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Aims. This study aims to explore the relationship between the physical properties of different galaxy subclasses and their environment based on the analysis of 31 631 VIMOS Public Extragalactic Redshift Survey (VIPERS) galaxies observed at 0.5 < z < 0.9. Methods. We use the results of an unsupervised clustering algorithm to distinguish 11 subclasses of VIPERS galaxies based on the multi-dimensional feature space defined by rest-frame UV to NIR colours presented in Siudek et al (2018a). We investigate the relationship between the properties of these subclasses of galaxies and their local environment, defined as the galaxy density contrast derived from the 5th nearest neighbour technique. Results. We confirm that the galaxy population-density relation is already in place at z ~ 0.9, with the blue galaxy fraction decreasing with density, compensated by an increase of the red fraction. On average red galaxies in the high-density environment are larger by 28% than the ones in low-density environments. In particular, we find one group of galaxies, subclass C3, whose increase of size with time can be explained mainly as the result of mergers; for other red subclasses, mergers would not seem to play a major role (subclass C2) or play a negligible role (subclass C1). The properties of the green galaxies (subclasses C4-6) depend on whether their stellar mass is above or below a transition mass. Low-mass green galaxies appear to have grown through secular processes, while in high-mass green galaxies mass assembly appears to be dominated by mergers. When it comes to blue galaxies, the trend of decreasing fraction with denser environments seen for the group as a whole (subclasses C7-11) is found to be driven mostly by one group of galaxies, subclass C10. These are compact low-mass galaxies with high sSFRs, that are preferentially found in low-density environments., Comment: Accepted for publication in A&A. 15 pages, 14 figures

Redshift measurement of active galactic nuclei (AGNs) remains a time-consuming and challenging task, as it requires follow up spectroscopic observations and detailed analysis. Hence, there exists an urgent requirement for alternative redshift estimation techniques. The use of machine learning (ML) for this purpose has been growing over the last few years, primarily due to the availability of large-scale galactic surveys. However, due to observational errors, a significant fraction of these data sets often have missing entries, rendering that fraction unusable for ML regression applications. In this study, we demonstrate the performance of an imputation technique called Multivariate Imputation by Chained Equations (MICE), which rectifies the issue of missing data entries by imputing them using the available information in the catalog. We use the Fermi-LAT Fourth Data Release Catalog (4LAC) and impute 24% of the catalog. Subsequently, we follow the methodology described in Dainotti et al. (2021) and create an ML model for estimating the redshift of 4LAC AGNs. We present results which highlight positive impact of MICE imputation technique on the machine learning models performance and obtained redshift estimation accuracy., Comment: 21 Pages, 11 Figures, 3 Tables

We aim to generate a catalogue of merging galaxies within the 5.4 sq. deg. North Ecliptic Pole over the redshift range $0.0 < z < 0.3$. To do this, imaging data from the Hyper Suprime-Cam are used along with morphological parameters derived from these same data. The catalogue was generated using a hybrid approach. Two neural networks were trained to perform binary merger non-merger classifications: one for galaxies with $z < 0.15$ and another for $0.15 \leq z < 0.30$. Each network used the image and morphological parameters of a galaxy as input. The galaxies that were identified as merger candidates by the network were then visually checked by experts. The resulting mergers will be used to calculate the merger fraction as a function of redshift and compared with literature results. We found that 86.3% of galaxy mergers at $z < 0.15$ and 79.0% of mergers at $0.15 \leq z < 0.30$ are expected to be correctly identified by the networks. Of the 34 264 galaxies classified by the neural networks, 10 195 were found to be merger candidates. Of these, 2109 were visually identified to be merging galaxies. We find that the merger fraction increases with redshift, consistent with literature results from observations and simulations, and that there is a mild star-formation rate enhancement in the merger population of a factor of $1.102 \pm 0.084$., Comment: Accepted to A&A, 26 pages, 20 figures, 8 tables, 3 appendixes, full tables 1 and 4 will be available on CDS

We investigate how different mid-infrared (mid-IR) properties of galaxies trace the environment in which the galaxies are located. For this purpose, we first study the dependence of galaxy clustering on the absolute magnitude at 3.4 $\mu$m and redshift. Then, we look into the environmental dependence of mid-IR luminosities and the galaxy properties derived from these luminosities. We also explore how various infrared galaxy luminosity selections influence the galaxy clustering measurements. We use a set of W1 (3.4 $\mu$m) absolute magnitude ($M_\text{W1}$) selected samples from the Galaxy and Mass Assembly (GAMA) survey matched with mid-IR properties from the Wide-field Infrared Survey Explorer (WISE) in the redshift range $0.07 \leq z < 0.43$. We compute the galaxy two-point correlation function (2pCF) and compare the clustering lengths between subsamples binned in $M_\text{W1}$ and in redshift. We also measure the marked correlation functions (MCFs) using the luminosities in the WISE W1 to W4 (3.4 to 22 $\mu$m) bands as marks. Additionally, we compare the measurements of MCFs with different estimates of stellar mass and star formation rate used as marks. Finally, we check how different selections applied to the sample affect the clustering measurements. We show strong clustering dependence on the W1 absolute magnitude: galaxies brighter in the W1 band are more strongly clustered than their fainter counterparts. We also observe a lack of significant redshift dependence of clustering in the redshift range $0.07 \leq z < 0.43$. We show that although W1 and W2 bands are direct indicators of stellar mass, a galaxy sample selected based on W1 or W2 bands does not perfectly show the clustering behaviour of a stellar mass selected sample. Similar is the case with the proxy relation between W3 and W4 bands and star formation rate., Comment: 18 pages, 12 figures, matches the version published in A&A

Measuring the redshift of active galactic nuclei (AGNs) requires the use of time-consuming and expensive spectroscopic analysis. However, obtaining redshift measurements of AGNs is crucial as it can enable AGN population studies, provide insight into the star formation rate, the luminosity function, and the density rate evolution. Hence, there is a requirement for alternative redshift measurement techniques. In this project, we aim to use the Fermi gamma-ray space telescope's 4LAC Data Release (DR2) catalog to train a machine learning model capable of predicting the redshift reliably. In addition, this project aims at improving and extending with the new 4LAC Catalog the predictive capabilities of the machine learning (ML) methodology published in Dainotti et al. (2021). Furthermore, we implement feature engineering to expand the parameter space and a bias correction technique to our final results. This study uses additional machine learning techniques inside the ensemble method, the SuperLearner, previously used in Dainotti et al.(2021). Additionally, we also test a novel ML model called Sorted L-One Penalized Estimation (SLOPE). Using these methods we provide a catalog of estimated redshift values for those AGNs that do not have a spectroscopic redshift measurement. These estimates can serve as a redshift reference for the community to verify as updated Fermi catalogs are released with more redshift measurements., Comment: 26 pages, 16 figures, 3 tables

HE 0435-5304 from Hamburg European Southern Observatory survey is a quasar that appears in the literature with two conflicting redshift values: $\sim 1.2$ and $\sim 0.4$. It was used in the studies of the intergalactic medium through fitting of the narrow absorption lines in its ultraviolet (UV) spectrum. This source is also known historically as a luminous infrared galaxy. We present optical spectra of HE 0435-5304, aiming to precisely measure its redshift and to study its physical properties. In particular, properties of its active nucleus, which is studied in the context of the source being identified here as an ultra-luminous infrared galaxy, allow us to place this quasar in the context of the general population. Fitting the spectra, we focused on modeling H$\beta$ and [O III] lines. Based on these, we derived the virial black hole mass, bolometric luminosity, and Eddington ratio of the active galactic nucleus (AGN). Additionally, we performed broad band photometry fitting which allows us to quantify host galaxy parameters. The improved redshift value of HE 0435-5304 is estimated to $0.42788 \pm 0.00027$ based on the [O II] line, which is mostly consistent with the narrowest components of the other emission lines. The source was found to be a relatively massive and luminous AGN whose host galaxy is actively forming stars. Although its stellar population seems to be heavily obscured, we did not find evidence for significant obscuration of the nucleus. We conclude that the AGN HE 0435-5304 is a rather prominent iron emitter from the extreme type-A population very close to the narrow-line Seyfert 1 group. The fact that the width of the H$\beta$ line appears to be systematically growing in its broadest component with time may suggest that this AGN is changing its broad line region. Due to the influence of atmospheric effects, this finding is uncertain., Comment: 19 pages, 11 figures, accepted for publication in A&A

We present a study of optically-selected Type II AGN at 0.5 < z < 0.9 from the VIPERS and VVDS surveys, to investigate the connection between AGN activity and physical properties of their host galaxies. The host stellar mass is estimated through spectral energy distribution fitting with the CIGALE code, and star formation rates are derived from the [OII]$\lambda$3727 $\r{A}$ line luminosity. We find that 49% of the AGN host galaxies are on or above the main sequence (MS), 40% lie in the sub-MS locus, and 11% in the quiescent locus. Using the [OIII]$\lambda$5007 $\r{A}$ line luminosity as a proxy of the AGN power, we find that at fixed AGN power Type II AGN host galaxies show a bimodal behaviour: systems with host galaxy stellar mass <10$^{10}$ M$_{\odot}$, reside along the MS or in the starbursts locus (high-SF Type II AGN), while systems residing in massive host-galaxies (>10$^{10}$ M$_{\odot}$) show a lower level of star formation (low-SF Type II AGN). At all stellar masses, the offset from the MS is positively correlated with the AGN power. We interpret this correlation as evidence of co-evolution between the AGN and the host, possibly due to the availability of cold gas. In the most powerful AGN with host galaxies below the MS we find a hint, though weak, of asymmetry in the [OIII] line profile, likely due to outflowing gas, consistent with a scenario in which AGN feedback removes the available gas and halts the star formation in the most massive hosts., Comment: Accepted for publication in A&A

The upcoming large scale surveys like LSST are expected to find approximately $10^5$ strong gravitational lenses by analysing data of many orders of magnitude larger than those in contemporary astronomical surveys. In this case, non-automated techniques will be highly challenging and time-consuming, even if they are possible at all. We propose a new automated architecture based on the principle of self-attention to find strong gravitational lenses. The advantages of self-attention-based encoder models over convolution neural networks are investigated, and ways to optimise the outcome of encoder models are analysed. We constructed and trained 21 self-attention based encoder models and five convolution neural networks to identify gravitational lenses from the Bologna Lens Challenge. Each model was trained separately using 18,000 simulated images, cross-validated using 2,000 images, and then applied to a test set with 100,000 images. We used four different metrics for evaluation: classification accuracy, area under the receiver operating characteristic curve (AUROC), the TPR$_0$ score and the TPR$_{10}$ score. The performances of self-attention-based encoder models and CNNs participating in the challenge are compared. They were able to surpass the CNN models that participated in the Bologna Lens Challenge by a high margin for the TPR$_0$ and TPR_${10}$. Self-Attention based models have clear advantages compared to simpler CNNs. They have highly competing performance in comparison to the currently used residual neural networks. Compared to CNNs, self-attention based models can identify highly confident lensing candidates and will be able to filter out potential candidates from real data. Moreover, introducing the encoder layers can also tackle the over-fitting problem present in the CNNs by acting as effective filters., Comment: 18 Pages, 4 tables and 19 Figures

Despite several decades of multi-wavelength and multi-messenger spectral observations, Gamma-Ray Bursts (GRBs) remain one of the big mysteries of modern astrophysics. Polarization measurements are essential to gain a more clear and complete picture of the emission processes at work in these extremely powerful transient events. In this regard, a first generation of dedicated gamma-ray polarimeters, POLAR and GAP, were launched into space in the last decade. After 6 months of operation, the POLAR mission detected 55 GRBs, among which 14 have been analyzed in detail, reporting a low polarization degree and a hint of a temporal evolution of the polarization angle. Starting early 2024 and based on the legacy of the POLAR results, the POLAR-2 instrument will aim to provide a catalog of high quality measurements of the energy and temporal evolution of the GRB polarization thanks to its large and efficient polarimeter. Several spectrometer modules will additionally allow to perform joint spectral and polarization analyzes. The mission is foreseen to make high precision polarization measurements of about 50 GRBs every year on board of the China Space Station (CSS). The technical design of the polarimeter modules will be discussed in detail, as well as the expected scientific performances based on the first results of the developed prototype modules., Comment: Proceeding from the 37th International Cosmic Ray Conference (ICRC2021), 9 pages, 5 figures

Despite over 50 years of Gamma-Ray Burst (GRB) observations many open questions remain about their nature and the environments in which the emission takes place. Polarization measurements of the GRB prompt emission have long been theorized to be able to answer most of these questions. The POLAR detector was a dedicated GRB polarimeter developed by a Swiss, Chinese and Polish collaboration. The instrument was launched, together with the second Chinese Space Lab, the Tiangong-2, in September 2016 after which it took 6 months of scientific data. During this period POLAR detected 55 GRBs as well as several pulsars. From the analysis of the GRB polarization catalog we see that the prompt emission is lowly polarized or fully unpolarized. There is, however, the caveat that within single pulses there are strong hints of an evolving polarization angle which washes out the polarization degree in the time integrated analysis. Building on the success of the POLAR mission, the POLAR-2 instrument is currently under development. POLAR-2 is a Swiss, Chinese, Polish and German collaboration and was recently approved for launch in 2024. Thanks to its large sensitivity POLAR-2 will produce polarization measurements of at least 50 GRBs per year with a precision equal or higher than the best results published by POLAR. POLAR-2 thereby aims to make the prompt polarization a standard observable and produce catalogs of the gamma-ray polarization of GRBs. Here we will present an overview of the POLAR mission and all its scientific measurement results. Additionally, we will present an overview of the future POLAR-2 mission, and how it will answer some of the questions raised by the POLAR results., Comment: Presented at the 37th International Cosmic Ray Conference (ICRC 2021)

How does the environment affect active galactic nucleus (AGN) activity? We investigated this question in an extinction-free way, by selecting 1120 infrared galaxies in the $AKARI$ North Ecliptic Pole Wide field at redshift $z$ $\leq$ 1.2. A unique feature of the $AKARI$ satellite is its continuous 9-band infrared (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 (normalised local density) does not greatly affect either definitions of AGN activity of our IRG/LIRG samples (log ${\rm L}_{\rm TIR}$ $\leq$ 12). However, we found a different behavior for ULIRGs (log ${\rm L}_{\rm TIR}$ $>$ 12). At our highest redshift bin (0.7 $\lesssim$ z $\lesssim$ 1.2), AGN activity increases with denser environments, but at the intermediate redshift bin (0.3 $\lesssim$ z $\lesssim$ 0.7), the opposite is observed. These results may hint at a different physical mechanism for ULIRGs. The trends are not statistically significant (p $\geq$ 0.060 at the intermediate redshift bin, and p $\geq$ 0.139 at the highest redshift bin). Possible different behavior of ULIRGs is a key direction to explore further with future space missions (e.g., $JWST$, $Euclid$, $SPHEREx$)., Comment: Accepted for publication in MNRAS. A summary video is available at https://www.youtube.com/watch?v=7Y_a0kJkLI4&ab_channel=NthuCosmology

AGNs are very powerful galaxies characterized by extremely bright emissions coming out from their central massive black holes. Knowing the redshifts of AGNs provides us with an opportunity to determine their distance to investigate important astrophysical problems such as the evolution of the early stars, their formation along with the structure of early galaxies. The redshift determination is challenging because it requires detailed follow-up of multi-wavelength observations, often involving various astronomical facilities. Here, we employ machine learning algorithms to estimate redshifts from the observed gamma-ray properties and photometric data of gamma-ray loud AGN from the Fourth Fermi-LAT Catalog. The prediction is obtained with the Superlearner algorithm, using LASSO selected set of predictors. We obtain a tight correlation, with a Pearson Correlation Coefficient of 71.3% between the inferred and the observed redshifts, an average {\Delta}z_norm = 11.6 x 10^-4. We stress that notwithstanding the small sample of gamma-ray loud AGNs, we obtain a reliable predictive model using Superlearner, which is an ensemble of several machine learning models., Comment: 29 pages, 19 Figures with a total of 39 panels

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 coordinates and photometric redshift (photo-$z$) of galaxies. We used galaxies detected in 5 optical bands ($g$, $r$, $i$, $z$, and $Y$) by the Subaru Hyper Suprime-Cam (HSC), assisted with $u$-band from Canada-France-Hawaii Telescope (CFHT) MegaPrime/MegaCam, and IRAC1 and IRAC2 bands from the $Spitzer$ space telescope for photo-$z$ estimation. We calculated the local density around every galaxy using the 10$^{th}$-nearest neighbourhood. Cluster candidates were determined by applying the friends-of-friends algorithm to over-densities. 88 cluster candidates containing 4390 member galaxies below redshift 1.1 in 5.4 deg$^2$ have been detected. The reliability of our method was examined through false detection tests, redshift uncertainty tests, and applications on the COSMOS data, giving false detection rates of 0.01 to 0.05 and recovery rate of 0.9 at high richness. 3 X-ray clusters previously observed by $ROSAT$ and $Chandra$ were recovered. The cluster galaxies show higher stellar mass and lower star formation rate (SFR) compared to 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 9-band mid-infrared photometry for tens of thousands of galaxies., Comment: 19 pages, 16 figures, has been accepted for publication in MNRAS

In this work we study how to employ the upcoming Legacy Survey of Space and Time (LSST) data to constrain physical properties of normal, star forming galaxies. We use simulated LSST data and existing real observations to test the estimations of the physical properties of galaxies, such as star formation rate (SFR), stellar mass ($M_{star}$), and dust luminosity ($L_{dust}$). We focus on normal star-forming galaxies, as they form the majority of the galaxy population in the universe and therefore are more likely to be observed by the LSST. We perform a simulation of LSST observations and uncertainties of 50,385 real galaxies within redshift range $0

This study is focused on the observational measurement of the viewing angle of individual quasars by modeling the broadband quasar spectrum ranging from the infra-red (IR) to the soft X-ray band. Sources are selected from various published catalogs, and their broadband quasi-simultaneous spectral data points were collected and used to model. We started with a COSMOS sample of type-1 sources which have broadband photometric points. Then, to include more data points, we cross-matched the COSMOS with the SDSS DR14 quasar catalog, and eventually, we find 90 sources that have broadband data ranging from IR to soft X-ray. The broadband spectral energy distribution (SED) modeling is done in Xspec by using the optxagnf and the SKIRTOR models for the X-ray, UV, Optical, and IR regimes for each source. The whole sample is divided into four bins with respect to redshift, black hole (BH) mass, and Eddington ratio with an equal number of sources in each bin. The viewing angle is estimated in each bin, and its evolution with respect to redshift, BH mass, and Eddington ratio is examined. As a result, we did not find any significant evolution of viewing angle with those parameters within the 95$\%$ confidence interval. We conclude that the use of quasars in cosmology to determine the expansion rate of the universe is therefore justified, and biases are not expected., Comment: 13 pages, 4 figures, 2 tables, Accepted for publication in ApJ

Context. The North Ecliptic Pole (NEP) field provides a unique set of panchromatic data, well suited for active galactic nuclei (AGN) studies. Selection of AGN candidates is often based on mid-infrared (MIR) measurements. Such method, despite its effectiveness, strongly reduces a catalog volume 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. Aims of this work were 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. A set of supervised machine learning algorithms was tested in order to perform an efficient AGN selection. Best of the models were formed into a majority voting scheme, which used the most popular classification result to produce the final AGN catalog. Additional analysis of catalog properties was performed in form of the 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 shows consistency with the MIR-based selection. Moreover, 76% of the obtained catalog can be found only with the new method due to the lack of MIR detection for most of the new AGN candidates. Training data, codes and final catalog are available via the github repository. Final AGN candidates catalog will be also available via the CDS service after publication., Comment: Accepted for publication in Astronomy and Astrophysics

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; $z \sim 0.06$), and the other from the VIMOS Public Extragalactic Redshift Survey (VIPERS; $z \sim 0.65$). 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., Comment: Accepted for publication in MNRAS. 22 pages, 15 figures

We investigate how different galaxy properties - luminosities in u, g, r, J, K-bands, stellar mass, star formation rate and specific star formation rate trace the environment in the local universe. We also study the effect of survey flux limits on galaxy clustering measurements. We measure the two-point correlation function (2pCF) and marked correlation functions (MCFs) using the aforementioned properties as marks. We use nearly stellar-mass-complete galaxy sample in the redshift range 0.1 < z < 0.16 from the Galaxy And Mass Assembly (GAMA) survey with a flux limit of r < 19.8. Further, we impose a brighter flux limit of r < 17.8 to our sample and repeat the measurements to study how this affects galaxy clustering analysis. We compare our results to measurements from the Sloan Digital Sky Survey (SDSS) with flux limits of r < 17.8 and r < 16.8. We show that the stellar mass is the best tracer of galaxy environment, the K-band luminosity being a good substitute, although such a proxy sample misses close pairs of evolved, red galaxies. We also confirm that the u-band luminosity is a good, but not a perfect proxy of star formation rate in the context of galaxy clustering. We observe an effect of the survey flux limit on clustering studies - samples with a higher flux limit (smaller magnitude) miss some information about close pairs of starburst galaxies., Comment: 15 pages, 7 figures, matches the version published in A&A

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%., Comment: 12 pages, 12 figures

The prompt emission of GRBs has been investigated for more than 50 years but remains poorly understood. Commonly, spectral and temporal profiles of {\gamma}-ray emission are analysed. However, they are insufficient for a complete picture on GRB-related physics. The addition of polarization measurements provides invaluable information towards the understanding of these astrophysical sources. In recent years, dedicated polarimeters, such as POLAR and GAP, were built. The former of which observed low levels of polarization as well as a temporal evolution of the polarization angle. It was understood that a larger sample of GRB polarization measurements and time resolved studies are necessary to constrain theoretical models. The POLAR-2 mission aims to address this by increasing the effective area by an order of magnitude compared to POLAR. POLAR-2 is manifested for launch on board the China Space Station in 2024 and will operate for at least 2 years. Insight from POLAR will aid in the improvement of the overall POLAR-2 design. Major improvements (compared to POLAR) will include the replacement of multi-anode PMTs (MAPMTs) with SiPMs, increase in sensitive volume and further technological upgrades. POLAR-2 is projected to measure about 50 GRBs per year with equal or better quality compared to the best seen by POLAR. The instrument design, preliminary results and anticipated scientific potential of this mission will be discussed., Comment: 16 pages

The symmetry axes of active galactic nuclei (AGN) are randomly distributed in space but highly inclined sources are heavily obscured and are not seen as quasars with broad emission lines. The obscuring torus geometry determines the average viewing angle, and if the torus geometry changes with the redshift, this average viewing angle will also change. Thus the ratio between the isotropic luminosity and observed luminosity may change systematically with redshift. Therefore, if we use quasars to measure the luminosity distance by evaluating the isotropic absolute luminosity and measuring the observed flux, we can have a redshift-dependent bias which can propagate to cosmological parameters. We propose a toy model for testing the effect of viewing angle uncertainty on measurement of the luminosity distance. The model is based on analytical description of the obscuring torus applied to one-parameter observational data. It illustrates the possible change of the torus covering factor between the two chosen redshift ranges. We have estimated the possible error on specific cosmological parameters (H0,Omega_m) for the flat Lambda-CDM cosmology if a method is calibrated at low redshift and applied to the higher redshift. The errors on cosmological parameters due to potential dependence of viewing angle on redshift are found to be potentially significant, and the effect will have to be accommodated in the future in all quasar-based cosmological methods. A careful systematic study of AGN mean viewing angle across redshift is necessary, with the use of appropriate samples and models which uniquely determine the inclination of each source., Comment: 15 pages, 10 figures, Accepted for publication in ApJ

The $AKARI$ space infrared telescope has performed near- to mid-infrared (MIR) observations on the North Ecliptic Pole Wide (NEPW) field (5.4 deg$^2$) for about one year. $AKARI$ took advantage of its continuous nine photometric bands, compared with NASA's $Spitzer$ and WISE space telescopes, which had only four filters with a wide gap in the MIR. The $AKARI$ NEPW field lacked deep and homogeneous optical data, limiting the use of nearly half of the IR sources for extra-galactic studies owing to the absence of photometric redshifts (photo-zs). To remedy this, we have recently obtained deep optical imaging over the NEPW field with 5 bands ($g$, $r$, $i$, $z$, and $Y$) of the Hyper Suprime-Camera (HSC) on the Subaru 8m telescope. We optically identify AKARI-IR sources along with supplementary $Spitzer$ and WISE data as well as pre-existing optical data. In this work, we derive new photo-zs using a $\chi^2$ template-fitting method code ($Le$ $Phare$) and reliable photometry from 26 selected filters including HSC, $AKARI$, CFHT, Maidanak, KPNO, $Spitzer$ and WISE data. We take 2026 spectroscopic redshifts (spec-z) from all available spectroscopic surveys over the NEPW to calibrate and assess the accuracy of the photo-zs. At z < 1.5, we achieve a weighted photo-z dispersion of $\sigma_{\Delta{z/(1+z)}}$ = 0.053 with $\eta$ = 11.3% catastrophic errors., Comment: 20 pages, 13 figures, accepted for publication in MNRAS. For summary video, please see http://youtu.be/hjNJRCoBIgg

The north ecliptic pole (NEP) field is a natural deep field location for many satellite observations. It has been targeted manytimes since it was surveyed by the AKARI space telescope with its unique wavelength coverage from the near- to mid-infrared(mid-IR). Many follow-up observations have been carried out and made this field one of the most frequently observed areas witha variety of facilities, accumulating abundant panchromatic data from X-ray to radio wavelength range. Recently, a deep opticalsurvey with the Hyper Suprime-Cam (HSC) at the Subaru telescope covered the NEP-Wide (NEPW) field, which enabled us toidentify faint sources in the near- and mid-IR bands, and to improve the photometric redshift (photo-z) estimation. In this work,we present newly identified AKARI sources by the HSC survey, along with multi-band photometry for 91,861 AKARI sourcesobserved over the NEPW field. We release a new band-merged catalogue combining various photometric data from GALEXUV to the submillimetre (sub-mm) bands (e.g., Herschel/SPIRE, JCMT/SCUBA-2). About 20,000 AKARI sources are newlymatched to the HSC data, most of which seem to be faint galaxies in the near- to mid-infrared AKARI bands. This cataloguemotivates a variety of current research, and will be increasingly useful as recently launched (eROSITA/ART-XC) and futurespace missions (such as JWST, Euclid, and SPHEREx) plan to take deep observations in the NEP field., Comment: 17 pages, 12 Figures, accepted in MNRAS, (in press)