[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126990-en":3,"doc-seo-126990-105":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":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},126990,687207024643,"Oliver","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Galactic center GeV excess and classification of Fermi-LAT sources with machine learning","Gamma-ray excess with GeV energies around the Galactic center observed by Fermi-LAT is a key feature that has been interpreted through annihilating dark matter or emission from unresolved millisecond pulsars. This work applies multi-class machine-learning classification to study how millisecond-pulsar-like sources are distributed among unassociated Fermi-LAT sources near the Galactic center. The resulting MSP-like source count distribution is found to be comparable with the MSP explanation of the GeV excess, addressing both population and statistical approaches to test hypotheses.","arXiv :2406 .03990v1 [ astro-ph .HE] 6 Jun 2024  \nGalactic center GeV excess and classification of Fermi-LAT sources with machine  \nlearning  \nDmitry V. Malyshev  \nErlangen Centre for Astroparticle Physics,  \nNikolaus-Fiebiger-Str. 2, Erlangen 91058, Germany  \nExcess of gamma rays with a spherical morphology around the Galactic center (GC) observed in the Fermi large area telescope (LAT) data is one of the most intriguing features in the gamma-ray sky. The excess has been interpreted by annihilating dark matter as well as emission from a population of unresolved millisecond pulsars (MSPs) . We use a multi-class classification of Fermi-LAT sources with machine learning to study the distribution of MSPlike sources among unassociated Fermi-LAT sources near the GC. We find that the source count distribution of MSP-like sources is comparable with the MSP explanation of the GC excess.  \n1 Introduction  \nExcess of gamma rays with GeV energies near the GC observed in the Fermi-LAT data has sparked a significant interest as it has spectral and spatial distributions expected for dark matter (DM) annihilation 1 ,2 ,3 ,4 ,5 ,6 ,7 ,8 . Nevertheless, there also exist astrophysical explanations of the excess, e.g. , due to a population of unresolved MSPs 9 ,3 ,4 , 10 , 11 . Approaches to test the MSP hypothesis can be separated into two general classes: (1) population studies based on physical modeling or on the extrapolation from associated MSPs and globular clusters 11 , 12 , 13 , 14 , 15 ;  \n(2) statistical methods that exploit the properties of distributions of gamma rays near the GC 16 , 17 , 18 , 19 ,20 ,21 ,22 ,23 ,24 ,25 ,26 . Advantages and challenges in these two classes of methods are:  \n1. Population studies  \nPros: The methods are based on observed properties of local MSPs and MSPs in globular clusters and can be used to separate the contribution of MSPs near the GC from contributions of other sources.  \nCons: Extrapolation from the distribution of nearby MSPs or observed globular clusters is needed.  \n2. Statistical methods  \nPros: Sensitive to sources both above and below the detection threshold. It is possible to determine sources correlated with a particular distribution, e.g., spherical profile around  \nSdN/dSd (sr 1 )  \nAll sky Nph, 14 years  \n101 102 103  \nGalactic center Nph, 14 years  \n101 102 103  \n100  \n80  \n60  \n40  \n20  \n0  \n\n|  |  |  |  |\n| --- | --- | --- | --- |\n|  |  | \u003Cbr>~~ ~~ 4FGL-DR4 PS (x0 .4) ~~ ~~ Unas 4FGL-DR4 PS\u003Cbr>~~ ~~ Unas  msp+ ~~ ~~ Assoc msp+ |  |\n|  |  |  |  |\n\n1200  \n1000  \n800  \n600  \n400  \n200  \n0  \n10 ~~ ~~ 11 10 ~~ ~~ 10 10 ~~ ~~ 9 10 ~~ ~~ 8  \n2 )  \nSdN/dS  \nSdN/dSd (deg  \n1.0  \n0.8  \n0.6  \n0.4  \n0.2  \n0.0  \n300  \n250  \n200  \n150  \n100  \n50  \n0  \n10 ~~ ~~ 11 10 ~~ ~~ 10 10 ~~ ~~ 9 10 ~~ ~~ 8  \nS2   5 GeV (cm ~~ ~~ 2s ~~ ~~ 1 ) S2   5 GeV (cm ~~ ~~ 2s ~~ ~~ 1 )  \nSdN/dS  \nFigure 1 – Source count distributions of 4FGL-DR4 27 sources for the whole sky (left panel) and in the GC ROI (right panel) . Blue solid lines – all sources, orange dashed lines – unassociated sources, green dotted lines – associated MSP-like sources, red dash-dotted lines – estimated source count for MSP-like sources among unassociated sources. Purple dash-dot-dotted line (right panel)– estimated source count distribution from photon count statistics 23 .  \nthe GC.  \nCons: Not specific to MSPs, i.e., only the overall distribution of sources is determined ina relatively large energy bin.  \nWe illustrate the challenges in the population studies in Fig. 1. On the left panel we show the distribution of 4FGL data release 4 (DR4) catalog 27 source counts as a function of flux in energy bin between 2 and 5 GeV. The corresponding number of photons in this energy range for 14 years of data taking is shown on the top x-axis. It is estimated using the acceptance of 2.5 m2 sr around a few GeV reduced by 20% to account for dead time (mostly due to passage of South Atlantic Anomaly)a. On average, the fluxes of associated MSPs and globul","cbCaivP6jQaKTjA8","https://ap.wps.com/l/cbCaivP6jQaKTjA8","pdf",667077,1,4,"English","en",105,"# Introduction\n## Population studies\n## Statistical methods\n## Figure 1: source count distributions and challenges","[{\"question\":\"What two main explanations for the Galactic center GeV gamma-ray excess are discussed?\",\"answer\":\"The excess is interpreted either as dark matter annihilation or as emission from a population of unresolved millisecond pulsars.\"},{\"question\":\"How does the work use machine learning in this study?\",\"answer\":\"It applies multi-class classification to determine the distribution of MSP-like sources among unassociated Fermi-LAT sources near the Galactic center.\"},{\"question\":\"What is the comparison result between the predicted MSP-like source counts and the MSP explanation?\",\"answer\":\"The source count distribution of MSP-like sources is found comparable with the MSP explanation of the Galactic center excess.\"}]","Galactic center GeV excess and classification of Fermi-LAT sources with machine learning | PDF",1785936065,10,{"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},"galactic-center-gev-excess-and-classification-of-fermi-lat-sources-with-machine-learning","",{"@graph":36,"@context":85},[37,53,68],{"@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":21},"https://docshare.wps.com/document/galactic-center-gev-excess-and-classification-of-fermi-lat-sources-with-machine-learning/126990/",{"url":52,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-22","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What two main explanations for the Galactic center GeV gamma-ray excess are discussed?","Question",{"text":75,"@type":76},"The excess is interpreted either as dark matter annihilation or as emission from a population of unresolved millisecond pulsars.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the work use machine learning in this study?",{"text":80,"@type":76},"It applies multi-class classification to determine the distribution of MSP-like sources among unassociated Fermi-LAT sources near the Galactic center.",{"name":82,"@type":73,"acceptedAnswer":83},"What is the comparison result between the predicted MSP-like source counts and the MSP explanation?",{"text":84,"@type":76},"The source count distribution of MSP-like sources is found comparable with the MSP explanation of the Galactic center 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