Statistical Data Retrieval Technique in Astronomy Computational Physics
Ruben Cornelius Siagian*1, Pandu Pribadi2, Goldberd Harmuda Duva Sinaga3, Arip Nurahman4, Budiman Nasution5.
1,5Physics departement, Medan State University, Medan, North Sumatera, Indonesian,
2Physics education, STIT Muhammadiyah Banjar, Indonesian,
3Department of mechanical engineering, University of HKBP Nommensen Medan, North Sumatera, Indonesian,
4Department of Science education, Indonesian education university, Bandung, West Java, Indonesian.
e-mail: *1Rubensiagian775@gmail.com, 2pandupribadi2384@gmail.com,
3goldberghdsinaga@gmail.com, 4aripnurahman@institutpendidikan.ac.id,
5Budimannasution@unimed.ac.id.
Abstrak
Komputasi astronomi adalah cabang yang sangat penting di era sekarang ini, di mana fisikawan atau peneliti dapat menggunakan komputer untuk memproses statistik dalam fisika astronomi. peneliti dapat mengolah data abstrak dari data mentah dan dapat mengubah data menjadi visualisasi data. Fisika komputasi astronomi adalah metode yang canggih dan mapan, cabang ilmu ini dapat menyediakan dan mengolah data, memecahkan masalah yang kompleks, dan sangat membantu bagi ahli statistik dan ilmuwan komputer. Fisikawan astronomi memiliki banyak masalah, antara lain; terdapat permasalahan yang bersifat hierarkis, dan kompleks, sehingga makalah ini akan memberikan dasar bagi metode optimasi metode dalam pengolahan data statistika fisika. Harapan penulis adalah para fisikawan astronomi dapat melakukan pengolahan data astronomi yang penting dan efektif secara optimal dan efektif.
Kata kunci—Fisika Komputasi, Metode astronomi statistik, data Astronomi
Abstract
Computational astronomy is a very important branch in today's era, where physicists or researchers can use computers to process statistics in astronomical physics. researchers can process abstract data from raw data and can convert data into data visualizations.
Computational physics astronomy is a sophisticated and well-established method, this branch of science can provide and process data, solve complex problems, and is very helpful for statisticians and computer scientists. Astronomical physicists have many problems, among others; there is a problem that is hierarchical, and complex, so that this paper will provide a basis for methods for optimizing methods in processing statistical data on physics. The author's hope is that astronomical physicists can perform an important and effective processing of astronomical data optimally and effectively.
Keywords—Computational Physics, Statistical astronomy methods, Astronomy data.
1. INTRODUCTION
In carrying out a data measurement in physics in particular, it is necessary to have a fairly long process, where physicists or researchers need a data exploitation stage in observations, in physics modeling a parameter is needed to measure the uncertainty [1]. In finding astronomical research, such as; modeling how galaxies form, looking for stellar data, looking for habitable planets that are similar to Earth, estimating the area between stars and planets, or researching transient classifications [2]. From these things it is very necessary to collect data in the form of data collection. In today's century the development of physical data collection, especially this century, namely the 21st century, data collection has been so complex in research, among others; increasing the size of the data, converting or increasing statistical improvements in methodological development, as well as the use of CCD cameras, and telescope automation [3]. From the things mentioned above, namely to achieve the goals of astronomical physicists and statisticians in processing data, this article is very suitable as a reference and library [4]. In this paper, we will explain several methods of processing astronomical applications, such as how to review statistical data methodology, looking for general data in astronomical physics.
2. RESULT AND DISCUSSION
2.1 Several Types of Astronomical Data 2.1.1 Spectral and image data
To measure astronomical data such as radiation spectrum, including visible light, ultraviolet, X-rays, infrared and waves, a method called Spectroscopy is needed [5], [6]. In the method of astronomical spectroscopy, a spectral data is needed. spectral data researchers can analyze the nature of the universe even though it cannot be seen closely and directly [7], the last few decades have; measuring the Doppler shift; reveal many star traits; shows the speed of movement towards or away from observing the physical properties of many types of celestial bodies, galaxies, stars, and black holes [8].
Figure 1. DECam take clear pictures of the night sky. The image is like several parts of the image, it works to classify, identify, or estimate the luminosity of celestial objects.
Source: Cliff Johnson, Clara Martínez-Vázquez, DELVE Survey (2019).
In the universe physicists of very rapid progress have known astonishing data by cloud people, one of which is dark energy [9]. However, from this data, it is very difficult to find and detect and it is very necessary for a new method to capture the data [10]. As for the current decade, there is an instrument facility called the Dark Energy Camera (DECam) [11], which also mostly comes from the Dark Energy Survey (DES) [12]. In this astronomical instrument
facility, in capturing data, a photometric filter is needed [13], this filter can classify, and only take certain length data that you want to use. One of the advantages of Dark Energy Camera (DECam) is that it can capture about 400 one gigabyte images per night [14]. We can see in Figure 1. The image is the result of an astronomical instrument facility. This camera can capture images well and effectively especially the night sky and far away, this data is produced from the obtained length light data, and converted into images from a permanent and tested process.
Figure 2. Galactic spectral data from the Andromeda Galaxy that can find the distance from its eclipsing binary
Source: Vilardell (2010)
We can see in Figure 2, which is the spectral data of the Andromeda galaxy, from the graph of the data we can get physical information so that even though the distance from Earth to Andromeda is 2.537 million light years [15], we can know the chemical composition, temperature, and mass and age of the galaxy [16]. We can analyze the spectral as shown in the image from the displacement of the spectral feature, namely the Doppler shift, usually referred to as the red shift [17]. Spectral data is one that is often used by physical astronomers to understand the evolution and statistical data of the universe[18]. Certain wavelengths or what you want to take can absorb light in atomic elements, from this spectral data can determine the physical composition of chemical objects [19].
2.1.2 Data function and time series
In finding and obtaining data types, a basis or foundation is needed, including; integrated flux, photometric information classified by filter [20], foundation or base can be obtained from 2 raw materials for astronomical research, namely; spectrum and image. The luminosity or brightness of various light objects in the universe as a function of time can be classified as a data type [21]. As for getting the data results, it is necessary to capture the raw data, repeatedly from time to time, so that an analysis of the temporal brightness of variations obtained from the light curve and time series captured in each object will be obtained [22].
Figure 3. The events of the OGLE-2005-BLG-390 micro-lensing model plotted as a function of time to obtain the light curve
Source: Briliant stephane (2005)
In the experimental image, which is usually called a gravitational lens, we can see data that shows the Optical Time Series (OGLE) [23]. Based on these data, we can see the data collected from the raw data by comparing the time series and the brilliance of astronomical objects. so astronomers physics researchers can examine the characterization of changes in star brilliance or magnitude per period of time. Active development is a type of data in estimating brightness (magnitude) versus time period. We can also see the data represented by the cross- like sign in Figure 4 in looking for the star variable on the light curve. The time range and the rhythm of data observations in time series are usually irregular, so a fairly long period of time is needed so that the characteristics of changes in the magnitude and phase of the observed stars can be known [24]. The Optical Gravity Lens Experiment (OGLE) can collect approximately 400,000 light curve data which can be plotted on time series analysis [25].
Figure 4. Light Curves of Variable Stars, Source: Mendez (2005)
2.2 methods and data processes in searching for astronomical statistics
The challenge for both physical astronomers and astronomical statisticians is to analyze complex and abstract raw data. As for processing complex data and astral in astronomy, this paper will explain and discuss several methods, especially in the field of astronomical statistics with the latest applications, so that they become a reference and reference for statisticians, astronomers and physicists in processing and analyzing complex data in astronomy.
2.2.1 Model measurement error
In varying the research model, there are often cases of measurement errors, this is a trivial thing, while this error is explicit in the form of a regression model in processing data in research variables [26], it is very important to pay attention to in order to avoid a measurement bias. The hierarchical structure is often similar to a data processing model, which is also treated as a parameter that has a true predictor value [27]. Usually there is often the term homoscedasticity in modeling a data, while errors in the same variant are usually called homoscedasticity, but usually this error is common and has become the norm in data processing [28].
Figure 5. Data statistics Function Maximum brightness curve on the supernova spectrum Source: James (2004)
Model Hierarchical Bayesian errors, bivariate correlates and intrinsic scatter are approaches that are often used in astronomical research models [29]. To estimate the density of observations subject to measurement error is the development of mixed models in the application model. As for the probabilistic classification of quasars on flux uncertainty, it is also a modeling application [30]. To investigate global clusters in various galaxies, we can use the Bayesian model where this model serves to handle discrete measurement uncertainty in the negative binomial model [31].
2.2.2 Survive analysis method
Astronomers data statisticians urgently need multivariate data to analyze survival in the universe. In the object data population we can obtain a data sample that is unbiased in the construction of an astronomical survey [32]. The definition of bias here is an observation of data by a telescope which passes the sensitivity limit of the telescope because there is an object that has a brighter magnitude to detect and cuts occur [33]. But behind it there is an object that is too dim to be detected so the sensor is the observation. Persistence analysis is so necessary in statistics especially in astronomy because it cannot be ignored in general [34]. The decrease can be a nonparametric density estimate, or we use a regression model for the selection.
Figure 6. Example of survival analysis in python implementation Source: Pandey (2019)
2.2.3 Bayesian Computational modeling method
In the current rapid development of astronomy, astronomical methods, especially in statistical Bayesian methodology, have also improved, especially in the last three decades. As for models such as; posterior samples, hierarchical models, as well as for images and functions that are complex data types, are the scope of the Bayesian methodology [35]. If in the
observation the data does not know the existence of a population or the number of individual objects, then the Bayesian Hierarchy Model (HBM) can be a solution [36].
By using an intervention variation of data that aims to approach the posterior, we can find the modeling of the supernova light curve and we can characterize, as well as collect data on galaxies [37], so that the Hierarchical Bayesian Model (HBM) can be interpreted to be applicable to many types of data in astronomy [38]. As for reducing a computational data requires an expensive cost, by using Estimated Bayesian Computing (ABC) we can use it, (ABC) using distance comparisons and simulating the actual data [39]. Astronomers also use Estimated Bayesian Computing (ABC) to assess the evolution of galaxies, as well as infer cosmological parameters [40].
In the current decade, the development of astronomical computing has made it easier for astronomers and statisticians to process data, such as; ABC sampler through Population Monte Carlo, cosmoabc [40]. As for software such as Markov Chain Monte Carlo sampler, Diffusive Nested Sampling, affine-invariant ensemble sampler, a development has been carried out by the computators where a feature is added to add nested samplers [41].
2.2.4 Common linear modeling methods
If the data is obtained from the distribution of the other exponential part of Gauss, then the assumption of this distribution is that the linear regression model fails [42]. Meanwhile, there is a response relationship on the variables x and y, through a link function that is connected to each other in a general linear model [43]. We can solve problems in modeling galactic distances from a function, modeling a spherical cluster on the parent galaxy property (negative binomial) with a function, or modeling a Seyfert galaxy (Bernouli) with a function (Gamma) using the general linear model [44].
2.2.5 Machine learning method
In observing astronomical problems, there are things that are more important than parameter estimates, namely pattern recognition and prediction [45]. In today's times and decades, there are many things in computing that are very helpful and often used by humans, one of which is machine learning [46]. The things that statisticians and astronomers can use in using machine learning in astronomical statistics are computational feature extraction, as well as real-time classification [47]. So in this paper, we have explained and summarized the challenges that exist in machine learning
We already know that to classify a light source curve variable we use machine learning.
From this we can tell that there is a functional classification problem because we know that the function is part of the light curve. We can use a human classification in several levels, where we must first build a set of training objects in the class [48]. The purpose of this classification is to obtain a new object in the survey. To find the approximate data of photometric redshift, find the source in the image, the clustering of spectral data, we can use machine learning to solve this problem [49].
In the astronomy problem, there is a special problem, namely domain adaptation, as well as active learning, so machine learning computators are very enthusiastic in developing this feature [50]. The problem that must be considered in machine learning is the lack of representation of photometric and spectroscopic variables [51]. Machine learning is often in this situation because of the size of its validation [52]. In astronomy it is so necessary to match the training sample data, but in machine learning so often there is a mismatch of training samples and in non- exclusive testing [53]. The existence of and cutting into machine learning algorithms, missing data, censorship, and feature measurement errors are some of the challenges machine learning has in its use in astronomical statistics [54].
2.2.6 Information Visualization method
We have to get the right tools to apply to uncover non-trivial correlations and undetectable patterns in table-based data [55].
Figure 6. N-body/hydro cosmological simulation on galaxy catalog consisting of 3 visualizations (cladogram, graph, and chord diagram)
Source: Pandey (2019)
The existence of multidimensional data visualization, visualization belonging to the basic analysis of astronomy [56], as well as new paradigms in astronomy are underutilized. The human visual system requires a visualization method to optimize intuition into the data structure [57]. As for starfish diagrams, phylogenetic trees, chords, which are also multivariate astronomical data have been developed to facilitate the exploration of astronomical data [58].
3. Conclusion
Explanation Based on what has been conveyed relating to the processing of astronomical data, it aims to explain the methods in processing raw data which at the beginning are complicated and use methods that are more modern and easier than before. In this article the author has provided an explanation of the types and types in data processing, especially astronomical statistical data. The methods that can make it easier for scientists and statisticians are to use methods; Model measurement error, Survive analysis method, Bayesian Computational modeling method, General linear modeling method, Machine learning method, and Information Visualization Method. This paper will provide and direction for challenging complex properties, as well as hierarchical properties of data, so that astronomers and statisticians in astronomy can overcome these challenges.
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