About

Jordan Gerard Starck is an Assistant Professor of Psychology at Stanford University. His research focuses on social psychology topics including racial bias, moralization processes, diversity, and legal decision-making. He investigates how messages and social group values influence moral perceptions and attitudes related to consent, diversity, and racial advantage. His work includes empirical studies on the moralization of consent at college parties, attitudes supporting the racial status quo in higher education, and implicit biases within criminal defense attorneys. Starck's contributions aim to deepen understanding of the psychological underpinnings of social and legal issues, emphasizing the importance of locally tailored messages and the influence of dominant group preferences on diversity practices.

Research topics

• Data Mining
• Artificial Intelligence
• Computer Science
• Algorithm
• Programming language
• Astrophysics
• Physics

Selected publications

• <i>Euclid</i> preparation

  Astronomy and Astrophysics · 2026-01-06

  preprintOpen access

  This is the second paper in the HOWLS (higher-order weak lensing statistics) series exploring the usage of non-Gaussian statistics for cosmology inference within Euclid . With respect to our first paper, we develop a full tomographic analysis based on realistic photometric redshifts that allows us to derive Fisher forecasts in the ( σ 8 , w 0 ) plane for a Euclid -like data release 1 (DR1) setup. We find that the five higher-order statistics (HOS) that satisfy the Gaussian likelihood assumption of the Fisher formalism (one-point probability distribution function, ℓ 1-norm, peak counts, Minkowski functionals, and Betti numbers) each outperform the shear two-point correlation functions by a factor of 2.5 on the w 0 forecasts, with only marginal improvement when used in combination with two-point estimators, suggesting that every HOS is able to retrieve both the non-Gaussian and Gaussian information of the matter density field. The similar performance of the different estimators is explained by a homogeneous use of multi-scale and tomographic information, optimized to lower computational costs. These results hold for the three mass mapping techniques of the Euclid pipeline, aperture mass, Kaiser–Squires, and Kaiser–Squires plus, and they are unaffected by the application of realistic star masks. Finally, we explored the use of HOS with the Bernardeau–Nishimichi–Taruya (BNT) nulling scheme approach, finding promising results toward applying physical scale cuts to HOS.

  PublisherDOI

• Generative modeling of convergence maps based on predicted one-point statistics

  HAL (Le Centre pour la Communication Scientifique Directe) · 2025-07-16

  preprint

  International audience

  Publisher

• Generative modelling of convergence maps based on predicted one-point statistics

  Astronomy and Astrophysics · 2025-08-06

  articleOpen access

  Context. Weak gravitational lensing is a key cosmological probe for current and future large-scale surveys. While power spectra are commonly used for analyses, they fail to capture non-Gaussian information from non-linear structure formation, which necessitates higher-order statistics and methods for an efficient map generation. Aims. We develop an emulator that generates accurate convergence ( κ ) maps directly from an input power spectrum and wavelet ℓ 1 -norm without relying on computationally intensive simulations. Methods. We used either numerical or theoretical predictions to construct κ maps by iteratively adjusting the wavelet coefficients to match the marginal distributions of the target and their inter-scale dependences by incorporating higher-order statistical information. Results. The resulting κ maps accurately reproduce the input power spectrum, and their higher-order statistical properties are consistent with the input predictions. They thus provide an efficient tool for weak-lensing analyses.

  PublisherDOI

• Joint multiband deconvolution for Euclid and Vera C. Rubin images

  Astronomy and Astrophysics · 2025-04-08

  articleOpen accessSenior author

  With the advent of surveys like Euclid and Vera C. Rubin, astrophysicists will have access to both deep, high-resolution images and multiband images. However, these two types are not simultaneously available in any single dataset. It is therefore vital to devise image deconvolution algorithms that exploit the best of both worlds and that can jointly analyze datasets spanning a range of resolutions and wavelengths. In this work we introduce a novel multiband deconvolution technique aimed at improving the resolution of ground-based astronomical images by leveraging higher-resolution space-based observations. The method capitalizes on the fortunate fact that the Rubin r, i , and z bands lie within the Euclid VIS band. The algorithm jointly de-convolves all the data to convert the r -, i -, and z -band Rubin images to the resolution of Euclid by leveraging the correlations between the different bands. We also investigate the performance of deep-learning-based denoising with DRUNet to further improve the results. We illustrate the effectiveness of our method in terms of resolution and morphology recovery, flux preservation, and generalization to different noise levels. This approach extends beyond the specific Euclid-Rubin combination, offering a versatile solution to improving the resolution of ground-based images in multiple photometric bands by jointly using any space-based images with overlapping filters.

  PublisherDOI

• Breaking the degeneracy in stellar spectral classification from single wide-band images

  arXiv (Cornell University) · 2025-01-27

  preprintOpen access

  The spectral energy distribution (SED) of observed stars in wide-field images is crucial for chromatic point spread function (PSF) modelling methods, which use unresolved stars as integrated spectral samples of the PSF across the field of view. This is particularly important for weak gravitational lensing studies, where precise PSF modelling is essential to get accurate shear measurements. Previous research has demonstrated that the SED of stars can be inferred from low-resolution observations using machine-learning classification algorithms. However, a degeneracy exists between the PSF size, which can vary significantly across the field of view, and the spectral type of stars, leading to strong limitations of such methods. We propose a new SED classification method that incorporates stellar spectral information by using a preliminary PSF model, thereby breaking this degeneracy and enhancing the classification accuracy. Our method involves calculating a set of similarity features between an observed star and a preliminary PSF model at different wavelengths and applying a support vector machine to these similarity features to classify the observed star into a specific stellar class. The proposed approach achieves a 91\% top-two accuracy, surpassing machine-learning methods that do not consider the spectral variation of the PSF. Additionally, we examined the impact of PSF modelling errors on the spectral classification accuracy.

  PublisherDOI

• Breaking the degeneracy in stellar spectral classification from single wide-band images

  Astronomy and Astrophysics · 2025-01-28

  articleOpen access

  The spectral energy distribution (SED) of observed stars in wide-field images is crucial for chromatic point spread function (PSF) modelling methods, which use unresolved stars as integrated spectral samples of the PSF across the field of view. This is particularly important for weak gravitational lensing studies, where precise PSF modelling is essential to get accurate shear measurements. Previous research has demonstrated that the SED of stars can be inferred from low-resolution observations using machine-learning classification algorithms. However, a degeneracy exists between the PSF size, which can vary significantly across the field of view, and the spectral type of stars, leading to strong limitations of such methods. We propose a new SED classification method that incorporates stellar spectral information by using a preliminary PSF model, thereby breaking this degeneracy and enhancing the classification accuracy. Our method involves calculating a set of similarity features between an observed star and a preliminary PSF model at different wavelengths and applying a support vector machine to these similarity features to classify the observed star into a specific stellar class. The proposed approach achieves a 91% top-two accuracy, surpassing machine-learning methods that do not consider the spectral variation of the PSF. Additionally, we examined the impact of PSF modelling errors on the spectral classification accuracy.

  PublisherOA PDFDOI

• Distribution-free uncertainty quantification for inverse problems: Application to weak lensing mass mapping

  Astronomy and Astrophysics · 2025-01-22

  articleOpen accessSenior author

  Aims. In inverse problems, the aim of distribution-free uncertainty quantification (UQ) is to obtain error bars in the reconstruction with coverage guarantees that are independent of any prior assumptions about the data distribution. This allows for a better understanding of how intermediate errors introduced during the process affect subsequent stages and ultimately influence the final reconstruction. In the context of mass mapping, uncertainties could lead to errors that affect how the underlying mass distribution is understood or that propagate to cosmological parameter estimation, thereby impacting the precision and reliability of cosmological models. Current surveys, such as Euclid or Rubin , will provide new weak lensing datasets of very high quality. Accurately quantifying uncertainties in mass maps is therefore critical to fully exploit their scientific potential and to perform reliable cosmological parameter inference. Methods. In this paper, we extend the conformalized quantile regression (CQR) algorithm, initially proposed for scalar regression, to inverse problems. We compared our approach with another distribution-free approach based on risk-controlling prediction sets (RCPS). Both methods are based on a calibration dataset, and they offer finite-sample coverage guarantees that are independent of the data distribution. Furthermore, they are applicable to any mass mapping method, including black box predictors. In our experiments, we applied UQ to three mass-mapping methods: the Kaiser-Squires inversion, iterative Wiener filtering, and the MCALens algorithm. Results. Our experiments reveal that RCPS tends to produce overconservative confidence bounds with small calibration sets, whereas CQR is designed to avoid this issue. Although the expected miscoverage rate is guaranteed to stay below a user-prescribed threshold regardless of the mass mapping method, selecting an appropriate reconstruction algorithm remains crucial for obtaining accurate estimates, especially around peak-like structures, which are particularly important for inferring cosmological parameters. Additionally, the choice of mass mapping method influences the size of the error bars.

  PublisherOA PDFDOI

• LCS: A learnlet-based sparse framework for blind source separation

  Astronomy and Astrophysics · 2025-12-12 · 1 citations

  articleOpen access

  Blind source separation (BSS) plays a pivotal role in modern astrophysics by enabling the extraction of scientifically meaningful signals from multi-frequency observations. Traditional BSS methods, such as those that rely on fixed wavelet dictionaries, enforce sparsity during component separation but can fall short when faced with the inherent complexity of real astrophysical signals. In this work, we introduce the learnlet component separator (LCS), a novel BSS framework that bridges classical sparsity-based techniques and modern deep learning. LCS utilises the learnlet transform – a structured convolutional neural network designed to serve as a learned, wavelet-like multi-scale representation. This hybrid design preserves the interpretability and sparsity-promoting properties of wavelets while gaining the adaptability and expressiveness of learned models. The LCS algorithm integrates this learned sparse representation into an iterative source separation process, enabling the effective decomposition of multi-channel observations. While conceptually inspired by sparse BSS methods, LCS introduces a learned representation layer that significantly departs from classical fixed-basis assumptions. We evaluated LCS on both synthetic and real datasets and in this paper demonstrate its superior separation performance compared to state-of-the-art methods (average gain of about 5 dB on toy model examples). Our results highlight the potential of hybrid approaches that combine signal processing priors with deep learning to address the challenges of next-generation cosmological experiments.

  PublisherDOI

• Impact of weak-lensing mass-mapping algorithms on cosmology inference

  Astronomy and Astrophysics · 2025-04-22 · 1 citations

  articleOpen access

  Context. Weak gravitational lensing is a powerful tool for probing the distribution of dark matter in the Universe. Mass-mapping algorithms, which reconstruct the convergence field from galaxy shear measurements, play a crucial role in extracting higher-order statistics from weak-lensing data to constrain cosmological parameters. However, only limited research has been done on whether the choice of mass-mapping algorithm affects the inference of cosmological parameters from weak-lensing higher-order statistics. Aims. This study aims to evaluate the impact of different mass-mapping algorithms on the inference of cosmological parameters measured with weak-lensing peak counts. Methods. We employed Kaiser-Squires, inpainting Kaiser-Squires, and MCALens mass-mapping algorithms to reconstruct the convergence field from simulated weak-lensing data, generated from cosmo-SLICS simulations. Using these maps, we computed the peak counts and multi-scale wavelet peak counts as our data vectors. We performed Bayesian analysis with Markov chain Monte Carlo sampling to estimate the posterior distributions of cosmological parameters, including the matter density, amplitude of matter fluctuations, and dark energy equation of state parameter. Results. Our results indicate that the choice of mass-mapping algorithm significantly affects the constraints on cosmological parameters, with the MCALens method improving constraints by up to 157% compared to the standard Kaiser-Squires method. This improvement arises from MCALens’s ability to better capture small-scale structures. In contrast, inpainting Kaiser-Squires yields constraints similar to Kaiser-Squires, indicating a limited benefit from inpainting for cosmological parameter estimation with peaks. Conclusions. The accuracy of mass-mapping algorithms is critical for cosmological inference from weak-lensing data. Advanced algorithms like MCALens, which offer superior reconstruction of the convergence field, can substantially enhance the precision of cosmological parameter estimates. These findings underscore the importance of selecting appropriate mass-mapping techniques in weak-lensing studies to fully exploit the potential of higher-order statistics for cosmological research.

  PublisherDOI

• Foreground removal in HI 21 cm intensity mapping under frequency-dependent beam distortions

  arXiv (Cornell University) · 2025-11-17

  preprintOpen access

  Neutral hydrogen (HI) intensity mapping with single-dish experiments is a powerful approach for probing cosmology in the post-reionization epoch. However, the presence of bright foregrounds over four orders of magnitude stronger than the HI signal makes its extraction highly challenging. While all methods perform well when assuming a Gaussian beam degraded to the worst resolution, most of them degrade significantly in the presence of a more realistic beam model. In this work, we investigate the performance of SDecGMCA. This method extends DecGMCA to spherical data, combining sparse component separation with beam deconvolution. Our goal is to evaluate this method in comparison with established foreground removal techniques, assessing its ability to recover the cosmological HI signal from single-dish intensity mapping observations under varying beam conditions. We use simulated HI signal and foregrounds, covering the frequency ranges relevant to MeerKAT and SKA-Mid. The foreground removal techniques tested fall into two main categories: model-fitting methods (polynomial and parametric) and blind source separation methods (PCA, ICA, GMCA, and SDecGMCA). Their effectiveness is evaluated based on the recovery of the HI angular and frequency power spectra under progressively more realistic beam conditions. While all methods perform adequately under a uniform degraded beam, SDecGMCA remains robust when frequency-dependent beam distortions are introduced. In the oscillating beam case, SDecGMCA suppresses the spurious spectral peak at $k_ν\sim 0.3$ and achieves $\lesssim 5\%$ accuracy at intermediate angular scales ($10 &lt; \ell &lt; 200$), outperforming other methods. Beam inversion, however, remains intrinsically unstable beyond $\ell \sim 200$, setting a practical limit on the method.

  PublisherOA PDFDOI

Frequent coauthors

• J.-F. Cardoso

  2946 shared

• F. Sureau

  Commissariat à l'Énergie Atomique et aux Énergies Alternatives

  2560 shared

• G. de Zotti

  Osservatorio Astronomico di Padova

  2273 shared

• A. Chamballu

  Université Paris Cité

  2237 shared

• J. F. Macías–Pérez

  Laboratoire de Physique Subatomique et de Cosmologie

  2164 shared

• M. Arnaud

  Astrophysique, Instrumentation et Modélisation

  2115 shared

• A. Catalano

  Institut polytechnique de Grenoble

  2034 shared

• M. Remazeilles

  2016 shared

Labs

• Vice Provost for Student AffairsPI