A machine-learning approach for identifying the counterparts of submillimetre galaxies and applications to the GOODS-North field

ABSTRACT Identifying the counterparts of submillimetre (submm) galaxies (SMGs) in multiwavelength images is a critical step towards building accurate models of the evolution of strongly star-forming galaxies in the early Universe. However, obtaining a statistically significant sample of robust associations is very challenging due to the poor angular resolution of single-dish submm facilities. Recently, a large sample of single-dish-detected SMGs in the UKIDSS UDS field, a subset of the SCUBA-2 Cosmology Legacy Survey (S2CLS), was followed up with the Atacama Large Millimeter/submillimeter Array (ALMA), which has provided the resolution necessary for identification in optical and near-infrared images. We use this ALMA sample to develop a training set suitable for machine-learning (ML) algorithms to determine how to identify SMG counterparts in multiwavelength images, using a combination of magnitudes and other derived features. We test several ML algorithms and find that a deep neural network performs the best, accurately identifying 85 per cent of the ALMA-detected optical SMG counterparts in our cross-validation tests. When we carefully tune traditional colour-cut methods, we find that the improvement in using machine learning is modest (about 5 per cent), but importantly it comes at little additional computational cost. We apply our trained neural network to the GOODS-North field, which also has single-dish submm observations from the S2CLS and deep multiwavelength data but little high-resolution interferometric submm imaging, and we find that we are able to classify SMG counterparts for 36/67 of the single-dish submm sources. We discuss future improvements to our ML approach, including combining ML with spectral energy distribution fitting techniques and using longer wavelength data as additional features.

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The effects of star formation history in the SFR–M* relation of H ii galaxies

Monthly Notices of the Royal Astronomical Society ◽

10.1093/mnras/staa3333 ◽

2020 ◽

Vol 500 (3) ◽

pp. 3240-3253

Author(s):

Amanda R Lopes ◽

Eduardo Telles ◽

Jorge Melnick

Keyword(s):

Star Formation ◽

Spectral Energy Distribution ◽

Formation Rate ◽

Star Formation History ◽

Spectral Energy ◽

Distribution Fitting ◽

Good Tool ◽

Star Forming ◽

Additional Information ◽

Star Formation Histories

ABSTRACT We discuss the implications of assuming different star formation histories (SFH) in the relation between star formation rate (SFR) and mass derived by the spectral energy distribution fitting (SED). Our analysis focuses on a sample of H ii galaxies, dwarf starburst galaxies spectroscopically selected through their strong narrow emission lines in SDSS DR13 at z < 0.4, cross-matched with photometric catalogues from GALEX, SDSS, UKIDSS, and WISE. We modelled and fitted the SEDs with the code CIGALE adopting different descriptions of SFH. By adding information from different independent studies, we find that H ii galaxies are best described by episodic SFHs including an old (10 Gyr), an intermediate age (100−1000 Myr) and a recent population with ages < 10 Myr. H ii galaxies agree with the SFR−M* relation from local star-forming galaxies, and only lie above such relation when the current SFR is adopted as opposed to the average over the entire SFH. The SFR−M* demonstrated not to be a good tool to provide additional information about the SFH of H ii galaxies, as different SFH present a similar behaviour with a spread of <0.1 dex.

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Towards a census of high-redshift dusty galaxies with Herschel

Astronomy and Astrophysics ◽

10.1051/0004-6361/201731888 ◽

2018 ◽

Vol 614 ◽

pp. A33 ◽

Cited By ~ 9

Author(s):

D. Donevski ◽

V. Buat ◽

F. Boone ◽

C. Pappalardo ◽

M. Bethermin ◽

...

Keyword(s):

Galaxy Evolution ◽

High Redshift ◽

Spectral Energy Distribution ◽

Formation Rate ◽

Far Infrared ◽

Virgo Cluster ◽

Spectral Energy ◽

Distribution Fitting ◽

Redshift Distribution ◽

Star Forming

Context. Over the last decade a large number of dusty star-forming galaxies has been discovered up to redshift z = 2 − 3 and recent studies have attempted to push the highly confused Herschel SPIRE surveys beyond that distance. To search for z ≥ 4 galaxies they often consider the sources with fluxes rising from 250 μm to 500 μm (so-called “500 μm-risers”). Herschel surveys offer a unique opportunity to efficiently select a large number of these rare objects, and thus gain insight into the prodigious star-forming activity that takes place in the very distant Universe. Aims. We aim to implement a novel method to obtain a statistical sample of 500 μm-risers and fully evaluate our selection inspecting different models of galaxy evolution. Methods. We consider one of the largest and deepest Herschel surveys, the Herschel Virgo Cluster Survey. We develop a novel selection algorithm which links the source extraction and spectral energy distribution fitting. To fully quantify selection biases we make end-to-end simulations including clustering and lensing. Results. We select 133 500 μm-risers over 55 deg2, imposing the criteria: S500 > S350 > S250, S250 > 13.2 mJy and S500 > 30 mJy. Differential number counts are in fairly good agreement with models, displaying a better match than other existing samples. The estimated fraction of strongly lensed sources is 24+6-5% based on models. Conclusions. We present the faintest sample of 500 μm-risers down to S250 = 13.2 mJy. We show that noise and strong lensing have an important impact on measured counts and redshift distribution of selected sources. We estimate the flux-corrected star formation rate density at 4 < z < 5 with the 500 μm-risers and find it to be close to the total value measured in far-infrared. This indicates that colour selection is not a limiting effect to search for the most massive, dusty z > 4 sources.

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The MUSE Hubble Ultra Deep Field Survey

Astronomy and Astrophysics ◽

10.1051/0004-6361/201833281 ◽

2018 ◽

Vol 617 ◽

pp. A62 ◽

Cited By ~ 9

Author(s):

Anna Feltre ◽

Roland Bacon ◽

Laurence Tresse ◽

Hayley Finley ◽

David Carton ◽

...

Keyword(s):

Spectral Energy Distribution ◽

Gas Content ◽

Spectral Energy ◽

Distribution Fitting ◽

Space Telescope ◽

Star Forming ◽

Neutral Gas ◽

Near Ultraviolet ◽

Hubble Ultra Deep Field ◽

Stellar Masses

The physical origin of the near-ultraviolet Mg II emission remains an underexplored domain, unlike more typical emission lines that are detected in the spectra of star-forming galaxies. We explore the nebular and physical properties of a sample of 381 galaxies between 0.70 < z < 2.34 drawn from the MUSE Hubble Ultra Deep Survey. The spectra of these galaxies show a wide variety of profiles of the Mg II λλ2796, 2803 resonant doublet, from absorption to emission. We present a study on the main drivers for the detection of Mg II emission in galaxy spectra. By exploiting photoionization models, we verified that the emission-line ratios observed in galaxies with Mg II in emission are consistent with nebular emission from HII regions. From a simultaneous analysis of MUSE spectra and ancillary Hubble Space Telescope information through spectral energy distribution fitting, we find that galaxies with Mg II in emission have lower stellar masses, smaller sizes, bluer spectral slopes, and lower optical depth than those with absorption. This leads us to suggest that Mg II emission is a potential tracer of physical conditions that are not merely related to those of the ionized gas. We show that these differences in Mg II emission and absorption can be explained in terms of a higher dust and neutral gas content in the interstellar medium (ISM) of galaxies showing Mg II in absorption, which confirms the extreme sensitivity of Mg II to the presence of the neutral ISM. We conclude with an analogy between the Mg II doublet and the Ly α line that lies in their resonant nature. Further investigations with current and future facilities, including the James Webb Space Telescope, are promising because the detection of Mg II emission and its potential connection with Lyα could provide new insights into the ISM content in the early Universe.

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Timing the earliest quenching events with a robust sample of massive quiescent galaxies at 2 < z < 5

Monthly Notices of the Royal Astronomical Society ◽

10.1093/mnras/staa1535 ◽

2020 ◽

Vol 496 (1) ◽

pp. 695-707 ◽

Cited By ~ 3

Author(s):

A C Carnall ◽

S Walker ◽

R J McLure ◽

J S Dunlop ◽

D J McLeod ◽

...

Keyword(s):

Energy Distribution ◽

Selection Criteria ◽

Spectral Energy Distribution ◽

Photometric Data ◽

Spectral Energy ◽

Distribution Fitting ◽

Star Forming ◽

Photometric Redshift ◽

The Times ◽

Lower Redshift

ABSTRACT We present a sample of 151 massive (M* > 1010 M⊙) quiescent galaxies at 2 < z < 5, based on a sophisticated Bayesian spectral energy distribution fitting analysis of the CANDELS UDS and GOODS-South fields. Our sample includes a robust sub-sample of 61 objects for which we confidently exclude low-redshift and star-forming solutions. We identify 10 robust objects at z > 3, of which 2 are at z > 4. We report formation redshifts, demonstrating that the oldest objects formed at z > 6; however, individual ages from our photometric data have significant uncertainties, typically ∼0.5 Gyr. We demonstrate that the UVJ colours of the quiescent population evolve with redshift at z > 3, becoming bluer and more similar to post-starburst galaxies at lower redshift. Based upon this, we construct a model for the time evolution of quiescent galaxy UVJ colours, concluding that the oldest objects are consistent with forming the bulk of their stellar mass at z ∼ 6–7 and quenching at z ∼ 5. We report spectroscopic redshifts for two of our objects at z = 3.440 and 3.396, which exhibit extremely weak Ly α emission in ultra-deep VANDELS spectra. We calculate star formation rates based on these line fluxes, finding that these galaxies are consistent with our quiescent selection criteria, provided their Ly α escape fractions are >3 and >10 per cent, respectively. We finally report that our highest redshift robust object exhibits a continuum break at λ ∼ 7000 Å in a spectrum from VUDS, consistent with our photometric redshift of $z_\mathrm{phot}=4.72^{+0.06}_{-0.04}$. If confirmed as quiescent, this object would be the highest redshift known quiescent galaxy. To obtain stronger constraints on the times of the earliest quenching events, high-SNR spectroscopy must be extended to z ≳ 3 quiescent objects.

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An Active Galactic Nucleus Recognition Model based on Deep Neural Network

Monthly Notices of the Royal Astronomical Society ◽

10.1093/mnras/staa3865 ◽

2020 ◽

Author(s):

Bo Han Chen ◽

Tomotsugu Goto ◽

Seong Jin Kim ◽

Ting Wen Wang ◽

Daryl Joe D Santos ◽

...

Keyword(s):

Neural Network ◽

Deep Neural Network ◽

Spectral Energy Distribution ◽

Experimental Result ◽

Spectral Energy ◽

Neural Net ◽

Star Forming ◽

The North ◽

History Of ◽

Reliable Solution

Abstract To understand the cosmic accretion history of supermassive black holes, separating the radiation from active galactic nuclei (AGNs) and star-forming galaxies (SFGs) is critical. However, a reliable solution on photometrically recognising AGNs still remains unsolved. In this work, we present a novel AGN recognition method based on Deep Neural Network (Neural Net; NN). The main goals of this work are (i) to test if the AGN recognition problem in the North Ecliptic Pole Wide (NEPW) field could be solved by NN; (ii) to shows that NN exhibits an improvement in the performance compared with the traditional, standard spectral energy distribution (SED) fitting method in our testing samples; and (iii) to publicly release a reliable AGN/SFG catalogue to the astronomical community using the best available NEPW data, and propose a better method that helps future researchers plan an advanced NEPW database. Finally, according to our experimental result, the NN recognition accuracy is around 80.29% - 85.15%, with AGN completeness around 85.42% - 88.53% and SFG completeness around 81.17% - 85.09%.

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CPz: Classification-aided photometric-redshift estimation

Astronomy and Astrophysics ◽

10.1051/0004-6361/201730763 ◽

2018 ◽

Vol 619 ◽

pp. A14 ◽

Cited By ~ 10

Author(s):

S. Fotopoulou ◽

S. Paltani

Keyword(s):

Galaxy Evolution ◽

Near Infrared ◽

Spectral Energy Distribution ◽

Ultra Violet ◽

Cost Effective ◽

Continuum Emission ◽

Spectral Energy ◽

Distribution Fitting ◽

Optical Photometry ◽

Photometric Redshift

Broadband photometry offers a time and cost effective method to reconstruct the continuum emission of celestial objects. Thus, photometric redshift estimation has supported the scientific exploitation of extragalactic multiwavelength surveys for more than twenty years. Deep fields have been the backbone of galaxy evolution studies and have brought forward a collection of various approaches in determining photometric redshifts. In the era of precision cosmology, with the upcoming Euclid and LSST surveys, very tight constraints are put on the expected performance of photometric redshift estimation using broadband photometry, thus new methods have to be developed in order to reach the required performance. We present a novel automatic method of optimizing photometric redshift performance, the classification-aided photometric redshift estimation (CPz). The main feature of CPz is the unified treatment of all classes of objects detected in extragalactic surveys: galaxies of any type (passive, starforming and starbursts), active galactic nuclei (AGN), quasi-stellar objects (QSO), stars and also includes the identification of potential photometric redshift catastrophic outliers. The method operates in three stages. First, the photometric catalog is confronted with star, galaxy and QSO model templates by means of spectral energy distribution fitting. Second, three machine-learning classifiers are used to identify 1) the probability of each source to be a star, 2) the optimal photometric redshift model library set-up for each source and 3) the probability to be a photometric redshift catastrophic outlier. Lastly, the final sample is assembled by identifying the probability thresholds to be applied on the outcome of each of the three classifiers. Hence, with the final stage we can create a sample appropriate for a given science case, for example favoring purity over completeness. We apply our method to the near-infrared VISTA public surveys, matched with optical photometry from CFHTLS, KIDS and SDSS, mid-infrared WISE photometry and ultra-violet photometry from the Galaxy Evolution Explorer (GALEX). We show that CPz offers improved photometric redshift performance for both normal galaxies and AGN without the need for extra X-ray information.

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Predicting the global far-infrared SED of galaxies via machine learning techniques

Astronomy and Astrophysics ◽

10.1051/0004-6361/201936695 ◽

2020 ◽

Vol 634 ◽

pp. A57 ◽

Cited By ~ 3

Author(s):

W. Dobbels ◽

M. Baes ◽

S. Viaene ◽

S. Bianchi ◽

J. I. Davies ◽

...

Keyword(s):

Machine Learning ◽

Energy Balance ◽

Near Infrared ◽

Spectral Energy Distribution ◽

Far Infrared ◽

Space Mission ◽

Machine Learning Techniques ◽

Spectral Energy ◽

Learning Framework ◽

Learning Techniques

Context. Dust plays an important role in shaping a galaxy’s spectral energy distribution (SED). It absorbs ultraviolet (UV) to near-infrared radiation and re-emits this energy in the far-infrared (FIR). The FIR is essential to understand dust in galaxies. However, deep FIR observations require a space mission, none of which are still active today. Aims. We aim to infer the FIR emission across six Herschel bands, along with dust luminosity, mass, and effective temperature, based on the available UV to mid-infrared (MIR) observations. We also want to estimate the uncertainties of these predictions, compare our method to energy balance SED fitting, and determine possible limitations of the model. Methods. We propose a machine learning framework to predict the FIR fluxes from 14 UV–MIR broadband fluxes. We used a low redshift sample by combining DustPedia and H-ATLAS, and extracted Bayesian flux posteriors through SED fitting. We trained shallow neural networks to predict the far-infrared fluxes, uncertainties, and dust properties. We evaluated them on a test set using a root mean square error (RMSE) in log-space. Results. Our results (RMSE = 0.19 dex) significantly outperform UV–MIR energy balance SED fitting (RMSE = 0.38 dex), and are inherently unbiased. We can identify when the predictions are off, for example when the input has large uncertainties on WISE 22 μm, or when the input does not resemble the training set. Conclusions. The galaxies for which we have UV–FIR observations can be used as a blueprint for galaxies that lack FIR data. This results in a “virtual FIR telescope”, which can be applied to large optical-MIR galaxy samples. This helps bridge the gap until the next FIR mission.

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The post-Herschel view of intrinsic AGN emission: constructing templates for galaxy and AGN emission at IR wavelengths

Monthly Notices of the Royal Astronomical Society ◽

10.1093/mnras/stab419 ◽

2021 ◽

Vol 503 (2) ◽

pp. 2598-2621

Author(s):

E Bernhard ◽

C Tadhunter ◽

J R Mullaney ◽

L P Grimmett ◽

D J Rosario ◽

...

Keyword(s):

Galaxy Formation ◽

Spectral Energy Distribution ◽

Far Infrared ◽

Infrared Emission ◽

Host Galaxy ◽

Spectral Energy ◽

Distribution Fitting ◽

Star Forming ◽

Galaxy Formation And Evolution ◽

Infrared Diagnostics

ABSTRACT Measuring the star-forming properties of active galactic nucleus (AGN) hosts is key to our understanding of galaxy formation and evolution. However, this topic remains debated, partly due to the difficulties in separating the infrared (i.e. 1–1000 ${\rm \mu m}$) emission into AGN and star-forming components. Taking advantage of archival far-infrared data from Herschel, we present a new set of AGN and galaxy infrared templates and introduce the spectral energy distribution fitting code iragnsep. Both can be used to measure infrared host galaxy properties, free of AGN contamination. To build these, we used a sample of 100 local (z < 0.3), low-to-high luminosity AGNs (i.e. Lbol$\ \sim \ 10^{42-46}$ erg s−1), selected from the 105-month Swift–BAT X-ray survey, which have archival Spitzer–IRS spectra and Herschel photometry. We first built a set of seven galaxy templates using a sample of 55 star-forming galaxies selected via infrared diagnostics. Using these templates, combined with a flexible model for the AGN contribution, we extracted the intrinsic infrared emission of our AGN sample. We further demonstrate that we can reduce the diversity in the intrinsic shapes of AGN spectral energy distributions down to a set of three AGN templates, of which two represent AGN continuum, and one represents silicate emission. Our results indicate that, on average, the contribution of AGNs to the far-infrared (λ ≳ 50 ${\rm \mu m}$) is not as high as suggested by some recent work. We further show that the need for two infrared AGN continuum templates could be related to nuclear obscuration, where one of our templates appears dominated by the emission of the extended polar dust.

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WISE J044232.92+322734.9: A star-forming galaxy at redshift 1.1 seen through a Galactic dust clump?

Astronomy and Astrophysics ◽

10.1051/0004-6361/202038747 ◽

2020 ◽

Vol 643 ◽

pp. A97

Author(s):

O. Miettinen

Keyword(s):

Physical Properties ◽

Near Infrared ◽

Mass Difference ◽

Spectral Energy Distribution ◽

Formation Rate ◽

Stellar Mass ◽

Radio Continuum ◽

Spectral Energy ◽

Wide Field ◽

Star Forming

Context. Physically unassociated background or foreground objects seen towards submillimetre sources are potential contaminants of both the studies of young stellar objects embedded in Galactic dust clumps and multiwavelength counterparts of submillimetre galaxies (SMGs). Aims. We aim to search for and characterise the properties of a potential extragalactic object seen in projection towards a Galactic dust clump. Methods. We employed the near-infrared (3.4 μm and 4.6 μm) and mid-infrared (12 μm and 22 μm) data from the Wide-field Infrared Survey Explorer (WISE) and the submillimetre data from the Planck satellite. Results. We uncovered a source, namely the WISE source J044232.92+322734.9 (hereafter J044232.92), which is detected in the W1–W3 bands of WISE, but undetected at 22 μm (W4), and whose WISE infrared (IR) colours suggest that it is a star-forming galaxy (SFG). This source is seen in projection towards the Planck-detected dust clump PGCC G169.20-8.96, which likely belongs to the Taurus-Auriga cloud complex, at a distance of 140 pc. We used the MAGPHYS+photo-z spectral energy distribution (SED) code to derive the photometric redshift and physical properties of J044232.92. The redshift was derived to be zphot = 1.132−0.165+0.280, while, for example, the stellar mass, IR (8–1000 μm) luminosity, and star formation rate were derived to be M⋆ = 4.6−2.5+4.7 × 1011 M⊙, LIR = 2.8−1.5+5.7 × 1012 L⊙, and SFR = 191−146+580 M⊙ yr−1 (or 281−155+569 M⊙ yr−1 when estimated from the IR luminosity). The derived value of LIR suggests that J044232.92 could be an ultraluminous IR galaxy, and we found that it is consistent with a main sequence SFG at a redshift of 1.132. Conclusions. The estimated physical properties of J044232.92 are comparable to those of SMGs, except that the derived stellar mass of J044232.92 appears somewhat higher (by a factor of 4–5) than the average stellar masses of SMGs. However, the stellar mass difference could just reflect the poorly sampled SED in the ultraviolet, optical, and near-IR regimes. Indeed, the SED of J044232.92 could not be well constrained using the currently available data (WISE only), and hence the derived redshift of the source and its physical properties should be taken as preliminary estimates. Further observations, in particular high-resolution (sub-)millimetre and radio continuum imaging, are needed to better constrain the redshift and physical properties of J044232.92 and to see if the source really is a galaxy seen through a Galactic dust clump, in particular an SMG population member at z ∼ 1.1.

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The high-redshift SFR–M* relation is sensitive to the employed star formation rate and stellar mass indicators: towards addressing the tension between observations and simulations

Monthly Notices of the Royal Astronomical Society ◽

10.1093/mnras/staa157 ◽

2020 ◽

Vol 492 (4) ◽

pp. 5592-5606 ◽

Cited By ~ 7

Author(s):

A Katsianis ◽

V Gonzalez ◽

D Barrientos ◽

X Yang ◽

C D P Lagos ◽

...

Keyword(s):

Star Formation ◽

Star Formation Rate ◽

High Redshift ◽

Spectral Energy Distribution ◽

Formation Rate ◽

Stellar Mass ◽

Spectral Energy ◽

Distribution Fitting ◽

Star Forming ◽

Stellar Masses

ABSTRACT There is a severe tension between the observed star formation rate (SFR)–stellar mass (M⋆) relations reported by different authors at z = 1–4. In addition, the observations have not been successfully reproduced by state-of-the-art cosmological simulations that tend to predict a factor of 2–4 smaller SFRs at a fixed M⋆. We examine the evolution of the SFR–M⋆ relation of z = 1–4 galaxies using the skirt simulated spectral energy distributions of galaxies sampled from the Evolution and Assembly of GaLaxies and their Environments simulations. We derive SFRs and stellar masses by mimicking different observational techniques. We find that the tension between observed and simulated SFR–M⋆ relations is largely alleviated if similar methods are used to infer the galaxy properties. We find that relations relying on infrared wavelengths (e.g. 24 ${\rm \, \mu m}$, MIPS – 24, 70, and 160 ${\rm \, \mu m}$ or SPIRE – 250, 350, and 500 ${\rm \, \mu m}$) have SFRs that exceed the intrinsic relation by 0.5 dex. Relations that rely on the spectral energy distribution fitting technique underpredict the SFRs at a fixed stellar mass by −0.5 dex at z ∼ 4 but overpredict the measurements by 0.3 dex at z ∼ 1. Relations relying on dust-corrected rest-frame ultraviolet luminosities, are flatter since they overpredict/underpredict SFRs for low/high star-forming objects and yield deviations from the intrinsic relation from 0.10 to −0.13 dex at z ∼ 4. We suggest that the severe tension between different observational studies can be broadly explained by the fact that different groups employ different techniques to infer their SFRs.

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