Quasar Factor Analysis -- An Unsupervised and Probabilistic Quasar Continuum Prediction Algorithm with Latent Factor Analysis

Since their first discovery, quasars have been essential probes of the distant Universe. However, due to our limited knowledge of its nature, predicting the intrinsic quasar continua has bottlenecked their usage. Existing methods of quasar continuum recovery often rely on a limited number of high-quality quasar spectra, which might not capture the full diversity of the quasar population. In this study, we propose an unsupervised probabilistic model, \textit{Quasar Factor Analysis} (QFA), which combines factor analysis (FA) with physical priors of the intergalactic medium (IGM) to overcome these limitations. QFA captures the posterior distribution of quasar continua through generatively modeling quasar spectra. We demonstrate that QFA can achieve the state-of-the-art performance, $\sim 2\%$ relative error, for continuum prediction in the Ly$\alpha$ forest region compared to previous methods. We further fit 90,678 $22$ quasar spectra from Sloan Digital Sky Survey Data Release 16 and found that for $\sim 30\%$ quasar spectra where the continua were ill-determined with previous methods, QFA yields visually more plausible continua. QFA also attains $\lesssim 1\%$ error in the 1D Ly$\alpha$ power spectrum measurements at $\mathrm{z}\sim 3$ and $\sim 4\%$ in $\mathrm{z}\sim 2.4$. In addition, QFA determines latent factors representing more physically motivated than PCA. We investigate the evolution of the latent factors and report no significant redshift or luminosity dependency except for the Baldwin effect. The generative nature of QFA also enables outlier detection robustly; we showed that QFA is effective in selecting outlying quasar spectra, including damped Ly$\alpha$ systems and potential Type II quasar spectra.Comment: Main body is 23 pages with 14 figures. Much more detailed exposition of the method originally presented in the short conference workshop paper arXiv:2207.02788 . All source codes are made publicly available at https://github.com/ZechangSun/QFA . Submitted to ApJS. Comments are welcome

Zephyr : Stitching Heterogeneous Training Data with Normalizing Flows for Photometric Redshift Inference

We present zephyr, a novel method that integrates cutting-edge normalizing flow techniques into a mixture density estimation framework, enabling the effective use of heterogeneous training data for photometric redshift inference. Compared to previous methods, zephyr demonstrates enhanced robustness for both point estimation and distribution reconstruction by leveraging normalizing flows for density estimation and incorporating careful uncertainty quantification. Moreover, zephyr offers unique interpretability by explicitly disentangling contributions from multi-source training data, which can facilitate future weak lensing analysis by providing an additional quality assessment. As probabilistic generative deep learning techniques gain increasing prominence in astronomy, zephyr should become an inspiration for handling heterogeneous training data while remaining interpretable and robustly accounting for observational uncertainties.Comment: 10 pages, 5 figures, accepted to NeurIPS 2023 workshop on Machine Learning and the Physical Science

The Mass-Metallicity Relation of Dwarf Galaxies at the Cosmic Noon in the JWST Era

We present the mass-metallicity relation (MZR) at $z=2-3$ in the stellar mass range of $M_\star\approx 10^{6.5}-10^{9.5}M_\odot$ using 55 dwarf galaxies in the Abell 2744 and SMACS J0723-3732 galaxy cluster fields. These dwarf galaxies are identified and confirmed by deep JWST/NIRISS imaging and slitless grism spectroscopic observations. Taking advantage of the gravitational lensing effect, we extend the previous MZR relation at $z=2-3$ to a much lower mass regime by more than 2.5 orders of magnitude compared with previous studies. We find that the MZR has a shallower slope at the low-mass end ($M_\star<10^{9}M_\odot$) compared to that at the high-mass end ($M_\star>10^{9}M_\odot$), with a slope turnover point at around the stellar mass of $10^9 M_\odot$. This implies that dominating feedback processes in dwarf galaxies may be different from that in galaxies with higher mass. From $z=3$ to $z=2$, the metallicity of the dwarf galaxies is enhanced by $\approx0.1$ dex for a given stellar mass, consistent with the mild evolution found in galaxies with higher mass. Further, we confirm the existence of a 3D relation between the gas-phase metallicity, stellar mass, and star formation rate, i.e., fundamental metallicity relation (FMR), in dwarf galaxies at $z=2-3$. Our derived FMR, which has no significant redshift evolution, can be used as a benchmark to understand the origin of the anti-correlation between SFR and metallicity of dwarf galaxies in the high-redshift Universe.Comment: 16 pages, 4 figures, 1 table, submitted to AAS Journal; welcome comment

Deep Learning of Dark Energy Spectroscopic Instrument Mock Spectra to Find Damped Ly alpha Systems

We have updated and applied a convolutional neural network (CNN) machine-learning model to discover and characterize damped Lyα systems (DLAs) based on Dark Energy Spectroscopic Instrument (DESI) mock spectra. We have optimized the training process and constructed a CNN model that yields a DLA classification accuracy above 99% for spectra that have signal-to-noise ratios (S/N) above 5 per pixel. The classification accuracy is the rate of correct classifications. This accuracy remains above 97% for lower S/N ≈1 spectra. This CNN model provides estimations for redshift and H i column density with standard deviations of 0.002 and 0.17 dex for spectra with S/N above 3 pixel-1. Also, this DLA finder is able to identify overlapping DLAs and sub-DLAs. Further, the impact of different DLA catalogs on the measurement of baryon acoustic oscillations (BAO) is investigated. The cosmological fitting parameter result for BAO has less than 0.61% difference compared to analysis of the mock results with perfect knowledge of DLAs. This difference is lower than the statistical error for the first year estimated from the mock spectra: above 1.7%. We also compared the performances of the CNN and Gaussian Process (GP) models. Our improved CNN model has moderately 14% higher purity and 7% higher completeness than an older version of the GP code, for S/N > 3. Both codes provide good DLA redshift estimates, but the GP produces a better column density estimate by 24% less standard deviation. A credible DLA catalog for the DESI main survey can be provided by combining these two algorithms

DESI Survey Validation Data in the COSMOS/Hyper Suprime-Cam Field: Cool Gas Trace Main-sequence Star-forming Galaxies at the Cosmic Noon

We present the first result in exploring the gaseous halo and galaxy correlation using the Dark Energy Spectroscopic Instrument survey validation data in the Cosmic Evolution Survey (COSMOS) and Hyper Suprime-Cam field. We obtain multiphase gaseous halo properties in the circumgalactic medium by using 115 quasar spectra (signal-to-noise ratio > 3). We detect Mg ii absorption at redshift 0.6 < z < 2.5, C iv absorption at 1.6 < z < 3.6, and H i absorption associated with the Mg ii and C iv. By crossmatching the COSMOS2020 catalog, we identify the Mg ii and C iv host galaxies in 10 quasar fields at 0.9< z < 3.1. We find that within the impact parameter of 250 kpc, a tight correlation is seen between the strong Mg ii equivalent width and the host galaxy star formation rate. The covering fraction f c of the strong Mg ii selected galaxies, which is the ratio of the absorbing galaxy in a certain galaxy population, shows significant evolution in the main-sequence galaxies and marginal evolution in all the galaxy populations within 250 kpc at 0.9 < z < 2.2. The f c increase in the main-sequence galaxies likely suggests the coevolution of strong Mg ii absorbing gas and the main-sequence galaxies at the cosmic noon. Furthermore, Mg ii and C iv absorbing gas is detected out of the galaxy virial radius, tentatively indicating the feedback produced by the star formation and/or the environmental effects

Battery Internal Temperature Estimation for LiFePO4 Battery Based on Impedance Phase Shift under Operating Conditions

An impedance-based temperature estimation method is investigated considering the electrochemical non-equilibrium with short-term relaxation time for facilitating the vehicular application. Generally, sufficient relaxation time is required for battery electrochemical equilibrium before the impedance measurement. A detailed experiment is performed to investigate the regularity of the battery impedance in short-term relaxation time after switch-off current excitation, which indicates that the impedance can be measured and also has systematical decrement with the relaxation time growth. Based on the discussion of impedance variation in electrochemical perspective, as well as the monotonic relationship between impedance phase shift and battery internal temperature in the electrochemical equilibrium state, an exponential equation that accounts for both measured phase shift and relaxation time is established to correct the measuring deviation caused by electrochemical non-equilibrium. Then, a multivariate linear equation coupled with ambient temperature is derived considering the temperature gradients between the active part and battery surface. Equations stated above are all identified with the embedded thermocouple experimentally. In conclusion, the temperature estimation method can be a valuable alternative for temperature monitoring during cell operating, and serve the functionality as an efficient implementation in battery thermal management system for electric vehicles (EVs) and hybrid electric vehicles (HEVs)

Online Reliable Peak Charge/Discharge Power Estimation of Series-Connected Lithium-Ion Battery Packs

The accurate peak power estimation of a battery pack is essential to the power-train control of electric vehicles (EVs). It helps to evaluate the maximum charge and discharge capability of the battery system, and thus to optimally control the power-train system to meet the requirement of acceleration, gradient climbing and regenerative braking while achieving a high energy efficiency. A novel online peak power estimation method for series-connected lithium-ion battery packs is proposed, which considers the influence of cell difference on the peak power of the battery packs. A new parameter identification algorithm based on adaptive ratio vectors is designed to online identify the parameters of each individual cell in a series-connected battery pack. The ratio vectors reflecting cell difference are deduced strictly based on the analysis of battery characteristics. Based on the online parameter identification, the peak power estimation considering cell difference is further developed. Some validation experiments in different battery aging conditions and with different current profiles have been implemented to verify the proposed method. The results indicate that the ratio vector-based identification algorithm can achieve the same accuracy as the repetitive RLS (recursive least squares) based identification while evidently reducing the computation cost, and the proposed peak power estimation method is more effective and reliable for series-connected battery packs due to the consideration of cell difference

Activation of ACLY by SEC63 deploys metabolic reprogramming to facilitate hepatocellular carcinoma metastasis upon endoplasmic reticulum stress

Abstract Background Tumor cells display augmented capability to maintain endoplasmic reticulum (ER) homeostasis and hijack ER stress pathway for malignant phenotypes under microenvironmental stimuli. Metabolic reprogramming is a well-known hallmark for tumor cells to provide specific adaptive traits to the microenvironmental alterations. However, it’s unknown how tumor cells orchestrate metabolic reprogramming and tumor progression in response to ER stress. Herein, we aimed to explore the pivotal roles of SEC63-mediated metabolic remodeling in hepatocellular carcinoma (HCC) cell metastasis after ER stress. Methods The expression levels of SEC63 in HCC tissues and adjacent non-cancerous tissues were determined by immunohistochemistry and western blot. The regulatory roles of SEC63 in HCC metastasis were investigated both in vitro and in vivo by RNA-sequencing, metabolites detection, immunofluorescence, and transwell migration/invasion analyses. GST pull-down, immunoprecipitation/mass spectrometry and in vivo ubiquitination/phosphorylation assay were conducted to elucidate the underlying molecular mechanisms. Results We identified SEC63 as a new regulator of HCC cell metabolism. Upon ER stress, the phosphorylation of SEC63 at T537 by IRE1α pathway contributed to SEC63 activation. Then, the stability of ACLY was upregulated by SEC63 to increase the supply of acetyl-CoA and lipid biosynthesis, which are beneficial for improving ER capacity. Meanwhile, SEC63 also entered into nucleus for increasing nuclear acetyl-CoA production to upregulate unfolded protein response targets to improve ER homeostasis. Importantly, SEC63 coordinated with ACLY to epigenetically modulate expression of Snail1 in the nucleus. Consequently, SEC63 promoted HCC cell metastasis and these effects were reversed by ACLY inhibition. Clinically, SEC63 expression was significantly upregulated in HCC tissue specimens and was positively correlated with ACLY expression. Importantly, high expression of SEC63 predicted unfavorable prognosis of HCC patients. Conclusions Our findings revealed that SEC63-mediated metabolic reprogramming plays important roles in keeping ER homeostasis upon stimuli in HCC cells. Meanwhile, SEC63 coordinates with ACLY to upregulate the expression of Snail1, which further promotes HCC metastasis. Metastasis is crucial for helping cancer cells seek new settlements upon microenvironmental stimuli. Taken together, our findings highlight a cancer selective adaption to ER stress as well as reveal the potential roles of the IRE1α-SEC63-ACLY axis in HCC treatment