Bringing Manifold Learning and Dimensionality Reduction to SED Fitters

Hemmati, Shoubaneh; Capak, P.; Pourrahmani, Milad; Nayyeri, Hooshang; Stern, Daniel; Mobasher, Bahram; Darvish, Behnam; Davidzon, I.; Ilbert, O.; Masters, D. C.; Shahidi, Abtin

doi:10.3847/2041-8213/ab3418

Cited by 31 publications

(20 citation statements)

References 46 publications

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“…Clustering has been used, for example, to partition galaxies on the basis of their pixel data (Hocking et al 2017(Hocking et al , 2018Martin et al 2020), their spectra (Sánchez Almeida et al 2010;de Souza et al 2017), their SEDs (Siudek et al 2018b,a), and their derived astrophysical features (Barchi et al 2016;Turner et al 2019). Dimensionality reduction, which can extract important or discriminative information from large ensembles of input features, has been used, for example, to produce simplified projections of galaxy samples based on their multi-wavelength photometry (Steinhardt et al 2020) and their estimated SEDs (Davidzon et al 2019;Hemmati et al 2019), and to classify their spectra (Yip et al 2004;Marchetti et al 2013).…”

Section: Introductionmentioning

confidence: 99%

Synergies between low- and intermediate-redshift galaxy populations revealed with unsupervised machine learning

Turner

Siudek

Salim

et al. 2021

Monthly Notices of the Royal Astronomical Society

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The colour bimodality of galaxies provides an empirical basis for theories of galaxy evolution. However, the balance of processes that begets this bimodality has not yet been constrained. A more detailed view of the galaxy population is needed, which we achieve in this paper by using unsupervised machine learning to combine multi-dimensional data at two different epochs. We aim to understand the cosmic evolution of galaxy subpopulations by uncovering substructures within the colour bimodality. We choose a clustering algorithm that models clusters using only the most discriminative data available, and apply it to two galaxy samples: one from the second edition of the GALEX-SDSS-WISE Legacy Catalogue (GSWLC-2; z ∼ 0.06), and the other from the VIMOS Public Extragalactic Redshift Survey (VIPERS; z ∼ 0.65). We cluster within a nine-dimensional feature space defined purely by rest-frame ultraviolet-through-near-infrared colours. Both samples are similarly partitioned into seven clusters, breaking down into four of mostly star-forming galaxies (including the vast majority of green valley galaxies) and three of mostly passive galaxies. The separation between these two families of clusters suggests differences in the evolution of their galaxies, and that these differences are strongly expressed in their colours alone. The samples are closely related, with star-forming/green-valley clusters at both epochs forming morphological sequences, capturing the gradual internally-driven growth of galaxy bulges. At high stellar masses, this growth is linked with quenching. However, it is only in our low-redshift sample that additional, environmental processes appear to be involved in the evolution of low-mass passive galaxies.

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Section: Introductionmentioning

confidence: 99%

Synergies between low- and intermediate-redshift galaxy populations revealed with unsupervised machine learning

Turner

Siudek

Salim

et al. 2021

Monthly Notices of the Royal Astronomical Society

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“…Therefore, it is computationally more feasible than SED or UVJ based selections when a measure of the sensitivity to photometric uncertainties is needed. Hemmati et al (2019) has proposed a faster method of measuring the physical properties of galaxies and their uncertainties using Kohonen's Self-Organizing Map (SOM) (Kohonen 1982). SOM is a neural network that maps the entire color space of galaxies into lower dimensions while preserving the topology of the input space (Kiviluoto 1996;Villmann 1999).…”

Section: Discussionmentioning

confidence: 99%

Selection of massive evolved galaxies at $3 \leq z \leq 4.5$ in the CANDELS fields

Shahidi,

Mobasher,

Nayyeri

et al. 2020

Preprint

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Using the CANDELS photometric catalogs for the HST /ACS and WFC3, we identified massive evolved galaxies at 3 < z < 4.5, employing three different selection methods. We find the comoving number density of these objects to be ∼ 2 × 10 −5 and 8 × 10 −6 M pc −3 after correction for completeness for two redshift bins centered at z = 3.4, 4.7. We quantify a measure of how much confidence we should have for each candidate galaxy from different selections and what are the conservative error estimates propagated into our selection. Then we compare the evolution of the corresponding number densities and their stellar mass density with numerical simulations, semi-analytical models, and previous observational estimates, which shows slight tension at higher redshifts as the models tend to underestimate the number and mass densities. By estimating the average halo masses of the candidates (M h ≈ 4.2, 1.9, 1.3 × 10 12 M for redshift bins centered at z = 3.4, 4.1, 4.7), we find them to be consistent with halos that were efficient in turning baryons to stars and were relatively immune to the feedback effects and on the verge of transition into hot-mode accretion. This can suggest the relative cosmological starvation of the cold gas followed by an overconsumption phase in which the galaxy consumes the available cold gas rapidly as one of the possible drivers for the quenching of the massive evolved population at high redshift.

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“…Recently, ML has been applied to derive various physical parameters of galaxies (e.g., Masters et al 2015;Krakowski et al 2016;D'Isanto & Polsterer 2018;Hemmati et al 2019;Davidzon et al 2019;Bonjean et al 2019). In particular, classification is one of the important issues on galaxy and AGN properties (e.g., Padovani et al 2017;Hickox & Alexander 2018, and references therein).…”

Section: Introductionmentioning

confidence: 99%

Identifying AGN host galaxies by Machine Learning with HSC+WISE

Chang,

Hsieh,

Wang

et al. 2021

Preprint

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We use machine learning techniques to investigate their performance in classifying active galactic nuclei (AGNs), including X-ray selected AGNs (XAGNs), infrared selected AGNs (IRAGNs), and radio selected AGNs (RAGNs). Using known physical parameters in the Cosmic Evolution Survey (COSMOS) field, we are able to well-established training samples in the region of Hyper Suprime-Cam (HSC) survey. We compare several Python packages (e.g., scikit-learn, Keras, and XGBoost), and use XGBoost to identify AGNs and show the performance (e.g., accuracy, precision, recall, F1 score, and AUROC). Our results indicate that the performance is high for bright XAGN and IRAGN host galaxies. The combination of the HSC (optical) information with the Wide-field Infrared Survey Explorer (WISE) band-1 and WISE band-2 (near-infrared) information perform well to identify AGN hosts. For both type-1 (broad-line) XAGNs and type-1 (unobscured) IRAGNs, the performance is very good by using optical to infrared information. These results can apply to the five-band data from the wide regions of the HSC survey, and future all-sky surveys.

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Bringing Manifold Learning and Dimensionality Reduction to SED Fitters

Cited by 31 publications

References 46 publications

Synergies between low- and intermediate-redshift galaxy populations revealed with unsupervised machine learning

Synergies between low- and intermediate-redshift galaxy populations revealed with unsupervised machine learning

Selection of massive evolved galaxies at $3 \leq z \leq 4.5$ in the CANDELS fields

Identifying AGN host galaxies by Machine Learning with HSC+WISE

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