Distribution Fitting Research Articles

Tully-Fisher relation of late-type galaxies at 0.6 ≤ z ≤ 2.5

We present a study of the stellar and baryonic Tully-Fisher relation within the redshift range of 0.6 ≤ z ≤ 2.5, utilizing observations of star-forming galaxies. This dataset comprises of disk-like galaxies spanning a stellar mass range of 8.89 ≤ log(Mstar [M⊙]) ≤ 11.5, a baryonic mass range of 9.0 ≤ log(Mbar [M⊙]) ≤ 11.5, and a circular velocity range of 1.65 ≤ log(Vc [km/s]) ≤ 2.85. We estimated the stellar masses of these objects using spectral energy distribution fitting techniques, while the gas masses were determined via scaling relations. Circular velocities were directly derived from the rotation curves (RCs), after meticulously correcting for beam smearing and pressure support. Our analysis confirms that our sample adheres to the fundamental mass-size relations of galaxies and reflects the evolution of velocity dispersion in galaxies, in line with previous findings. This reaffirms the reliability of our photometric and kinematic parameters (i.e., Mstar and Vc), thereby enabling a comprehensive examination of the Tully-Fisher relation. To attain robust results, we employed a novel orthogonal likelihood fitting technique designed to minimize intrinsic scatter around the best-fit line, as required at high redshifts. For the stellar Tully-Fisher relation, we obtained a slope of α = 3.03 ± 0.25, an offset of β = 3.34 ± 0.53, and an intrinsic scatter of ζint = 0.08 dex. Correspondingly, the baryonic Tully-Fisher relation yielded α = 3.21 ± 0.28, β = 3.16 ± 0.61, and ζint = 0.09 dex. Our findings indicate a subtle deviation in the stellar and baryonic Tully-Fisher relation with respect to local studies, which is most likely due to the evolutionary processes governing disk formation.

Not-so-little Red Dots: Two Massive and Dusty Starbursts at z ∼ 5–7 Pushing the Limits of Star Formation Discovered by JWST in the COSMOS-Web Survey

We present the properties of two candidate massive (M ⋆ ∼ 1011 M ⊙) and dusty (A v > 2.5 mag) galaxies at z = 5–7 in the first 0.28 deg2 of the COSMOS-Web survey. One object is spectroscopically confirmed at z spec = 5.051, while the other has a robust z phot = 6.7 ± 0.3. Thanks to their extremely red colors (F277W–F444W ∼ 1.7 mag), these galaxies satisfy the nominal color selection for the widely studied “little red dot” (LRD) population with the exception of their spatially resolved morphologies. The morphology of our targets allows us to conclude that their red continuum is dominated by highly obscured stellar emission and not by reddened nuclear activity. Using a variety of spectral energy distribution fitting tools and star formation histories, we estimate the stellar masses to be log(M⋆)=11.32−0.15+0.07M⊙ and log(M⋆)=11.2−0.2+0.1M⊙ , respectively, with a red continuum emission dominated by a recent episode of star formation. We then compare their number density to the halo mass function to infer stellar baryon fractions of ϵ ⋆ ∼ 0.25 and ϵ ⋆ ∼ 0.5. Both are significantly higher than what is commonly observed in lower-z galaxies or more dust-obscured galaxies at similar redshifts. With very bright ultra-high-z Lyman-Break Galaxies and some non-AGN-dominated LRDs, such “extended” LRDs represent another population that may require very efficient star formation at early times.

Uncertainty Estimation and Out-of-Distribution Detection for Deep Learning-Based Image Reconstruction Using the Local Lipschitz.

Accurate image reconstruction is at the heart of diagnostics in medical imaging. Supervised deep learning-based approaches have been investigated for solving inverse problems including image reconstruction. However, these trained models encounter unseen data distributions that are widely shifted from training data during deployment. Therefore, it is essential to assess whether a given input falls within the training data distribution. Current uncertainty estimation approaches focus on providing an uncertainty map to radiologists, rather than assessing the training distribution fit. In this work, we propose a method based on the local Lipschitz metric to distinguish out-of-distribution images from in-distribution with an area under the curve of 99.94% for True Positive Rate versus False Positive Rate. We demonstrate a very strong relationship between the local Lipschitz value and mean absolute error (MAE), supported by a Spearman's rank correlation coefficient of 0.8475, to determine an uncertainty estimation threshold for optimal performance. Through the identification of false positives, we demonstrate the local Lipschitz and MAE relationship can guide data augmentation and reduce uncertainty. Our study was validated using the AUTOMAP architecture for sensor-to-image Magnetic Resonance Imaging (MRI) reconstruction. We demonstrate our approach outperforms baseline techniques of Monte-Carlo dropout and deep ensembles as well as the state-of-the-art Mean Variance Estimation network approach. We expand our application scope to MRI denoising and Computed Tomography sparse-to-full view reconstructions using UNET architectures. We show our approach is applicable to various architectures and applications, especially in medical imaging, where preserving diagnostic accuracy of reconstructed images remains paramount.

Positronium lifetime validation measurements using a long-axial field-of-view positron emission tomography scanner

BackgroundPositron emission tomography (PET) traditionally uses coincident annihilation photons emitted from a positron interacting with an electron to localize cancer within the body. The formation of positronium (Ps), a bonded electron-positron pair, has not been utilized in clinical applications of PET due to the need to detect either the emission of a prompt gamma ray or the decay of higher-order coincident events. Assessment of the lifetime of the formed Ps, however, can potentially yield additional diagnostic information of the surrounding tissue because Ps properties vary due to void size and molecular composition. To assess the feasibility of measuring Ps lifetimes with a PET scanner, experiments were performed in a Biograph Vision Quadra (Siemens Healthineers). Quadra is a long-axial field-of-view (LA-FOV) PET scanner capable of producing list-mode data from single interaction events.ResultsOrtho-Ps (o-Ps) lifetimes were measured for quartz-glass and polycarbonate samples using a 22Na\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{22}\ ext{Na}$$\\end{document} positron source. Results produced o-Ps lifetimes of 1.538 ± 0.036 ns for the quartz glass and 1.927 ± 0.042 ns for the polycarbonate. Both o-Ps lifetimes were determined using a double-exponential fit to the time-difference distribution between the emission of a prompt gamma ray and the annihilation of the correlated positron. The measured values match within a single standard deviation of previously published results. The quartz-glass samples were additional measured with 82Rb\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${^{82}}\ ext{Rb}$$\\end{document}, 68\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{68}$$\\end{document}Ga and 124I\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{124}\ ext{I}$$\\end{document} to validate the lifetime using clinically available sources. A double-exponential fit was initially chosen as a similar methodology to previously published works, however, an exponentially-modified Gaussian distribution fit to each lifetime more-accurately models the data. A Bayesian method was used to estimate the variables of the fit and o-Ps lifetime results are reported using this methodology for the three clinical isotopes: 1.59 ± 0.03 ns for 82Rb\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{82}\ ext{Rb}$$\\end{document}, 1.58 ± 0.07 ns for 68Ga\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{68}\ ext{Ga}$$\\end{document} and 1.62 ± 0.01 ns for 124I\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{124}\ ext{I}$$\\end{document}. The impact of scatter and attenuation on the o-Ps lifetime was also assessed by analyzing a water-filled uniform cylinder (20 ϕ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\phi$$\\end{document}×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes$$\\end{document} 30 cm3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{3}$$\\end{document}) with an added 82Rb\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{82}\ ext{Rb}$$\\end{document} solution. Lifetimes were extracted for various regions of the cylinder and while there is a shape difference in the lifetime due to scatter, the extracted o-Ps lifetime of the water, 1.815 ± 0.013 ns, agrees with previously published results.ConclusionOverall, the methodology presented in this manuscript demonstrates the repeatability of Ps lifetime measurements with clinically available isotopes in a commercially-available LA-FOV PET scanner. This validation work lays the foundation for future in-vivo patient scans with Quadra.

Streamflow trends and flood frequency analysis: a regional study of the UK.

In recent years, the escalating effects of climate change on surface water bodies have underscored the critical importance of analyzing streamflow trends for effective water resource planning and management. This study conducts a comprehensive regional investigation into the streamflow rate trends of 18 rivers across the United Kingdom (UK). An enhanced Mann-Kendall (MK) test was employed to meticulously analyze both rainfall and streamflow trends on monthly and annual scales. Additionally, the Innovative Trend Analysis (ITA) method was applied to elucidate the variability of streamflow rates, providing a more nuanced understanding of hydrological changes in response to climatic shifts. MK test reveals statistically significant positive trends in streamflow rates, particularly for rivers in south-central Scotland and northern England. Specifically, in January, rivers such as the Tay at Ballathie, Tweed at Peebles, and Teviot at Ormiston showed Z-scores above 2. Annually, similar positive trends were observed, with the Tay at Ballathie (Z = 3.42) and Nith at Friars Carse (Z = 3.35) exhibiting the highest increases in streamflow rates. The ITA method showed no relevant trends for the lowest values of streamflow, except for the Thames at Kingston, while considerable variability was observed for the highest streamflow rates, with several rivers showing positive trends and, however, some England rivers, like Bure at Ingworth, Test at Broadlands, and Trent at Colwick, showing negative trends. From this perspective, a more in-depth analysis of the extreme streamflow trends was carried out. In particular, the flood frequency of the maximum annual streamflow was assessed, based on the fitting of the Generalized Extreme Value (GEV) distribution on the annual maxima. Increasing location parameter (μ) and return period trends were observed for several rivers across the UK. In particular, the Tay at Ballathie (Scotland) showed the most marked increase, with μ that ranged from about 730 m3/s to more than 900 m3/s. At the same time, slight decreasing trends were observed for the Trent River (μ from 378 m3/s to 341 m3/s). The critical comparison of the MK test, ITA, and GEV distribution fitting revealed both agreements and discrepancies among the methods. While the analyses generally aligned in detecting significant trends in streamflow rates, notable discrepancies were observed, particularly in rivers with negligible trends. These inconsistencies highlight the complexity of hydrological responses and the limitations of individual methods. Overall, the study provides a comprehensive view of how streamflow dynamics are evolving in UK rivers, highlighting regional variations in the impact of climate change. This understanding can improve water resource management strategies by integrating diverse analytical approaches.

Evidence for a Shallow Evolution in the Volume Densities of Massive Galaxies at z = 4–8 from CEERS

We analyze the evolution of massive (log10[M ⋆/M ⊙] > 10) galaxies at z ∼ 4–8 selected from JWST Cosmic Evolution Early Release Survey (CEERS). We infer the physical properties of all galaxies in the CEERS NIRCam imaging through spectral energy distribution (SED) fitting with dense basis to select a sample of high-redshift massive galaxies. Where available we include constraints from additional CEERS observing modes, including 18 sources with MIRI photometric coverage, and 28 sources with spectroscopic confirmations from NIRSpec or NIRCam WFSS. We sample the recovered posteriors in stellar mass from SED fitting to infer the volume densities of massive galaxies across cosmic time, taking into consideration the potential for sample contamination by active galactic nuclei. We find that the evolving abundance of massive galaxies tracks expectations based on a constant baryon conversion efficiency in dark matter halos for z ∼ 4–8. At higher redshifts, we observe an excess abundance of massive galaxies relative to this simple model, resulting in a shallower decline of observed volume densities of massive galaxies. These higher abundances can be explained by modest changes to star formation physics and/or the efficiencies with which star formation occurs in massive dark matter halos, and are not in tension with modern cosmology.

The Massive and Distant Clusters of WISE Survey 2: A Stacking Analysis Investigating the Evolution of Star Formation Rates and Stellar Masses in Groups and Clusters

The evolution of galaxies depends on their masses and local environments; understanding when and how environmental quenching starts to operate remains a challenge. Furthermore, studies of the high-redshift regime have been limited to massive cluster members, owing to sensitivity limits or small fields of view when the sensitivity is sufficient, intrinsically biasing the picture of cluster evolution. In this work, we use stacking to investigate the average star formation history of more than 10,000 groups and clusters drawn from the Massive and Distant Clusters of WISE Survey 2. Our analysis covers near-ultraviolet to far-infrared wavelengths, for galaxy overdensities at 0.5 ≲ z ≲ 2.54. We employ spectral energy distribution fitting to measure the specific star formation rates (sSFRs) in four annular apertures with radii between 0 and 1000 kpc. At z ≳ 1.6, the average sSFR evolves similarly to the field in both the core and the cluster outskirts. Between z¯=1.60 and z¯=1.35 , the sSFR in the core drops sharply, and it continues to fall relative to the field sSFR at lower redshifts. We interpret this change as evidence that the impact of environmental quenching dramatically increases at z ∼ 1.5, with the short time span of the transition suggesting that the environmental quenching mechanism dominant at this redshift operates on a rapid timescale. We find indications that the sSFR may decrease with increasing host halo mass, but lower-scatter mass tracers than the signal-to-noise ratio are needed to confirm this relationship.

A New Generalization of the Inverse Generalized Weibull Distribution with Different Methods of Estimation and Applications in Medicine and Engineering

Limitations inherent to existing statistical distributions in capturing the complexities of real-world data often necessitate the development of novel models. This paper introduces the new exponential generalized inverse generalized Weibull (NEGIGW) distribution. The NEGIGW distribution boasts significant flexibility with symmetrical and asymmetrical shapes, allowing its hazard rate function to be adapted to many failure patterns observed in various fields such as medicine, biology, and engineering. Some statistical properties of the NEGIGW distribution, such as moments, quantile function, and Renyi entropy, are studied. Three methods are used for parameter estimation, including maximum likelihood, maximum product of spacing, and percentile methods. The performance of the estimation methods is evaluated via Monte Carlo simulations. The NEGIGW distribution excels in its ability to fit real-world data accurately. Five medical and engineering datasets are applied to demonstrate the superior fit of NEGIGW distribution compared to competing models. This compelling evidence suggests that the NEGIGW distribution is promising for lifetime data analysis and reliability assessments across different disciplines.

A Transformer Maintenance Interval Optimization Method Considering Imperfect Maintenance and Dynamic Maintenance Costs

As one of the most critical components of the power grid system, transformer maintenance strategy planning significantly influences the safe, economical, and sustainable operation of the power system. Periodic imperfect maintenance strategies have become a research focus in preventive maintenance strategies for large power equipment due to their ease of implementation and better alignment with engineering realities. However, power transformers are characterized by long lifespans, high reliability, and limited defect samples. Existing maintenance methods have not accounted for the dynamic changes in maintenance costs over a transformer’s operational lifetime. Therefore, we propose a maintenance interval optimization method that considers imperfect maintenance and dynamic maintenance costs. Utilizing defect and maintenance cost data from 400 220 KV oil-immersed transformers in northern China, we employed Bayesian estimation for the first time to address the distribution fitting of defect data under small sample conditions. Subsequently, we introduced imperfect maintenance improvement factors to influence the number of defects occurring in each maintenance cycle, resulting in more realistic maintenance cost estimations. Finally, we established an optimization model for transformer maintenance cycles, aiming to minimize maintenance costs throughout the transformer’s entire lifespan while maintaining reliability constraints. Taking a transformer’s strong oil circulation cooling system as an example, our method demonstrates that while meeting the reliability threshold recognized by the power grid company, the system’s maintenance cost can be reduced by 41.443% over the transformer’s entire life cycle. Through parameter analysis of the optimization model, we conclude that as the maintenance cycle increases, the factors dominating maintenance costs shift from corrective maintenance to preventive maintenance.

Seasonal solar irradiance forecasting using artificial intelligence techniques with uncertainty analysis

Renewable integration in utility grid is crucial in the current energy scenario. Optimized utilization of renewable energy can minimize the energy consumption from the grid. This demands accurate forecasting of renewable contribution and planning. Most of the researches aim to find a suitable forecasting model in terms of accuracy and error metrics. However, the uncertainty and variability in these forecasts are also significant. This work combines point forecast with interval forecast to provide comprehensive information about the forecast uncertainty. In this work, solar irradiance forecasting is carried out using artificial intelligence (AI) techniques. Forecasting is done using seasonal auto-regressive moving average with exogenous factors (SARIMAX), support vector regression (SVR), long short term memory (LSTM) techniques and performance is evaluated. SVR model exhibited the best performance with R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^2$$\\end{document} values of 0.97 and 0.96 for winter and summer respectively and 0.85 for monsoon and post-monsoon seasons. This is followed by forecast error distribution studies and uncertainty analysis. For this, SVR forecast error data is fitted using laplace distribution. Uncertainty study is carried out using confidence intervals and coverage rates. Excellent coverage rates are obtained for various confidence levels for all seasons, indicating the appropriate fitting of error distribution. For the narrow 85% confidence band, coverage rates of 89%, 95%, 90%, and 88% are obtained for winter, summer, monsoon and post-monsoon respectively. The work emphasizes the need for error-distribution studies, modeling of forecast errors and their application in providing reliable forecast intervals with the perspective of enhancing system reliability.

Radiative and mechanical energies in galaxies

Context. Atomic and molecular lines emitted from galaxies are fundamental tracers of the medium responsible for the emission and contain valuable information regarding the energy budget and the strength of the different feedback mechanisms. Aims. The goal of this work is to provide a new framework for the interpretation of atomic and molecular lines originating from extragalactic sources and a robust method to deduce the mechanical and radiative energy budget from a set of observations. Methods. Atomic and molecular lines detected in a given object are assumed to result from the combination of distributions of shocks and photo-dissociation regions (PDRs) within the observational beam. The emission of individual structures is computed using the Paris-Durham shock code and the Meudon PDR code over a wide range of parameters. The total emission is then calculated assuming probability distribution functions for shocks and PDRs. A distance between the observational dataset and the model is finally defined based on the ratios of the observed to the predicted intensities. Results. As a test case scenario, we consider the radio galaxy 3C 326 N. The dataset is composed of 12 rotational and ro-vibrational lines of H2 and the fine structure lines of C+ and O. Our interpretative framework shows that both shocks and PDRs are required to explain the line fluxes. Surprisingly, viable solutions are obtained at low density only (nH < 100 cm−3), indicating that most of the emission originates from diffuse interstellar matter. The optimal solution, obtained for nH = 10 cm−3, corresponds to a distribution of low-velocity shocks (between 5 and 20 km s−1) propagating in PDR environments illuminated by a UV radiation field ten times larger than that in the solar neighborhood. This solution implies that at least 4% of the total mass carried by the PDRs is shocked. The H2 0-0 S(0) 28 μm, [CII] 158 μm, and [OI] 63 μm lines originate from the PDR components, while all the other H2 lines are mostly emitted by shocks. The total solid angles sustained by PDRs and shocks imply that the radiative and mechanical energies reprocessed by these structures are LUV = 6.3 × 109 L⊙ and LK = 3.9 × 108 L⊙, respectively, in remarkable agreement with the values of the IR luminosity deduced from the fit of the spectral energy distribution (SED) of 3C 326 N, and consistent with a small fraction of the active galactic nuclei (AGN) jet kinetic power dissipated in the interstellar medium (≈1%). Conclusions. This work shows that the radiative and mechanical energy budget of galaxies can be derived from the sole observations of atomic and molecular lines. It reveals the unexpected importance of the diffuse medium for 3C 326 N, in contrast to previous studies. A last-minute comparison of the model to new JWST data obtained in 3C 326 N shows a striking agreement and demonstrates the ability of the model to make accurate predictions. This framework opens new prospects for the prediction and interpretation of extragalactic observations, in particular in the context of JWST observations.

Molecular cloud matching in CO and dust in M33

This study is aimed to contribute to a more comprehensive understanding of the molecular hydrogen distribution in the galaxy M33 by introducing novel methods for generating high angular resolution (18.2″, equivalent to 75 pc for a distance of 847 kpc) column density maps of molecular hydrogen (NH2). M33 is a local group galaxy that has been observed with Herschel in the far-infrared (FIR) wavelength range from 70 to 500 μm. Previous studies have presented total hydrogen column density maps (NH), using these FIR data (partly combined with mid-IR maps), employing various methods. We first performed a spectral energy distribution (SED) fit to the 160, 250, 350, and 500 μm continuum data obtain NH, using a technique similar to one previously reported in the literature. We also use a second method which involves translating only the 250 μm map into a NH map at the same angular resolution of 18.2″. An NH2 map via each method is then obtained by subtracting the H I component. Distinguishing our study from previous ones, we adopt a more versatile approach by considering a variable emissivity index, β, and dust absorption coefficient, κ0. This choice enables us to construct a κ0 map, thereby enhancing the depth and accuracy of our investigation of the hydrogen column density. We address the inherent biases and challenges within both methods (which give similar results) and compare them with existing maps available in the literature. Moreover, we calculate a map of the carbon monoxide CO(1 − 0)-to-molecular hydrogen (H2) conversion factor (XCO factor), which shows a strong dispersion around an average value of 1.8 × 1020 cm−2/(K km s−1) throughout the disk. We obtain column density probability distribution functions (N-PDFs) from the NH, NH2, and NH I maps and discuss their shape, consisting of several log-normal and power-law tail components.

Identifying and Characterizing Infrared Excesses in the Spitzer Kepler Survey (SpiKeS)

We report our search for infrared excesses in the Spitzer/Infrared Array Camera survey of the Kepler field at 3.6 and 4.5 μm. The Spitzer Kepler Survey contains ∼190,000 targets, which we reduce to ∼117,000 targets after applying multiple filters to arrive at a high-quality sample for spectral energy distribution fitting. Of the ∼117,000, we find 11 Sun-like stars (T eff ∼ 6000 K) with infrared excesses of high significance (>4σ) ranging from 10% to 40% above the photosphere at 4.5 μm, which is characteristic of debris disk systems. Blackbody fits of the infrared excesses for the 11 debris disk candidates suggest dust temperatures of 400–1400 K. None of the candidates have reported exoplanet detections. High-quality 1–5 μm or longer spectra or photometry would be necessary to confirm the candidates as debris disk systems.

Blueberry galaxies up to 200 Mpc and their optical and infrared properties

Context. Dwarf highly star-forming galaxies (SFGs) dominated the early Universe and are considered the main driver of its reionization. However, direct observations of these distant galaxies are mainly confined to rest-frame ultraviolet and visible light, limiting our understanding of their complete properties. Therefore, it is still paramount to study their local analogs, the green pea (GP) and blueberry (BB) galaxies. Aims. This work aims to expand our knowledge of BBs by identifying a new sample that is closer and in the southern sky. Methods. In addition to the already known BBs, this new sample will allow for a statistically significant study of their properties probed by visible and infrared (IR) light. By utilizing the HECATE catalog, which provides optical and IR photometry and characterization of galaxies, along with data from Pan-STARSS and SDSS, this study selects and analyzes a new sample of BBs. We employed spectral energy distribution fitting to derive homogeneous measurements of star-formation rates and stellar masses. Additionally, we measured emission-line fluxes, including He II λ4686, through spectral fitting. Results. Through this work, we identified 48 BBs, of which 40 were first recognized as such, with the nearest at 19 Mpc. Fourteen of the BBs are in the south sky. The BBs tend to be extremely IR red in both WISE W1 – W2 and W2 – W3 colors, distinguishing them from typical SFGs. Dwarf SFGs with higher specific star-formation rates tend to have redder IR colors. Conclusions. Blueberry galaxies stand out as the most intensely star-forming sources in the local Universe among dwarf galaxies. They are intrinsically bluer in visible light, redder in the infrared, and less massive. They also have higher specific star-formation rates, equivalent widths, lower metallicities, and the most strongly ionized interstellar medium compared to typical SFGs and GPs.

A method for assessing the operational status of a microseismic monitoring system using energy distribution changes of observed data

Abstract The operational status of geophones plays a pivotal role in ensuring the accuracy and reliability of microseismic monitoring systems. However, conventional techniques used to evaluate the operational status of geophones require human intervention or significant time delays. To address this issue, we propose a method for online monitoring of geophone status using observed data obtained from a microseismic system. Firstly, the energy features of the preprocessed observation data are extracted via wavelet packet decomposition. Subsequently, the distribution parameters of energy features are obtained through loglogistic distribution fitting. These parameters are then applied to a change-point detection model, enabling the online monitoring of seismic geophones. In addition, we selected long short-term memory (LSTM) network to classify the operational status of the geophones, which is trained using the obtained energy distribution data and the time-frequency characteristics of the observed data. The experimental results indicate that the model achieves an accuracy of 98.33%, surpassing the 89.58% accuracy of the support vector machine (SVM). The proposed method not only contributes to online monitoring and precise determination of the operating status of detectors, but also has enormous application potential in other fields that require monitoring and evaluating the operating status of instruments.

Application of Queue Theory in Cafe Services with Erlang Distribution

As urban lifestyles evolve, culinary businesses, particularly cafes, have experienced rapid growth. This surge in popularity has led to an increase in customers and, consequently, longer queues. These extended wait times can frustrate customers and pose challenges to cafe management. To address this issue, we conducted a comprehensive eval___uation and optimization of the service system at a Samarinda cafe using the Erlang distribution queuing system. Primary data was meticulously collected over six days, amounting to a total of 12 hours of observation. Kolmogorov-Smirnov distribution fitting tests were employed, revealing that customer service times adhered to an exponential distribution. The average customer arrival rate was determined to be 0.351 per minute, while the average service time was calculated at 5.546 minutes per customer. Our analysis confirmed that the system operates in a steady state with a utility value of 0.06, indicating sufficient service capacity to handle the current customer load. Therefore, the study concludes that the cafe's service system is currently optimal.

Live fitting of process data within digital twins of manufacturing to use simulation and optimisation

ABSTRACT In production scenarios, uncertainty in production times, and scrap rates is common. Uncertainty can be described by stochastic models that need continuous updates due to changing conditions. This paper models probability distributions and estimates their parameters from real-world data on processing times and scrap rates. It uses live fitting to identify timely changes in the data sets, comparing different distributions. The fitting and live fitting approaches are applied to data from a real production system to compare the goodness of fit for different distributions and to demonstrate the reliability of the reaction to changes in the input data. Data and probability estimations are categorised, and a concept combining simulation and optimisation models is developed to optimise scheduling. The vision of the research presented in this paper is an online approach that determines quasi-optimal schedules for production systems based on current data from the system and its environment.

DILA: Dynamic Gaussian Distribution Fitting and Imitation Learning-Based Label Assignment for tiny object detection

With the rapid advancement of deep learning technology, significant achievements have been made in the field of general object detection. However, challenges still remain in the detection of tiny objects. There are two main drawbacks: (1) static prior information makes the localization of tiny objects relatively fixed; (2) Intersection over Union (IoU) is highly sensitive to deviations in tiny objects. To this end, we propose a Dynamic Gaussian Distribution Fitting and Imitation Learning-Based Label Assignment (DILA) strategy for Tiny Object Detection. Specifically, to address the positional deviation of effective receptive fields at different network layers during Gaussian modeling, DILA first designs an Adaptive Dynamic Calculation Strategy (ADCS) to compute estimation factors for the effective receptive field in different feature spaces, dynamically modeling prior information using Gaussian distribution. Then, DILA introduces a new Balance of Gaussian Scaling-aware Metric (BGSM) to measure the similarity between tiny bounding boxes and predefined anchors, instead of using IoU, which is highly sensitive to tiny pixel shifts, for sample assignment, thereby providing a more accurate basis for label assignment. Finally, a Detail Information Imitation Compensation Module (DIM) is presented to improve and compensate for the detailed information of tiny objects that troubles label assignment, achieving balanced learning for tiny objects. The proposed DILA strategy can be seamlessly integrated into various anchor-based detectors. Extensive experiments were conducted on three publicly available datasets for tiny object detection. The results indicate that when DILA is embedded into Faster RCNN, it outperforms other state-of-the-art methods in terms of detection performance for tiny objects, achieving an improvement in average precision of 10.3%, 1.5%, and 4.2% on AI-TOD, SODA-D, and VisDrone2019, respectively. The source codes and results are available at: https://github.com/chnu-cpl/DILA.

Probability of maximum values of stress concentration factors in tubular DKT-joints reinforced with FRP under axial loads

In the paper, the most suitable probability distributions for SCFmax in DKT-Joints stiffened with FRP under axial loading conditions are studied. To this end, 1296 models were generated and then analyzed in the ANSYS to provide five reliable samples. Created FE models were validated using previous laboratory and FE data. Afterward, to predict the appropriate probability distribution models, 12 different kinds of distributions were utilized to check the fit distribution curve to the samples. Next, the accuracy and reliability of the distribution curves were assessed with the help of goodness-of-fit tests, such as the Kolmogorov-Smirnov (KS) and the Chi-squared (CS). The results of the analysis reveal that the Birnbaum-Saunders distribution is the best option. Furthermore, based on the probability graphs, it was concluded that the developed distribution is suitable enough for the given data sets.

Nearest neighbour analysis as a new probe for fuzzy dark matter

ABSTRACT Fuzzy dark matter (FDM) is a promising candidate for dark matter (DM), characterized by its ultra-light mass, which gives rise to wave effects at astrophysical scales. These effects offer potential solutions to the small-scale issues encountered within the standard cold dark matter (CDM) paradigm. In this paper, we investigate the large-scale structure of the cosmic web using FDM simulations, comparing them to CDM-only simulations and a simulation incorporating baryonic effects. Our study employs the nearest neighbour (NN) analysis as a new statistical tool for examining the structure and statistics of the cosmic web in an FDM universe. This analysis could capture the information absent in the two-point correlation functions. In particular, we analyse data related to the spherical contact, nearest neighbour distances (NND), and the angle between the first and second nearest neighbours of haloes (NNA). Specifically, we utilize probability distribution functions, statistical moments, and fitting parameters, as well as G(x), F(x), and J(x) functions to analyse the above data. Remarkably, the results from the FDM simulations differ significantly from the others across these analyses, while no noticeable distinction is observed between the baryonic and CDM-only simulations. Moreover, the lower FDM mass leads to more significant deviations from the CDM simulations. These compelling results highlight the efficiency of the NN analysis – mainly through the use of the J(x) function, $s_3$, $l_{3}$, and $a_4$ parameters – as a prominent new tool for investigating FDM on large scales and making observational predictions.