arXiv subjects

G. Valdes

Publications and source records attributed to G. Valdes.

4 recordsLinked to original sources

arXiv2021

Environmental Noise in Advanced LIGO Detectors

The sensitivity of the Advanced LIGO detectors to gravitational waves can be affected by environmental disturbances external to the detectors themselves. Since the transition from the former initial LIGO phase, many improvements have been made to the equipment and techniques used to investigate these environmental effects. These methods have aided in tracking down and mitigating noise sources throughout the first three observing runs of the advanced detector era, keeping the ambient contribution of environmental noise below the background noise levels of the detectors. In this paper we describe the methods used and how they have led to the mitigation of noise sources, the role that environmental monitoring has played in the validation of gravitational wave events, and plans for future observing runs.

astro-ph.IM↗

arXiv2021

LIGO Detector Characterization in the Second and Third Observing Runs

The characterization of the Advanced LIGO detectors in the second and third observing runs has increased the sensitivity of the instruments, allowing for a higher number of detectable gravitational-wave signals, and provided confirmation of all observed gravitational-wave events. In this work, we present the methods used to characterize the LIGO detectors and curate the publicly available datasets, including the LIGO strain data and data quality products. We describe the essential role of these datasets in LIGO-Virgo Collaboration analyses of gravitational-waves from both transient and persistent sources and include details on the provenance of these datasets in order to support analyses of LIGO data by the broader community. Finally, we explain anticipated changes in the role of detector characterization and current efforts to prepare for the high rate of gravitational-wave alerts and events in future observing runs.

astro-ph.IM↗

arXiv2021

A search for a 5th planet around HR 8799 using the star-hopping RDI technique at VLT/SPHERE

The direct imaging of extrasolar giant planets demands the highest possible contrasts (dH ~10 magnitudes) at the smallest angular separations (~0.1'') from the star. We present an adaptive optics observing method, called star-hopping, recently offered as standard queue observing for the SPHERE instrument at the VLT. The method uses reference difference imaging (RDI) but unlike earlier works, obtains images of a reference star for PSF subtraction, within minutes of observing the target star. We aim to significantly gain in contrast over the conventional angular differencing imaging (ADI) method, to search for a fifth planet at separations less than 10 au, interior to the four giant planets of the HR 8799 system. We obtained a total of 4.5 hours of simultaneous integral field spectroscopy (R~30, Y-H band with IFS) and dual-band imaging (K1 and K2-band with IRDIS) of the HR 8799 system and a reference star. The reference star was observed for ~1/3 of the total time, and should have dR~1 mag and separated on sky by ~1-2 deg. The star hops were made every 6-10 minutes, with only 1 minute gaps in on-sky integration per hop. We did not detect the hypothetical fifth planet at the most plausible separations, 7.5 and 9.7 au, down to mass limits of 3.6 MJup high signal-to-noise ratios. As noted in previous works, the planet spectra are matched very closely by some red field dwarfs. We also demonstrated that with star-hopping RDI, the contrast improvement at 0.1'' separation can be up to 2 magnitudes. Since ADI, meridian transit and the concomitant sky rotation are not needed, the time of observation can be chosen from within a 2-3 times larger window. In general, star-hopping can be used for stars fainter than R=4 magnitudes, since for these a reference star of suitable brightness and separation is usually available. The reduction software used in this paper has been made available online.

astro-ph.EP↗

arXiv2021

Expert-Augmented Machine Learning

Machine Learning is proving invaluable across disciplines. However, its success is often limited by the quality and quantity of available data, while its adoption by the level of trust that models afford users. Human vs. machine performance is commonly compared empirically to decide whether a certain task should be performed by a computer or an expert. In reality, the optimal learning strategy may involve combining the complementary strengths of man and machine. Here we present Expert-Augmented Machine Learning (EAML), an automated method that guides the extraction of expert knowledge and its integration into machine-learned models. We use a large dataset of intensive care patient data to predict mortality and show that we can extract expert knowledge using an online platform, help reveal hidden confounders, improve generalizability on a different population and learn using less data. EAML presents a novel framework for high performance and dependable machine learning in critical applications.

stat.ML↗