Zobrazeno 1 - 10
of 20 898
pro vyhledávání: '"A. Riess"'
Boubel et al. 2024 (B24) recently used the Tully-Fisher (TF) relation to measure calibrated distances in the Hubble flow and found $H_0= 73.3 \pm 2.1 (stat) \pm 3.5 (sys)$ km/s/Mpc. The large systematic uncertainty was the result of propagating the c
Externí odkaz:
http://arxiv.org/abs/2412.08449
Autor:
Kudritzki, Rolf-Peter, Urbaneja, Miguel A., Bresolin, Fabio, Macri, Lucas M., Yuan, Wenlong, Li, Siyang, Anand, Gagandeep S., Riess, Adam G.
A quantitative spectroscopic study of blue supergiant stars in the Hubble constant anchor galaxy NGC 4258 is presented. The non-LTE analysis of Keck I telescope LRIS spectra yields a central logarithmic metallicity (in units of the solar value) of [Z
Externí odkaz:
http://arxiv.org/abs/2411.07974
Autor:
Konti, Xenia, Riess, Hans, Giannopoulos, Manos, Shen, Yi, Pencina, Michael J., Economou-Zavlanos, Nicoleta J., Zavlanos, Michael M.
In this paper, we address the challenge of heterogeneous data distributions in cross-silo federated learning by introducing a novel algorithm, which we term Cross-silo Robust Clustered Federated Learning (CS-RCFL). Our approach leverages the Wasserst
Externí odkaz:
http://arxiv.org/abs/2410.07039
Autor:
Scolnic, Daniel, Riess, Adam G., Murakami, Yukei S., Peterson, Erik R., Brout, Dillon, Acevedo, Maria, Carreres, Bastien, Jones, David O., Said, Khaled, Howlett, Cullan, Anand, Gagandeep S.
The Dark Energy Spectroscopic Instrument (DESI) collaboration measured a tight relation between the Hubble constant ($H_0$) and the distance to the Coma cluster using the fundamental plane (FP) relation of the deepest, most homogeneous sample of earl
Externí odkaz:
http://arxiv.org/abs/2409.14546
Autor:
Peterson, Erik R., Carreres, Bastien, Carr, Anthony, Scolnic, Daniel, Bailey, Ava, Davis, Tamara M., Brout, Dillon, Howlett, Cullan, Jones, David O., Riess, Adam G., Said, Khaled, Taylor, Georgie
At the low-redshift end ($z<0.05$) of the Hubble diagram with Type Ia Supernovae (SNe Ia), the contribution to Hubble residual scatter from peculiar velocities is of similar size to that due to the standardization of the SN Ia light curve. A way to i
Externí odkaz:
http://arxiv.org/abs/2408.14560
Autor:
Riess, Adam G., Scolnic, Dan, Anand, Gagandeep S., Breuval, Louise, Casertano, Stefano, Macri, Lucas M., Li, Siyang, Yuan, Wenlong, Huang, Caroline D., Jha, Saurabh, Murakami, Yukei S., Beaton, Rachael, Brout, Dillon, Wu, Tianrui, Addison, Graeme E., Bennett, Charles, Anderson, Richard I., Filippenko, Alexei V., Carr, Anthony
JWST provides new opportunities to cross-check the HST Cepheid/SNeIa distance ladder, which yields the most precise local measure of H0. We analyze early JWST subsamples (~1/4 of the HST sample) from the SH0ES and CCHP groups, calibrated by a single
Externí odkaz:
http://arxiv.org/abs/2408.11770
Image-based biometrics can aid law enforcement in various aspects, for example in iris, fingerprint and soft-biometric recognition. A critical precondition for recognition is the availability of sufficient biometric information in images. It is visua
Externí odkaz:
http://arxiv.org/abs/2408.10823
Autor:
Li, Siyang, Anand, Gagandeep S., Riess, Adam G., Casertano, Stefano, Yuan, Wenlong, Breuval, Louise, Macri, Lucas M., Scolnic, Daniel, Beaton, Rachael, Anderson, Richard I.
Publikováno v:
ApJ 976 177 2024
The Hubble Tension, a >5 sigma discrepancy between direct and indirect measurements of the Hubble constant (H0), has persisted for a decade and motivated intense scrutiny of the paths used to infer H0. Comparing independently-derived distances for a
Externí odkaz:
http://arxiv.org/abs/2408.00065
Autor:
Daum, Deniz, Osuala, Richard, Riess, Anneliese, Kaissis, Georgios, Schnabel, Julia A., Di Folco, Maxime
Generally, the small size of public medical imaging datasets coupled with stringent privacy concerns, hampers the advancement of data-hungry deep learning models in medical imaging. This study addresses these challenges for 3D cardiac MRI images in t
Externí odkaz:
http://arxiv.org/abs/2407.16405
Autor:
Osuala, Richard, Lang, Daniel M., Riess, Anneliese, Kaissis, Georgios, Szafranowska, Zuzanna, Skorupko, Grzegorz, Diaz, Oliver, Schnabel, Julia A., Lekadir, Karim
Deep learning holds immense promise for aiding radiologists in breast cancer detection. However, achieving optimal model performance is hampered by limitations in availability and sharing of data commonly associated to patient privacy concerns. Such
Externí odkaz:
http://arxiv.org/abs/2407.12669