arXiv subjects

Rui Qiao

Publications and source records attributed to Rui Qiao.

At least 55 records · Page 3Linked to original sources

arXiv2022

Search for gamma-ray spectral lines with the DArk Matter Particle Explorer

The DArk Matter Particle Explorer (DAMPE) is well suitable for searching for monochromatic and sharp $γ$-ray structures in the GeV$-$TeV range thanks to its unprecedented high energy resolution. In this work, we search for $γ$-ray line structures using five years of DAMPE data. To improve the sensitivity, we develop two types of dedicated data sets (including the BgoOnly data which is the first time to be used in the data analysis for the calorimeter-based gamma-ray observatories) and adopt the signal-to-noise ratio optimized regions of interest (ROIs) for different DM density profiles. No line signals or candidates are found between 10 and 300 GeV in the Galaxy. The constraints on the velocity-averaged cross section for $χχ\to γγ$ and the decay lifetime for $χ\to γν$, both at 95% confidence level, have been calculated and the systematic uncertainties have been taken into account. Comparing to the previous Fermi-LAT results, though DAMPE has an acceptance smaller by a factor of $\sim 10$, similar constraints on the DM parameters are achieved and below 100 GeV the lower limits on the decay lifetime are even stronger by a factor of a few. Our results demonstrate the potential of high-energy-resolution observations on dark matter detection.

astro-ph.HE↗

arXiv2022

Temperature effect on non-Darcian flow in low-permeability porous media

In low-permeability porous media, the velocity of a fluid flow exhibits a nonlinear dependence on the imposed pressure gradient. This non-Darcian flow behavior has important implications to geological disposal of nuclear waste, hydrocarbon extraction from shale, and flow and transport in clay-rich aquifers. Temperature has been postulated to affect the threshold pressure gradient of a non-Darcian flow; however, the supporting data is very limited. In this study we for the first time report a systematic measurement of the threshold pressure gradient under various permeabilities and temperatures. The results show that a higher temperature leads to a lower threshold pressure gradient under the same permeability and a faster reduction of the threshold pressure gradient with increasing permeability. The experimental data are fitted to a two-parameter model to determine the parameters, h0 and a, which characterize the interfacial fluid-solid interactions and the transition between the Darcy and non-Darcian regimes.

physics.geo-ph↗

arXiv2021

The In-Flight Realtime Trigger and Localization Software of GECAM

Realtime trigger and localization of bursts are the key functions of GECAM, which is an all-sky gamma-ray monitor launched in Dec 10, 2020. We developed a multifunctional trigger and localization software operating on the CPU of the GECAM electronic box (EBOX). This onboard software has the following features: high trigger efficiency for real celestial bursts with a suppression of false triggers caused by charged particle bursts and background fluctuation, dedicated localization algorithm optimized for short and long bursts respetively, short time latency of the trigger information which is downlinked throught the BeiDou satellite navigation System (BDS). This paper presents the detailed design and deveopment of this trigger and localization software system of GECAM, including the main functions, general design, workflow and algorithms, as well as the verification and demonstration of this software, including the on-ground trigger tests with simulated gamma-ray bursts made by a dedicated X-ray tube and the in-flight performance to real gamma-ray bursts and magnetar bursts.

astro-ph.IM↗

arXiv2021

Gain stabilization and consistency correction approach for multiple SiPM-based gamma-ray detectors on GECAM

Each satellite of the Gravitational wave high-energy Electromagnetic Counterpart All-sky Monitor (GECAM, mission) consists of 25 SiPM based gamma-ray detectors (GRDs). Although SiPM based GRD has merits of compact size and low bias-voltage, the drift of the SiPM gain with temperature is a severe problem for GRD performance. An adaptive voltage supply source was designed to automatically adjust the SiPM bias voltage to compensate the temperature effects and keep the gain stable. This approach has been proved to be effective during both the on-ground and in-flight tests. The in-flight measured variation of the SiPM gain is within 2%. To reduce the gain non-uniformity of GRDs, an iterative bias voltage adjustment approach is proposed and implemented. The gain non-uniformity is reduced from 17% to 0.6%. In this paper, the gain stabilization and consistency correction approach are presented and discussed in detail.

physics.ins-det↗

arXiv2021

Observations of Forbush Decreases of cosmic ray electrons and positrons with the Dark Matter Particle Explorer

The Forbush Decrease (FD) represents the rapid decrease of the intensities of charged particles accompanied with the coronal mass ejections (CMEs) or high-speed streams from coronal holes. It has been mainly explored with ground-based neutron monitors network which indirectly measure the integrated intensities of all species of cosmic rays by counting secondary neutrons produced from interaction between atmosphere atoms and cosmic rays. The space-based experiments can resolve the species of particles but the energy ranges are limited by the relative small acceptances except for the most abundant particles like protons and helium. Therefore, the FD of cosmic ray electrons and positrons have just been investigated by the PAMELA experiment in the low energy range ($<5$ GeV) with limited statistics. In this paper, we study the FD event occurred in September, 2017, with the electron and positron data recorded by the Dark Matter Particle Explorer. The evolution of the FDs from 2 GeV to 20 GeV with a time resolution of 6 hours are given. We observe two solar energetic particle events in the time profile of the intensity of cosmic rays, the earlier and weak one has not been shown in the neutron monitor data. Furthermore, both the amplitude and recovery time of fluxes of electrons and positrons show clear energy-dependence, which is important in probing the disturbances of the interplanetary environment by the coronal mass ejections.

astro-ph.HE↗

arXiv2021

GRB 210121A: A Typical Fireball Burst Detected by Two Small Missions

The Chinese CubeSat Mission, Gamma Ray Integrated Detectors (GRID), recently detected its first gamma-ray burst, GRB 210121A, which was jointly observed by the Gravitational wave high-energy Electromagnetic Counterpart All-sky Monitor (GECAM). This burst is confirmed by several other missions, including \fermi and \textit{Insight}-HXMT. We combined multi-mission observational data and performed a comprehensive analysis of the burst's temporal and spectral properties. Our results show that the burst is relatively special in its high peak energy, thermal-like low energy indices, and large fluence. By putting it to the $E_{\rm p}$-$E_{\rmγ, iso}$ relation diagram with assumed distance, we found this burst can be constrained at the redshift range of [0.3,3.0]. The thermal spectral component is also confirmed by the direct fit of the physical models to the observed spectra. Interestingly, the physical photosphere model also constrained a redshift of $z\sim$ 0.3 for this burst, which help us to identify a host galaxy candidate at such a distance within the location error box. Assuming the host galaxy is real, we found the burst can be best explained by the photosphere emission of a typical fireball with an initial radius of $r_0\sim$ 3.2 $\times 10^7$ cm.

astro-ph.HE↗

arXiv2021

Uncovering Main Causalities for Long-tailed Information Extraction

Information Extraction (IE) aims to extract structural information from unstructured texts. In practice, long-tailed distributions caused by the selection bias of a dataset, may lead to incorrect correlations, also known as spurious correlations, between entities and labels in the conventional likelihood models. This motivates us to propose counterfactual IE (CFIE), a novel framework that aims to uncover the main causalities behind data in the view of causal inference. Specifically, 1) we first introduce a unified structural causal model (SCM) for various IE tasks, describing the relationships among variables; 2) with our SCM, we then generate counterfactuals based on an explicit language structure to better calculate the direct causal effect during the inference stage; 3) we further propose a novel debiasing approach to yield more robust predictions. Experiments on three IE tasks across five public datasets show the effectiveness of our CFIE model in mitigating the spurious correlation issues.

cs.CL↗

arXiv2021

Interventional Video Grounding with Dual Contrastive Learning

Video grounding aims to localize a moment from an untrimmed video for a given textual query. Existing approaches focus more on the alignment of visual and language stimuli with various likelihood-based matching or regression strategies, i.e., P(Y|X). Consequently, these models may suffer from spurious correlations between the language and video features due to the selection bias of the dataset. 1) To uncover the causality behind the model and data, we first propose a novel paradigm from the perspective of the causal inference, i.e., interventional video grounding (IVG) that leverages backdoor adjustment to deconfound the selection bias based on structured causal model (SCM) and do-calculus P(Y|do(X)). Then, we present a simple yet effective method to approximate the unobserved confounder as it cannot be directly sampled from the dataset. 2) Meanwhile, we introduce a dual contrastive learning approach (DCL) to better align the text and video by maximizing the mutual information (MI) between query and video clips, and the MI between start/end frames of a target moment and the others within a video to learn more informative visual representations. Experiments on three standard benchmarks show the effectiveness of our approaches. Our code is available on GitHub: https://github.com/nanguoshun/IVG.

cs.CV↗

arXiv2020

Personalized workflow to identify optimal T-cell epitopes for peptide-based vaccines against COVID-19

Traditional vaccines against viruses are designed to target their surface proteins, i.e., antigens, which can trigger the immune system to produce specific antibodies to capture and neutralize the viruses. However, viruses often evolve quickly, and their antigens are prone to mutations to avoid recognition by the antibodies (antigenic drift). This limitation of the antibody-mediated immunity could be addressed by the T-cell mediated immunity, which is able to recognize conserved viral HLA peptides presented on virus-infected cells. Thus, by targeting conserved regions on the genome of a virus, T-cell epitope-based vaccines are less subjected to mutations and may work effectively on different strains of the virus. Here we propose a personalized workflow to identify an optimal set of T-cell epitopes based on the HLA alleles and the immunopeptidome of an individual person. Specifically, our workflow trains a machine learning model on the immunopeptidome and then predicts HLA peptides from conserved regions of a virus that are most likely to trigger responses from the person T cells. We applied the workflow to identify T-cell epitopes for the SARS-COV-2 virus, which has caused the recent COVID-19 pandemic in more than 100 countries across the globe.

q-bio.PE↗

arXiv2020

Drying of porous media by concurrent drainage and evaporation: A pore network modeling study

Drainage and evaporation can occur simultaneously during the drying of porous media, but the interactions between these processes and their effects on drying are rarely studied. In this work, we develop a pore network model that considers drainage, evaporation, and rarefied multi-component gas transport in porous media with nanoscale pores. Using this model, we investigate the drying of a liquid solvent-saturated porous medium enabled by the flow of purge gas through it. Simulations show that drying progresses in three stages, and the solvent removal by drainage effects (evaporation effects) becomes increasingly weak (strong) as drying progresses through these stages. Interestingly, drainage can contribute considerably to solvent removal even after evaporation effects become very strong, especially when the applied pressure difference across the porous medium is low. We show that these phenomena are the results of the coupling between the drainage and evaporation effects and this coupling depends on the operating conditions and the stage of drying.

cond-mat.soft↗

arXiv2019

Predicting Effective Diffusivity of Porous Media from Images by Deep Learning

We report the application of machine learning methods for predicting the effective diffusivity (De) of two-dimensional porous media from images of their structures. Pore structures are built using reconstruction methods and represented as images, and their effective diffusivity is computed by lattice Boltzmann (LBM) simulations. The datasets thus generated are used to train convolutional neural network (CNN) models and evaluate their performance. The trained model predicts the effective diffusivity of porous structures with computational cost orders of magnitude lower than LBM simulations. The optimized model performs well on porous media with realistic topology, large variation of porosity (0.28-0.98), and effective diffusivity spanning more than one order of magnitude (), e.g., >95% of predicted De have truncated relative error of <10% when the true De is larger than 0.2. The CNN model provides better prediction than the empirical Bruggeman equation, especially for porous structure with small diffusivity. The relative error of CNN predictions, however, is rather high for structures with De < 0.1. To address this issue, the porosity of porous structures is encoded directly into the neural network but the performance is enhanced marginally. Further improvement, i.e., 70% of the CNN predictions for structures with true De < 0.1 have relative error <30%, is achieved by removing trapped regions and dead-end pathways using a simple algorithm. These results suggest that deep learning augmented by field knowledge can be a powerful technique for predicting the transport properties of porous media. Directions for future research of machine learning in porous media are discussed based on detailed analysis of the performance of CNN models in the present work.

physics.geo-ph↗

arXiv2019

Implications on the origin of cosmic rays in light of 10 TV spectral softenings

Precise measurements of the energy spectra of cosmic rays (CRs) show various kinds of features deviating from single power-laws, which give very interesting and important implications on their origin and propagation. Previous measurements from a few balloon and space experiments indicate the existence of spectral softenings around 10 TV for protons (and probably also for Helium nuclei). Very recently, the DArk Matter Particle Explorer (DAMPE) measurement about the proton spectrum clearly reveals such a softening with a high significance. Here we study the implications of these new measurements, as well as the groundbased indirect measurements, on the origin of CRs. We find that a single component of CRs fails to fit the spectral softening and the air shower experiment data simultaneously. In the framework of multiple components, we discuss two possible scenarios, the multiple source population scenario and the background plus nearby source scenario. Both scenarios give reasonable fits to the wide-band data from TeV to 100 PeV energies. Considering the anisotropy observations, the nearby source model is favored.

astro-ph.HE↗

arXiv2019

DeepNovoV2: Better de novo peptide sequencing with deep learning

Personalized cancer vaccines are envisioned as the next generation rational cancer immunotherapy. The key step in developing personalized therapeutic cancer vaccines is to identify tumor-specific neoantigens that are on the surface of tumor cells. A promising method for this is through de novo peptide sequencing from mass spectrometry data. In this paper we introduce DeepNovoV2, the state-of-the-art model for peptide sequencing. In DeepNovoV2, a spectrum is directly represented as a set of (m/z, intensity) pairs, therefore it does not suffer from the accuracy-speed/memory trade-off problem. The model combines an order invariant network structure (T-Net) and recurrent neural networks and provides a complete end-to-end training and prediction framework to sequence patterns of peptides. Our experiments on a wide variety of data from different species show that DeepNovoV2 outperforms previous state-of-the-art methods, achieving 13.01-23.95\% higher accuracy at the peptide level.

cs.LG↗

arXiv2019

Double Helical Conformation and Extreme Rigidity in a Rodlike Polyelectrolyte

The ubiquitous biomacromolecule DNA has an axial rigidity persistence length of ~50 nm, driven by its elegant double helical structure. While double and multiple helix structures appear widely in nature, only rarely are these found in synthetic non-chiral macromolecules. Here we describe a double helical conformation in the densely charged aromatic polyamide poly(2,2'-disulfonyl-4,4'-benzidine terephthalamide) or PBDT. This double helix macromolecule represents one of the most rigid simple molecular structures known, exhibiting an extremely high axial persistence length (~1 micrometer). We present X-ray diffraction, NMR spectroscopy, and molecular dynamics (MD) simulations that reveal and confirm the double helical conformation. The discovery of this extreme rigidity in combination with high charge density gives insight into the self-assembly of molecular ionic composites with high mechanical modulus (~1 GPa) yet with liquid-like ion motions inside, and provides fodder for formation of new 1D-reinforced composites.

cond-mat.soft↗

arXiv2018

Charge Measurement of Cosmic Ray Nuclei with the Plastic Scintillator Detector of DAMPE

One of the main purposes of the DArk Matter Particle Explorer (DAMPE) is to measure the cosmic ray nuclei up to several tens of TeV or beyond, whose origin and propagation remains a hot topic in astrophysics. The Plastic Scintillator Detector (PSD) on top of DAMPE is designed to measure the charges of cosmic ray nuclei from H to Fe and serves as a veto detector for discriminating gamma-rays from charged particles. We propose in this paper a charge reconstruction procedure to optimize the PSD performance in charge measurement. Essentials of our approach, including track finding, alignment of PSD, light attenuation correction, quenching and equalization correction are described detailedly in this paper after a brief description of the structure and operational principle of the PSD. Our results show that the PSD works very well and almost all the elements in cosmic rays from H to Fe are clearly identified in the charge spectrum.

astro-ph.IM↗

arXiv2018

A machine learning method to separate cosmic ray electrons from protons from 10 to 100 GeV using DAMPE data

DArk Matter Particle Explorer (DAMPE) is a general purpose high energy cosmic ray and gamma ray observatory, aiming to detect high energy electrons and gammas in the energy range 5 GeV to 10 TeV and hundreds of TeV for nuclei. This paper provides a method using machine learning to identify electrons and separate them from gammas,protons,helium and heavy nuclei with the DAMPE data from 2016 January 1 to 2017 June 30, in energy range from 10 to 100 GeV.

astro-ph.IM↗

arXiv2017

A new charge reconstruction algorithm for the DAMPE silicon microstrip detector

The DArk Matter Particle Explorer (DAMPE) is one of the four satellites within the Strategic Pioneer Research Program in Space Science of the Chinese Academy of Science (CAS). The Silicon-Tungsten Tracker (STK), which is composed of 768 singled-sided silicon microstrip detectors, is one of the four subdetectors in DAMPE, providing track reconstruction and charge identification for relativistic charged particles. The charge response of DAMPE silicon microstrip detectors is complicated, depending on the incident angle and impact position. A new charge reconstruction algorithm for the DAMPE silicon microstrip detector is introduced in this paper. This algorithm can correct the complicated charge response, and was proved applicable by the ion test beam.

astro-ph.IM↗

arXiv2017

Interpretations of the DAMPE electron data

The DArk Matter Particle Explorer (DAMPE), a high energy cosmic ray and $γ$-ray detector in space, has recently reported the new measurement of the total electron plus positron flux between 25 GeV and 4.6 TeV. A spectral softening at $\sim0.9$ TeV and a tentative peak at $\sim1.4$ TeV have been reported. We study the physical implications of the DAMPE data in this work. The presence of the spectral break significantly tightens the constraints on the model parameters to explain the electron/positron excesses. The spectral softening can either be explained by the maximum acceleration limits of electrons by astrophysical sources, or a breakdown of the common assumption of continuous distribution of electron sources at TeV energies in space and time. The tentive peak at $\sim1.4$ TeV implies local sources of electrons/positrons with quasi-monochromatic injection spectrum. We find that the cold, ultra-relativistic $e^+e^-$ winds from pulsars may give rise to such a structure. The pulsar is requird to be middle-aged, relatively slowly-rotated, mildly magnetized, and isolated in a density cavity. The annihilation of DM particles ($m_χ\sim1.5$ TeV) into $e^+e^-$ pairs in a nearby clump or an over-density region may also explain the data. In the DM scenario, the inferred clump mass (or density enhancement) is about $10^7-10^8$ M$_\odot$ (or $17-35$ times of the canonical local density) assuming a thermal production cross section, which is relatively extreme compared with the expectation from numerical simulations. A moderate enhancement of the annihilation cross section via, e.g., the Sommerfeld mechanism or non-thermal production, is thus needed.

astro-ph.HE↗