SearcharxivSearch

arXiv subjects

Ryan O'Connor

Publications and source records attributed to Ryan O'Connor.

3 recordsLinked to original sources

Combining the Mass--Radius Posteriors of J0030+0451 Allowing for Unknown Model Systematics

The NASA Neutron star Interior Composition Explorer (\emph{NICER}) mission measures the X-ray pulse profiles of select millisecond pulsars and uses sophisticated pulse profile modeling (PPM) techniques to constrain their masses ($M$) and radii ($R$), in order to probe the state of matter in their interiors. One of the most studied pulsars, PSR J0030+0451, has been analyzed by multiple groups using different choices of hotspot models. The different choices of hotspot prescriptions to fit the same observational data led to different $M$--$R$ posteriors that do not completely agree with one another, resulting in a practical bottleneck for dense-matter equation-of-state (EoS) inference. In this paper, we adapt a robust Bayesian combination framework to the published $M$--$R$ posteriors of PSR J0030+0451 while allowing for unknown systematic uncertainties that might have led to the apparently divergent results. Using this technique, we combine eight existing $M$--$R$ posteriors into a single conservative and reproducible posterior that incorporates unknown model systematics across the currently available analyses and is suitable for direct use in EoS studies. The resulting constraint is $M = 1.46^{+0.09}_{-0.08}\,M_\odot$, $R = 12.69^{+0.64}_{-0.55}\,\mathrm{km}$, and compactness $C = 0.172^{+0.006}_{-0.007}$ (68\% credible interval). Incorporating this combined J0030+0451 constraint in an EoS-agnostic joint analysis with PSR~J0437--4715 and GW170817 yields $R_{1.4} = 11.98^{+0.58}_{-0.68}\,\mathrm{km}$ and $\Lambda_{1.4} = 320^{+216}_{-138}$. Our results provide a combined $M$--$R$ constraint for J0030+0451 and a practical framework for incorporating cross-model uncertainty into neutron star EoS inference pipelines.

astro-ph.HE

Extending Polaris to Support Transactions

In Polaris, we introduced a cloud-native distributed query processor to perform analytics at scale. In this paper, we extend the underlying Polaris distributed computation framework, which can be thought of as a read-only transaction engine, to execute general transactions (including updates, deletes, inserts and bulk loads, in addition to queries) for Tier 1 warehousing workloads in a highly performant and predictable manner. We take advantage of the immutability of data files in log-structured data stores and build on SQL Server transaction management to deliver full transactional support with Snapshot Isolation semantics, including multi-table and multi-statement transactions. With the enhancements described in this paper, Polaris supports both query processing and transactions for T-SQL in Microsoft Fabric.

cs.DB

A Cleanroom in a Glovebox

The exploration of new materials, novel quantum phases, and devices requires ways to prepare cleaner samples with smaller feature sizes. Initially, this meant the use of a cleanroom that limits the amount and size of dust particles. However, many materials are highly sensitive to oxygen and water in the air. Furthermore, the ever-increasing demand for a quantum workforce, trained and able to use the equipment for creating and characterizing materials, calls for a dramatic reduction in the cost to create and operate such facilities. To this end, we present our cleanroom-in-a-glovebox, a system which allows for the fabrication and characterization of devices in an inert argon atmosphere. We demonstrate the ability to perform a wide range of characterization as well as fabrication steps, without the need for a dedicated room, all in an argon environment. Connection to a vacuum suitcase is also demonstrated to enable receiving from and transfer to various ultra-high vacuum (UHV) equipment including molecular-beam epitaxy (MBE) and scanning tunneling microscopy (STM).

physics.app-ph