Clustering of the AKARI NEP deep field 24<i>μ</i>m selected galaxies

Solarz, A.; Pollo, A.; Takeuchi, Tsutomu T.; Małek, K.; Matsuhara, Hideo; White, G. J.; Pępiak, Agata; Goto, Tomotsugu; Wada, Takehiko; Oyabu, Shinki; Takagi, Toshinobu; Ohyama, Youichi; Pearson, Chris; Hanami, H.; Ishigaki, T.; Malkan, Matthew A.

doi:10.1051/0004-6361/201423370

Cited by 10 publications

(12 citation statements)

References 94 publications

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“…Our result provides a meaningful constraint on the clustering amplitude of SMGs at z;0.5-1, a key redshift range where downsizing effects are expected to take place. At z;0.5-1, the clustering signal of our SMG candidates appears to be a little higher than in the previous studies of Magliocchetti et al (2013), Dolley et al (2014), and Solarz et al (2015); although, there are large uncertainties. It is worth noting that the majority of the sources in the aforementioned studies represent a fainter population with L IR ; 10 11 L ☉ .…”

Section: Clustering Signalscontrasting

confidence: 69%

“…Figure 7 shows the values of r 0 as a function of redshift for our SMG candidates and for comparison samples. We also plot the measurements from the literature for 24 μm-selected galaxies (Dolley et al 2014;Solarz et al 2015), 100 μm-selected Herschel SMGs (Magliocchetti et al 2013), 250 μm-selected Herschel sources (Amvrosiadis et al 2019), 850 μm-selected SMGs (Webb et al 2003;Blain et al 2004;Weiß et al 2009;Williams et al 2011;Hickox et al 2012;Wilkinson et al 2017;An et al 2019), and quasars (Myers et al 2006;Porciani & Norberg 2006;Shen et al 2007;Eftekharzadeh et al 2015). The measured r 0 (or b) of our SMG candidates and the comparison samples decline with decreasing redshift (Table 3).…”

Section: Dark Matter Halo Massmentioning

confidence: 99%

“…The question now arises: are the SMGs at z<1 the same dusty population as those at z>1? The dusty star-forming galaxy population seems to have lower L IR at low redshifts (e.g., Magliocchetti et al 2013;Dolley et al 2014;Solarz et al 2015), which prevents us from making a direct comparison with the SMGs at high redshifts. Nevertheless, our understanding of the clustering properties in SMGs is still far from complete.…”

Section: Clustering Signalsmentioning

confidence: 99%

“…Observations at mid-infrared (e.g., 24 μm) can be used to overcome the aforementioned selection bias and probe the clustering signals in the key redshift range of 0.3<z<1.0 (Gilli et al 2007;Magliocchetti et al 2008;Starikova et al 2012;Dolley et al 2014;Solarz et al 2015). These studies found relatively lower clustering strengths at z<1 with clustering length r 0 =3-6 h −1 Mpc compared to the highredshift measurements.…”

Section: Introductionmentioning

confidence: 98%

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SCUBA-2 Ultra Deep Imaging EAO Survey (STUDIES). IV. Spatial Clustering and Halo Masses of Submillimeter Galaxies

Lim

Chen

Smail

et al. 2020

ApJ

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We analyze an extremely deep 450 μm image (1σ=0.56 mJy beam −1) of a ;300 arcmin 2 area in the CANDELS/ COSMOS field as part of the Sub-millimeter Common User Bolometric Array-2 Ultra Deep Imaging EAO Survey. We select a robust (signal-to-noise ratio 4) and flux-limited (4 mJy) sample of 164 submillimeter galaxies (SMGs) at 450 μm that have K-band counterparts in the COSMOS2015 catalog identified from radio or midinfrared imaging. Utilizing this SMG sample and the 4705 K-band-selected non-SMGs that reside within the noise level 1 mJy beam −1 region of the 450 μm image as a training set, we develop a machine-learning classifier using K-band magnitude and color-color pairs based on the 13-band photometry available in this field. We apply the trained machine-learning classifier to the wider COSMOS field (1.6 deg 2) using the same COSMOS2015 catalog and identify a sample of 6182 SMG candidates with similar colors. The number density, radio and/or mid-infrared detection rates, redshift and stellar-mass distributions, and the stacked 450 μm fluxes of these SMG candidates, from the S2COSMOS observations of the wide field, agree with the measurements made in the much smaller CANDELS field, supporting the effectiveness of the classifier. Using this SMG candidate sample, we measure the two-point autocorrelation functions from z=3 down to z=0.5. We find that the SMG candidates reside in halos with masses of ;(2.0±0.5)×10 13 h −1 M ☉ across this redshift range. We do not find evidence of downsizing that has been suggested by other recent observational studies.

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Section: Clustering Signalscontrasting

confidence: 69%

Section: Dark Matter Halo Massmentioning

confidence: 99%

Section: Clustering Signalsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

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SCUBA-2 Ultra Deep Imaging EAO Survey (STUDIES). IV. Spatial Clustering and Halo Masses of Submillimeter Galaxies

Lim

Chen

Smail

et al. 2020

ApJ

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“…For many applications SVM have shown better performance and accuracy than other learning machines and have been used in many branches of astrophysics to solve classification problems and build catalogues (e.g. Beaumont et al 2011;Fadely et al 2012;Małek et al 2013;Solarz et al 2015;Kovács & Szapudi 2015;Heinis et al 2016;Marton et al 2016;Kurcz et al 2016;Krakowski et al 2016). Support vector machines maps input points onto a high-dimensional feature space and finds a hyperplane separating two or more classes with as large a margin as possible between points belonging to each category in this space.…”

Section: Support Vector Machinesmentioning

confidence: 99%

Automated novelty detection in the WISE survey with one-class support vector machines

Solarz

Bilicki

Gromadzki

et al. 2017

A&A

Self Cite

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Wide-angle photometric surveys of previously uncharted sky areas or wavelength regimes will always bring in unexpected sourcesnovelties or even anomalies -whose existence and properties cannot be easily predicted from earlier observations. Such objects can be efficiently located with novelty detection algorithms. Here we present an application of such a method, called one-class support vector machines (OCSVM), to search for anomalous patterns among sources preselected from the mid-infrared AllWISE catalogue covering the whole sky. To create a model of expected data we train the algorithm on a set of objects with spectroscopic identifications from the SDSS DR13 database, present also in AllWISE. The OCSVM method detects as anomalous those sources whose patterns -WISE photometric measurements in this case -are inconsistent with the model. Among the detected anomalies we find artefacts, such as objects with spurious photometry due to blending, but more importantly also real sources of genuine astrophysical interest. Among the latter, OCSVM has identified a sample of heavily reddened AGN/quasar candidates distributed uniformly over the sky and in a large part absent from other WISE-based AGN catalogues. It also allowed us to find a specific group of sources of mixed types, mostly stars and compact galaxies. By combining the semi-supervised OCSVM algorithm with standard classification methods it will be possible to improve the latter by accounting for sources which are not present in the training sample, but are otherwise well-represented in the target set. Anomaly detection adds flexibility to automated source separation procedures and helps verify the reliability and representativeness of the training samples. It should be thus considered as an essential step in supervised classification schemes to ensure completeness and purity of produced catalogues.

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Active galactic nuclei catalog from the AKARI NEP-Wide field

Poliszczuk

Pollo

Małek

et al. 2021

A&A

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Context. The north ecliptic pole (NEP) field provides a unique set of panchromatic data that are well suited for active galactic nuclei (AGN) studies. The selection of AGN candidates is often based on mid-infrared (MIR) measurements. Such methods, despite their effectiveness, strongly reduce the breadth of resulting catalogs due to the MIR detection condition. Modern machine learning techniques can solve this problem by finding similar selection criteria using only optical and near-infrared (NIR) data. Aims. The aim of this study is to create a reliable AGN candidates catalog from the NEP field using a combination of optical SUBARU/HSC and NIR AKARI/IRC data and, consequently, to develop an efficient alternative for the MIR-based AKARI/IRC selection technique. Methods. We tested set of supervised machine learning algorithms for the purposes of carrying out an efficient process for AGN selection. The best models were compiled into a majority voting scheme, which used the most popular classification results to produce the final AGN catalog. An additional analysis of the catalog properties was performed as a spectral energy distribution (SED) fitting via the CIGALE software. Results. The obtained catalog of 465 AGN candidates (out of 33 119 objects) is characterized by 73% purity and 64% completeness. This new classification demonstrates a suitable consistency with the MIR-based selection. Moreover, 76% of the obtained catalog can be found solely using the new method due to the lack of MIR detection for most of the new AGN candidates. The training data, codes, and final catalog are available via the github repository. The final catalog of AGN candidates is also available via the CDS service. Conclusions. The new selection methods presented in this paper are proven to be a better alternative for the MIR color AGN selection. Machine learning techniques not only show similar effectiveness, but also involve less demanding optical and NIR observations, substantially increasing the extent of available data samples.

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Clustering of the AKARI NEP deep field 24μm selected galaxies

Cited by 10 publications

References 94 publications

SCUBA-2 Ultra Deep Imaging EAO Survey (STUDIES). IV. Spatial Clustering and Halo Masses of Submillimeter Galaxies

SCUBA-2 Ultra Deep Imaging EAO Survey (STUDIES). IV. Spatial Clustering and Halo Masses of Submillimeter Galaxies

Automated novelty detection in the WISE survey with one-class support vector machines

Active galactic nuclei catalog from the AKARI NEP-Wide field

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