[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119965-en":3,"doc-seo-119965-105":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},119965,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Microlensing signatures of extended dark objects using machine learning","A machine learning-based approach detects distinctive gravitational microlensing signatures produced by extended dark-matter objects, including boson stars, axion miniclusters, and subhalos. The method adapts MicroLIA to low-cadence microlensing survey data, training on simulated light curves with realistic observational time stamps. Models separate pointlike lenses from extended lenses and distinguish additional variable-magnitude object classes. Results emphasize identification of boson stars for 0.8 ≲ r/rE ≲ 3 and show NFW subhalos can be recognized from point lenses under regular cadence, with code and datasets provided.","Microlensing signatures of extended dark objects using machine learning  \nMiguel Crispim Romao* and Djuna Croon†  \nInstitute for Particle Physics Phenomenology, Department of Physics, Durham University, Durham DH1 3LE, United Kingdom  \n (Received 8 February 2024; accepted 26 April 2024; published 3 June 2024)  \nThis paper presents a machine learning-based method for the detection of the unique gravitational microlensing signatures of extended dark objects, such as boson stars, axion miniclusters and subhalos. We adapt MicroLIA, a machine learning-based package tailored to handle the challenges posed by low-cadence data in microlensing surveys. Using realistic observational time stamps, our models are trained on simulated light curves to distinguish between microlensing by pointlike and extended lenses, as well as from other object classes which give a variable magnitude. We focus on boson stars and Navarro-FrenkWhite (NFW) subhalos and show that the former, which are examples of objects with a relatively flat mass distribution, can be confidently identified for 0.8 ≲ r=rE ≲ 3. Intriguingly, we also find that more sharply peaked structures, such as NFW subhalos, can be distinctly recognized from point lenses under regular observation cadence. Our findings significantly advance the potential of microlensing data in uncovering the elusive nature of extended dark objects. The code and dataset used are also provided.  \nDOI: 10.1103/PhysRevD.109.123004  \nI. INTRODUCTION  \nMacroscopic dark matr candidates, with asses rang  \ning from large asteroids ( 10−15M⊙ ) to stars ( M⊙ ), offer compelling alternatives to the traditional particle-based theories. These celestial objects, potentially formed in the early Universe, are primarily detectable via their gravitational effects: gravitational lensing (e.g. [1–4]) and gravitational waves (e.g. [5–12]) . Indeed, gravitational microlensing is one of the most important ways of probing compact objects such as “machos” or primordial black holes (PBHs), a dark matter (DM) candidate consisting of compact objects formed in the early Universe. Through surveys of a range of sources, microlensing of such pointlike lenses has been used to constrain the fraction of DM such objects can comprise in a wide range of masses (see e.g. [4]) .  \nIt has also been proposed that dark matter can instead be comprised of extended objects, such as boson stars (e.g. [13–16]), axion miniclusters [17], and subhalos [18–22] . Like compact objects, such objects can also bend the light of distant stars. Whether this effect can be probed by a microlensing survey depends on the comparison  \n*[miguel.romao@durham.ac.uk](miguel.romao@durham.ac.uk)[ ](miguel.romao@durham.ac.uk)†[djuna.l.croon@durham.ac.uk](djuna.l.croon@durham.ac.uk)  \nPublished by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.  \nbetween the object radius and the Einstein radius—the characteristic length scale which is a function of the mass of the dark matter lens and the distance to the light source. The effectiveness of (micro-)lensing then depends on the size of the object compared to the Einstein radius: dilute dark objects, which are transparent to light, are ineffective lenses. Using conservative assumptions about the number of events observed, Refs. [23,24] derived modified constraints on extended dark matter objects.  \nInterestingly, structures with radii close to the Einstein radius may give distinct microlensing signatures. How the mass is spread within these structures affects the lensing effect. This was demonstrated explicitly for microlensing of various dark matter structures in [23–25] . In objects with aflatter density profile, such as boson stars, the microlensing magnification time series can deviate significantly from that expected from a pointli","cbCaigynI3hoqtJJ","https://ap.wps.com/l/cbCaigynI3hoqtJJ","pdf",3280346,1,12,"English","en",105,"# Introduction\n# Microlensing signatures of extended lenses","[{\"question\":\"What does the paper propose for detecting extended dark objects?\",\"answer\":\"It proposes a machine learning-based analysis pipeline that identifies gravitational microlensing signatures of extended dark-matter objects in time-series data.\"},{\"question\":\"How does MicroLIA support this work?\",\"answer\":\"It adapts MicroLIA, a machine learning package designed to handle the challenges of low-cadence data common in microlensing surveys.\"},{\"question\":\"Which extended objects are studied and what identification ranges are reported?\",\"answer\":\"The study focuses on boson stars and Navarro-Frenk-White (NFW) subhalos, showing confident boson star identification for 0.8 ≲ r/rE ≲ 3 and distinct recognition of NFW subhalos from point lenses under regular cadence.\"}]","Microlensing signatures of extended dark objects using machine learning | 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does the paper propose for detecting extended dark objects?","Question",{"text":75,"@type":76},"It proposes a machine learning-based analysis pipeline that identifies gravitational microlensing signatures of extended dark-matter objects in time-series data.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does MicroLIA support this work?",{"text":80,"@type":76},"It adapts MicroLIA, a machine learning package designed to handle the challenges of low-cadence data common in microlensing surveys.",{"name":82,"@type":73,"acceptedAnswer":83},"Which extended objects are studied and what identification ranges are reported?",{"text":84,"@type":76},"The study focuses on boson stars and Navarro-Frenk-White (NFW) subhalos, showing confident boson star identification for 0.8 ≲ r/rE ≲ 3 and distinct recognition of NFW subhalos from point lenses under regular 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