[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119592-en":3,"doc-seo-119592-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"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},119592,687197207919,"Theodora","https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552",8,"Research & Report","GalaxiesML - a dataset of galaxy images, photometry, redshifts, and structural parameters for machine learning","GalaxiesML is a public machine learning dataset designed for astrophysical and data-driven research, combining galaxy images, photometry, spectroscopic redshifts, and structural properties. It contains 286,401 galaxy samples from the Hyper-Suprime-Cam Survey PDR2 across five filters (g, r, i, z, y) with spectroscopically confirmed redshifts as ground truth. The dataset is built to be uniform and consistent with minimal outliers while covering a realistic signal-to-noise range. It supports training and validation challenges including outlier rejection, duplication handling, ground-truth establishment, and careful sample selection, spanning redshifts from 0.01 to 4.","arXiv :2410 .00271v1 [ astro-ph .CO] 30 Sep 2024  \nGalaxiesML: a dataset of galaxy images, photometry, redshifts, and structural parameters for machine  \nlearning  \nTuan Do  \nPhysics and Astronomy Department UCLA Los Angeles, CA 90095 [tdo@astro.ucla.edu](tdo@astro.ucla.edu)  \nBernie Boscoe  \nComputer Science Department Southern Oregon University Ashland, OR 97520 [boscoeb@sou.edu](boscoeb@sou.edu)  \nEvan Jones  \nPhysics and Astronomy Department  \nUCLA  \nLos Angeles, CA 90095  \n[ejones@astro.ucla.edu](ejones@astro.ucla.edu)  \nYun Qi Li  \nPhysics and Astronomy Department  \nUCLA  \nLos Angeles, CA 90095  \nyunqil@g.ucla .edus  \nKevin Alfaro  \nPhysics and Astronomy Department  \nUCLA  \nLos Angeles, CA 90095  \n[keal1885@gmail.com](keal1885@gmail.com)  \nAbstract  \nWe present a dataset built for machine learning applications consisting of galaxy photometry, images, spectroscopic redshifts, and structural properties. This dataset comprises 286,401 galaxy images and photometry from the Hyper-Suprime-Cam Survey PDR2 in five imaging filters (g, r, i, z, y) with spectroscopically confirmed redshifts as ground truth. Such a dataset is important for machine learning applications because it is uniform, consistent, and has minimal outliers but still contains a realistic range of signal-to-noise ratios. We make this dataset public to help spur development of machine learning methods for the next generation of surveys such as Euclid and LSST. The aim of GalaxiesML is to provide a robust dataset that can be used not only for astrophysics but also for machine learning, where image properties cannot be validated by the human eye and are instead governed by physical laws. We describe the challenges associated with putting together adataset from publicly available archives, including outlier rejection, duplication, establishing ground truths, and sample selection. This is one of the largest public machine learning-ready training sets of its kind with redshifts ranging from 0.01 to 4 . The redshift distribution of this sample peaks at redshift of 1.5 and falls off rapidly beyond redshift 2.5 . We also include an example application of this dataset  \nPreprint. Under review.  \nfor redshift estimation, demonstrating that using images for redshift estimation produces more accurate results compared to using photometry alone. For example, the bias in redshift estimate is a factor of 10 lower when using images between redshift of 0.1 to 1.25 compared to photometry alone. Results from dataset such as this will help inform us on how to best make use of data from the next generation of galaxy surveys.  \nFigure 1: Example of galaxies at different redshifts from the GalaxiesML dataset. The top row shows the images in a linear intensity scale while the bottom row shows the images in a logarithmic scale to show lower surface brightness features like nearby galaxies .  \n1 Introduction  \nOne of the major questions in physics and astrophysics is the nature of dark matter and dark energy. Together, dark matter and dark energy make up over 95% of the energy density of the universe, yet we do not know their particle or field nature [12] . The most promising approach to investigating their nature is through observations of the Universe on cosmic scales, pushing the limits of not only our telescopes and instrumentation, but also of our data analysis techniques.  \nMachine learning holds potential to help achieve the ambitious science goals of the large experiments in Astrophysics that are coming online now and in the next few years. The vast majority of data we have about the Universe is in the form of images, which are not always easily interpreted. For example, a key measurement required is how galaxies are spatially distributed across different cosmic times, but the distance to a galaxy (redshift) is not easily determined from its images alone. Fortunately, because the properties of galaxies evolve over time according to physical laws, the images of galaxies at different ","cbCaicuEekv8ULl3","https://ap.wps.com/l/cbCaicuEekv8ULl3","pdf",2683362,1,19,"English","en",105,"# Abstract\n# 1 Introduction\n## Dark matter and dark energy on cosmic scales\n## Why machine learning for astronomical images\n## Data curation needs for ML-ready datasets\n## Upcoming large surveys and expected scale","[{\"question\":\"What does the GalaxiesML dataset include?\",\"answer\":\"GalaxiesML provides galaxy images, photometry, spectroscopic redshifts, and structural properties built for machine learning applications.\"},{\"question\":\"How is ground truth for redshift provided in GalaxiesML?\",\"answer\":\"The dataset uses spectroscopically confirmed redshifts as ground truth, paired with images and photometry from the Hyper-Suprime-Cam Survey PDR2.\"},{\"question\":\"What is the benefit of using images versus photometry for redshift estimation?\",\"answer\":\"An example application shows that redshift estimates using images are more accurate than using photometry alone, with the redshift estimate bias reduced by about a factor of 10 for images within redshift 0.1 to 1.25.\"}]","GalaxiesML - a dataset of galaxy images, photometry, redshifts, and structural parameters for machine learning | PDF",1785725178,48,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"galaxiesml-a-dataset-of-galaxy-images-photometry-redshifts-and-structural-parameters-for-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/galaxiesml-a-dataset-of-galaxy-images-photometry-redshifts-and-structural-parameters-for-machine-learning/119592/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What does the GalaxiesML dataset include?","Question",{"text":75,"@type":76},"GalaxiesML provides galaxy images, photometry, spectroscopic redshifts, and structural properties built for machine learning applications.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is ground truth for redshift provided in GalaxiesML?",{"text":80,"@type":76},"The dataset uses spectroscopically confirmed redshifts as ground truth, paired with images and photometry from the Hyper-Suprime-Cam Survey PDR2.",{"name":82,"@type":73,"acceptedAnswer":83},"What is the benefit of using images versus photometry for redshift estimation?",{"text":84,"@type":76},"An example application shows that redshift estimates using images are more accurate than using photometry alone, with the redshift estimate bias reduced by about a factor of 10 for images within redshift 0.1 to 1.25.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},"General","general"]