Machine learning synthetic spectra for probabilistic redshift estimation: SYTH-Z

Ramachandra, Nesar; Chaves-Montero, Jonás; Alarcón, A.; Fadikar, Arindam; Habib, Salman; Heitmann, Katrin

doi:10.1093/mnras/stac1790

Cited by 6 publications

(3 citation statements)

References 69 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The origin of such differences could be due to limitations in computational power, the limited power of some statistical technique applied, our ignorance about the exact state of a detector, or our inability to correctly model certain physics processes accurately within the simulation. The nature of the systematic uncertainty informs the appropriate mitigation/quantification mechanism [55].…”

Section: Bias Mitigationmentioning

confidence: 99%

Interpretable Uncertainty Quantification in AI for HEP

Chen¹,

Dey²,

Ghosh³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Estimating uncertainty is at the core of performing scientific measurements in HEP: a measurement is not useful without an estimate of its uncertainty. The goal of uncertainty quantification (UQ) is inextricably linked to the question, "how do we physically and statistically interpret these uncertainties?" The answer to this question depends not only on the computational task we aim to undertake, but also on the methods we use for that task. For artificial intelligence (AI) applications in HEP, there are several areas where interpretable methods for UQ are essential, including inference, simulation, and control/decision-making. There exist some methods for each of these areas, but they have not yet been demonstrated to be as trustworthy as more traditional approaches currently employed in physics (e.g., non-AI frequentist and Bayesian methods).Shedding light on the questions above requires additional understanding of the interplay of AI systems and uncertainty quantification. We briefly discuss the existing methods in each area and relate them to tasks across HEP. We then discuss recommendations for avenues to pursue to develop the necessary techniques for reliable widespread usage of AI with UQ over the next decade.

show abstract

Section: Bias Mitigationmentioning

confidence: 99%

Interpretable Uncertainty Quantification in AI for HEP

Chen¹,

Dey²,

Ghosh³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…The use of SPS models in analyzing large samples of galaxies has only recently become feasible, thanks to fast neural emulators (e.g., speculator; Alsing et al 2020). The use of physically motivated priors (e.g., Tanaka 2015 andRamachandra et al 2022) and continuous physical models for galaxy spectra (Ramachandra et al 2022) has already led to promising improvements in photometric redshift inferences for individual galaxies.…”

Section: Introductionmentioning

confidence: 99%

Forward Modeling of Galaxy Populations for Cosmological Redshift Distribution Inference

Alsing

Peiris

Mortlock

et al. 2023

ApJS

View full text Add to dashboard Cite

We present a forward-modeling framework for estimating galaxy redshift distributions from photometric surveys. Our forward model is composed of: a detailed population model describing the intrinsic distribution of the physical characteristics of galaxies, encoding galaxy evolution physics; a stellar population synthesis model connecting the physical properties of galaxies to their photometry; a data model characterizing the observation and calibration processes for a given survey; and explicit treatment of selection cuts, both into the main analysis sample and for the subsequent sorting into tomographic redshift bins. This approach has the appeal that it does not rely on spectroscopic calibration data, provides explicit control over modeling assumptions and builds a direct bridge between photo-z inference and galaxy evolution physics. In addition to redshift distributions, forward modeling provides a framework for drawing robust inferences about the statistical properties of the galaxy population more generally. We demonstrate the utility of forward modeling by estimating the redshift distributions for the Galaxy And Mass Assembly (GAMA) survey and the Vimos VLT Deep Survey (VVDS), validating against their spectroscopic redshifts. Our baseline model is able to predict tomographic redshift distributions for GAMA and VVDS with respective biases of Δz ≲ 0.003 and Δz ≃ 0.01 on the mean redshift—comfortably accurate enough for Stage III cosmological surveys—without any hyperparameter tuning (i.e., prior to doing any fitting to those data). We anticipate that with additional hyperparameter fitting and modeling improvements, forward modeling will provide a path to accurate redshift distribution inference for Stage IV surveys.

show abstract

“…Note that emulators are not the only approach to accelerating inference. For example,Hahn & Melchior (2022) andRamachandra et al (2022) showed how to approximate posterior distributions directly for the estimation of SPS parameters or redshifts from broadband photometry.…”

mentioning

confidence: 99%

Hierarchical Bayesian Inference of Photometric Redshifts with Stellar Population Synthesis Models

Leistedt

Alsing

Peiris

et al. 2023

ApJS

View full text Add to dashboard Cite

We present a Bayesian hierarchical framework to analyze photometric galaxy survey data with stellar population synthesis (SPS) models. Our method couples robust modeling of spectral energy distributions with a population model and a noise model to characterize the statistical properties of the galaxy populations and real observations, respectively. By self-consistently inferring all model parameters, from high-level hyperparameters to SPS parameters of individual galaxies, one can separate sources of bias and uncertainty in the data. We demonstrate the strengths and flexibility of this approach by deriving accurate photometric redshifts for a sample of spectroscopically confirmed galaxies in the COSMOS field, all with 26-band photometry and spectroscopic redshifts. We achieve a performance competitive with publicly released photometric redshift catalogs based on the same data. Prior to this work, this approach was computationally intractable in practice due to the heavy computational load of SPS model calls; we overcome this challenge by the addition of neural emulators. We find that the largest photometric residuals are associated with poor calibration for emission-line luminosities and thus build a framework to mitigate these effects. This combination of physics-based modeling accelerated with machine learning paves the path toward meeting the stringent requirements on the accuracy of photometric redshift estimation imposed by upcoming cosmological surveys. The approach also has the potential to create new links between cosmology and galaxy evolution through the analysis of photometric data sets.

show abstract

Machine learning synthetic spectra for probabilistic redshift estimation: SYTH-Z

Cited by 6 publications

References 69 publications

Interpretable Uncertainty Quantification in AI for HEP

Interpretable Uncertainty Quantification in AI for HEP

Forward Modeling of Galaxy Populations for Cosmological Redshift Distribution Inference

Hierarchical Bayesian Inference of Photometric Redshifts with Stellar Population Synthesis Models

Contact Info

Product

Resources

About