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Black Hole

Lensing of space time around a black hole. At Oxford we study black holes observationally and theoretically on all size and time scales - it is some of our core work.

Credit: ALAIN RIAZUELO, IAP/UPMC/CNRS. CLICK HERE TO VIEW MORE IMAGES.

Dr Chiara Spiniello

Ernest Rutherford Fellow

Research theme

  • Astronomy and astrophysics

Sub department

  • Astrophysics

Research groups

  • Galaxy formation and evolution
  • Hintze Centre for Astrophysical Surveys
  • Rubin-LSST
chiara.spiniello@physics.ox.ac.uk
Telephone: 0865 273309
Denys Wilkinson Building, room 562
Chiara's Website
  • About
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  • Prizes, awards and recognition
  • Publications
The INvestigating Stellar Population In RElics

an ESO Observational Large Program (ID: 1104.B-0370, PI: C. Spiniello) with the X-Shooter spectrograph at the ESO Very Large Telescope targeting "Relic Galaxies", the ancient fossil of the early Universe

INSPIRE

E-INSPIRE - I. Bridging the gap with the local Universe: Stellar population of a statistical sample of ultra-compact massive galaxies at z < 0.3

Monthly Notices of the Royal Astronomical Society Oxford University Press (OUP) (2025) staf516

Authors:

John Mills, Chiara Spiniello, Alexey Sergeyev, Crescenzo Tortora, Vladyslav Khramtsov, Giuseppe D’Ago, Michalina Maksymowicz-Maciata, João PV Benedetti, Anna Ferré-Mateu, Michele Cappellari, Roger Davies, Johanna Hartke, Charles Rosen
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Looking into the faintEst WIth MUSE (LEWIS): Exploring the nature of ultra-diffuse galaxies in the Hydra I cluster

Astronomy & Astrophysics EDP Sciences 695 (2025) a91

Authors:

J Hartke, E Iodice, M Gullieuszik, M Mirabile, C Buttitta, G Doll, G D’Ago, CC de la Casa, KM Hess, R Kotulla, B Poggianti, M Arnaboldi, M Cantiello, EM Corsini, J Falcón-Barroso, DA Forbes, M Hilker, S Mieske, M Rejkuba, M Spavone, C Spiniello
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Looking into the faintEst WIth MUSE (LEWIS): Exploring the nature of ultra-diffuse galaxies in the Hydra-I cluster

Astronomy & Astrophysics EDP Sciences 694 (2025) a276

Authors:

Chiara Buttitta, Enrichetta Iodice, Goran Doll, Johanna Hartke, Michael Hilker, Duncan A Forbes, Enrico M Corsini, Luca Rossi, Magda Arnaboldi, Michele Cantiello, Giuseppe D’Ago, Jesus Falcón-Barroso, Marco Gullieuszik, Antonio La Marca, Steffen Mieske, Marco Mirabile, Maurizio Paolillo, Marina Rejkuba, Marilena Spavone, Chiara Spiniello, Marc Sarzi
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Strong Lensing by Galaxies

Space Science Reviews Springer 220:8 (2024) 87

Authors:

AJ Shajib, G Vernardos, TE Collett, V Motta, D Sluse, LLR Williams, P Saha, S Birrer, C Spiniello, T Treu

Abstract:

Strong gravitational lensing at the galaxy scale is a valuable tool for various applications in astrophysics and cosmology. Some of the primary uses of galaxy-scale lensing are to study elliptical galaxies’ mass structure and evolution, constrain the stellar initial mass function, and measure cosmological parameters. Since the discovery of the first galaxy-scale lens in the 1980s, this field has made significant advancements in data quality and modeling techniques. In this review, we describe the most common methods for modeling lensing observables, especially imaging data, as they are the most accessible and informative source of lensing observables. We then summarize the primary findings from the literature on the astrophysical and cosmological applications of galaxy-scale lenses. We also discuss the current limitations of the data and methodologies and provide an outlook on the expected improvements in both areas in the near future.
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Retrieval of the physical parameters of galaxies from WEAVE-StePS-like data using machine learning

Astronomy and Astrophysics EDP Sciences 690 (2024) A198

Authors:

J Angthopo, B Granett, F La Barbera, M Longhetti, A Iovino, M Fossati, Chiara Spiniello, Gavin Dalton, S Jin

Abstract:

Context

The William Herschel Telescope Enhanced Area Velocity Explorer (WEAVE) is a new, massively multiplexing spectrograph that allows us to collect about one thousand spectra over a 3 square degree field in one observation. The WEAVE Stellar Population Survey (WEAVE-StePS) in the next 5 years will exploit this new instrument to obtain high-S/N spectra for a magnitude-limited (IAB = 20.5) sample of ∼25 000 galaxies at moderate redshifts (z ≥ 0.3), providing insights into galaxy evolution in this as yet unexplored redshift range.

Aims

We aim to test novel techniques for retrieving the key physical parameters of galaxies from WEAVE-StePS spectra using both photometric and spectroscopic (spectral indices) information for a range of noise levels and redshift values.

Methods

We simulated ∼105 000 galaxy spectra assuming star formation histories with an exponentially declining star formation rate, covering a wide range of ages, stellar metallicities, specific star formation rates (sSFRs), and dust extinction values. We considered three redshifts (i.e. z = 0.3, 0.55, and 0.7), covering the redshift range that WEAVE-StePS will observe. We then evaluated the ability of the random forest and K-nearest neighbour algorithms to correctly predict the average age, metallicity, sSFR, dust attenuation, and time since the bulk of formation, assuming no measurement errors. We also checked how much the predictive ability deteriorates for different noise levels, with S/NI,obs = 10, 20, and 30, and at different redshifts. Finally, the retrieved sSFR was used to classify galaxies as part of the blue cloud, green valley, or red sequence.

Results

We find that both the random forest and K-nearest neighbour algorithms accurately estimate the mass-weighted ages, u-band-weighted ages, and metallicities with low bias. The dispersion varies from 0.08–0.16 dex for age and 0.11–0.25 dex for metallicity, depending on the redshift and noise level. For dust attenuation, we find a similarly low bias and dispersion. For the sSFR, we find a very good constraining power for star-forming galaxies, log sSFR ≳ −11, where the bias is ∼0.01 dex and the dispersion is ∼0.10 dex. However, for more quiescent galaxies, with log sSFR ≲ −11, we find a higher bias, ranging from 0.61 to 0.86 dex, and a higher dispersion, ∼0.4 dex, depending on the noise level and redshift. In general, we find that the random forest algorithm outperforms the K-nearest neighbours. Finally, we find that the classification of galaxies as members of the green valley is successful across the different redshifts and S/Ns.

Conclusions

We demonstrate that machine learning algorithms can accurately estimate the physical parameters of simulated galaxies for a WEAVE-StePS-like dataset, even at relatively low S/NI, obs = 10 per Å spectra with available ancillary photometric information. A more traditional approach, Bayesian inference, yields comparable results. The main advantage of using a machine learning algorithm is that, once trained, it requires considerably less time than other methods.
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