Landasan Metodologis Teologi Pendidikan Islam

This paper aims to describe that there is a number of conventions/rules that must be considered by the researcher of Islamic education in interpreting the verses of the Qur'an related to education. The conventions/rules can be in the form of methodological and theological. Methodologically, an attempt to interpret the verses of education contained in the Qur'an comes from the Qur'an interpretation methods. In general, the method of interpretation of the Qur'an is not different from the method of interpreting the text/script in general. The most distinguishing thing is the text itself in the form of the holy book. Therefore, there needs to be a certain theological understanding to treat the holy Qurán as an object of study/interpretation/research. The Qur'an is the word of Allah SWT that contains instructions for all humans. As the word of Allah, only God knows all intents contained in each verse of the Qur'an. But as a guide, the word must be understood in order to be carried out. On that basis, efforts to understand the Qur'an should be done, includingto understand the verses of the Qur'an related to education. Variety of these efforts including the basic principles, methods of analysis and research methods are summarized in the concept of theology methodological foundation of Islamic education

Kemampuan Membaca Puisi Siswa Kelas VII Mts Al-uswah Pekanbaru

This research was intended to find out the ability of the first year students of Al-Uswah Pekanbaru. in reading poetry. The problem in this research is how the student's ability in reading poetry consists of the aspects of vocal precision, intonation, and appreciation (expression). The method used in this research is descriptive quantitative with the sample were 44 students of the first year of MTs Al-Uswah Pekanbaru. The data was collected by provide a test reading text of the poem to the sample, and then provide an assessment in accordance with the assessment criteria that have been established, then look for the percentage, mean, two different tests, standard deviation, and standard deviation for each aspect combined assessed and a recapitulation result of data analysis. The research finding showed that (1) the ability to read poetry in aspects of vocal accuracy category with the average being 60.99, equivalent to 76.24 per cent (2) the ability to read poetry in intonation aspect being categorized with the average of 47.26, equivalent to 78 , 77 percent (3) the ability to read poetry in the aspect of appreciation (expression) low category with a mean of 41.22, equivalent to 68.71 percent. Overall, it can be concluded that the ability of the first year students of Al-Uswah Pekanbaru. in reading poetry is categorized with average 143.59 (for two poems), equivalent to 71.80 percent

Processing GOTO data with the Rubin Observatory LSST Science Pipelines I: Production of coadded frames

The past few decades have seen the burgeoning of wide field, high cadence surveys, the most formidable of which will be the Legacy Survey of Space and Time (LSST) to be conducted by the Vera C. Rubin Observatory. So new is the field of systematic time-domain survey astronomy, however, that major scientific insights will continue to be obtained using smaller, more flexible systems than the LSST. One such example is the Gravitational-wave Optical Transient Observer (GOTO), whose primary science objective is the optical follow-up of Gravitational Wave events. The amount and rate of data production by GOTO and other wide-area, high-cadence surveys presents a significant challenge to data processing pipelines which need to operate in near real-time to fully exploit the time-domain. In this study, we adapt the Rubin Observatory LSST Science Pipelines to process GOTO data, thereby exploring the feasibility of using this "off-the-shelf" pipeline to process data from other wide-area, high-cadence surveys. In this paper, we describe how we use the LSST Science Pipelines to process raw GOTO frames to ultimately produce calibrated coadded images and photometric source catalogues. After comparing the measured astrometry and photometry to those of matched sources from PanSTARRS DR1, we find that measured source positions are typically accurate to sub-pixel levels, and that measured L-band photometries are accurate to ∼50 mmag at mL∼16 and ∼200 mmag at mL∼18. These values compare favourably to those obtained using GOTO's primary, in-house pipeline, GOTOPHOTO, in spite of both pipelines having undergone further development and improvement beyond the implementations used in this study. Finally, we release a generic "obs package" that others can build-upon should they wish to use the LSST Science Pipelines to process data from other facilities

Searching for electromagnetic counterparts to gravitational-wave merger events with the prototype Gravitational-wave Optical Transient Observer (GOTO-4)

We report the results of optical follow-up observations of 29 gravitational-wave (GW) triggers during the first half of the LIGO–Virgo Collaboration (LVC) O3 run with the Gravitational-wave Optical Transient Observer (GOTO) in its prototype 4-telescope configuration (GOTO-4). While no viable electromagnetic (EM) counterpart candidate was identified, we estimate our 3D (volumetric) coverage using test light curves of on- and off-axis gamma-ray bursts and kilonovae. In cases where the source region was observable immediately, GOTO-4 was able to respond to a GW alert in less than a minute. The average time of first observation was 8.79 h after receiving an alert (9.90 h after trigger). A mean of 732.3 square degrees were tiled per event, representing on average 45.3 per cent of the LVC probability map, or 70.3 per cent of the observable probability. This coverage will further improve as the facility scales up alongside the localization performance of the evolving GW detector network. Even in its 4-telescope prototype configuration, GOTO is capable of detecting AT2017gfo-like kilonovae beyond 200 Mpc in favourable observing conditions. We cannot currently place meaningful EM limits on the population of distant (⁠D^L=1.3 Gpc) binary black hole mergers because our test models are too faint to recover at this distance. However, as GOTO is upgraded towards its full 32-telescope, 2 node (La Palma & Australia) configuration, it is expected to be sufficiently sensitive to cover the predicted O4 binary neutron star merger volume, and will be able to respond to both northern and southern triggers

Self-Supervised Clustering on Image-Subtracted Data with Deep-Embedded Self-Organizing Map

Developing an effective automatic classifier to separate genuine sources from artifacts is essential for transient follow-ups in wide-field optical surveys. The identification of transient detections from the subtraction artifacts after the image differencing process is a key step in such classifiers, known as real-bogus classification problem. We apply a self-supervised machine learning model, the deep-embedded self-organizing map (DESOM) to this "real-bogus" classification problem. DESOM combines an autoencoder and a self-organizing map to perform clustering in order to distinguish between real and bogus detections, based on their dimensionality-reduced representations. We use 32x32 normalized detection thumbnails as the input of DESOM. We demonstrate different model training approaches, and find that our best DESOM classifier shows a missed detection rate of 6.6% with a false positive rate of 1.5%. DESOM offers a more nuanced way to fine-tune the decision boundary identifying likely real detections when used in combination with other types of classifiers, for example built on neural networks or decision trees. We also discuss other potential usages of DESOM and its limitations

Machine learning for transient recognition in difference imaging with minimum sampling effort

The amount of observational data produced by time-domain astronomy is exponentially in-creasing. Human inspection alone is not an effective way to identify genuine transients fromthe data. An automatic real-bogus classifier is needed and machine learning techniques are commonly used to achieve this goal. Building a training set with a sufficiently large number of verified transients is challenging, due to the requirement of human verification. We presentan approach for creating a training set by using all detections in the science images to be thesample of real detections and all detections in the difference images, which are generated by the process of difference imaging to detect transients, to be the samples of bogus detections. This strategy effectively minimizes the labour involved in the data labelling for supervised machine learning methods. We demonstrate the utility of the training set by using it to train several classifiers utilizing as the feature representation the normalized pixel values in 21-by-21pixel stamps centered at the detection position, observed with the Gravitational-wave Optical Transient Observer (GOTO) prototype. The real-bogus classifier trained with this strategy can provide up to 95% prediction accuracy on the real detections at a false alarm rate of 1%

Machine learning for transient recognition in difference imaging with minimum sampling effort

The amount of observational data produced by time-domain astronomy is exponentially increasing. Human inspection alone is not an effective way to identify genuine transients from the data. An automatic real-bogus classifier is needed and machine learning techniques are commonly used to achieve this goal. Building a training set with a sufficiently large number of verified transients is challenging, due to the requirement of human verification. We present an approach for creating a training set by using all detections in the science images to be the sample of real detections and all detections in the difference images, which are generated by the process of difference imaging to detect transients, to be the samples of bogus detections. This strategy effectively minimizes the labour involved in the data labelling for supervised machine learning methods. We demonstrate the utility of the training set by using it to train several classifiers utilizing as the feature representation the normalized pixel values in 21 x 21 pixel stamps centred at the detection position, observed with the Gravitational-wave Optical Transient Observer (GOTO) prototype. The real-bogus classifier trained with this strategy can provide up to 95 per cent prediction accuracy on the real detections at a false alarm rate of 1 per cent

Processing GOTO data with the Rubin Observatory LSST Science Pipelines I: Production of coadded frames

The past few decades have seen the burgeoning of wide-field, high-cadence surveys, the most formidable of which will be the Legacy Survey of Space and Time (LSST) to be conducted by the Vera C. Rubin Observatory. So new is the field of systematic time-domain survey astronomy; however, that major scientific insights will continue to be obtained using smaller, more flexible systems than the LSST. One such example is the Gravitational-wave Optical Transient Observer (GOTO) whose primary science objective is the optical follow-up of gravitational wave events. The amount and rate of data production by GOTO and other wide-area, high-cadence surveys presents a significant challenge to data processing pipelines which need to operate in near-real time to fully exploit the time domain. In this study, we adapt the Rubin Observatory LSST Science Pipelines to process GOTO data, thereby exploring the feasibility of using this ‘off-the-shelf’ pipeline to process data from other wide-area, high-cadence surveys. In this paper, we describe how we use the LSST Science Pipelines to process raw GOTO frames to ultimately produce calibrated coadded images and photometric source catalogues. After comparing the measured astrometry and photometry to those of matched sources from PanSTARRS DR1, we find that measured source positions are typically accurate to subpixel levels, and that measured L-band photometries are accurate to ∼ 50 mmag at mL ∼ 16 and ∼ 200 mmag at mL ∼ 18. These values compare favourably to those obtained using GOTO’s primary, in-house pipeline, GOTOPHOTO, in spite of both pipelines having undergone further development and improvement beyond the implementations used in this study. Finally, we release a generic ‘obs package’ that others can build upon, should they wish to use the LSST Science Pipelines to process data from other facilities. Keywords: astronomy data analysis – surveys – atrometry – photometry</p

Searching for Fermi GRB optical counterparts with the prototype Gravitational-wave Optical Transient Observer (GOTO)

The typical detection rate of ∼1 gamma-ray burst (GRB) per day by the Fermi Gamma-ray Burst Monitor (GBM) provides a valuable opportunity to further our understanding of GRB physics. However, the large uncertainty of the Fermi localization typically prevents rapid identification of multiwavelength counterparts. We report the follow-up of 93 Fermi GRBs with the Gravitational-wave Optical Transient Observer (GOTO) prototype on La Palma. We selected 53 events (based on favourable observing conditions) for detailed analysis, and to demonstrate our strategy of searching for optical counterparts. We apply a filtering process consisting of both automated and manual steps to 60 085 candidates initially, rejecting all but 29, arising from 15 events. With ≈3 GRB afterglows expected to be detectable with GOTO from our sample, most of the candidates are unlikely to be related to the GRBs. Since we did not have multiple observations for those candidates, we cannot confidently confirm the association between the transients and the GRBs. Our results show that GOTO can effectively search for GRB optical counterparts thanks to its large field of view of ≈40 deg2 and its depth of ≈20 mag. We also detail several methods to improve our overall performance for future follow-up programmes of Fermi GRBs.</p

Searching for Fermi GRB optical counterparts with the prototype gravitational-wave optical transient observer (GOTO)

The typical detection rate of ∼1 gamma-ray burst (GRB) per day by the Fermi Gamma-ray Burst Monitor (GBM) provides a valuable opportunity to further our understanding of GRB physics. However, the large uncertainty of the Fermi localization typically prevents rapid identification of multi-wavelength counterparts. We report the follow-up of 93 Fermi GRBs with the Gravitational-wave Optical Transient Observer (GOTO) prototype on La Palma. We selected 53 events (based on favourable observing conditions) for detailed analysis, and to demonstrate our strategy of searching for optical counterparts. We apply a filtering process consisting of both automated and manual steps to 60 085 candidates initially, rejecting all but 29, arising from 15 events. With ≈3 GRB afterglows expected to be detectable with GOTO from our sample, most of the candidates are unlikely to be related to the GRBs. Since we did not have multiple observations for those candidates, we cannot confidently confirm the association between the transients and the GRBs. Our results show that GOTO can effectively search for GRB optical counterparts thanks to its large field of view of ≈40 square degrees and its depth of ≈20 mag. We also detail several methods to improve our overall performance for future follow-up programs of Fermi GRBs