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A machine learning approach to galaxy properties: Joint redshift - stellar mass probability distributions with Random Forest
[article]
2020
arXiv
pre-print
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We demonstrate that highly accurate joint redshift - stellar mass PDFs can be obtained using the Random Forest (RF) machine learning (ML) algorithm, even with few photometric bands available. ...
As an example, we use the Dark Energy Survey (DES), combined with the COSMOS2015 catalogue for redshifts and stellar masses. ...
In this work we tackle the problem by using a machine learning (ML) based approach. We introduce a novel method based on the Random Forest (RF) algorithm to generate joint PDFs. ...
arXiv:2012.05928v1
fatcat:a43ftabs6jcdxbco7kujnr7u6a
A machine learning approach to galaxy properties: Joint redshift - stellar mass probability distributions with Random Forest
2021
Monthly notices of the Royal Astronomical Society
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We demonstrate that highly accurate joint redshift - stellar mass PDFs can be obtained using the Random Forest (RF) machine learning (ML) algorithm, even with few photometric bands available. ...
As an example, we use the Dark Energy Survey (DES), combined with the COSMOS2015 catalogue for redshifts and stellar masses. ...
OL acknowledges support from a European Research Council Advanced grant TESTDE FP7/291329 and an STFC Consolidated grants ST/M001334/1 and ST/R000476/1. ...
doi:10.1093/mnras/stab164
fatcat:pabkwutmdjapfgt67io7qft3va
A machine learning approach to infer the accreted stellar mass fractions of galaxies
[article]
2021
arXiv
pre-print
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We propose a random forest (RF) machine learning approach to determine the accreted stellar mass fractions (f_acc) of central galaxies, based on various dark matter halo and galaxy features. ...
For galaxies with log_10M_∗/M_⊙>10.6, global features such as halo mass, size and stellar mass are more important in determining f_acc, whereas for galaxies with log_10M_∗/M_⊙⩽ 10.6, features related to ...
RS is grateful for discussions on the random forest machine learning method and algorithm with Yanrui Zhou and Wei Zhang, on halo angular momentum with Yifeng Zhou and Fuyu Dong and on galaxy images with ...
arXiv:2112.07203v1
fatcat:mcr6qwu2pnckph5p4n74pdrgvq
A Novel Machine Learning Approach to Disentangle Multi-Temperature Regions in Galaxy Clusters
[article]
2020
arXiv
pre-print
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In this paper, we present a novel approach to determining the number of components using an amalgam of machine learning techniques. ...
The dimensions of the training set was initially reduced using the Principal Component Analysis and then categorized based on the number of underlying components using a Random Forest Classifier. ...
PERSEUS CLUSTER OBSERVATIONS In Fig. 8 , we show the Chandra X-ray observations of the Perseus cluster used to test our machine learning approach. ...
arXiv:2009.00643v1
fatcat:on2h6i27j5bavfrno5psfjsepq
OUP accepted manuscript
2020
Monthly notices of the Royal Astronomical Society
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2020 pre-print arXiv:2002.03578v1 fatcat:avcd4uxq5jf3nlq7vlz66jdxei
As multiband flux measurements become available for thousands of galaxies, an alternative approach to characterising star formation using machine learning becomes feasible. ...
In this work, we present the use of deep learning techniques to predict three important star formation properties -- stellar mass, star formation rate and dust luminosity. ...
GAMA is a joint European-Australasian project based around a spectroscopic campaign using the Anglo-Australian Telescope. ...
doi:10.1093/mnras/staa537
fatcat:jn7r2hikabfahptgerlon7sbva
PHOTOMETRIC REDSHIFT WITH BAYESIAN PRIORS ON PHYSICAL PROPERTIES OF GALAXIES
2015
Astrophysical Journal
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2015 pre-print arXiv:1501.02047v1 fatcat:vxdn5fcfb5azleahuyheqe3xfa
We construct model templates of galaxies using a stellar population synthesis code and apply Bayesian priors on physical properties such as stellar mass and star formation rate. ...
We present a proof-of-concept analysis of photometric redshifts with Bayesian priors on physical properties of galaxies. ...
This work is based on observations obtained with MegaPrime/MegaCam, a joint project of CFHT and CEA/IRFU, at the Canada-France-Hawaii Telescope (CFHT) which is operated by the National Research Council ...
doi:10.1088/0004-637x/801/1/20
fatcat:gpuzfuldsbaljnzajfyut2r77u
Galaxy interactions trigger rapid black hole growth: An unprecedented view from the Hyper Suprime-Cam survey
2017
Nippon Tenmon Gakkai obun kenkyu hokoku
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2017 pre-print arXiv:1706.07436v1 fatcat:mjvtycie4fgqrluxkihbpyjyqeOther Versions
We identify galaxy interaction signatures using a novel machine-learning random forest decision tree technique allowing us to select statistically significant samples of major-mergers, minor-mergers/irregular-systems ...
We use these samples to show that galaxies undergoing mergers are a factor ~2-7 more likely to contain luminous obscured AGN than non-interacting galaxies, and this is independent of both stellar mass ...
This publication makes use of data products from the Wide-field Infrared Survey Explorer, which is a joint project of the University of California, Los Angeles, and the Jet Propulsion Laboratory/California ...
doi:10.1093/pasj/psx135
fatcat:aru6ymqevbf7njyis6ieb6wifu
Machine learning technique for morphological classification of galaxies at z<0.1 from the SDSS
[article]
2020
arXiv
pre-print
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We applied its to the sample of galaxies from the SDSS DR9 with redshifts of 0.02 < z < 0.1 and absolute stellar magnitudes of 24m < Mr < 19.4m. ...
The methods of Support Vector Machine and Random Forest with Scikit-learn machine learning in Python provide the highest accuracy for the binary galaxy morphological classification: 96.4% correctly classified ...
Makarov et al. (2014) and SDSS IV Blanton et al. (2017) were helpful to our study. ...
arXiv:1712.08955v2
fatcat:ucenkhz7hzezdelenbwfdvxjee
Constraining the recent star formation history of galaxies: an Approximate Bayesian Computation approach
2020
Astronomy and Astrophysics
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The stellar mass distribution of galaxies with a strong to decisive evidence against the smooth delayed-τ SFH peaks at lower M * than for galaxies where the smooth delayed-τ SFH is preferred. ...
We applied approximate Bayesian computation, a custom statistical method for performing model choice, which is associated with machine-learning algorithms to provide the probability that a flexible SFH ...
The research leading to these results was partially financed via the PEPS Astro-Info program of the CNRS. P. ...
doi:10.1051/0004-6361/201936788
fatcat:qaammzjwqffi3mtwrvv5yfrfjy
Constraining the recent star formation history of galaxies : an Approximate Bayesian Computation approach
[article]
2020
arXiv
pre-print
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The stellar mass distribution of galaxies with a strong to decisive evidence against the smooth delayed-τ SFH peaks at lower M* compared to galaxies where the smooth delayed-τ SFH is preferred. ...
We apply Approximate Bayesian Computation, a state-of-the-art statistical method to perform model choice, associated to machine learning algorithms to provide the probability that a flexible SFH is preferred ...
The research leading to these results was partially financed via the PEPS Astro-Info program of the CNRS. P. ...
arXiv:2002.07815v1
fatcat:64kw6pbsvzhhfpyzqxy5vk5u5q
Multi-phase outflows in post starburst E+A galaxies – I. General sample properties and the prevalence of obscured starbursts
[article]
2021
arXiv
pre-print
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E+A galaxies are believed to be a short phase connecting major merger ULIRGs with red and dead elliptical galaxies. ...
The mean mass outflow rate and kinetic power of the ionized outflows in our sample (Ṁ∼ 1 M_⊙/yr, Ė∼ 10^41 erg/sec) are larger than those derived for active galaxies of similar AGN luminosity and stellar ...
presented a novel method to estimate distances between spectra using an Unsupervised Random Forest algorithm. ...
arXiv:2105.08071v2
fatcat:wbgoix26hratrbhiu2dxlinliu
Galaxy tagging: photometric redshift refinement and group richness enhancement
2018
Monthly notices of the Royal Astronomical Society
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We present a new scheme, galtag, for refining the photometric redshift measurements of faint galaxies by probabilistically tagging them to observed galaxy groups constructed from a brighter, magnitude-limited ...
This region contains Galaxy and Mass Assembly (GAMA) deep spectroscopic observations (i-band<22) and an accompanying group catalogue to r-band<19.8. ...
We like to thank Violeta Gonzalez-perez for providing us DESI light-cones, Gary Mamon (IAP) for maggie related Q&A, Maciej Bilicki for comments on the photometric redshift aspects, and Rob Finnegan and ...
doi:10.1093/mnras/sty1536
fatcat:shsq67sex5dvjkto3ncjbtfwne
A machine learning approach to mapping baryons onto dark matter haloes using the EAGLE and C-EAGLE simulations
[article]
2021
arXiv
pre-print
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2021 pre-print arXiv:2106.04980v1 fatcat:hwqyhw7vhzf3hldi7g35hs3dmy
We train a tree based machine learning method to predict the baryonic properties of galaxies based on their host dark matter halo properties. ...
We propose a new approach, using a machine learning framework to explore the halo-galaxy relationship in the periodic EAGLE simulations, and zoom C-EAGLE simulations of galaxy clusters. ...
access the subhalo properties for the periodic hydrodynamic simulations in this paper (McAlpine et al. 2016) . ...
arXiv:2106.04980v2
fatcat:tx5e63h4czb3pa3pl3tudlkyru
Modeling the Impact of Baryons on Subhalo Populations with Machine Learning
2018
Astrophysical Journal
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We identify subhalos in dark matter-only (DMO) zoom-in simulations that are likely to be disrupted due to baryonic effects by using a random forest classifier trained on two hydrodynamic simulations of ...
Our five-property classifier identifies disrupted subhalos in the FIRE simulations with an 85% out-of-bag classification score. ...
We have made our code and trained classifier publicly available atgithub.com/ollienad/subhalo_randomforest; please contact the authors with data requests. ...
doi:10.3847/1538-4357/aac266
fatcat:wntd6d4nt5hg5kok43lg5vfo6q
Angular clustering properties of the DESI QSO target selection using DR9 Legacy Imaging Surveys
[article]
2021
arXiv
pre-print
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To model this complex relation, we explore machine learning algorithms (Random Forest and Multi-Layer Perceptron) as an alternative to the standard linear regression. ...
area is due to a stellar contamination which should be removed with DESI spectroscopic data. ...
ACKNOWLEDGEMENTS The authors are honored to be permitted to conduct astronomical research on Iolkam Du'ag (Kitt Peak), a mountain with particular significance to the Tohono O'odham Nation. ...
arXiv:2108.03640v1
fatcat:qyep7noplrbopj4v4kmhmcxija
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