Abstract Conventional studies of galaxy clustering within the framework of halo models typically assume that the density profile of all dark matter halos can be approximated by the ...Navarro–Frenk–White (NFW) spherically symmetric profile. However, both modern N -body simulations and observational data suggest that most halos are either oblate or prolate, and almost never spherical. In this paper we present a modified model of the galaxy correlation function. In addition to the five “classical” halo occupation distribution (HOD) parameters proposed by Zheng et al., it includes an additional free parameter ϕ in the modified NFW density profile describing the asymmetry of the host dark matter halo. Using a subhalo abundance matching model, we populate galaxies within BolshoiP N -body simulations. We compute the projected two-point correlation function w p ( r p ) for six stellar-mass volume-limited galaxy samples. We fit our model to the results and then compare the best-fit asymmetry parameter ϕ (and other halo parameters) to the asymmetry of dark matter halos measured directly from the simulations and find that they agree within 1 σ . We then fit our model to the w p ( r p ) results from Zehavi et al. and compare halo parameters. We show that our model accurately retrieves the halo asymmetry and other halo parameters. Additionally, we find 2%–6% differences between the halo masses ( log M min and log M 1 ) estimated by our model and those estimated by “classical” HOD models. The model proposed in this paper can serve as an alternative to multiparameter HOD models, since it can be used for relatively small samples of galaxies.
Abstract
Gamma-ray bursts (GRBs), as they are observed at high redshift (
z
= 9.4), are vital to cosmological studies and investigating Population III stars. To tackle these studies, we need ...correlations among relevant GRB variables with the requirement of small uncertainties on their variables. Thus, we must have good coverage of GRB light curves (LCs). However, gaps in the LC hinder the precise determination of GRB properties and are often unavoidable. Therefore, extensive categorization of GRB LCs remains a hurdle. We address LC gaps using a stochastic reconstruction, wherein we fit two preexisting models (the Willingale model; W07; and a broken power law; BPL) to the observed LC, then use the distribution of flux residuals from the original data to generate data to fill in the temporal gaps. We also demonstrate a model-independent LC reconstruction via Gaussian processes. At 10% noise, the uncertainty of the end time of the plateau, its correspondent flux, and the temporal decay index after the plateau decreases by 33.3%, 35.03%, and 43.32% on average for the W07, and by 33.3%, 30.78%, 43.9% for the BPL, respectively. The uncertainty of the slope of the plateau decreases by 14.76% in the BPL. After using the Gaussian process technique, we see similar trends of a decrease in uncertainty for all model parameters for both the W07 and BPL models. These improvements are essential for the application of GRBs as standard candles in cosmology, for the investigation of theoretical models, and for inferring the redshift of GRBs with future machine-learning analyses.
Abstract Gamma-ray bursts (GRBs), due to their high luminosities, are detected up to a redshift of 10, and thus have the potential to be vital cosmological probes of early processes in the Universe. ...Fulfilling this potential requires a large sample of GRBs with known redshifts, but due to observational limitations, only 11% have known redshifts ( z ). There have been numerous attempts to estimate redshifts via correlation studies, most of which have led to inaccurate predictions. To overcome this, we estimated GRB redshift via an ensemble-supervised machine-learning (ML) model that uses X-ray afterglows of long-duration GRBs observed by the Neil Gehrels Swift Observatory. The estimated redshifts are strongly correlated (a Pearson coefficient of 0.93) and have an rms error, namely, the square root of the average squared error 〈Δ z 2 〉, of 0.46 with the observed redshifts showing the reliability of this method. The addition of GRB afterglow parameters improves the predictions considerably by 63% compared to previous results in peer-reviewed literature. Finally, we use our ML model to infer the redshifts of 154 GRBs, which increase the known redshifts of long GRBs with plateaus by 94%, a significant milestone for enhancing GRB population studies that require large samples with redshift.
Abstract Gamma-ray bursts (GRBs) can be probes of the early Universe, but currently, only 26% of GRBs observed by the Neil Gehrels Swift Observatory have known redshifts ( z ) due to observational ...limitations. To address this, we estimated the GRB redshift (distance) via a supervised statistical learning model that uses optical afterglow observed by Swift and ground-based telescopes. The inferred redshifts are strongly correlated (a Pearson coefficient of 0.93) with the observed redshifts, thus proving the reliability of this method. The inferred and observed redshifts allow us to estimate the number of GRBs occurring at a given redshift (GRB rate) to be 8.47–9 yr −1 Gpc −1 for 1.9 < z < 2.3. Since GRBs come from the collapse of massive stars, we compared this rate with the star formation rate, highlighting a discrepancy of a factor of 3 at z < 1.
ABSTRACT
In order to understand the interaction between the central black hole and the whole galaxy or their co-evolution history along with cosmic time, a complete census of active galactic nucleus ...(AGN) is crucial. However, AGNs are often missed in optical, UV, and soft X-ray observations since they could be obscured by gas and dust. A mid-infrared (MIR) survey supported by multiwavelength data is one of the best ways to find obscured AGN activities because it suffers less from extinction. Previous large IR photometric surveys, e.g. Wide field Infrared Survey Explorer and Spitzer, have gaps between the MIR filters. Therefore, star-forming galaxy-AGN diagnostics in the MIR were limited. The AKARI satellite has a unique continuous nine-band filter coverage in the near to MIR wavelengths. In this work, we take advantage of the state-of-the-art spectral energy distribution modelling software, cigale, to find AGNs in MIR. We found 126 AGNs in the North Ecliptic Pole-Wide field with this method. We also investigate the energy released from the AGN as a fraction of the total IR luminosity of a galaxy. We found that the AGN contribution is larger at higher redshifts for a given IR luminosity. With the upcoming deep IR surveys, e.g. JWST, we expect to find more AGNs with our method.
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 recognizing 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 show 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 data base. Finally, according to our experimental result, the NN recognition accuracy is around 80.29 per cent–85.15 per cent, with AGN completeness around 85.42 per cent–88.53 per cent and SFG completeness around 81.17 per cent–85.09 per cent.
ABSTRACT
Galaxy clusters provide an excellent probe in various research fields in astrophysics and cosmology. However, the number of galaxy clusters detected so far in the AKARI North Ecliptic Pole ...(NEP) field is limited. In this work, we provide galaxy cluster candidates in the AKARI NEP field with the minimum requisites based only on the coordinates and photometric redshift (photo-z) of galaxies. We used galaxies detected in five optical bands (g, r, i, z, and Y) by the Subaru Hyper Suprime-Cam (HSC), with additional data from the u band obtained from the Canada-France-Hawaii Telescope (CFHT) MegaPrime/MegaCam, and from the IRAC1 and IRAC2 bands from the Spitzer space telescope for photo-z estimation. We calculated the local density around every galaxy using the 10th-nearest neighbourhood. Cluster candidates were determined by applying the friends-of-friends algorithm to over-densities. A total of 88 cluster candidates containing 4390 member galaxies below redshift 1.1 in 5.4 deg2 were identified. The reliability of our method was examined through false-detection tests, redshift-uncertainty tests, and applications on the Cosmic Evolution Survey (COSMOS) data, giving false-detection rates of 0.01 to 0.05 and a recovery rate of 0.9 at high richness. Three X-ray clusters previously observed by ROSAT and Chandra were recovered. The cluster galaxies show a higher stellar mass and lower star formation rate compared with the field galaxies in two-sample Z-tests. These cluster candidates are useful for environmental studies of galaxy evolution and future astronomical surveys in the NEP, where AKARI has performed unique nine-band mid-infrared photometry for tens of thousands of galaxies.
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 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.
Abstract
We use the new release of the AKARI Far-Infrared All-Sky Survey (FIS) matched with the NVSS radio database to investigate the local ($z$ < 0.25) far-infrared–radio correlation (FIRC) of ...different types of extragalactic sources. To obtain the redshift information for the AKARI FIS sources we cross-match the catalogue with the SDSS DR8. This also allows us to use emission line properties to divide sources into four categories: (i) star-forming galaxies (SFGs), (ii) composite galaxies (displaying both star formation and active nucleus components), (iii) Seyfert galaxies, and (iv) low-ionization nuclear emission-line region (LINER) galaxies. We find that the Seyfert galaxies have the lowest far-infrared/radio flux ratios and display excess radio emission when compared to the SFGs. We conclude that the FIRC can be used to separate SFGs and AGNs only for the most radio-loud objects.
ABSTRACT
How does the environment affect active galactic nucleus (AGN) activity? We investigated this question in an extinction-free way by selecting 1120 infrared (IR) galaxies in the AKARI North ...Ecliptic Pole Wide field at redshift z ≤ 1.2. A unique feature of the AKARI satellite is its continuous nine-band IR filter coverage, providing us with an unprecedentedly large sample of IR spectral energy distributions (SEDs) of galaxies. By taking advantage of this, for the first time, we explored the AGN activity derived from SED modelling as a function of redshift, luminosity, and environment. We quantified AGN activity in two ways: AGN contribution fraction (ratio of AGN luminosity to the total IR luminosity), and AGN number fraction (ratio of number of AGNs to the total galaxy sample). We found that galaxy environment (normalized local density) does not greatly affect either definitions of AGN activity of our IRG/LIRG samples (log LTIR ≤ 12). However, we found a different behaviour for ULIRGs (log LTIR > 12). At our highest redshift bin (0.7 ≲ z ≲ 1.2), AGN activity increases with denser environments, but at the intermediate redshift bin (0.3 ≲ z ≲ 0.7), the opposite is observed. These results may hint at a different physical mechanism for ULIRGs. The trends are not statistically significant (p ≥ 0.060 at the intermediate redshift bin, and p ≥ 0.139 at the highest redshift bin). Possible different behaviour of ULIRGs is a key direction to explore further with future space missions (e.g. JWST, Euclid, SPHEREx).
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