arXiv subjects

Haoran Feng

Publications and source records attributed to Haoran Feng.

28 records · Page 2Linked to original sources

arXiv2024

New method for estimating molecular cloud distances based on Gaia, 2MASS, and the TRILEGAL galaxy model

We propose a new method for estimating the distances of molecular clouds traced by CO line emission. Stars from 2MASS and Gaia EDR3 are selected as on-cloud stars when they are projected on a cloud. The background on-cloud stars have redder colors on average than the foreground stars. Instead of searching for stars projected away from the cloud, we employed the TRILEGA galaxy model to mimic the stellar population without cloud extinction along the sightline toward the cloud. Our method does not require an exact boundary of a cloud. The boundaries are highly variable and depend on the sensitivity of the molecular line data. For each cloud, we compared the distributions of on-cloud stars to the TRILEGAL stellar populations in the diagram of $J-K_s$ color versus distance. The intrinsic $J-K_s$ colors of main-sequence and evolved stars from TRILEGAL were considered separately, and they were used as the baseline for subtracting the observed $J-K_s$ colors. The baseline-corrected $J-K_s$ color was deployed with the Bayesian analysis and Markov chain Monte Carlo sampling to determine the distance at which the $J-K_s$ color jump is largest. This method was successfully applied to measure the distances of 27 molecular clouds, which were selected from previously published cloud samples. By replacing TRILEGAL with the GALAXIA galaxy model, we were able to measure the distances for 21 of the 27 clouds. The distances of the 21 clouds based on the GALAXIA model agree well with those based on the TRILEGAL model. The distances of the 27 clouds estimated by this method are consistent with previous estimates. We will apply this new method to a larger region of the gaseous galactic plane, in particular, for the inner galactic region, where a region free of CO emission is hard to separate from the crowded field of clouds.

astro-ph.GA↗

arXiv2024

The Multilayer Nature of Molecular Gas toward the Cygnus Region

We study the physical properties and 3D distribution of molecular clouds (MCs) toward the Cygnus region using the MWISP CO survey and Gaia DR3 data. Based on Gaussian decomposition and clustering for $\rm ^{13}CO$ lines, over 70% of the fluxes are recovered. With the identification result of $\rm ^{13}CO$ structures, two models are designed to measure the distances of the molecular gas in velocity crowding regions. The distances of more than 200 large $\rm ^{13}CO$ structures are obtained toward the 150 square degree region. Additionally, tens of the identified MC structures coincide well with masers and/or intense mid-IR emission. We find multiple gas layers toward the region: (1) the extensive gas structures composing the Cygnus Rift from 700 pc to 1 kpc across the whole region; (2) the $\sim$ 1.3 kpc gas layer mainly in the Cygnus X South region; and (3) the 1.5 kpc dense filament at the Cygnus X North region and many cometary clouds shaped by Cygnus OB2. We also note that the spatial distribution of YSO candidates is generally consistent with the molecular gas structures. The total molecular mass of the Cygnus region is estimated to be $\sim 2.7\times10^{6}~M_{\odot}$ assuming an X-factor ratio $X_{\rm CO} = 2 \times 10^{20} \rm cm^{-2} (K\cdot km\cdot s^{-1})^{-1}$. The foreground Cygnus Rift contributes $\sim$25% of the molecular mass in the whole region. Our work presents a new 3D view of the MCs' distribution toward the Cygnus X region, as well as the exact molecular gas mass distribution in the foreground Cygnus Rift.

astro-ph.GA↗

arXiv2023

The molecular clouds in a section of the third Galactic quadrant: observational properties and chemical abundance ratio between CO and its isotopologues

We compare the observational properties between $^{12}$CO, $^{13}$CO, and C$^{18}$O and summarize the observational parameters based on 7069 clouds sample from the Milky Way Imaging Scroll Painting (MWISP) CO survey in a section of the third Galactic quadrant. We find that the $^{13}$CO angular area ($A_{\rm ^{13}CO}$) generally increases with that of $^{12}$CO ($A_{\rm ^{12}CO}$), and the ratio of $A_{\rm ^{13}CO}$ to $A_{\rm ^{12}CO}$ is 0.38 by linear fitting. We find that the $^{12}$CO and $^{13}$CO flux are tightly correlated as $F_{\rm ^{13}CO}~=~0.17~ F_{\rm ^{12}CO}$ with both fluxes calculated within the $^{13}$CO-bright region. This indicates that the abundance $X_{\rm ^{13}CO}$ is a constant to be 6.5$^{+0.1}_{-0.5}$ $\times 10^{-7}$ for all samples under assumption of local thermodynamic equilibrium (LTE). Additionally, we observed that the X-factor is approximately constant in large sample molecular clouds. Similarly, we find $F_{\rm C^{18}O}~=~0.11~F_{\rm ^{13}CO}$ with both fluxes calculated within C$^{18}$O-bright region, which indicates that the abundance ratios ${X_{\rm ^{13}CO}/X_{\rm C^{18}O}}$ stays the same value 9.7$^{+0.6}_{-0.8}$ across the molecular clouds under LTE assumption. The linear relationships of $F_{\rm ^{12}CO}$ vs. $F_{\rm ^{13}CO}$ and $F_{\rm ^{13}CO}$ vs. $F_{\rm C^{18}O}$ hold not only for the $^{13}$CO-bright region or C$^{18}$O-bright region, but also for the entire molecular cloud scale with lower flux ratio. The abundance ratio ${X_{\rm ^{13}CO}/X_{\rm C^{18}O}}$ inside clouds shows a strong correlation with column density and temperature. This indicates that the ${X_{\rm ^{13}CO}/X_{\rm C^{18}O}}$ is dominated by a combination of chemical fractionation, selectively dissociation, and self-shielding effect inside clouds.

astro-ph.GA↗

arXiv2023

M$^3$Fair: Mitigating Bias in Healthcare Data through Multi-Level and Multi-Sensitive-Attribute Reweighting Method

In the data-driven artificial intelligence paradigm, models heavily rely on large amounts of training data. However, factors like sampling distribution imbalance can lead to issues of bias and unfairness in healthcare data. Sensitive attributes, such as race, gender, age, and medical condition, are characteristics of individuals that are commonly associated with discrimination or bias. In healthcare AI, these attributes can play a significant role in determining the quality of care that individuals receive. For example, minority groups often receive fewer procedures and poorer-quality medical care than white individuals in US. Therefore, detecting and mitigating bias in data is crucial to enhancing health equity. Bias mitigation methods include pre-processing, in-processing, and post-processing. Among them, Reweighting (RW) is a widely used pre-processing method that performs well in balancing machine learning performance and fairness performance. RW adjusts the weights for samples within each (group, label) combination, where these weights are utilized in loss functions. However, RW is limited to considering only a single sensitive attribute when mitigating bias and assumes that each sensitive attribute is equally important. This may result in potential inaccuracies when addressing intersectional bias. To address these limitations, we propose M3Fair, a multi-level and multi-sensitive-attribute reweighting method by extending the RW method to multiple sensitive attributes at multiple levels. Our experiments on real-world datasets show that the approach is effective, straightforward, and generalizable in addressing the healthcare fairness issues.

cs.LG↗

arXiv2023

In search for infalling clumps in molecular clouds -- A catalogue of CO blue-profiles

We have started a systematic survey of molecular clumps with infall motions to study the very early phase of star formation. Our first step is to utilize the data products by MWISP to make an unbiased survey for blue asymmetric line profiles of CO isotopical molecules. Within a total area of $\sim$ 2400 square degrees nearby the Galactic plane, we have found 3533 candidates showing blue-profiles, in which 3329 are selected from the $^{12}$CO&$^{13}$CO pair and 204 are from the $^{13}$CO&C$^{18}$O pair. Exploration of the parametric spaces suggests our samples are in the cold phase with relatively high column densities ready for star formation. Analysis of the spatial distribution of our samples suggests that they exist virtually in all major components of the Galaxy. The vertical distribution suggest that the sources are located mainly in the thick disk of $\sim$ 85 parsec, but still a small part are located far beyond Galactic midplane. Our follow-up observation indicates that these candidates are a good sample to start a search for infall motions, and to study the condition of very early phase of star formation.

astro-ph.GA↗

arXiv2022

A Catalog of Molecular Clumps and Cores with Infall Signatures

The research of infall motion is a common means to study molecular cloud dynamics and the early process of star formation. Many works had been done in-depth research on infall. We searched the literature related to infall study of molecular cloud since 1994, summarized the infall sources identified by the authors. A total of 456 infall sources are catalogued. We classify them into high-mass and low-mass sources, in which the high-mass sources are divided into three evolutionary stages: prestellar, protostellar and HII region. We divide the sources into clumps and cores according to their sizes. The H$_2$ column density values range from 1.21$\times$ 10$^{21}$ to 9.75 $\times$ 10$^{24}$ cm$^{-2}$, with a median value of 4.17$\times$ 10$^{22}$ cm$^{-2}$. The H$_2$ column densities of high-mass and low-mass sources are significantly separated. The median value of infall velocity for high-mass clumps is 1.12 km s$^{-1}$, and the infall velocities of low-mass cores are virtually all less than 0.5 km s$^{-1}$. There is no obvious difference between different stages of evolution. The mass infall rates of low-mass cores are between 10$^{-7}$ and 10$^{-4}$ M$_{\odot} \text{yr}^{-1}$, and those of high-mass clumps are between 10$^{-4}$ and 10$^{-1}$ M$_{\odot} \text{yr}^{-1}$ with only one exception. We do not find that the mass infall rates vary with evolutionary stages.

astro-ph.GA↗

arXiv2021

Preliminary analysis on the noise characteristics of MWISP data

Noise is a significant part within a millimeter-wave molecular line datacube. Analyzing the noise improves our understanding of noise characteristics, and further contributes to scientific discoveries. We measure the noise level of a single datacube from MWISP and perform statistical analyses. We identified major factors which increase the noise level of a single datacube, including bad channels, edge effects, baseline distortion and line contamination. Cleaning algorithms are applied to remove or reduce these noise components. As a result, we obtained the cleaned datacube in which noise follows a positively skewed normal distribution. We further analyzed the noise structure distribution of a 3D mosaicked datacube in the range l = 40°.7 to 43°.3 and b = -2°.3 to 0°.3 and found that noise in the final mosaicked datacube is mainly characterized by noise fluctuation among the cells.

astro-ph.GA↗

arXiv2013

Hybrid Radio-map for Noise Tolerant Wireless Indoor Localization

In wireless networks, radio-map based locating techniques are commonly used to cope the complex fading feature of radio signal, in which a radio-map is built by calibrating received signal strength (RSS) signatures at training locations in the offline phase. However, in severe hostile environments, such as in ship cabins where severe shadowing, blocking and multi-path fading effects are posed by ubiquitous metallic architecture, even radio-map cannot capture the dynamics of RSS. In this paper, we introduced multiple feature radio-map location method for severely noisy environments. We proposed to add low variance signature into radio map. Since the low variance signatures are generally expensive to obtain, we focus on the scenario when the low variance signatures are sparse. We studied efficient construction of multi-feature radio-map in offline phase, and proposed feasible region narrowing down and particle based algorithm for online tracking. Simulation results show the remarkably performance improvement in terms of positioning accuracy and robustness against RSS noises than the traditional radio-map method.

cs.NI↗

arXiv2013

Sense, Model and Identify the Load Signatures of HVAC Systems in Metro Stations

The HVAC systems in subway stations are energy consuming giants, each of which may consume over 10, 000 Kilowatts per day for cooling and ventilation. To save energy for the HVAC systems, it is critically important to firstly know the "load signatures" of the HVAC system, i.e., the quantity of heat imported from the outdoor environments and by the passengers respectively in different periods of a day, which will significantly benefit the design of control policies. In this paper, we present a novel sensing and learning approach to identify the load signature of the HVAC system in the subway stations. In particular, sensors and smart meters were deployed to monitor the indoor, outdoor temperatures, and the energy consumptions of the HVAC system in real-time. The number of passengers was counted by the ticket checking system. At the same time, the cooling supply provided by the HVAC system was inferred via the energy consumption logs of the HVAC system. Since the indoor temperature variations are driven by the difference of the loads and the cooling supply, linear regression model was proposed for the load signature, whose coefficients are derived via a proposed algorithm . We collected real sensing data and energy log data from HaiDianHuangZhuang Subway station, which is in line 4 of Beijing from the duration of July 2012 to Sept. 2012. The data was used to evaluate the coefficients of the regression model. The experiment results show typical variation signatures of the loads from the passengers and from the outdoor environments respectively, which provide important contexts for smart control policies.

eess.SY↗

arXiv2013

SEED: Public Energy and Environment Dataset for Optimizing HVAC Operation in Subway Stations

For sustainability and energy saving, the problem to optimize the control of heating, ventilating, and air-conditioning (HVAC) systems has attracted great attentions, but analyzing the signatures of thermal environments and HVAC systems and the evaluation of the optimization policies has encountered inefficiency and inconvenient problems due to the lack of public dataset. In this paper, we present the Subway station Energy and Environment Dataset (SEED), which was collected from a line of Beijing subway stations, providing minute-resolution data regarding the environment dynamics (temperature, humidity, CO2, etc.) working states and energy consumptions of the HVAC systems (ventilators, refrigerators, pumps), and hour-resolution data of passenger flows. We describe the sensor deployments and the HVAC systems for data collection and for environment control, and also present initial investigation for the energy disaggregation of HVAC system, the signatures of the thermal load, cooling supply, and the passenger flow using the dataset.

eess.SY↗