[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123829-en":3,"doc-seo-123829-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123829,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Improving Photometric Redshifts by Merging Probability Density Functions from Template-Based and Machine Learning Algorithms","This study improves galaxy photometric redshifts (photo-zs) by combining two established approaches: template-fitting and machine learning. The work compares the ANNz2 algorithm (machine learning) and BPz (template-based) using multiple photometric and spectroscopic samples from the Sloan Digital Sky Survey (SDSS). Results show ANNz2 outperforms BPz alone, while the proposed probability-density-function merging strategy yields additional gains in photometric-redshift accuracy, reducing RMS and 68% errors. The approach is demonstrated as a practical method for deeper, fainter surveys, with further validation needed for those regimes.","Improving Photometric Redshifts by Merging Probability Density Functions from Template-Based  \nand Machine Learning Algorithms  \nIshaq Y. K. ALSHUAILI ([https://orcid.org/0000-0003-4064-9253](https://orcid.org/0000-0003-4064-9253)) a , John Y. H. SOO ([https://orcid.org/0000-0001-5328-0892](https://orcid.org/0000-0001-5328-0892)) a,*, Mohd. Zubir MAT JAFRI ([https://orcid.org/0000-0001-](https://orcid.org/0000-0001-)  \n9313-7548)a and Yasmin RAFIDa  \na School of Physics, Universiti Sains Malaysia, 11800 USM, Pulau Pinang, Malaysia  \n*[e-mail: ](e-mail: johnsooyh@usm.my)[johnsooyh@usm.my](e-mail: johnsooyh@usm.my)  \nAbstract—This study aims to improve the photometric redshifts (photo-zs) of galaxies by integrating two contemporary methods: template-fitting and machine learning. Finding the synergy between these two methods was not a high priority in the past, but now that our computer processing power and observational accuracy have increased, we deem it worth investigating. We compared two methods to improve galaxy photometric redshift estimations by using the algorithms ANNz2 and BPz on different photometric and spectroscopic samples from the Sloan Digital Sky Survey (SDSS) . We find that the photometric redshift performance of ANNz2 (machine learning) is better than that of BPz (galactic templates), and with the utilization of the merging technique we introduced, we see that there is an improvement in photo-z when the two strategies are consolidated, providing improvements in 􀟪RMS and 􀟪68 up to [0.0265 & 0.0222] in LRG sample and [0.0471 & 0.0471] in the Stripe-82 sample. This simple demonstration can be used for photo-zs of galaxies in fainter and deeper sky surveys, and future work is required to prove its viability in these samples.  \nKeywords: Galaxies: distances and redshifts, Methods: photometric, methods: data analysis.  \n1. INTRODUCTION  \nThe measurement of galaxy redshifts represents an important topic to be studied, since it is required in many cosmological research work as it is also essentially adding a third, radial dimension to cosmological investigations. They make it possible to examine phenomena as a function of time and distance, as well as to identify structure formations like galaxy clusters, measuring distancedependent quantities such as luminosities and masses. Redshifts are also necessary to separate largescale structures and galaxies along the line of sight. Even today, the issue of obtaining photometric redshift (photo-z) estimations that are precise enough to suit the needs of cosmology and galaxy evolution investigations drives active development of photometric methods and photo-z algorithms.  \nIn the past, redshifts of galaxies have been calculated in various ways. One of the ways is to look at properties in redshifted galaxy spectra and compare them to the known rest-frame spectra of molecules and atoms on Earth using spectroscopic redshifts, often denoted as spec-zs or 􀝖spec. As a result, spectroscopic galaxy studies have been contributed towards the understanding of the origin, composition, and evolution of the Universe. The current set of spec-zs is insufficient for most current cosmological studies, mainly because spectroscopy, unlike photometry, is a time-consuming and expensive technique, and spectroscopic measurements are constantly limited by currently available  \ntechnology and optics (Soo. 2018) . As such, in order to produce redshifts ideally for all objects in large galaxy samples, the concept of photometric redshifts (photo-zs) was born.  \nThe photometric redshift technique described in the literature can be classified into two broad categories: the empirical training set method and the fitting of spectral energy distributions (SED) by synthetic or empirical template spectra. The first approach is also known as the machine learning method, an empirical relationship between magnitudes and redshifts is derived using a subsample of objects (the training set) in which both the redshifts ","cbCaivJIbv12oEJk","https://ap.wps.com/l/cbCaivJIbv12oEJk","pdf",1871010,1,14,"English","en",105,"# Introduction\n## Background: spectroscopic vs photometric redshifts\n## Two method families: machine learning and SED-template fitting\n## Motivation for combining methods via PDF merging","[{\"question\":\"What is the main goal of this study?\",\"answer\":\"To improve photometric redshift estimates of galaxies by integrating template-fitting and machine learning methods through a probability-density-function merging approach.\"},{\"question\":\"Which photo-z algorithms are compared and used?\",\"answer\":\"The study uses ANNz2 for machine learning and BPz for template-based fitting, evaluated on different photometric and spectroscopic samples from SDSS.\"},{\"question\":\"How does the merging of probability density functions affect performance?\",\"answer\":\"Consolidating the two strategies improves photo-z accuracy beyond using either method alone, with gains reflected in RMS and 68% error reductions for the tested samples.\"}]","Improving Photometric Redshifts by Merging Probability Density Functions from Template-Based and Machine Learning Algorithms | 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is the main goal of this study?","Question",{"text":76,"@type":77},"To improve photometric redshift estimates of galaxies by integrating template-fitting and machine learning methods through a probability-density-function merging approach.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which photo-z algorithms are compared and used?",{"text":81,"@type":77},"The study uses ANNz2 for machine learning and BPz for template-based fitting, evaluated on different photometric and spectroscopic samples from SDSS.",{"name":83,"@type":74,"acceptedAnswer":84},"How does the merging of probability density functions affect performance?",{"text":85,"@type":77},"Consolidating the two strategies improves photo-z accuracy beyond using either method alone, with gains reflected in RMS and 68% error reductions for the tested 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