we use a lightcone of semianalytic
galaxies tuned to match CANDELS observations from Lu et al. (2014) to train a
set of neural networks on a set of galaxy physical properties. We add realistic
photometric noise and use trained neural networks to predict stellar masses and
average star formation rates on real CANDELS galaxies, comparing our
predictions to SED fitting results. On semianalytic galaxies, we are nearly
competitive with template-fitting methods, with biases of dex for
stellar mass, dex for star formation rate, and dex for
metallicity. For the observed CANDELS data, our results are consistent with
template fits on the same data at dex bias in and
dex bias in star formation rate. Some of the bias is driven by SED-fitting
limitations, rather than limitations on the training set, and some is intrinsic
to the neural network method. Further errors are likely caused by differences
in noise properties between the semianalytic catalogs and data. Our results
show that galaxy physical properties can in principle be measured with neural
networks at a competitive degree of accuracy and precision to template-fitting
methods.Comment: 19 pages, 10 figures, 6 tables. Accepted for publication in Ap
Unsupervised feature-learning for galaxy SEDs with denoising autoencoders
With the increasing number of deep multi-wavelength galaxy surveys, the
spectral energy distribution (SED) of galaxies has become an invaluable tool
for studying the formation of their structures and their evolution. In this
context, standard analysis relies on simple spectro-photometric selection
criteria based on a few SED colors. If this fully supervised classification
already yielded clear achievements, it is not optimal to extract relevant
information from the data. In this article, we propose to employ very recent
advances in machine learning, and more precisely in feature learning, to derive
a data-driven diagram. We show that the proposed approach based on denoising
autoencoders recovers the bi-modality in the galaxy population in an
unsupervised manner, without using any prior knowledge on galaxy SED
classification. This technique has been compared to principal component
analysis (PCA) and to standard color/color representations. In addition,
preliminary results illustrate that this enables the capturing of extra
physically meaningful information, such as redshift dependence, galaxy mass
evolution and variation over the specific star formation rate. PCA also results
in an unsupervised representation with physical properties, such as mass and
sSFR, although this representation separates out. less other characteristics
(bimodality, redshift evolution) than denoising autoencoders.Comment: 11 pages and 15 figures. To be published in A&
A Method to Distinguish Quiescent and Dusty Star-forming Galaxies with Machine Learning
Large photometric surveys provide a rich source of observations of quiescent galaxies, including a surprisingly large population at z > 1. However, identifying large, but clean, samples of quiescent galaxies has proven difficult because of their near-degeneracy with interlopers such as dusty, star-forming galaxies. We describe a new technique for selecting quiescent galaxies based upon t-distributed stochastic neighbor embedding (t-SNE), an unsupervised machine-learning algorithm for dimensionality reduction. This t-SNE selection provides an improvement both over UVJ, removing interlopers that otherwise would pass color selection, and over photometric template fitting, more strongly toward high redshift. Due to the similarity between the colors of high- and low-redshift quiescent galaxies, under our assumptions, t-SNE outperforms template fitting in 63% of trials at redshifts where a large training sample already exists. It also may be able to select quiescent galaxies more efficiently at higher redshifts than the training sample
Predicting spectral features in galaxy spectra from broad-band photometry
We explore the prospects of predicting emission line features present in
galaxy spectra given broad-band photometry alone. There is a general consent
that colours, and spectral features, most notably the 4000 A break, can predict
many properties of galaxies, including star formation rates and hence they
could infer some of the line properties. We argue that these techniques have
great prospects in helping us understand line emission in extragalactic objects
and might speed up future galaxy redshift surveys if they are to target
emission line objects only. We use two independent methods, Artifical Neural
Neworks (based on the ANNz code) and Locally Weighted Regression (LWR), to
retrieve correlations present in the colour N-dimensional space and to predict
the equivalent widths present in the corresponding spectra. We also investigate
how well it is possible to separate galaxies with and without lines from broad
band photometry only. We find, unsurprisingly, that recombination lines can be
well predicted by galaxy colours. However, among collisional lines some can and
some cannot be predicted well from galaxy colours alone, without any further
redshift information. We also use our techniques to estimate how much
information contained in spectral diagnostic diagrams can be recovered from
broad-band photometry alone. We find that it is possible to classify AGN and
star formation objects relatively well using colours only. We suggest that this
technique could be used to considerably improve redshift surveys such as the
upcoming FMOS survey and the planned WFMOS survey.Comment: 10 pages 7 figures summitted to MNRA
An Active Instance-based Machine Learning method for Stellar Population Studies
We have developed a method for fast and accurate stellar population
parameters determination in order to apply it to high resolution galaxy
spectra. The method is based on an optimization technique that combines active
learning with an instance-based machine learning algorithm. We tested the
method with the retrieval of the star-formation history and dust content in
"synthetic" galaxies with a wide range of S/N ratios. The "synthetic" galaxies
where constructed using two different grids of high resolution theoretical
population synthesis models. The results of our controlled experiment shows
that our method can estimate with good speed and accuracy the parameters of the
stellar populations that make up the galaxy even for very low S/N input. For a
spectrum with S/N=5 the typical average deviation between the input and fitted
spectrum is less than 10**{-5}. Additional improvements are achieved using
prior knowledge.Comment: 14 pages, 25 figures, accepted by Monthly Notice
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