arXiv subjects

Jiajun Zhang

Publications and source records attributed to Jiajun Zhang.

At least 181 records · Page 10Linked to original sources

arXiv2022

Improvement of cosmological constraints with the cross correlation between line-of-sight optical galaxy and FRB dispersion measure

Fast Radio Bursts (hereafter FRBs) can be used in cosmology by studying the Dispersion Measure (hereafter DM) as a function of redshift. The large scale structure of matter distribution is regarded as a major error budget for such application. Using optical galaxy and dispersion measure mocks built from N-body simulations, we have shown that the galaxy number density can be used as a tracer for large scale electron density and help improve the measurement of DM as a function of redshift. We have shown that, using the line-of-sight galaxy number counts within 1' around the given localized FRB source can help improve the cosmological parameter constraints by more than 20%.

astro-ph.CO↗

arXiv2022

A Roadmap for Big Model

With the rapid development of deep learning, training Big Models (BMs) for multiple downstream tasks becomes a popular paradigm. Researchers have achieved various outcomes in the construction of BMs and the BM application in many fields. At present, there is a lack of research work that sorts out the overall progress of BMs and guides the follow-up research. In this paper, we cover not only the BM technologies themselves but also the prerequisites for BM training and applications with BMs, dividing the BM review into four parts: Resource, Models, Key Technologies and Application. We introduce 16 specific BM-related topics in those four parts, they are Data, Knowledge, Computing System, Parallel Training System, Language Model, Vision Model, Multi-modal Model, Theory&Interpretability, Commonsense Reasoning, Reliability&Security, Governance, Evaluation, Machine Translation, Text Generation, Dialogue and Protein Research. In each topic, we summarize clearly the current studies and propose some future research directions. At the end of this paper, we conclude the further development of BMs in a more general view.

cs.LG↗

arXiv2022

Pre-Training on Dynamic Graph Neural Networks

The pre-training on the graph neural network model can learn the general features of large-scale networks or networks of the same type by self-supervised methods, which allows the model to work even when node labels are missing. However, the existing pre-training methods do not take network evolution into consideration. This paper proposes a pre-training method on dynamic graph neural networks (PT-DGNN), which uses dynamic attributed graph generation tasks to simultaneously learn the structure, semantics, and evolution features of the graph. The method includes two steps: 1) dynamic sub-graph sampling, and 2) pre-training with dynamic attributed graph generation task. Comparative experiments on three realistic dynamic network datasets show that the proposed method achieves the best results on the link prediction fine-tuning task.

cs.LG↗

arXiv2022

N-body simulations, halo mass functions, and halo density profile in $f(T)$ gravity

We perform N-body simulations for $f(T)$ gravity using the ME-Gadget code, in order to investigate for the first time the structure formation process in detail. Focusing on the power-law model, and considering the model-parameter to be consistent within 1$σ$ with all other cosmological datasets (such as SNIa, BAO, CMB, CC), we show that there are clear observational differences between $Λ$CDM cosmology and $f(T)$ gravity, due to the modifications brought about the latter in the Hubble function evolution and the effective $Newton\prime s$ constant. We extract the matter density distribution, matter power spectrum, counts-in-cells, halo mass function and excess surface density (ESD) around low density positions (LDPs) at present time. Concerning the matter power spectrum we find a difference from $Λ$CDM scenario, which is attributed to about 2/3 to the different expansion and to about 1/3 to the effective gravitational constant. Additionally, we find a difference in the cells, which is significantly larger than the Poisson error, which may be distinguishable with weak-lensing reconstructed mass maps. Moreover, we show that there are different massive halos with mass $M>10^{14}M_{\odot}/h$, which may be distinguishable with statistical measurements of cluster number counting, and we find that the ESD around LDPs is mildly different. In conclusion, high-lighting possible smoking guns, we show that large scale structure can indeed lead us to distinguish General Relativity and $Λ$CDM cosmology from $f(T)$ gravity.

astro-ph.CO↗

arXiv2022

The BINGO Project V: Further steps in Component Separation and Bispectrum Analysis

Observing the neutral hydrogen distribution across the Universe via redshifted 21cm line intensity mapping constitutes a powerful probe for cosmology. However, the redshifted 21cm signal is obscured by the foreground emission from our Galaxy and other extragalactic foregrounds. This paper addresses the capabilities of the BINGO survey to separate such signals. Specifically, this paper looks in detail at the different residuals left over by foreground components, shows that a noise-corrected spectrum is unbiased, and shows that we understand the remaining systematic residuals by analyzing nonzero contributions to the three-point function. We use the generalized needlet internal linear combination, which we apply to sky simulations of the BINGO experiment for each redshift bin of the survey. We present our recovery of the redshifted 21cm signal from sky simulations of the BINGO experiment, including foreground components. We test the recovery of the 21cm signal through the angular power spectrum at different redshifts, as well as the recovery of its non-Gaussian distribution through a bispectrum analysis. We find that non-Gaussianities from the original foreground maps can be removed down to, at least, the noise limit of the BINGO survey with such techniques. Our component separation methodology allows us to subtract the foreground contamination in the BINGO channels down to levels below the cosmological signal and the noise, and to reconstruct the 21cm power spectrum for different redshift bins without significant loss at multipoles $20 \lesssim \ell \lesssim 500$. Our bispectrum analysis yields strong tests of the level of the residual foreground contamination in the recovered 21cm signal, thereby allowing us to both optimize and validate our component separation analysis. (Abridged)

astro-ph.CO↗

arXiv2022

Learning Confidence for Transformer-based Neural Machine Translation

Confidence estimation aims to quantify the confidence of the model prediction, providing an expectation of success. A well-calibrated confidence estimate enables accurate failure prediction and proper risk measurement when given noisy samples and out-of-distribution data in real-world settings. However, this task remains a severe challenge for neural machine translation (NMT), where probabilities from softmax distribution fail to describe when the model is probably mistaken. To address this problem, we propose an unsupervised confidence estimate learning jointly with the training of the NMT model. We explain confidence as how many hints the NMT model needs to make a correct prediction, and more hints indicate low confidence. Specifically, the NMT model is given the option to ask for hints to improve translation accuracy at the cost of some slight penalty. Then, we approximate their level of confidence by counting the number of hints the model uses. We demonstrate that our learned confidence estimate achieves high accuracy on extensive sentence/word-level quality estimation tasks. Analytical results verify that our confidence estimate can correctly assess underlying risk in two real-world scenarios: (1) discovering noisy samples and (2) detecting out-of-domain data. We further propose a novel confidence-based instance-specific label smoothing approach based on our learned confidence estimate, which outperforms standard label smoothing.

cs.CL↗

arXiv2022

The BINGO Project III: Optical design and optimisation of the focal plane

The BINGO telescope was designed to measure the fluctuations of the 21-cm radiation arising from the hyperfine transition of neutral hydrogen and aims to measure the Baryon Acoustic Oscillations (BAO) from such fluctuations, therefore serving as a pathfinder to future deeper intensity mapping surveys. The requirements for the Phase 1 of the projects consider a large reflector system (two 40 m-class dishes in a crossed-Dragone configuration), illuminating a focal plane with 28 horns to measure the sky with two circular polarisations in a drift scan mode to produce measurements of the radiation in intensity as well as the circular polarisation. In this paper we present the optical design for the instrument. We describe the intensity and polarisation properties of the beams and the optical arrangement of the horns in the focal plane to produce a homogeneous and well-sampled map after the end of Phase 1. Our analysis provides an optimal model for the location of the horns in the focal plane, producing a homogeneous and Nyquist sampled map after the nominal survey time. We arrive at an optimal configuration for the optical system, including the focal plane positioning and the beam behavior of the instrument. We present an estimate of the expected side lobes both for intensity and polarisation, as well as the effect of band averaging on the final side lobes. The cross polarisation leakage values for the final configuration allow us to conclude that the optical arrangement meets the requirements of the project. We conclude that the chosen optical design meets the requirements for the project in terms of polarisation purity, area coverage as well as homogeneity of coverage so that BINGO can perform a successful BAO experiment. We further conclude that the requirements on the placement and r.m.s. error on the mirrors are also achievable so that a successful experiment can be conducted.(Abridged)

astro-ph.IM↗

arXiv2022

Dark Matter Halos in Interacting Dark Energy Models: Formation History, Density Profile, Spin and Shape

The interacting dark energy (IDE) model, which considers the interaction between dark energy and dark matter, provides a natural mechanism to alleviate the coincidence problem and can also relieve the observational tensions under the $Λ$CDM model. Previous studies have put constraints on IDE models by observations of cosmic expansion history, cosmic microwave background and large-scale structures. However, these data are not yet enough to distinguish IDE models from $Λ$CDM effectively. Because the non-linear structure formation contains rich cosmological information, it can provide additional means to differentiate alternative models. In this paper, based on a set of $N$-body simulations for IDE models, we investigate the formation histories and properties of dark matter halos, and compare with their $Λ$CDM counterparts. For the model with dark matter decaying into dark energy and the parameters being the best-fit values from previous constraints, the structure formation is markedly slowed down, and the halos have systematically lower mass, looser internal structure, higher spin and anisotropy. This is inconsistent with the observed structure formation, and thus this model can be safely ruled out from the perspective of non-linear structure formation. Moreover, we find that the ratio of halo concentrations between IDE and $Λ$CDM counterparts depends sensitively on the interaction parameter and is independent of halo mass. This can act as a powerful probe to constrain IDE models. Our results concretely demonstrate that the interaction of the two dark components can affect the halo formation considerably, and therefore the constraints from non-linear structures are indispensable.

astro-ph.CO↗

arXiv2022

Instance-aware Prompt Learning for Language Understanding and Generation

Recently, prompt learning has become a new paradigm to utilize pre-trained language models (PLMs) and achieves promising results in downstream tasks with a negligible increase of parameters. The current usage of discrete and continuous prompts assumes that the prompt is fixed for a specific task and all samples in the task share the same prompt. However, a task may contain quite diverse samples in which some are easy and others are difficult, and diverse prompts are desirable. In this paper, we propose an instance-aware prompt learning method that learns a different prompt for each instance. Specifically, we suppose that each learnable prompt token has a different contribution to different instances, and we learn the contribution by calculating the relevance score between an instance and each prompt token. The contribution weighted prompt would be instance aware. We apply our method to both unidirectional and bidirectional PLMs on both language understanding and generation tasks. Extensive experiments demonstrate that our method obtains considerable improvements compared to strong baselines. Especially, our method achieves the state-of-the-art on the SuperGLUE few-shot learning benchmark.

cs.CL↗

arXiv2021

Parameter Differentiation based Multilingual Neural Machine Translation

Multilingual neural machine translation (MNMT) aims to translate multiple languages with a single model and has been proved successful thanks to effective knowledge transfer among different languages with shared parameters. However, it is still an open question which parameters should be shared and which ones need to be task-specific. Currently, the common practice is to heuristically design or search language-specific modules, which is difficult to find the optimal configuration. In this paper, we propose a novel parameter differentiation based method that allows the model to determine which parameters should be language-specific during training. Inspired by cellular differentiation, each shared parameter in our method can dynamically differentiate into more specialized types. We further define the differentiation criterion as inter-task gradient similarity. Therefore, parameters with conflicting inter-task gradients are more likely to be language-specific. Extensive experiments on multilingual datasets have demonstrated that our method significantly outperforms various strong baselines with different parameter sharing configurations. Further analyses reveal that the parameter sharing configuration obtained by our method correlates well with the linguistic proximities.

cs.CL↗

arXiv2021

The BINGO Project II: Instrument Description

The measurement of diffuse 21-cm radiation from the hyperfine transition of neutral hydrogen (HI signal) in different redshifts is an important tool for modern cosmology. However, detecting this faint signal with non-cryogenic receivers in single-dish telescopes is a challenging task. The BINGO (Baryon Acoustic Oscillations from Integrated Neutral Gas Observations) radio telescope is an instrument designed to detect baryonic acoustic oscillations (BAOs) in the cosmological HI signal, in the redshift interval $0.127 \le z \le 0.449$. This paper describes the BINGO radio telescope, including the current status of the optics, receiver, observational strategy, calibration, and the site. BINGO has been carefully designed to minimize systematics, being a transit instrument with no moving dishes and 28 horns operating in the frequency range $980 \le ν\le 1260$ MHz. Comprehensive laboratory tests were conducted for many of the BINGO subsystems and the prototypes of the receiver chain, horn, polarizer, magic tees, and transitions have been successfully tested between 2018 - 2020. The survey was designed to cover $\sim 13\%$ of the sky, with the primary mirror pointing at declination $δ=-15^{\circ}$. The telescope will see an instantaneous declination strip of $14.75^{\circ}$. The results of the prototype tests closely meet those obtained during the modeling process, suggesting BINGO will perform according to our expectations. After one year of observations with a $60\%$ duty cycle and 28 horns, BINGO should achieve an expected sensitivity of 102 $μK$ per 9.33 MHz frequency channel, one polarization, and be able to measure the HI power spectrum in a competitive time frame.

astro-ph.IM↗

arXiv2021

The BINGO Project VII: Cosmological Forecasts from 21cm Intensity Mapping

The 21cm line of neutral hydrogen (HI) opens a new avenue in our exploration of the structure and evolution of the Universe. It provides complementary data to the current large-scale structure observations with different systematics, and thus it will be used to improve our understanding of the $Λ$CDM model. Among several radio cosmological surveys designed to measure this line, BINGO is a single-dish telescope mainly designed to detect baryon acoustic oscillations (BAOs) at low redshifts ($0.127< z<0.449$). Our goal is to assess the fiducial BINGO setup and its capabilities of constraining the cosmological parameters, and to analyze the effect of different instrument configurations. We used the Phase 1 fiducial configuration of the BINGO telescope to perform our cosmological forecasts. In addition, we investigated the impact of several instrumental setups, taking into account some instrumental systematics, and different cosmological models. Combining BINGO with Planck temperature and polarization data, the projected constraint improves from a $13\%$ and $25\%$ precision measurement at the $68\%$ confidence level with Planck only to $1\%$ and $3\%$ for the Hubble constant and the dark energy equation of state (EoS), respectively, within the wCDM model. Assuming a Chevallier-Polarski-Linder parameterization, the EoS parameters have standard deviations given by $σ_{w_0} = 0.30$ and $σ_{w_a} = 1.2$, which are improvements on the order of $30\%$ with respect to Planck alone. Also, we can access information about the HI density and bias, obtaining $\sim 8.5\%$ and $\sim 6\%$ precision, respectively, assuming they vary with redshift at three independent bins. The fiducial BINGO configuration will be able to extract significant cosmological information from the HI distribution and provide constraints competitive with current and future cosmological surveys. (Abridged)

astro-ph.CO↗

arXiv2021

White dwarfs identified in LAMOST Data Release 5

In this paper, we report white dwarfs identified in the 5th Data Release of the Large Area Multi-Object fibre Spectroscopic Telescope, including spectral types of DA, DB, DC, DZ, and so on. There are 2 625 DA spectra of 2 281 DA stars, 182 DB spectra of 166 DB stars, 62 DC spectra of 58 DC stars, 36 DZ spectra of 33 DZ stars and many other types identified, in addition to our previous paper (Data Release 2). Among those sources, 393 DA stars and 46 DB stars are new identifications after cross-matching with the literature. In order to select DA candidates, we use the classification result from the LAMOST pipeline, colour-colour cut method and a random forest machine learning method. For DBs, since there is no template for DB in the pipeline model, a random forest machine learning method is chosen to select candidates. All the WD candidates have been visually checked individually. The parameters of effective temperature, surface gravity, mass, and cooling age have been estimated for relatively high signal-to-noise ratio DAs and DBs. The peaks of the DA and DB mass distributions are found to be around 0.62Msun and 0.65Msun, respectively. Finally, the data and method we used to select white dwarf candidates for the second phase of LAMOST survey are also addressed in this paper.

astro-ph.SR↗

arXiv2021

Deep Learning-based Segmentation of Cerebral Aneurysms in 3D TOF-MRA using Coarse-to-Fine Framework

BACKGROUND AND PURPOSE: Cerebral aneurysm is one of the most common cerebrovascular diseases, and SAH caused by its rupture has a very high mortality and disability rate. Existing automatic segmentation methods based on DLMs with TOF-MRA modality could not segment edge voxels very well, so that our goal is to realize more accurate segmentation of cerebral aneurysms in 3D TOF-MRA with the help of DLMs. MATERIALS AND METHODS: In this research, we proposed an automatic segmentation framework of cerebral aneurysm in 3D TOF-MRA. The framework was composed of two segmentation networks ranging from coarse to fine. The coarse segmentation network, namely DeepMedic, completed the coarse segmentation of cerebral aneurysms, and the processed results were fed into the fine segmentation network, namely dual-channel SE_3D U-Net trained with weighted loss function, for fine segmentation. Images from ADAM2020 (n=113) were used for training and validation and images from another center (n=45) were used for testing. The segmentation metrics we used include DSC, HD, and VS. RESULTS: The trained cerebral aneurysm segmentation model achieved DSC of 0.75, HD of 1.52, and VS of 0.91 on validation cohort. On the totally independent test cohort, our method achieved the highest DSC of 0.12, the lowest HD of 11.61, and the highest VS of 0.16 in comparison with state-of-the-art segmentation networks. CONCLUSIONS: The coarse-to-fine framework, which composed of DeepMedic and dual-channel SE_3D U-Net can segment cerebral aneurysms in 3D TOF-MRA with a superior accuracy.

eess.IV↗

arXiv2021

Extended Tidal Tails of IC 4756 detected by {\it Gaia} EDR3

We report the discovery of emerged tidal tails around open cluster IC 4756 ($\sim$ 1 Gyr) based on 644 members identified from {\it Gaia} EDR3. Three-dimensional spatial positions, two-dimensional tangential velocities $\left( x, y, z, κ\cdot μ_α^{*}/\varpi, κ\cdot μ_δ/\varpi \right)$ are utilized to determine the co-moving member candidates of IC 4756. Using a Bayesian method, we correct the distance for each cluster member. Two tidal tails extend up to 180 pc and display a S-shape in $X^{\prime}Y^{\prime}$ space (Cartesian coordinates focused on cluster center). A clean sequence of our members in Color-Absolute-Magnitude Diagram (CAMD) indicates the coeval population and matches perfectly with the PARSEC isochrone with age from Bossini et al. (2019). Mass segregation is detected in this cluster as well. Finally, we derive the tidal radius and core radius of IC 4756 about $12.13$ pc and $4.33 \pm 0.75$ pc, respectively.

astro-ph.GA↗

arXiv2021

Tracing the Origin of Moving Groups. III. Detecting Moving Groups in The LAMOST DR7

We revisit the moving groups (MGs) in the solar neighborhood with a sample of 91969 nearby stars constructed from LAMOST DR7. Using the wavelet technique and Monte Carlo simulations, five MGs together with a new candidate located at $V \simeq$ -130 km s$^{-1}$ are detected simultaneously in $V-\sqrt{U^2+2V^2}$ space. Taking into account the other known MGs, we conclude that MGs in the Galactic disk are spaced by approximately 15 $\sim$ 25 km s$^{-1}$ along $V$ velocity. The origin of detected MGs is analysed through the distributions of [Fe/H]$-$[Mg/Fe] and ages. Our results support attributing the origin to the continuous resonant mechanisms probably induced by the bar or spiral arms of the Milky Way.

astro-ph.GA↗

arXiv2021

The BINGO Project IV: Simulations for mission performance assessment and preliminary component separation steps

The large-scale distribution of neutral hydrogen (HI) in the Universe is luminous through its 21 cm emission. The goal of the Baryon Acoustic Oscillations from Integrated Neutral Gas Observations -- BINGO -- radio telescope is to detect baryon acoustic oscillations (BAOs) at radio frequencies through 21 cm intensity mapping (IM). The telescope will span the redshift range 0.127 $< z <$ 0.449 with an instantaneous field-of-view of $14.75^{\circ} \times 6.0^{\circ}$. In this work we investigate different constructive and operational scenarios of the instrument by generating sky maps as they would be produced by the instrument. In doing this we use a set of end-to-end IM mission simulations. The maps will additionally be used to evaluate the efficiency of a component separation method (GNILC). We have simulated the kind of data that would be produced in a single-dish IM experiment such as BINGO. According to the results obtained, we have optimized the focal plane design of the telescope. In addition, the application of the GNILC method on simulated data shows that it is feasible to extract the cosmological signal across a wide range of multipoles and redshifts. The results are comparable with the standard principal component analysis method.

astro-ph.CO↗

arXiv2021

The BINGO Project I: Baryon Acoustic Oscillations from Integrated Neutral Gas Observations

Observations of the redshifted 21-cm line of neutral hydrogen (HI) are a new and powerful window of observation that offers us the possibility to map the spatial distribution of cosmic HI and learn about cosmology. BINGO (Baryon Acoustic Oscillations [BAO] from Integrated Neutral Gas Observations) is a new unique radio telescope designed to be one of the first to probe BAO at radio frequencies. BINGO has two science goals: cosmology and astrophysics. Cosmology is the main science goal and the driver for BINGO's design and strategy. The key of BINGO is to detect the low redshift BAO to put strong constraints in the dark sector models. Given the versatility of the BINGO telescope, a secondary goal is astrophysics, where BINGO can help discover and study Fast Radio Bursts (FRB) and other transients, Galactic and extragalactic science. In this paper, we introduce the latest progress of the BINGO project, its science goals, describing the scientific potential of the project in each science and the new developments obtained by the collaboration. We introduce the BINGO project and its science goals and give a general summary of recent developments in construction, science potential and pipeline development obtained by the BINGO collaboration in the past few years. We show that BINGO will be able to obtain competitive constraints for the dark sector, and also that will allow for the discovery of several FRBs in the southern hemisphere. The capacity of BINGO in obtaining information from 21-cm is also tested in the pipeline introduced here. There is still no measurement of the BAO in radio, and studying cosmology in this new window of observations is one of the most promising advances in the field. The BINGO project is a radio telescope that has the goal to be one of the first to perform this measurement and it is currently being built in the northeast of Brazil. (Abridged)

astro-ph.CO↗