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A machine learning approach to galaxy properties: Joint redshift - stellar mass probability distributions with Random Forest [article]

S. Mucesh, W. G. Hartley, A. Palmese, O. Lahav, L. Whiteway, A. Amon, K. Bechtol, G. M. Bernstein, A. Carnero Rosell, M. Carrasco Kind, A. Choi, K. Eckert (+61 others)
2020 arXiv   pre-print
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

S Mucesh, W G Hartley, A Palmese, O Lahav, L Whiteway, A F L Bluck, A Alarcon, A Amon, K Bechtol, G M Bernstein, A Carnero Rosell, M Carrasco Kind (+64 others)
2021 Monthly notices of the Royal Astronomical Society  
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]

Rui Shi, Wenting Wang, Zhaozhou Li, Jiaxin Han, Jingjing Shi, Vicente Rodriguez-Gomez, Yingjie Peng
2021 arXiv   pre-print
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]

Carter L. Rhea, Julie Hlavacek-Larrondo, Laurence Perreault-Levasseur, Marie-Lou Gendron-Marsolais, Ralph Kraft
2020 arXiv   pre-print
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  
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

Masayuki Tanaka
2015 Astrophysical Journal  
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

Andy D Goulding, Jenny E Greene, Rachel Bezanson, Johnny Greco, Sean Johnson, Alexie Leauthaud, Yoshiki Matsuoka, Elinor Medezinski, Adrian M Price-Whelan
2017 Nippon Tenmon Gakkai obun kenkyu hokoku  
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]

I.B. Vavilova, D.V. Dobrycheva, M.Yu. Vasylenko, A.A. Elyiv, O.V. Melnyk, V. Khramtsov
2020 arXiv   pre-print
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

G. Aufort, L. Ciesla, P. Pudlo, V. Buat
2020 Astronomy and Astrophysics  
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]

G. Aufort, L. Ciesla, P. Pudlo, V. Buat
2020 arXiv   pre-print
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]

Dalya Baron, Hagai Netzer, Dieter Lutz, J. Xavier Prochaska, Ric I. Davies
2021 arXiv   pre-print
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

P R Kafle, A S G Robotham, S P Driver, S Deeley, P Norberg, M J Drinkwater, L J Davies
2018 Monthly notices of the Royal Astronomical Society  
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]

Christopher C. Lovell, Stephen M. Wilkins, Peter A. Thomas, Matthieu Schaller, Carlton M. Baugh, Giulio Fabbian, Yannick Bahé
2021 arXiv   pre-print
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

Ethan O. Nadler, Yao-Yuan Mao, Risa H. Wechsler, Shea Garrison-Kimmel, Andrew Wetzel
2018 Astrophysical Journal  
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 atgithub.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]

Edmond Chaussidon, Christophe Yèche, Nathalie Palanque-Delabrouille, Arnaud de Mattia, Adam D. Myers, Mehdi Rezaie, Ashley J. Ross, Hee-Jong Seo, David Brooks, Enrique Gaztañaga, Robert Kehoe, Michael E. Levi (+3 others)
2021 arXiv   pre-print
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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