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Measuring the Unseen Universe with Statistical Vision : = Strong Lensing as a Probe of Small-Scale Structure.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Measuring the Unseen Universe with Statistical Vision :/
其他題名:	Strong Lensing as a Probe of Small-Scale Structure.
作者:	Wagner-Carena, Sebastian Matthias.
面頁冊數:	1 online resource (150 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-04, Section: B.
Contained By:	Dissertations Abstracts International85-04B.
標題:	Stars & galaxies. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30614633click for full text (PQDT)
ISBN:	9798380469951

Measuring the Unseen Universe with Statistical Vision : = Strong Lensing as a Probe of Small-Scale Structure.
Wagner-Carena, Sebastian Matthias.

Measuring the Unseen Universe with Statistical Vision :Strong Lensing as a Probe of Small-Scale Structure. - 1 online resource (150 pages)

Source: Dissertations Abstracts International, Volume: 85-04, Section: B.

Thesis (Ph.D.)--Stanford University, 2023.

Includes bibliographical references

For decades, modern cosmology has held that the majority of the matter in our Universe is cold, collisionless dark matter (CDM). Many of our dark matter theories impose scales at which the CDM paradigm breaks down, mainly by changing the distribution of collapsed structures (halos) at low masses. An investigative priority of modern astrophysics has been searching for these violations of CDM predictions. Probing dark matter at these scales is challenging; dark matter halos are traditionally traced by the galaxies they host, but at low masses, we do not fully understand the connection between halos and galaxies. However, strong gravitational lenses are sensitive to lowmass halos even if they host no galaxies. In this thesis, I develop the statistical tools that allow us to use strong lenses to measure the small-scale, dark matter structure underlying our Universe. I present work that leverages neural networks to produce posterior distributions for the parameters underlying strong gravitational lensing images. This work includes the development of a hierarchical inference framework that corrects for the implicit prior encoded into the network by the training distribution. After showing that we can use the technique to constrain the population statistics of lenses without low-mass halos, I present work that extends the methodology to measurements of the subhalo mass function (SHMF). With the aid of new simulation tools, the results demonstrate that we can reliably infer the SHMF across disparate configurations of hundreds of lenses. I then discuss the improvements that can be made as the methodology is extended to the data. I conclude by outlining the physics that can be measured with this new set of tools. I argue that the advances in this thesis serve as a foundation for turning strong gravitational lenses into a sensitive probe of dark matter physics.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798380469951Subjects--Topical Terms:

3683661
Stars & galaxies.
Index Terms--Genre/Form:

542853
Electronic books.

Measuring the Unseen Universe with Statistical Vision : = Strong Lensing as a Probe of Small-Scale Structure.
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504 $a Includes bibliographical references
520 $a For decades, modern cosmology has held that the majority of the matter in our Universe is cold, collisionless dark matter (CDM). Many of our dark matter theories impose scales at which the CDM paradigm breaks down, mainly by changing the distribution of collapsed structures (halos) at low masses. An investigative priority of modern astrophysics has been searching for these violations of CDM predictions. Probing dark matter at these scales is challenging; dark matter halos are traditionally traced by the galaxies they host, but at low masses, we do not fully understand the connection between halos and galaxies. However, strong gravitational lenses are sensitive to lowmass halos even if they host no galaxies. In this thesis, I develop the statistical tools that allow us to use strong lenses to measure the small-scale, dark matter structure underlying our Universe. I present work that leverages neural networks to produce posterior distributions for the parameters underlying strong gravitational lensing images. This work includes the development of a hierarchical inference framework that corrects for the implicit prior encoded into the network by the training distribution. After showing that we can use the technique to constrain the population statistics of lenses without low-mass halos, I present work that extends the methodology to measurements of the subhalo mass function (SHMF). With the aid of new simulation tools, the results demonstrate that we can reliably infer the SHMF across disparate configurations of hundreds of lenses. I then discuss the improvements that can be made as the methodology is extended to the data. I conclude by outlining the physics that can be measured with this new set of tools. I argue that the advances in this thesis serve as a foundation for turning strong gravitational lenses into a sensitive probe of dark matter physics.
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