scholarly journals New insights into the use of Ultra Long Period Cepheids as cosmological standard candles

2020 ◽  
Vol 501 (1) ◽  
pp. 866-874
Author(s):  
Ilaria Musella ◽  
Marcella Marconi ◽  
Roberto Molinaro ◽  
Giuliana Fiorentino ◽  
Vincenzo Ripepi ◽  
...  
Keyword(s):  
Time Series Data ◽  
Model Fitting ◽  
Hubble Constant ◽  
Variable Stars ◽  
Series Data ◽  
Long Period ◽  
Pulsating Variable Stars ◽  
Classical Cepheids ◽  
One Step ◽  
Homogeneous Data

ABSTRACT Ultra Long Period Cepheids (ULPs) are pulsating variable stars with a period longer than 80 d and have been hypothesized to be the extension of the Classical Cepheids (CCs) at higher masses and luminosities. If confirmed as standard candles, their intrinsic luminosities, ∼1 to ∼3 mag brighter than typical CCs, would allow to reach the Hubble flow and, in turn, to determine the Hubble constant, H0, in one step, avoiding the uncertainties associated with the calibration of primary and secondary indicators. To investigate the accuracy of ULPs as cosmological standard candles, we first collect all the ULPs known in the literature. The resulting sample includes 63 objects with a very large metallicity spread with 12 + log ([O/H]) ranging from 7.2 to 9.2 dex. The analysis of their properties in the VI period–Wesenheit plane and in the colour–magnitude diagram (CMD) supports the hypothesis that the ULPs are the extension of CCs at longer periods, higher masses and luminosities, even if, additional accurate and homogeneous data and a devoted theoretical scenario are needed to get firm conclusions. Finally, the three M31 ULPs, 8-0326, 8-1498, and H42, are investigated in more detail. For 8-1498 and H42, we cannot confirm their nature as ULPs, due to the inconsistency between their position in the CMD and the measured periods. For 8-0326, the light curve model fitting technique applied to the available time-series data allows us to constrain its intrinsic stellar parameters, distance, and reddening.

Ultralong-period Cepheids: a possible primary distance indicator?

2012 ◽  
Vol 8 (S289) ◽  
pp. 282-286 ◽  
Author(s):  
G. Fiorentino ◽  
F. Annibali ◽  
G. Clementini ◽  
R. Contreras Ramos ◽  
M. Marconi ◽  
...  
Keyword(s):  
Spiral Galaxies ◽  
Hubble Space Telescope ◽  
Hubble Constant ◽  
Variable Stars ◽  
Evolutionary Status ◽  
Stellar Systems ◽  
Classical Cepheids ◽  
Type Ia ◽  
Distance Indicator ◽  
Crucial Test

AbstractWe present a project that aims to provide a complete theoretical and observational framework for an as yet unexplored class of variable stars, the ultralong-period Cepheids (P longer than 80–100 days). Given their very high luminosities (MV up to −7 mag), with the Hubble Space Telescope we will be able to observe them easily in stellar systems located at large distances (~ 100 Mpc). This limit will be further increased, out to the Hubble flow (~ 350 Mpc), using future ground-based facilities such as the European Extremely Large Telescope. The nature of their pulsation is as yet unclear, as is their evolutionary status, which seems different from the central helium-burning phase generally associated with classical Cepheids. These objects have been found to cover a very large metallicity range, from [Fe/H] ~ −2 dex to solar values, and they are located in heterogeneous stellar systems, from dwarf to spiral galaxies. Once completely characterized, they could provide a crucial test, since they have been found in all Type Ia supernova host spiral galaxies that have been monitored for variability over long periods and that currently offer sound constraints on the estimated value of the Hubble constant.

scholarly journals Assessing the Impact of Satellite Revisit Rate on Estimation of Corn Phenological Transition Timing through Shape Model Fitting

2019 ◽  
Vol 11 (21) ◽  
pp. 2558 ◽  
Author(s):  
Emily Myers ◽  
John Kerekes ◽  
Craig Daughtry ◽  
Andrew Russ
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Time Series Data ◽  
Model Fitting ◽  
Series Data ◽  
Sigmoid Function ◽  
Absolute Deviation ◽  
Shape Model ◽  
Area Index ◽  
The Impact

Agricultural monitoring is an important application of earth-observing satellite systems. In particular, image time-series data are often fit to functions called shape models that are used to derive phenological transition dates or predict yield. This paper aimed to investigate the impact of imaging frequency on model fitting and estimation of corn phenological transition timing. Images (PlanetScope 4-band surface reflectance) and in situ measurements (Soil Plant Analysis Development (SPAD) and leaf area index (LAI)) were collected over a corn field in the mid-Atlantic during the 2018 growing season. Correlation was performed between candidate vegetation indices and SPAD and LAI measurements. The Normalized Difference Vegetation Index (NDVI) was chosen for shape model fitting based on the ground truth correlation and initial fitting results. Plot-average NDVI time-series were cleaned and fit to an asymmetric double sigmoid function, from which the day of year (DOY) of six different function parameters were extracted. These points were related to ground-measured phenological stages. New time-series were then created by removing images from the original time-series, so that average temporal spacing between images ranged from 3 to 24 days. Fitting was performed on the resampled time-series, and phenological transition dates were recalculated. Average range of estimated dates increased by 1 day and average absolute deviation between dates estimated from original and resampled time-series data increased by 1/3 of a day for every day of increase in average revisit interval. In the context of this study, higher imaging frequency led to greater precision in estimates of shape model fitting parameters used to estimate corn phenological transition timing.

scholarly journals Modeling the levels, trends, and connectivity of malaria transmission using genomic data from a health facility in Thiès, Senegal

2021 ◽  
Author(s):  
Albert Lee ◽  
Yaye Die Ndiaye ◽  
Aida Badiane ◽  
Awa Deme ◽  
Rachel F Daniels ◽  
...  
Keyword(s):  
Malaria Transmission ◽  
Malaria Parasite ◽  
Large Scale ◽  
Time Series Data ◽  
Model Fitting ◽  
Genetic Data ◽  
Series Data ◽  
Multiple Infections ◽  
Malaria Surveillance ◽  
Neighborhood Scale

Molecular data and analysis outputs are being integrated into malaria surveillance efforts to provide valuable programmatic insights for national malaria control programs (NMCPs). A plethora of studies from diverse geographies have demonstrated that malaria parasite genetic data can be an important tool for drug resistance monitoring, species identification, outbreak analysis, and transmission characterization. Despite many successful research efforts, there are still important knowledge gaps hindering practical translation of each of these use cases for NMCPs. Here, we leverage epidemiological modeling and time series data of 2035 genetic sequences collected in Thi`es, Senegal from 2006-2018 to provide a quantitative and setting-specific assessment of the levels, trends, and connectivity of malaria transmission. We also identify the genetic features that are the most informative for inferring transmission in Thi`es, such as the fraction of the population with multiple infections and the persistence of parasite lineages across multiple transmission seasons. The model fitting and uncertainty quantification framework also reveals a significant decrease in the level of malaria transmission around 2013. This difference coincides with a large-scale drought and bed net campaign by the NMCP and USAID and is independently corroborated by geo-spatial models of incidence in Thi`es. We find that genetically identical samples are more likely to be geographically clustered even at the neighborhood scale; and moreover, these lineages propagate non-randomly around the city. Our approach and results provide quantitative guidance for the interpretation of malaria parasite genetic data from Thi`es, Senegal and indicates the value of increased malaria genomic surveillance for NMCPs.

scholarly journals Daily Forecasting for Antam's Certified Gold Bullion Prices in 2018-2020 using Polynomial Regression and Double Exponential Smoothing

2021 ◽  
Vol 3 (4) ◽  
pp. 45-53
Author(s):  
Tresna Maulana Fahrudin ◽  
Prismahardi Aji Riyantoko ◽  
Kartika Maulida Hindrayani ◽  
I Gede Susrama Mas Diyasa
Keyword(s):  
Time Series Data ◽  
Polynomial Regression ◽  
Exponential Smoothing ◽  
Series Data ◽  
Percentage Error ◽  
Training Set ◽  
Double Exponential ◽  
Forecasting Performance ◽  
One Step ◽  
Double Exponential Smoothing

Gold investment is currently a trend in society, especially the millennial generation. Gold investment for the younger generation is an advantage for the future. Gold bullion is often used as a promising investment, on other hand, the digital gold is available which it is stored online on the gold trading platform. However, any investment certainly has risks, and the price of gold bullion fluctuates from day to day. People who invest in gold hopes to benefit from the initial purchase price even if they must wait up to five years. The problem is how they can notice the best time to sell and buy gold. Therefore, this research proposes a forecasting approach based on time series data and the selling of gold bullion prices per gram in Indonesia. The experiment reported that Holt’s double exponential smoothing provided better forecasting performance than polynomial regression. Holt’s double exponential smoothing reached the minimum of Mean Absolute Percentage Error (MAPE) 0.056% in the training set, 0.047% in one-step testing, and 0.898% in multi-step testing.

scholarly journals Bristlecone: An F# library for model-fitting and model-selection of ecological time-series data

2019 ◽  
Author(s):  
Andrew C Martin
Keyword(s):  
Time Series ◽  
Model Selection ◽  
Tree Rings ◽  
Time Series Data ◽  
Sediment Cores ◽  
Model Fitting ◽  
Series Data ◽  
Ecological Problems ◽  
Biodiversity And Ecosystem Function

Environmental archives such as sediment cores and tree rings provide important insights on the timing and rates of change in biodiversity and ecosystem function over the long-term. Such datasets are often analysed using empirical methods, which limits their ability to address ecological questions that seek to understand underlying ecological mechanisms and processes. Top down modelling approaches – where data is confronted with simple ecological models – can be used to infer the presence, form, and strength of mechanisms of interest. To aid adoption of time-series mechanistic modelling for long-term ecology, we created a F# library, Bristlecone, that can be used to apply this approach using a Model- Fitting and Model-Selection workflow. Our objective with Bristlecone was to create a library that could be used to efficiency and effectively conduct a full MFMS analysis for long-term ecological problems. We incorporated techniques to address specific challenges with environmental archives, including uneven time steps from age-depth models (for sediment cores), and allometry and seasonality (for tree rings). We include an example analysis to demonstrate functionality of Bristlecone. Our solution presents a straightforward, repeatable, and highly parallel method for conducting inference for long- term ecological problems.

scholarly journals Characterizing bracken fern phenological cycle using time series data derived from Sentinel-2 satellite sensor

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0257196
Author(s):  
Trylee Nyasha Matongera ◽  
Onisimo Mutanga ◽  
Mbulisi Sibanda
Keyword(s):  
Time Series ◽  
Invasive Plant ◽  
Time Series Data ◽  
Model Fitting ◽  
Quality Data ◽  
Series Data ◽  
Bracken Fern ◽  
Phenological Cycle ◽  
Phenological Metrics ◽  
Sentinel 2

Bracken fern is an invasive plant that has caused serious disturbances in many ecosystems due to its ability to encroach into new areas swiftly. Adequate knowledge of the phenological cycle of bracken fern is required to serve as an important tool in formulating management plans to control the spread of the fern. This study aimed to characterize the phenological cycle of bracken fern using NDVI and EVI2 time series data derived from Sentinel-2 sensor. The TIMESAT program was used for removing low quality data values, model fitting and for extracting bracken fern phenological metrics. The Sentinel-2 satellite-derived phenological metrics were compared with the corresponding bracken fern phenological events observed on the ground. Findings from our study revealed that bracken fern phenological metrics estimated from satellite data were in close agreement with ground observed phenological events with R2 values ranging from 0.53–0.85 (p < 0.05). Although they are comparable, our study shows that NDVI and EVI2 differ in their ability to track the phenological cycle of bracken fern. Overall, EVI2 performed better in estimating bracken fern phenological metrics as it related more to ground observed phenological events compared to NDVI. The key phenological metrics extracted in this study are critical for improving the precision in the controlling of the spread of bracken fern as well as in implementing active protection strategies against the invasion of highly susceptible rangelands.

scholarly journals Reassessment of long-period constituents for tidal predictions along the German North Sea coast and its tidally influenced rivers

Ocean Science ◽  
2019 ◽  
Vol 15 (5) ◽  
pp. 1363-1379
Author(s):  
Andreas Boesch ◽  
Sylvin Müller-Navarra
Keyword(s):  
North Sea ◽  
Time Series Data ◽  
Series Data ◽  
Tide Gauges ◽  
Mean Values ◽  
Long Period ◽  
Long Time ◽  
Harmonic Representation ◽  
Sea Coast ◽  
North Sea Coast

Abstract. The harmonic representation of inequalities (HRoI) is a procedure for tidal analysis and prediction that combines aspects of the non-harmonic and the harmonic method. With this technique, the deviations of heights and lunitidal intervals, especially of high and low waters, from their respective mean values are represented by superpositions of long-period tidal constituents. This article documents the preparation of a constituents list for the operational application of the harmonic representation of inequalities. Frequency analyses of observed heights and lunitidal intervals of high and low water from 111 tide gauges along the German North Sea coast and its tidally influenced rivers have been carried out using the generalized Lomb–Scargle periodogram. One comprehensive list of partial tides is realized by combining the separate frequency analyses and by applying subsequent improvements, e.g. through manual inspections of long time series data. The new set of 39 partial tides largely confirms the previously used set with 43 partial tides. Nine constituents are added and 13 partial tides, mostly in the close neighbourhood of strong spectral components, are removed. The effect of these changes has been studied by comparing predictions with observations from 98 tide gauges. Using the new set of constituents, the standard deviations of the residuals are reduced on average by 2.41 % (times) and 2.30 % (heights) for the year 2016. The new set of constituents will be used for tidal analyses and predictions starting with the German tide tables for the year 2020.

scholarly journals PENERAPAN ANALISIS REGRESI SPLINE UNTUK MENDUGA HARGA CABAI DI JAKARTA

2017 ◽  
Vol 1 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Hestiani Wulandari ◽  
Anang Kurnia ◽  
Bambang Sumantri ◽  
Dian Kusumaningrum ◽  
Budi Waryanto
Keyword(s):  
Time Series Data ◽  
Series Data ◽  
Percentage Error ◽  
Spline Regression ◽  
Regression Spline ◽  
Price Data ◽  
A Value ◽  
Spline Models ◽  
Order Spline ◽  
Homogeneous Data

The chili is an important commodity in Indonesia, which has a fairly large price fluctuations. Fluctuations in prices often raises the risk of loss even have contributed to inflation. Chili price data is time series data that is not independent between observations (autocorrelation) and do not spread to normal. In addition, chili price data does not have the diversity of homogeneous data. One method that can be used to predict the pattern of the data is spline regression. The data used in this study is data the average weekly price of chili in Jakarta from January, 2010 to October, 2015. The best spline model is a second order spline models with three knots. The model has a value of Mean Absolute Percentage Error (MAPE) of 9.57% and determination coefficient of 86.41%. The model obtained in this research is already well in predicting the pattern of the chili price, but it was only able to predict well for a period of one month. Prediction chili prices in Jakarta for November are in the range of Rp 35.565. Keywords: chili price, regression, spline.

scholarly journals Time-series forecasting with deep learning: a survey

2021 ◽  
Vol 379 (2194) ◽  
pp. 20200209
Author(s):  
Bryan Lim ◽  
Stefan Zohren
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Data ◽  
Time Series Forecasting ◽  
Series Data ◽  
Climate Modelling ◽  
Weather And Climate ◽  
Recent Developments ◽  
One Step ◽  
Learning Architectures

Numerous deep learning architectures have been developed to accommodate the diversity of time-series datasets across different domains. In this article, we survey common encoder and decoder designs used in both one-step-ahead and multi-horizon time-series forecasting—describing how temporal information is incorporated into predictions by each model. Next, we highlight recent developments in hybrid deep learning models, which combine well-studied statistical models with neural network components to improve pure methods in either category. Lastly, we outline some ways in which deep learning can also facilitate decision support with time-series data. This article is part of the theme issue ‘Machine learning for weather and climate modelling’.

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