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  1. ABSTRACT We have measured the wavelength-dependent lags between the X-ray, ultraviolet, and optical bands in the high-accretion rate ($L/L_{\rm Edd}\approx 40{{\ \rm per\ cent}}$) active galactic nucleus (AGN) Mrk 110 during two intensive monitoring campaigns in February and September 2019. After including the 2017 data published by Vincentelli et al., we divided the observations into three intervals with different X-ray luminosities. The first interval has the lowest X-ray luminosity and did not exhibit the U-band excess positive lag, or the X-ray excess negative lag that is seen in most AGNs. However, these excess lags are seen in the two subsequent intervalsmore »of higher X-ray luminosity. Although the data are limited, the excess lags appear to scale with X-ray luminosity. Our modelling shows that lags expected from reprocessing of X-rays by the accretion disc vary hardly at all with increasing luminosity. Therefore, as the U-band excess almost certainly arises from Balmer-continuum emission from the broad-line region (BLR), we attribute these lag changes to changes in the contribution from the BLR. The change is easily explained by the usual increase in the inner radius of the BLR with increasing ionizing luminosity.« less
    Free, publicly-accessible full text available March 4, 2023
  2. Brandt, M. ; Bauer, P. (Ed.)
    Past work has suggested that people prescribe optimism—believing it is better to be optimistic, instead of accurate or pessimistic, about uncertain future events. Here, we identified and addressed an important ambiguity about whether those findings reflect an endorsement of biased beliefs—i.e., whether people prescribe likelihood estimates that reflect overoptimism. In three studies, participants (total N = 663 U.S. university students) read scenarios about protagonists facing uncertain events with a desired outcome. Results replicated prescriptions of optimism when using the same solicitations as in past work. However, we found quite different prescriptions when using alternative solicitations that asked about potential biasmore »in likelihood estimations and that did not involve vague terms like “optimistic.” Participants generally prescribed being optimistic, feeling optimistic, and even thinking optimistically about the events, but they did not prescribe overestimating the likelihood of those events.« less
  3. ABSTRACT We present the first intensive continuum reverberation mapping study of the high accretion-rate Seyfert galaxy Mrk 110. The source was monitored almost daily for more than 200 d with the Swift X-ray and ultraviolet (UV)/optical telescopes, supported by ground-based observations from Las Cumbres Observatory, the Liverpool Telescope, and the Zowada Observatory, thus extending the wavelength coverage to 9100 Å. Mrk 110 was found to be significantly variable at all wavebands. Analysis of the intraband lags reveals two different behaviours, depending on the time-scale. On time-scales shorter than 10 d the lags, relative to the shortest UV waveband (∼1928 Å), increase with increasing wavelength up tomore »a maximum of ∼2 d lag for the longest waveband (∼9100 Å), consistent with the expectation from disc reverberation. On longer time-scales, however, the g-band lags the Swift BAT hard X-rays by ∼10 d, with the z-band lagging the g-band by a similar amount, which cannot be explained in terms of simple reprocessing from the accretion disc. We interpret this result as an interplay between the emission from the accretion disc and diffuse continuum radiation from the broad-line region.« less
  4. Kernel dimensionality reduction (KDR) algorithms find a low dimensional representation of the original data by optimizing kernel dependency measures that are capable of capturing nonlinear relationships. The standard strategy is to first map the data into a high dimensional feature space using kernels prior to a projection onto a low dimensional space. While KDR methods can be easily solved by keeping the most dominant eigenvectors of the kernel matrix, its features are no longer easy to interpret. Alternatively, Interpretable KDR (IKDR) is different in that it projects onto a subspace \textit{before} the kernel feature mapping, therefore, the projection matrix canmore »indicate how the original features linearly combine to form the new features. Unfortunately, the IKDR objective requires a non-convex manifold optimization that is difficult to solve and can no longer be solved by eigendecomposition. Recently, an efficient iterative spectral (eigendecomposition) method (ISM) has been proposed for this objective in the context of alternative clustering. However, ISM only provides theoretical guarantees for the Gaussian kernel. This greatly constrains ISM's usage since any kernel method using ISM is now limited to a single kernel. This work extends the theoretical guarantees of ISM to an entire family of kernels, thereby empowering ISM to solve any kernel method of the same objective. In identifying this family, we prove that each kernel within the family has a surrogate Φ matrix and the optimal projection is formed by its most dominant eigenvectors. With this extension, we establish how a wide range of IKDR applications across different learning paradigms can be solved by ISM. To support reproducible results, the source code is made publicly available on \url{https://github.com/ANONYMIZED}« less