Search

(Reduced) Using the recent cosmological high-resolution zoom-in simulations, NewHorizon and Galactica, in which the evolution of black hole spin is followed on the fly, we have tracked the cosmic history of a hundred of black holes (BHs) with a mass greater than 2x10^4 Ms. For each of them, we have studied the variations of the three dimensional angle (Psi) subtended between the BH spins and the angular momentum vectors of their host galaxies. The analysis of the individual evolution of the most massive BHs suggests that they are generally passing by three different regimes. First, for a short period after their birth, low mass BHs (<3x10^4 Ms) are rapidly spun up by gas accretion and their spin tends to be aligned with their host galaxy spin. Then follows a second phase in which the accretion of gas onto low mass BHs (<10^5 Ms) is quite chaotic and inefficient, reflecting the complex and disturbed morphologies of forming proto-galaxies at high redshifts. The variations of Psi are rather erratic during this phase and are mainly driven by the rapid changes of the direction of the galaxy angular momentum. Then, in a third and long phase, BHs are generally well settled in the center of galaxies around which the gas accretion becomes much more coherent (>10^5 Ms). In this case, the BH spins tend to be well aligned with the angular momentum of their host galaxy and this configuration is generally stable even though BH merger episodes can temporally induce misalignment. We have also derived the distributions of cos(Psi) at different redshifts and found that BHs and galaxy spins are generally aligned. Finally, based on a Monte Carlo method, we also predict statistics for the 2-d projected spin-orbit angles lambda. In particular, the distribution of lambda traces well the alignment tendency in the 3-d analysis. Such predictions provide an interesting background for future observational analyses., Comment: Accepted for publication in A&A (12/03/2024)

Superclusters, encompassing environments across a wide range of overdensities, can be regarded as unique laboratories for studying galaxy evolution. Although numerous supercluster catalogs have been published, none of them goes beyond redshift $z=0.7$. In this work, we adopt a physically motivated supercluster definition, requiring that superclusters should eventually collapse even in the presence of dark energy. Applying a friends-of-friends (FoF) algorithm to the CAMIRA cluster sample constructed using the Subaru Hyper Suprime-Cam survey data, we have conducted the first systematic search for superclusters at $z=0.5-1.0$ and identified $633$ supercluster candidates over an area of 1027 deg$^2$. The FoF algorithm is calibrated by evolving $N$-body simulations to the far future to ensure high purity. We found that these high-$z$ superclusters are mainly composed of $2-4$ clusters, suggesting the limit of gravitationally bound structures in the younger Universe. In addition, we studied the properties of the clusters and brightest cluster galaxies (BCGs) residing in different large-scale environments. We found that clusters associated with superclusters are typically richer, but no apparent dependence of the BCG properties on large-scale structures is found. We also compared the abundance of observed superclusters with mock superclusters extracted from halo light cones, finding that photometric redshift uncertainty is a limiting factor in the performance of superclusters detection., Comment: Submitted to ApJ. 29 pages, 22 figures, 6 tables

LLMs have gotten attention across various research domains due to their exceptional performance on a wide range of complex tasks. Therefore, refined methods to evaluate the capabilities of LLMs are needed to determine the tasks and responsibility they should undertake. Our study mainly discussed how LLMs, as useful tools, should be effectively assessed. We proposed the two-stage framework: from ``core ability'' to ``agent'', clearly explaining how LLMs can be applied based on their specific capabilities, along with the evaluation methods in each stage. Core ability refers to the capabilities that LLMs need in order to generate high-quality natural language texts. After confirming LLMs possess core ability, they can solve real-world and complex tasks as agent. In the "core ability" stage, we discussed the reasoning ability, societal impact, and domain knowledge of LLMs. In the ``agent'' stage, we demonstrated embodied action, planning, and tool learning of LLMs agent applications. Finally, we examined the challenges currently confronting the evaluation methods for LLMs, as well as the directions for future development.

The evaluation of large language models (LLMs) has drawn substantial attention in the field recently. This work focuses on evaluating LLMs in a Chinese context, specifically, for Traditional Chinese which has been largely underrepresented in existing benchmarks. We present TMLU, a holistic evaluation suit tailored for assessing the advanced knowledge and reasoning capability in LLMs, under the context of Taiwanese Mandarin. TMLU consists of an array of 37 subjects across social science, STEM, humanities, Taiwan-specific content, and others, ranging from middle school to professional levels. In addition, we curate chain-of-thought-like few-shot explanations for each subject to facilitate the evaluation of complex reasoning skills. To establish a comprehensive baseline, we conduct extensive experiments and analysis on 24 advanced LLMs. The results suggest that Chinese open-weight models demonstrate inferior performance comparing to multilingual proprietary ones, and open-weight models tailored for Taiwanese Mandarin lag behind the Simplified-Chinese counterparts. The findings indicate great headrooms for improvement, and emphasize the goal of TMLU to foster the development of localized Taiwanese-Mandarin LLMs. We release the benchmark and evaluation scripts for the community to promote future research., Comment: Preprint. Under review

We present the Liouville Flow Importance Sampler (LFIS), an innovative flow-based model for generating samples from unnormalized density functions. LFIS learns a time-dependent velocity field that deterministically transports samples from a simple initial distribution to a complex target distribution, guided by a prescribed path of annealed distributions. The training of LFIS utilizes a unique method that enforces the structure of a derived partial differential equation to neural networks modeling velocity fields. By considering the neural velocity field as an importance sampler, sample weights can be computed through accumulating errors along the sample trajectories driven by neural velocity fields, ensuring unbiased and consistent estimation of statistical quantities. We demonstrate the effectiveness of LFIS through its application to a range of benchmark problems, on many of which LFIS achieved state-of-the-art performance., Comment: 25 pages, 7 figures, 15 tables. Submitted to and accepted by the 41th International Conference on Machine Learning (Vienna, Austria)

We present a protocluster search covering $z\sim3$ to $z\sim5$ based on the combination of the Hyper SuprimeCam Subaru Strategic Programme and the CFHT Large Area $U$-band Deep Survey. We identify about 30 protocluster candidates per unit redshift over the $\sim25\,\mathrm{deg^2}$ area of the Deep/Ultra-Deep layer. Protocluster candidates are selected as regions with a significantly enhanced surface density of dropout galaxies. With this large sample, we characterise the properties of their individual member galaxies. We compare the number counts of dropout galaxies in protocluster candidates with that of coeval field galaxies. Rest-frame UV bright galaxies are over-abundant in protocluster candidates, a trend seen across the full redshift range studied. We do not find evidence for their spatial distribution within protocluster candidates to be distinct from their fainter counterparts, nor for their UV colour to be different from that of field galaxies with the same brightness. Cosmological simulations predict this bright-end excess, with the main cause being a richer population of massive galaxies, with only a minor contribution from an enhancement in star formation activity (and therefore UV emission) at fixed mass. $U$-to-$K$ SED modelling of our observed samples supports this interpretation. This environmental differentiation in number counts is already in place at $z\sim5$, with no significant redshift dependence over the range in lookback times probed. These observational results and model predictions suggest that the cosmic clock is ahead in high-density environments., Comment: 17 pages, 13 figures, accepted for publication in MNRAS

We present new Spitzer Infrared Array Camera (IRAC) 3.6 and 4.5{\mu}m mosaics of three fields, E-COSMOS, DEEP2-F3, and ELAIS-N1. Our mosaics include both new IRAC observations as well as re-processed archival data in these fields. These fields are part of the HSC-Deep grizy survey and have a wealth of additional ancillary data. The addition of these new IRAC mosaics is critical in allowing for improved photometric redshifts and stellar population parameters at cosmic noon and earlier epochs. The total area mapped by this work is {\sim} 17 deg2 with a mean integration time of {\sim}1200s, providing a median 5{\sigma} depth of 23.7(23.3) at 3.6(4.5){\mu}m in AB. We perform SExtractor photometry both on the combined mosaics as well as the single-epoch mosaics taken {\sim}6 months apart. The resultant IRAC number counts show good agreement with previous studies. In combination with the wealth of existing and upcoming spectro-photometric data in these fields, our IRAC mosaics will enable a wide range of galactic evolution and AGN studies. With that goal in mind, we make the combined IRAC mosaics and coverage maps of these three fields publicly available. counts show good agreement with previous studies., Comment: accepted for publication in AJ

We report CFHT/SITELLE imaging Fourier Transform Spectrograph observations of the Brightest Cluster Galaxy (BCG) of galaxy cluster Abell 2390 at z=0.228. The BCG displays a prominent cone of emission in H$\alpha$, H$\beta$, [NII], and [OII] to the North-West with PA = 42$^o$, 4.4 arcsec in length (15.9 kpc), which is associated with elongated and asymmetric Chandra soft X-ray emission. The H$\alpha$ flux map also contains a "hook" of H$\alpha$ and [NII] emission resulting in a broadened northern edge to the cone. Using SITELLE/LUCI software we extract emission line flux, velocity, velocity dispersion, and continuum maps, and utilize them to derive flux ratio maps to determine ionization mechanisms and dynamical information in the BCG's emission line region. The Baldwin-Phillips-Terlevich diagnostics on the BCG cone indicate a composite ionization origin of photoionization due to star formation and shock. Strong LINER-like emission is seen in the nuclear region which hosts an AGN. As Abell 2390 is a cool-core cluster, we suggest that the cooling flow is falling onto the central BCG and interacting with the central AGN. The AGN produces jets that inflate "bubbles" of plasma in the ICM, as is often observed in local galaxy clusters. Furthermore, combining signs of AGN activities from radio, optical emission line and X-ray data over a large range of physical scale, we find evidence for three possible episodes of AGN activity in different epochs associated with the Abell 2390 BCG., Comment: 15 pages, 13 figures, accepted by MNRAS

This work focuses on in-context data augmentation for intent detection. Having found that augmentation via in-context prompting of large pre-trained language models (PLMs) alone does not improve performance, we introduce a novel approach based on PLMs and pointwise V-information (PVI), a metric that can measure the usefulness of a datapoint for training a model. Our method first fine-tunes a PLM on a small seed of training data and then synthesizes new datapoints - utterances that correspond to given intents. It then employs intent-aware filtering, based on PVI, to remove datapoints that are not helpful to the downstream intent classifier. Our method is thus able to leverage the expressive power of large language models to produce diverse training data. Empirical results demonstrate that our method can produce synthetic training data that achieve state-of-the-art performance on three challenging intent detection datasets under few-shot settings (1.28% absolute improvement in 5-shot and 1.18% absolute in 10-shot, on average) and perform on par with the state-of-the-art in full-shot settings (within 0.01% absolute, on average)., Comment: Accepted at EACL 2023

We propose LLM-Eval, a unified multi-dimensional automatic evaluation method for open-domain conversations with large language models (LLMs). Existing evaluation methods often rely on human annotations, ground-truth responses, or multiple LLM prompts, which can be expensive and time-consuming. To address these issues, we design a single prompt-based evaluation method that leverages a unified evaluation schema to cover multiple dimensions of conversation quality in a single model call. We extensively evaluate the performance of LLM-Eval on various benchmark datasets, demonstrating its effectiveness, efficiency, and adaptability compared to state-of-the-art evaluation methods. Our analysis also highlights the importance of choosing suitable LLMs and decoding strategies for accurate evaluation results. LLM-Eval offers a versatile and robust solution for evaluating open-domain conversation systems, streamlining the evaluation process and providing consistent performance across diverse scenarios., Comment: Accepted at 5th NLP4ConvAI

Typical generative diffusion models rely on a Gaussian diffusion process for training the backward transformations, which can then be used to generate samples from Gaussian noise. However, real world data often takes place in discrete-state spaces, including many scientific applications. Here, we develop a theoretical formulation for arbitrary discrete-state Markov processes in the forward diffusion process using exact (as opposed to variational) analysis. We relate the theory to the existing continuous-state Gaussian diffusion as well as other approaches to discrete diffusion, and identify the corresponding reverse-time stochastic process and score function in the continuous-time setting, and the reverse-time mapping in the discrete-time setting. As an example of this framework, we introduce ``Blackout Diffusion'', which learns to produce samples from an empty image instead of from noise. Numerical experiments on the CIFAR-10, Binarized MNIST, and CelebA datasets confirm the feasibility of our approach. Generalizing from specific (Gaussian) forward processes to discrete-state processes without a variational approximation sheds light on how to interpret diffusion models, which we discuss., Comment: 29 pages, 13 figures, 2 tables. Accepted by the 40th International Conference on Machine Learning, Honolulu, Hawaii, USA

Galaxies play a key role in our endeavor to understand how structure formation proceeds in the Universe. For any precision study of cosmology or galaxy formation, there is a strong demand for huge sets of realistic mock galaxy catalogs, spanning cosmologically significant volumes. For such a daunting task, methods that can produce a direct mapping between dark matter halos from dark matter-only simulations and galaxies are strongly preferred, as producing mocks from full-fledged hydrodynamical simulations or semi-analytical models is too expensive. Here we present a Graph Neural Network-based model that is able to accurately predict key properties of galaxies such as stellar mass, $g-r$ color, star formation rate, gas mass, stellar metallicity, and gas metallicity, purely from dark matter properties extracted from halos along the full assembly history of the galaxies. Tests based on the TNG300 simulation of the IllustrisTNG project show that our model can recover the baryonic properties of galaxies to high accuracy, over a wide redshift range ($z = 0-5$), for all galaxies with stellar masses more massive than $10^9\,M_\odot$ and their progenitors, with strong improvements over the state-of-the-art methods. We further show that our method makes substantial strides toward providing an understanding of the implications of the IllustrisTNG galaxy formation model., Comment: 18 pages, 7 figures, 3 tables, 4 pages of appendices. Submitted to ApJ

In the realm of language models, the nuanced linguistic and cultural intricacies of Traditional Chinese, as spoken in Taiwan, have been largely overlooked. This paper introduces Taiwan LLM, a pioneering Large Language Model that specifically caters to the Traditional Chinese language, with a focus on the variant used in Taiwan. Leveraging a comprehensive pretraining corpus and instruction-finetuning datasets, we have developed a model that not only understands the complexities of Traditional Chinese but also embodies the cultural context of Taiwan. Taiwan LLM represents the first of its kind, a model that is not only linguistically accurate but also culturally resonant with its user base. Our evaluations demonstrate that Taiwan LLM achieves superior performance in understanding and generating Traditional Chinese text, outperforming existing models that are predominantly trained on Simplified Chinese or English. The open-source release of Taiwan LLM invites collaboration and further innovation, ensuring that the linguistic diversity of Chinese speakers is embraced and well-served. The model, datasets, and further resources are made publicly available to foster ongoing research and development in this field.

The aim of this short note is to show that Denoising Diffusion Probabilistic Model DDPM, a non-homogeneous discrete-time Markov process, can be represented by a time-homogeneous continuous-time Markov process observed at non-uniformly sampled discrete times. Surprisingly, this continuous-time Markov process is the well-known and well-studied Ornstein-Ohlenbeck (OU) process, which was developed in 1930's for studying Brownian particles in Harmonic potentials. We establish the formal equivalence between DDPM and the OU process using its analytical solution. We further demonstrate that the design problem of the noise scheduler for non-homogeneous DDPM is equivalent to designing observation times for the OU process. We present several heuristic designs for observation times based on principled quantities such as auto-variance and Fisher Information and connect them to ad hoc noise schedules for DDPM. Interestingly, we show that the Fisher-Information-motivated schedule corresponds exactly the cosine schedule, which was developed without any theoretical foundation but is the current state-of-the-art noise schedule.

Services of personalized TTS systems for the Mandarin-speaking speech impaired are rarely mentioned. Taiwan started the VoiceBanking project in 2020, aiming to build a complete set of services to deliver personalized Mandarin TTS systems to amyotrophic lateral sclerosis patients. This paper reports the corpus design, corpus recording, data purging and correction for the corpus, and evaluations of the developed personalized TTS systems, for the VoiceBanking project. The developed corpus is named after the VoiceBank-2023 speech corpus because of its release year. The corpus contains 29.78 hours of utterances with prompts of short paragraphs and common phrases spoken by 111 native Mandarin speakers. The corpus is labeled with information about gender, degree of speech impairment, types of users, transcription, SNRs, and speaking rates. The VoiceBank-2023 is available by request for non-commercial use and welcomes all parties to join the VoiceBanking project to improve the services for the speech impaired., Comment: submitted to 26th International Conference of the ORIENTAL-COCOSDA

The halo assembly bias, a phenomenon referring to dependencies of the large-scale bias of a dark matter halo other than its mass, is a fundamental property of our standard cosmological model. First discovered in 2005 via high-resolution numerical simulations, it has been proven very difficult to be detected observationally, with only a few convincing claims of detection thus far. The main obstacle lies in finding an accurate proxy of the halo formation time. In this study, by utilizing a constrained simulation that can faithfully reproduce the observed structures larger than $\sim 2\,$Mpc in the local universe, for a sample of about 630 massive clusters at $z\le 0.12$, we find their counterpart halos in the simulation and use the mass growth history of the matched halos to estimate the formation time of the observed clusters. This allows us to construct a pair of early- and late-forming clusters, with similar mass as measured via weak gravitational lensing, and large-scale bias differing at $\gtrsim 4\sigma$ level, clearly showing the signature of assembly bias., Comment: MNRAS letters, submitted; 5 pages and 3 figures

This paper documents the seventeenth data release (DR17) from the Sloan Digital Sky Surveys; the fifth and final release from the fourth phase (SDSS-IV). DR17 contains the complete release of the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey, which reached its goal of surveying over 10,000 nearby galaxies. The complete release of the MaNGA Stellar Library accompanies this data, providing observations of almost 30,000 stars through the MaNGA instrument during bright time. DR17 also contains the complete release of the Apache Point Observatory Galactic Evolution Experiment 2 survey that publicly releases infrared spectra of over 650,000 stars. The main sample from the Extended Baryon Oscillation Spectroscopic Survey (eBOSS), as well as the subsurvey Time Domain Spectroscopic Survey data were fully released in DR16. New single-fiber optical spectroscopy released in DR17 is from the SPectroscipic IDentification of ERosita Survey subsurvey and the eBOSS-RM program. Along with the primary data sets, DR17 includes 25 new or updated value-added catalogs. This paper concludes the release of SDSS-IV survey data. SDSS continues into its fifth phase with observations already underway for the Milky Way Mapper, Local Volume Mapper, and Black Hole Mapper surveys.

This paper documents the seventeenth data release (DR17) from the Sloan Digital Sky Surveys; the fifth and final release from the fourth phase (SDSS-IV). DR17 contains the complete release of the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey, which reached its goal of surveying over 10,000 nearby galaxies. The complete release of the MaNGA Stellar Library accompanies this data, providing observations of almost 30,000 stars through the MaNGA instrument during bright time. DR17 also contains the complete release of the Apache Point Observatory Galactic Evolution Experiment 2 survey that publicly releases infrared spectra of over 650,000 stars. The main sample from the Extended Baryon Oscillation Spectroscopic Survey (eBOSS), as well as the subsurvey Time Domain Spectroscopic Survey data were fully released in DR16. New single-fiber optical spectroscopy released in DR17 is from the SPectroscipic IDentification of ERosita Survey subsurvey and the eBOSS-RM program. Along with the primary data sets, DR17 includes 25 new or updated value-added catalogs. This paper concludes the release of SDSS-IV survey data. SDSS continues into its fifth phase with observations already underway for the Milky Way Mapper, Local Volume Mapper, and Black Hole Mapper surveys.

While rich, open-domain textual data are generally available and may include interesting phenomena (humor, sarcasm, empathy, etc.) most are designed for language processing tasks, and are usually in a non-conversational format. In this work, we take a step towards automatically generating conversational data using Generative Conversational Networks, aiming to benefit from the breadth of available language and knowledge data, and train open domain social conversational agents. We evaluate our approach on conversations with and without knowledge on the Topical Chat dataset using automatic metrics and human evaluators. Our results show that for conversations without knowledge grounding, GCN can generalize from the seed data, producing novel conversations that are less relevant but more engaging and for knowledge-grounded conversations, it can produce more knowledge-focused, fluent, and engaging conversations. Specifically, we show that for open-domain conversations with 10\% of seed data, our approach performs close to the baseline that uses 100% of the data, while for knowledge-grounded conversations, it achieves the same using only 1% of the data, on human ratings of engagingness, fluency, and relevance., Comment: Accepted at SIGDial 2022

This paper introduces Miutsu, National Taiwan University's Alexa Prize TaskBot, which is designed to assist users in completing tasks requiring multiple steps and decisions in two different domains -- home improvement and cooking. We overview our system design and architectural goals, and detail the proposed core elements, including question answering, task retrieval, social chatting, and various conversational modules. A dialogue flow is proposed to provide a robust and engaging conversation when handling complex tasks. We discuss the faced challenges during the competition and potential future work.

Dialogue systems are usually categorized into two types, open-domain and task-oriented. The first one focuses on chatting with users and making them engage in the conversations, where selecting a proper topic to fit the dialogue context is essential for a successful dialogue. The other one focuses on a specific task instead of casual talks, e.g., finding a movie on Friday night, or playing a song. These two directions have been studied separately due to their different purposes. However, how smoothly transitioning from social chatting to task-oriented dialogues is important for triggering business opportunities, and there is no public data focusing on such scenarios. Hence, this paper focuses on investigating the conversations starting from open-domain social chatting and then gradually transitioning to task-oriented purposes, and releases a large-scale dataset with detailed annotations for encouraging this research direction. To achieve this goal, this paper proposes a framework to automatically generate many dialogues without human involvement, in which any powerful open-domain dialogue generation model can be easily leveraged. The human evaluation shows that our generated dialogue data has a natural flow at a reasonable quality, showing that our released data has a great potential of guiding future research directions and commercial activities. Furthermore, the released models allow researchers to automatically generate unlimited dialogues in the target scenarios, which can greatly benefit semi-supervised and unsupervised approaches., Comment: ACL 2022

A number of recent studies have proposed that linear representations are appropriate for solving nonlinear dynamical systems with quantum computers, which fundamentally act linearly on a wave function in a Hilbert space. Linear representations, such as the Koopman representation and Koopman von Neumann mechanics, have regained attention from the dynamical-systems research community. Here, we aim to present a unified theoretical framework, currently missing in the literature, with which one can compare and relate existing methods, their conceptual basis, and their representations. We also aim to show that, despite the fact that quantum simulation of nonlinear classical systems may be possible with such linear representations, a necessary projection into a feasible finite-dimensional space will in practice eventually induce numerical artifacts which can be hard to eliminate or even control. As a result a practical, reliable and accurate way to use quantum computation for solving general nonlinear dynamical systems is still an open problem., Comment: 23 pages, 12 figures

To fit sparse linear associations, a LASSO sparsity inducing penalty with a single hyperparameter provably allows to recover the important features (needles) with high probability in certain regimes even if the sample size is smaller than the dimension of the input vector (haystack). More recently learners known as artificial neural networks (ANN) have shown great successes in many machine learning tasks, in particular fitting nonlinear associations. Small learning rate, stochastic gradient descent algorithm and large training set help to cope with the explosion in the number of parameters present in deep neural networks. Yet few ANN learners have been developed and studied to find needles in nonlinear haystacks. Driven by a single hyperparameter, our ANN learner, like for sparse linear associations, exhibits a phase transition in the probability of retrieving the needles, which we do not observe with other ANN learners. To select our penalty parameter, we generalize the universal threshold of Donoho and Johnstone (1994) which is a better rule than the conservative (too many false detections) and expensive cross-validation. In the spirit of simulated annealing, we propose a warm-start sparsity inducing algorithm to solve the high-dimensional, non-convex and non-differentiable optimization problem. We perform precise Monte Carlo simulations to show the effectiveness of our approach.

Subhalo abundance matching (SHAM) has played an important role in improving our understanding of how galaxies populate their host dark matter halos. In essence, the SHAM framework is to find a dark matter halo property that best correlates with an attribute of galaxies, such as stellar mass. The peak value of the maximum circular velocity ($V_{\rm max}$) a halo/subhalo has ever attained throughout its lifetime, $V_{\rm peak}$, has been a popular choice for SHAM. A recent study by Tonnesen & Ostriker (2021) suggested that quantity $\phi$, which combines the present-day $V_{\rm max}$ and the peak value of halo dark matter mass, performs better in predicting stellar mass than $V_{\rm peak}$. Inspired by their approach, in this work, we find that further improvement can be achieved by a quantity $\psi_5$ that combines the 90th percentile of $V_{\rm max}$ a halo/subhalo has ever achieved with the 60th percentile of the dark matter halo time variation rate. Tests based on the simulation IllustrisTNG300 show that our new SHAM scheme, with just three free parameters, can improve the stellar mass prediction and mass-dependent clustering by 15% and 16% from $\phi$, respectively, over the redshift range $z=0-2$., Comment: 16 pages, 8 figures, 3 tables, Accepted by The Astrophysical Journal

We propose a novel subhalo abundance matching (SHAM) model that uses the virial mass of the main progenitor of each (sub)halo $M_{\rm prog}$ as a proxy of the galaxy stellar mass $M_*$ at the time of observation. This $M_{\rm prog}$ model predicts the two-point correlation functions depending on the choice of the epoch $z_{\rm prog}$ at which $M_\mathrm{prog}$ is quoted. With $z_{\rm prog}$ as a fitting parameter, we apply the $M_{\rm prog}$ model to the angular correlation functions measured with varying stellar mass thresholds from $M_{*,~{\rm lim}}/(h^{-2}M_\odot)=10^{11}$ to $10^{8.6}$ using a sample of galaxies at $z\simeq0.4$ from the Subaru Hyper Suprime-Cam survey. The $M_{\rm prog}$ model can reproduce the observations very well over $10~h^{-1}{\rm kpc}\textrm{--}10~h^{-1}{\rm Mpc}$. We find that, for the samples of $10^{9.2}\leq M_{*,~{\rm lim}}/(h^{-2}M_\odot)\leq10^{10.2}$, the correlation functions predicted by the widely-used $V_{\rm peak}$ model lack amplitudes at $\lesssim1~h^{-1}{\rm Mpc}$, suggesting that $M_{\rm prog}$ is a better proxy of the galaxy stellar mass than conventional $V_{\rm peak}$. The $z_{\rm prog}$ parameter is highest ($z_{\rm prog}\simeq3$) for intermediate mass galaxies at $M_*\simeq10^{9.9}~h^{-2}M_\odot$, and becomes smaller down to $z_\mathrm{prog}\simeq1$ for both lower- and higher-mass galaxies. We interpret these trends as reflecting the downsizing in the in-situ star formation in lower-mass galaxies and the larger contribution of the ex-situ stellar mass growth in higher-mass galaxies., Comment: 12 pages, 8 figures, accepted for publication in MNRAS, the angular correlation function data of the HSC galaxies are available on https://sites.google.com/view/smasaki/works-%E7%A0%94%E7%A9%B6%E6%A5%AD%E7%B8%BE?authuser=0

We study spatially resolved scaling relations among stars, dust, and gas in ten nearby spiral galaxies. In a preceding paper Abdurro'uf et al. (2022), we have derived spatially resolved properties of the stellar population and dust by panchromatic spectral energy distribution (SED) fitting using piXedfit. Now, we investigate resolved star formation ($\Sigma_{\rm H_{2}}$--$\Sigma_{\rm SFR}$--$\Sigma_{*}$) and dust scaling relations. While the relations with all sub-galactic regions of the galaxies are reasonably tight ($\sigma \lesssim 0.3$ dex), we find that most of the scaling relations exhibit galaxy-to-galaxy variations in normalization and shape. Only two relations of $\Sigma_{\rm dust}$--$\Sigma_{\rm gas}$ and $\Sigma_{\rm dust}$--$\Sigma_{\rm H_{2}}$ do not show noticeable galaxy-to-galaxy variations among our sample galaxies. We further investigate correlations among the scaling relations. We find significant correlations among the normalization of the $\Sigma_{\rm H_{2}}$--$\Sigma_{\rm SFR}$--$\Sigma_{*}$ relations, which suggest that galaxies with higher levels of resolved $\text{H}_{2}$ fraction ($f_{\rm H_{2}}$) tend to have higher levels of resolved star formation efficiency (SFE) and specific star formation rate (sSFR). We also observe that galaxies with higher levels of resolved dust-to-stellar mass ratios tend to have higher levels of resolved sSFR, SFE, and $f_{\rm H_{2}}$. Moreover, we find that galaxies with higher global sSFR and less compact morphology tend to have higher levels of the resolved sSFR, SFE, and $f_{\rm H_{2}}$, which can explain the variations in the normalization of the $\Sigma_{\rm H_{2}}$--$\Sigma_{\rm SFR}$--$\Sigma_{*}$ relationships. Overall, we observe indications of the contributions of both global and local factors in governing the star formation process in galaxies., Comment: 28 pages, 13 figures, accepted for publication in ApJ. piXedfit v1.0 is publicly available at https://github.com/aabdurrouf/piXedfit. Documentation is available at https://pixedfit.readthedocs.io/en/latest/. Some tutorials of practical usages in jupyter notebooks can be found at https://github.com/aabdurrouf/piXedfit/tree/main/examples

The Subaru Hyper Suprime-Cam (HSC) Strategic Survey is the latest-generation multi-band optical imaging survey for galaxy evolution and structure formation. The "Ultra-Deep" component of the HSC survey provides $grizy$ broad-band images over $\sim3.4$ deg$^2$ to detection limits of $\sim26$-28 AB, along with narrow-band images, in the COSMOS and the SXDS fields. These images provide an unprecedented combination of depths and area coverage, for the studies galaxies up to $z\sim7$. However, the lack of coverage at $<4000$ Ang implies incomplete sampling of the rest-frame UV at $z\lesssim 3$, which is critically needed for understanding the buildup of stellar mass in the later cosmic time. We conducted a multi-year CFHT $u^\ast$-band imaging campaign in the two HSC Ultra-Deep fields with CFHT MegaCam. By including shallower archival data, we reach 5-$\sigma$ depths of $u^\ast=28.1$ and 28.4 (AB) at the centers of the COSMOS and SXDS fields, respectively, and $u^\ast=27.7$ and 27.8 in the central 1 deg$^2$ fields. The image quality is $\gtrsim0.90$ arcsec, fairly good for the $u^\ast$ band. Both the photometric and astrometric quality of our data are excellent. We show that the combination of our $u^\ast$-band and HSC data can lead to high-quality photometric redshifts at $z=0$-3, and robust measurements of rest-frame UV on galaxies at $0.4

The halo assembly bias, a phenomenon referring to dependencies of the large-scale bias of a dark matter halo other than its mass, is a fundamental property of the standard cosmological model. First discovered in 2005 from the Millennium Run simulation, it has been proven very difficult to be detected observationally, with only a few convincing claims of detection so far. The main obstacle lies in finding an accurate proxy of the halo formation time. In this study, by utilizing a constrained simulation that can faithfully reproduce the observed structures larger than $2\,$Mpc in the local universe, for a sample of 634 massive clusters at $z\le 0.12$, we find their counterpart halos in the simulation and use the mass growth history of the matched halos to estimate the formation time of the observed clusters. This allows us to construct a pair of early- and late-forming clusters, with similar mass as measured via weak gravitational lensing, and large-scale bias differing at $\approx 3\sigma$ level, suggestive of the signature of assembly bias, which is further corroborated by the properties of cluster galaxies, including the brightest cluster galaxy, and the spatial distribution and number of member galaxies. Our study paves a way to further detect assembly bias based on cluster samples constructed purely on observed quantities., Comment: 10 pages, 4 Figures. Accepted for publication in A&A

Although it is generally accepted that massive galaxies form in a two-phased fashion, beginning with a rapid mass buildup through intense starburst activities, followed by primarily dry mergers that mainly deposit stellar mass at outskirts, the late time stellar mass growth of brightest cluster galaxies (BCGs), the most massive galaxies in the universe, is still not well understood. Several independent measurements have indicated a slower mass growth rate than predictions from theoretical models. We attempt to resolve the discrepancy by measuring the frequency of BCGs with multiple-cores, which serve as a proxy of the merger rates in the central region and facilitate a more direct comparison with theoretical predictions. Using 79 BCGs at $z=0.06-0.15$ with integral field spectroscopic (IFS) data from the Mapping Nearby Galaxies at APO (MaNGA) project, we obtain a multiple-core fraction of $0.11 \pm 0.04$ at $z\approx 0.1$ within a 18 kpc radius from the center, which is comparable to the value of $0.08 \pm 0.04$ derived from mock observations of 218 simulated BCGs from the cosmological hydrodynamical simulation IllustrisTNG. We find that most of cores that appear close to the BCGs from imaging data turn out to be physically associated systems. Anchoring on the similarity in the multiple-core frequency between the MaNGA and IllustrisTNG, we discuss the mass growth rate of BCGs over the past 4.5 Gyr., Comment: 33 pages, 27 figures, 5 tables; Submitted to ApJ

This paper documents the seventeenth data release (DR17) from the Sloan Digital Sky Surveys; the fifth and final release from the fourth phase (SDSS-IV). DR17 contains the complete release of the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey, which reached its goal of surveying over 10,000 nearby galaxies. The complete release of the MaNGA Stellar Library (MaStar) accompanies this data, providing observations of almost 30,000 stars through the MaNGA instrument during bright time. DR17 also contains the complete release of the Apache Point Observatory Galactic Evolution Experiment 2 (APOGEE-2) survey which publicly releases infra-red spectra of over 650,000 stars. The main sample from the Extended Baryon Oscillation Spectroscopic Survey (eBOSS), as well as the sub-survey Time Domain Spectroscopic Survey (TDSS) data were fully released in DR16. New single-fiber optical spectroscopy released in DR17 is from the SPectroscipic IDentification of ERosita Survey (SPIDERS) sub-survey and the eBOSS-RM program. Along with the primary data sets, DR17 includes 25 new or updated Value Added Catalogs (VACs). This paper concludes the release of SDSS-IV survey data. SDSS continues into its fifth phase with observations already underway for the Milky Way Mapper (MWM), Local Volume Mapper (LVM) and Black Hole Mapper (BHM) surveys., Comment: 40 pages, 8 figures, 6 tables. In press at ApJSS (arxiv v2 corrects some minor typos and updates references)

We study spatially resolved properties (on spatial scales of $\sim 1-2$ kpc out to at least $3$ effective radii) of the stars, dust, and gas in ten nearby spiral galaxies. The properties of the stellar population and dust are derived by fitting the spatially resolved spectral energy distribution (SED) with more than 20 photometric bands ranging from far-ultraviolet to far-infrared. Our newly developed software piXedfit performs point spread function matching of images, pixel binning, and models the stellar light, dust attenuation, dust emission, and emission from a dusty torus heated by an active galactic nucleus simultaneously through the energy balance approach. With this self-consistent analysis, we present the spatially resolved version of the IRX--$\beta$ relation, finding that it is consistent with the relationship from the integrated photometry. We show that the old stellar populations contribute to the dust heating, which causes an overestimation of star formation rate (SFR) derived from the total ultraviolet and infrared luminosities on kpc scales. With archival high-resolution maps of atomic and molecular gas, we study the radial variation of the properties of the stellar populations (including stellar mass, age, metallicity, and SFR), dust (including dust mass, dust temperature, and abundance of polycyclic aromatic hydrocarbon), gas, as well as dust-to-stellar mass and dust-to-gas mass ratios. We observe a depletion of molecular gas mass fraction in the central region of the majority of the galaxies, suggesting that the lack of available fuel is an important factor in suppressing the specific SFR at the center., Comment: 35 pages, 19 figures, 4 tables. Accepted by ApJ (results remain the same). Complementary figures to Fig. 4 (for 10 galaxies) and high resolution version of Fig. 8 are available at https://github.com/aabdurrouf/maps-nearby-galaxies

The paper presents the third data release of Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP), a wide-field multi-band imaging survey with the Subaru 8.2m telescope. HSC-SSP has three survey layers (Wide, Deep, and UltraDeep) with different area coverages and depths, designed to address a wide array of astrophysical questions. This third release from HSC-SSP includes data from 278 nights of observing time and covers about 670 square degrees in all five broad-band filters at the full depth ($\sim26$~mag at $5\sigma$) in the Wide layer. If we include partially observed area, the release covers 1,470 square degrees. The Deep and UltraDeep layers have $\sim80\%$ of the originally planned integration times, and are considered done, as we have slightly changed the observing strategy in order to compensate for various time losses. There are a number of updates in the image processing pipeline. Of particular importance is the change in the sky subtraction algorithm; we subtract the sky on small scales before the detection and measurement stages, which has significantly reduced false detections. Thanks to this and other updates, the overall quality of the processed data has improved since the previous release. However, there are limitations in the data (for example, the pipeline is not optimized for crowded fields), and we encourage the user to check the quality assurance plots as well as a list of known issues before exploiting the data. The data release website is https://hsc-release.mtk.nao.ac.jp/., Comment: 25 pages, 19 figures, submitted to PASJ. Data available at https://hsc-release.mtk.nao.ac.jp

We study Brightest Cluster Galaxies (BCGs) in $\sim5000$ galaxy clusters from the Hyper Suprime-Cam (HSC) Subaru Strategic Program. The sample is selected over an area of 830 $\textrm{deg}^2$ and is uniformly distributed in redshift over the range $z=0.3-1.0$. The clusters have stellar masses in the range $10^{11.8} - 10^{12.9} M_{\odot}$. We compare the stellar mass of the BCGs in each cluster to what we would expect if their masses were drawn from the mass distribution of the other member galaxies of the clusters. The BCGs are found to be "special", in the sense that they are not consistent with being a statistical extreme of the mass distribution of other cluster galaxies. This result is robust over the full range of cluster stellar masses and redshifts in the sample, indicating that BCGs are special up to a redshift of $z=1.0$. However, BCGs with a large separation from the center of the cluster are found to be consistent with being statistical extremes of the cluster member mass distribution. We discuss the implications of these findings for BCG formation scenarios., Comment: 14 pages, 12 figures. This is a pre-copyedited, author-produced PDF of an article accepted for publication in MNRAS following peer review. The version of record is available online at: https://doi.org/10.1093/mnras/stab2363

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., Comment: accepted for publication in ApJ

If the formation of central galaxies in dark matter haloes traces the assembly history of their host haloes, in haloes of fixed mass, central galaxy clustering may show dependence on properties indicating their formation history. Such a galaxy assembly bias effect has been investigated by Lin et al. 2016, with samples of central galaxies constructed in haloes of similar mass and with mean halo mass verified by galaxy lensing measurements, and no significant evidence of assembly bias is found from the analysis of the projected two-point correlation functions of early- and late-forming central galaxies. In this work, we extend the the investigation of assembly bias effect from real space to redshift (velocity) space, with an extended construction of early- and late-forming galaxies. We carry out halo occupation distribution modelling to constrain the galaxy-halo connection to see whether there is any sign of the effect of assembly bias. We find largely consistent host halo mass for early- and late-forming central galaxies, corroborated by lensing measurements. The central velocity bias parameters, which are supposed to characterise the mutual relaxation between central galaxies and their host haloes, are inferred to overlap between early- and late-forming central galaxies. However, we find a large amplitude of velocity bias for early-forming central galaxies (e.g. with central galaxies moving at more than 50% that of dark matter velocity dispersion inside host haloes), which may signal an assembly bias effect. A large sample with two-point correlation functions and other clustering measurements and improved modelling will help reach a conclusive result., Comment: 15 pages, 12 figures, 1 table, Submitted to MNRAS on April 26, 2021

Rich, open-domain textual data available on the web resulted in great advancements for language processing. However, while that data may be suitable for language processing tasks, they are mostly non-conversational, lacking many phenomena that appear in human interactions and this is one of the reasons why we still have many unsolved challenges in conversational AI. In this work, we attempt to address this by using Generative Conversational Networks to automatically generate data and train social conversational agents. We evaluate our approach on TopicalChat with automatic metrics and human evaluators, showing that with 10% of seed data it performs close to the baseline that uses 100% of the data., Comment: Accepted at WeCNLP 2021

Although it is generally accepted that massive galaxies form in a two-phased fashion, beginning with a rapid mass buildup through intense starburst activities, followed by primarily dry mergers that mainly deposit stellar mass at outskirts, the late time stellar mass growth of brightest cluster galaxies (BCGs), the most massive galaxies in the universe, is still not well understood. Several independent measurements have indicated a slower mass growth rate than predictions from theoretical models. We attempt to resolve the discrepancy by measuring the frequency of BCGs with multiple-cores, which serve as a proxy of the merger rates in the central region and facilitate a more direct comparison with theoretical predictions. Using 79 BCGs at $z=0.06-0.15$ with integral field spectroscopic (IFS) data from the Mapping Nearby Galaxies at APO (MaNGA) project, we obtain a multiple-core fraction of $0.11 \pm 0.04$ at $z\approx 0.1$ within a 18 kpc radius from the center, which is comparable to the value of $0.08 \pm 0.04$ derived from mock observations of 218 simulated BCGs from the cosmological hydrodynamical simulation IllustrisTNG. We find that most of cores that appear close to the BCGs from imaging data turn out to be physically associated systems. Anchoring on the similarity in the multiple-core frequency between the MaNGA and IllustrisTNG, we discuss the mass growth rate of BCGs over the past 4.5 Gyr., Comment: 38 pages, 31 figures, 5 tables; accepted by ApJ

This paper documents the seventeenth data release (DR17) from the Sloan Digital Sky Surveys; the fifth and final release from the fourth phase (SDSS-IV). DR17 contains the complete release of the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey, which reached its goal of surveying over 10,000 nearby galaxies. The complete release of the MaNGA Stellar Library (MaStar) accompanies this data, providing observations of almost 30,000 stars through the MaNGA instrument during bright time. DR17 also contains the complete release of the Apache Point Observatory Galactic Evolution Experiment 2 (APOGEE-2) survey which publicly releases infra-red spectra of over 650,000 stars. The main sample from the Extended Baryon Oscillation Spectroscopic Survey (eBOSS), as well as the sub-survey Time Domain Spectroscopic Survey (TDSS) data were fully released in DR16. New single-fiber optical spectroscopy released in DR17 is from the SPectroscipic IDentification of ERosita Survey (SPIDERS) sub-survey and the eBOSS-RM program. Along with the primary data sets, DR17 includes 25 new or updated Value Added Catalogs (VACs). This paper concludes the release of SDSS-IV survey data. SDSS continues into its fifth phase with observations already underway for the Milky Way Mapper (MWM), Local Volume Mapper (LVM) and Black Hole Mapper (BHM) surveys., Comment: 40 pages, 8 figures, 6 tables. In press at ApJSS (arxiv v2 corrects some minor typos and updates references)

We study spatially resolved properties (on spatial scales of $\sim 1-2$ kpc out to at least $3$ effective radii) of the stars, dust, and gas in ten nearby spiral galaxies. The properties of the stellar population and dust are derived by fitting the spatially resolved spectral energy distribution (SED) with more than 20 photometric bands ranging from far-ultraviolet to far-infrared. Our newly developed software piXedfit performs point spread function matching of images, pixel binning, and models the stellar light, dust attenuation, dust emission, and emission from a dusty torus heated by an active galactic nucleus simultaneously through the energy balance approach. With this self-consistent analysis, we present the spatially resolved version of the IRX--$\beta$ relation, finding that it is consistent with the relationship from the integrated photometry. We show that the old stellar populations contribute to the dust heating, which causes an overestimation of star formation rate (SFR) derived from the total ultraviolet and infrared luminosities on kpc scales. With archival high-resolution maps of atomic and molecular gas, we study the radial variation of the properties of the stellar populations (including stellar mass, age, metallicity, and SFR), dust (including dust mass, dust temperature, and abundance of polycyclic aromatic hydrocarbon), gas, as well as dust-to-stellar mass and dust-to-gas mass ratios. We observe a depletion of molecular gas mass fraction in the central region of the majority of the galaxies, suggesting that the lack of available fuel is an important factor in suppressing the specific SFR at the center., Comment: 35 pages, 19 figures, 4 tables. Accepted by ApJ (results remain the same). Complementary figures to Fig. 4 (for 10 galaxies) and high resolution version of Fig. 8 are available at https://github.com/aabdurrouf/maps-nearby-galaxies

The paper presents the third data release of Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP), a wide-field multi-band imaging survey with the Subaru 8.2m telescope. HSC-SSP has three survey layers (Wide, Deep, and UltraDeep) with different area coverages and depths, designed to address a wide array of astrophysical questions. This third release from HSC-SSP includes data from 278 nights of observing time and covers about 670 square degrees in all five broad-band filters at the full depth ($\sim26$~mag at $5\sigma$) in the Wide layer. If we include partially observed area, the release covers 1,470 square degrees. The Deep and UltraDeep layers have $\sim80\%$ of the originally planned integration times, and are considered done, as we have slightly changed the observing strategy in order to compensate for various time losses. There are a number of updates in the image processing pipeline. Of particular importance is the change in the sky subtraction algorithm; we subtract the sky on small scales before the detection and measurement stages, which has significantly reduced false detections. Thanks to this and other updates, the overall quality of the processed data has improved since the previous release. However, there are limitations in the data (for example, the pipeline is not optimized for crowded fields), and we encourage the user to check the quality assurance plots as well as a list of known issues before exploiting the data. The data release website is https://hsc-release.mtk.nao.ac.jp/., Comment: 25 pages, 19 figures, submitted to PASJ. Data available at https://hsc-release.mtk.nao.ac.jp

We study Brightest Cluster Galaxies (BCGs) in $\sim5000$ galaxy clusters from the Hyper Suprime-Cam (HSC) Subaru Strategic Program. The sample is selected over an area of 830 $\textrm{deg}^2$ and is uniformly distributed in redshift over the range $z=0.3-1.0$. The clusters have stellar masses in the range $10^{11.8} - 10^{12.9} M_{\odot}$. We compare the stellar mass of the BCGs in each cluster to what we would expect if their masses were drawn from the mass distribution of the other member galaxies of the clusters. The BCGs are found to be "special", in the sense that they are not consistent with being a statistical extreme of the mass distribution of other cluster galaxies. This result is robust over the full range of cluster stellar masses and redshifts in the sample, indicating that BCGs are special up to a redshift of $z=1.0$. However, BCGs with a large separation from the center of the cluster are found to be consistent with being statistical extremes of the cluster member mass distribution. We discuss the implications of these findings for BCG formation scenarios., Comment: 14 pages, 12 figures. This is a pre-copyedited, author-produced PDF of an article accepted for publication in MNRAS following peer review. The version of record is available online at: https://doi.org/10.1093/mnras/stab2363

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., Comment: accepted for publication in ApJ

If the formation of central galaxies in dark matter haloes traces the assembly history of their host haloes, in haloes of fixed mass, central galaxy clustering may show dependence on properties indicating their formation history. Such a galaxy assembly bias effect has been investigated by Lin et al. 2016, with samples of central galaxies constructed in haloes of similar mass and with mean halo mass verified by galaxy lensing measurements, and no significant evidence of assembly bias is found from the analysis of the projected two-point correlation functions of early- and late-forming central galaxies. In this work, we extend the the investigation of assembly bias effect from real space to redshift (velocity) space, with an extended construction of early- and late-forming galaxies. We carry out halo occupation distribution modelling to constrain the galaxy-halo connection to see whether there is any sign of the effect of assembly bias. We find largely consistent host halo mass for early- and late-forming central galaxies, corroborated by lensing measurements. The central velocity bias parameters, which are supposed to characterise the mutual relaxation between central galaxies and their host haloes, are inferred to overlap between early- and late-forming central galaxies. However, we find a large amplitude of velocity bias for early-forming central galaxies (e.g. with central galaxies moving at more than 50% that of dark matter velocity dispersion inside host haloes), which may signal an assembly bias effect. A large sample with two-point correlation functions and other clustering measurements and improved modelling will help reach a conclusive result., Comment: 17 pages, 14 figures, 1 table, Published to MNRAS 13 Sept. 2021

Subaru Strategic Program with the Hyper-Suprime Cam (HSC-SSP) has proven to be successful with its extremely-wide area coverage in past years. Taking advantages of this feature, we report initial results from exploration and research of expansive over- and under-dense structures at $z=$ 0.3-1 based on the second Public Data Release where optical 5-band photometric data for $\sim$ eight million sources with $i<23$ mag are available over $\sim360$ square degrees. We not only confirm known superclusters but also find candidates of titanic over- and under-dense regions out to $z=1$. The mock data analysis suggests that the density peaks would involve one or more massive dark matter haloes ($>10^{14}$ M$_\odot$) of the redshift, and the density troughs tend to be empty of massive haloes over $>10$ comoving Mpc. Besides, the density peaks and troughs at $z<0.6$ are in part identified as positive and negative weak lensing signals respectively, in mean tangential shear profiles, showing a good agreement with those inferred from the full-sky weak lensing simulation. The coming extensive spectroscopic surveys will be able to resolve these colossal structures in three-dimensional space. The number density information over the entire survey field will be available as grid-point data on the website of the HSC-SSP data release (https://hsc.mtk.nao.ac.jp/ssp/data-release/)., Comment: 22 pages, 23 figures, accepted for publication in MNRAS

We present piXedfit, pixelized spectral energy distribution (SED) fitting, a Python package that provides tools for analyzing spatially resolved properties of galaxies using multiband imaging data alone or in combination with integral field spectroscopy (IFS) data. piXedfit has six modules that can handle all tasks in the spatially resolved SED fitting. The SED fitting module uses the Bayesian inference technique with two kinds of posteriors sampling methods: Markov Chain Monte Carlo (MCMC) and random densely-sampling of parameter space (RDSPS). We test the performance of the SED fitting module using mock SEDs of simulated galaxies from IllustrisTNG. The SED fitting with both posteriors sampling methods can recover physical properties and star formation histories of the IllustrisTNG galaxies well. We further test the performance of piXedfit modules by analyzing 20 galaxies observed by the CALIFA and MaNGA surveys. The data comprises of 12-band imaging data from GALEX, SDSS, 2MASS, and WISE, and the IFS data from CALIFA or MaNGA. piXedfit can spatially match (in resolution and sampling) of the imaging and IFS data. By fitting only the photometric SEDs, piXedfit can predict the spectral continuum, $\text{D}_{\rm n}4000$, $H_{\alpha}$, and $H_{\beta}$ well. The star formation rate (SFR) derived by piXedfit is consistent with that derived from $H_{\alpha}$ emission. The RDSPS method gives equally good fitting results as the MCMC and it is much faster than the MCMC. piXedfit is a versatile tool equipped with a parallel computing module for efficient analysis of large datasets, and will be made publicly available (https://github.com/aabdurrouf/piXedfit)., Comment: 40 pages, 21 figures, 1 table, Accepted for publication in The Astrophysical Journal Supplement Series (ApJS). piXedfit will be available at https://github.com/aabdurrouf/piXedfit and documented at https://pixedfit.readthedocs.io/en/latest/index.html

Environmental effects are crucial to the understanding of the evolution of galaxies in dense environments, such as galaxy clusters. Using the large field-of-view of SITELLE, the unique imaging fourier transform spectrograph at CFHT, we are able to obtain 2D spectral information for a large and complete sample of cluster galaxies out to the infall region. We describe a pipeline developed to identify emission line galaxies (ELGs) from the datacube using cross-correlation techniques. We present results based on the spatial offsets between the emission-line regions and stellar continua in ELGs from two z$\sim$0.25 galaxy clusters, Abell 2390 and Abell 2465. We find a preference in the offsets being pointed away from the cluster center. Combining the two clusters, there is a 3$\sigma$ excess for high-velocity galaxies within the virial radius having the offsets to be pointed away from the cluster center. Assuming the offset being a proxy for the velocity vector of a galaxy, as expected from ram pressure stripping, this excess indicates that ram pressure stripping occurs most effectively during the first passage of an infalling galaxy, leading to the quenching of its star formation. We also find that, outside the virial region, the continuum-normalized H$\alpha$ line flux for infalling galaxies with large offsets are on average lower than those with small or no measurable offset, further supporting ram pressure as a dominant quenching mechanism during the initial infall stages., Comment: 25 pages, 18 figures, accepted for publication in ApJ

In a dialogue system pipeline, a natural language generation (NLG) unit converts the dialogue direction and content to a corresponding natural language realization. A recent trend for dialogue systems is to first pre-train on large datasets and then fine-tune in a supervised manner using datasets annotated with application-specific features. Though novel behaviours can be learned from custom annotation, the required effort severely bounds the quantity of the training set, and the application-specific nature limits the reuse. In light of the recent success of data-driven approaches, we propose the novel future bridging NLG (FBNLG) concept for dialogue systems and simulators. The critical step is for an FBNLG to accept a future user or system utterance to bridge the present context towards. Future bridging enables self supervised training over annotation-free datasets, decoupled the training of NLG from the rest of the system. An FBNLG, pre-trained with massive datasets, is expected to apply in classical or new dialogue scenarios with minimal adaptation effort. We evaluate a prototype FBNLG to show that future bridging can be a viable approach to a universal few-shot NLG for task-oriented and chit-chat dialogues., Comment: 11 pages, 1 figure

There has been a rapid development in data-driven task-oriented dialogue systems with the benefit of large-scale datasets. However, the progress of dialogue systems in low-resource languages lags far behind due to the lack of high-quality data. To advance the cross-lingual technology in building dialog systems, DSTC9 introduces the task of cross-lingual dialog state tracking, where we test the DST module in a low-resource language given the rich-resource training dataset. This paper studies the transferability of a cross-lingual generative dialogue state tracking system using a multilingual pre-trained seq2seq model. We experiment under different settings, including joint-training or pre-training on cross-lingual and cross-ontology datasets. We also find out the low cross-lingual transferability of our approaches and provides investigation and discussion., Comment: DSTC9 Workshop - AAAI

We have conducted a comprehensive survey of emission-line galaxies at $z\lesssim1.6$ based on narrowband (NB) imaging data taken with Hyper Suprime-Cam (HSC) on the Subaru telescope. In this paper, we update the catalogs of H$\alpha$, [OIII], and [OII] emission-line galaxies using the data from the second Public Data Release (PDR2) of Subaru Strategic Program (SSP) of HSC and Cosmic HydrOgen Reionization Unveiled with Subaru (CHORUS) survey along with the spectroscopic redshifts for 2,019 emission-line galaxies selected with the PDR1 data. The wider effective coverage of NB816 and NB921, 16.3 deg$^2$ and 16.9 deg$^2$ respectively, are available in the Deep and UltraDeep layers of HSC-SSP from the PDR2. The CHORUS survey provides us with data with additional three NBs (NB527, NB718, and NB973) in the COSMOS field in the UltraDeep layer (1.37 deg$^2$). The five NB datasets allow us to investigate the star-forming galaxies presenting emission-lines at 14 specific redshifts ranging from $z\sim1.6$ down to $z\sim0.05$. We revisit the distribution of large-scale structures and luminosity functions (LFs) for the emission-line galaxies with the large samples of 75,377 emission-line galaxies selected. The redshift revolution of LFs shows that the star formation rate densities (SFRDs) decreases monotonically from $z\sim1.6$, which is consistent with the cosmic SFRD ever known. Our samples of emission-line galaxies covering a sufficiently large survey volume are useful to investigate the evolution of star-forming galaxies since the cosmic noon in a wide range of environments including galaxy clusters, filaments, and voids., Comment: 32 pages, 23 figures, 6 tables, accepted for publication in PASJ. The catalog of emission-line galaxies from HSC-SSP PDR2 data will be available soon at https://hsc-release.mtk.nao.ac.jp/, and the catalog from CHORUS data will be available after the CHORUS data are publicly released

We report the rest-frame ultraviolet luminosity function of $g$-dropout galaxies in 177 protocluster candidates (PC UVLF) at $z\sim4$ selected in the Hyper Suprime-Cam Subaru Strategic Program. Comparing with the UVLF of field galaxies at the same redshift, we find that the PC UVLF shows a significant excess towards the bright-end. This excess can not be explained by the contribution of only active galactic nuclei, and we also find that this is more significant in higher dense regions. Assuming that all protocluster members are located on the star formation main sequence, the PC UVLF can be converted into a stellar mass function. Consequently, our protocluster members are inferred to have a 2.8 times more massive characteristic stellar mass than that of the field Lyman break galaxies at the same redshift. This study, for the first time, clearly shows that the enhancement in star formation or stellar mass in overdense regions can generally be seen as early as at $z\sim4$. We also estimate the star formation rate density (SFRD) in protocluster regions as $\simeq 6-20\%$ of the cosmic SFRD, based on the measured PC UVLF after correcting for the selection incompleteness in our protocluster sample. This high value suggests that protoclusters make a non-negligible contribution to the cosmic SFRD at $z\sim4$, as previously suggested by simulations. Our results suggest that protoclusters are essential components for the galaxy evolution at $z\sim4$., Comment: 22 pages, 16 figures, Accepted for publication in ApJ