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Prerequisites include deepSIP, convolutional neural networks that infer phase and light-curve shape from single spectra, enabling precise SDM distances. The thesis applies SDM in a cosmological study of peculiar velocities and bulk flows.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/trash-to-treasure-extracting-cosmological-utility-from-sparsely-observed-type-ia-supernovae/505133/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/trash-to-treasure-extracting-cosmological-utility-from-sparsely-observed-type-ia-supernovae/505133.png","ImageObject",300,407,{"name":42,"@type":43},"Eliana","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-22","2026-10-04",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",11,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"What cosmological problem motivates this thesis about Type Ia supernovae?","Question",{"text":63,"@type":64},"Type Ia supernovae are valuable for cosmology, but sparse data limit conventional distance estimation. The thesis aims to extract reliable cosmological utility from such sparsely observed events.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"How does deepSIP enable distance estimation with limited observations?",{"text":68,"@type":64},"deepSIP uses three convolutional neural networks trained on publicly available SN Ia optical data to determine an SN Ia’s phase and light-curve shape from a single optical spectrum.",{"name":70,"@type":61,"acceptedAnswer":71},"What is the snapshot distance method (SDM) and how is it used?",{"text":72,"@type":64},"SDM estimates an SN Ia distance using an optical spectrum and only one epoch of multi-passband photometry. 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Filippenko, Co-chair Associate Professor Daniel Kasen, Co-chair Professor Saul Perlmutter Professor Uro􀀔s Seljak  \nSpring 2021  \nTrash to Treasure:  \nExtracting Cosmological Utility from Sparsely Observed Type Ia Supernovae  \nCopyright 2021  \nby  \nBenjamin Ernest Stahl  \n1  \nAbstract  \nTrash to Treasure:  \nExtracting Cosmological Utility from Sparsely Observed Type Ia Supernovae  \nby  \nBenjamin Ernest Stahl  \nDoctor of Philosophy in Physics  \nUniversity of California, Berkeley  \nProfessor Alexei V. Filippenko, Co-chair  \nAssociate Professor Daniel Kasen, Co-chair  \nType Ia supernovae (SNe Ia) are magni􀀌cent explosions in the Cosmos that are thought to result from the thermonuclear runaway of white dwarf stars in multistar systems (see, e.g. , Jha et al. 2019, for a recent review) . Though the exact details of the progenitor system(s) and explosion mechanism(s) remain elusive, SNe Ia have proven themselves to be immensely valuable in shaping our understanding of the physical laws that govern the evolution of the Universe (i.e., physical cosmology) . This value is manifested chie􀀍y in two empirical facts:(i) SNe Ia are incredibly luminous (reaching the equivalent of several billion Suns), and (ii) the relatively similar peak luminosities that all \\normal\" SNe Ia reach can be further homogenized by exploiting a correlation with the rate of photometric evolution (e.g., Phillips 1993) . Together, these facts make SNe Ia excellent extragalactic distance indicators, and their use as such led to the discovery of the accelerating expansion of the Universe (Riesset al. 1998; Perlmutter et al. 1999) . Through this, the current cosmological paradigm came into favor | the so-called 􀀃CDM model, where the Universe consists primarily of repulsive dark energy (of which a leading candidate is Einstein's cosmological constant, 􀀃) and cold dark matter (CDM) .  \nIn this thesis, I present a comprehensive study that follows the entire SN Ia cosmology lifecyle, from data acquisition to cosmological analysis (albeit of a di􀀋erent 􀀍avor than those mentioned above) . While these \\bookends\" provide natural segmentation points in this thesis, there is a third, intermediate segment which serves to present a complementary method for SN Ia distance measurement that is far less data intensive than conventional approaches. In this way, the segments are hierarchical, each depending on its predecessor and enabling its successor.  \nAfter appropriately setting the stage in Chapter 1, I delve into the 􀀌rst segment (data  \n2  \nacquisition) with Chapter 2, a data release and analysis of 93 multipassband SN Ia lightcurves collected between 2005 and 2018, and Chapter 3, a complementary release of 637 lowredshift SN Ia optical spectra from a similar time interval. In both, I describe open-source software I developed for data processing and analysis purposes, and make | in addition to the data themselves | useful, value-added data products (e.g., 􀀌tted parameters from lightcurves) available to the community. When combined with prior releases, the Berkeley SN Ia sample now reaches nearly 2000 optical spectra and more than 250 multiband light cur","cbCaiiG8WmvKTkhN","https://ap.wps.com/l/cbCaiiG8WmvKTkhN","pdf",7457744,211,"English","# Abstract\n## Thesis motivation and background\n## Dissertation structure and data releases\n## deepSIP: phase and light-curve inference from spectra\n## Snapshot distance method (SDM) for sparse observations\n## Cosmological analysis: peculiar velocities and bulk flows","[{\"question\":\"What cosmological problem motivates this thesis about Type Ia supernovae?\",\"answer\":\"Type Ia supernovae are valuable for cosmology, but sparse data limit conventional distance estimation. The thesis aims to extract reliable cosmological utility from such sparsely observed events.\"},{\"question\":\"How does deepSIP enable distance estimation with limited observations?\",\"answer\":\"deepSIP uses three convolutional neural networks trained on publicly available SN Ia optical data to determine an SN Ia’s phase and light-curve shape from a single optical spectrum.\"},{\"question\":\"What is the snapshot distance method (SDM) and how is it used?\",\"answer\":\"SDM estimates an SN Ia distance using an optical spectrum and only one epoch of multi-passband photometry. The thesis validates SDM precision across SN Ia parameters and applies it to constrain peculiar velocities and bulk flows.\"}]","Trash to Treasure - Extracting Cosmological Utility from Sparsely Observed Type Ia Supernovae | PDF",1791091395,532]