Periodic Reporting for period 1 - SCIPOL (Science from the large scale cosmic microwave background polarization structure)
Période du rapport: 2023-01-01 au 2025-06-30
Unveiling the Earliest Cosmic Signals
The Cosmic Microwave Background (CMB) is a crucial probe of the early Universe, carrying signatures of primordial fluctuations that shaped cosmic evolution. A key goal of modern cosmology is detecting B-mode polarization, a potential imprint of primordial gravitational waves and a direct test of cosmic inflation. However, this faint signal is obscured by astrophysical foregrounds, instrumental systematics, and atmospheric contamination, requiring advanced data analysis methods to extract meaningful information.
SciPol: A New Framework for CMB Data Analysis
The ERC SciPol project develops innovative computational tools and calibration techniques to enhance the precision of CMB polarization studies. Its core objectives are:
* Next-Generation Data Analysis:
- MICMAC: a non-parametric method for component separation, allowing for spatial variability in foregrounds.
- MegaTop: a Bayesian framework for B-mode signal extraction, reducing biases in parameter estimation.
* Improved Calibration and Systematics Control:
- HWP and Optical Modeling: refining telescope systematics for the Simons Observatory, CMB-S4 and LiteBIRD.
- Drone-Based Calibration: anovel artificial star method to characterize the instrumental response to unprecedented precision.
* High-Performance Computational Tools:
- FURAX: A GPU-accelerated differentiable programming framework for faster map-making and component separation.
- Bayesian Inference Techniques: advanced statistical tools for robust cosmological parameter estimation.
Expected Impact
SciPol’s methodologies are already being integrated into major CMB experiments, setting new standards for data analysis and calibration. The project’s key goals include:
* Advancing fundamental cosmology by improving constraints on inflation and primordial fluctuations.
* Enhancing CMB data quality, benefiting Simons Observatory, LiteBIRD, CMB-S4 and future missions.
* Introducing new calibration techniques, such as drone-based artificial stars for ground-based telescopes.
* Providing open-source softwares, with tools applicable to astrophysics, gravitational wave detection, and machine learning.
* Strengthening European leadership in CMB Science by fostering international collaborations and training the next generation of researchers.
* SciPol is actively engaged in outreach and education, collaborating with scientific illustrators and bridging the gap between cutting-edge research and the general public, fostering scientific curiosity and engagement.
By tackling key challenges in CMB research, SciPol is laying the foundation for next-generation cosmological discoveries, ensuring that future experiments extract the most precise insights into the early Universe and its fundamental physics.
The Cosmic Microwave Background (CMB) is a crucial probe of the early Universe, carrying signatures of primordial fluctuations that shaped cosmic evolution. A key goal of modern cosmology is detecting B-mode polarization, a potential imprint of primordial gravitational waves and a direct test of cosmic inflation. However, this faint signal is obscured by astrophysical foregrounds, instrumental systematics, and atmospheric contamination, requiring advanced data analysis methods to extract meaningful information.
SciPol: A New Framework for CMB Data Analysis
The ERC SciPol project develops innovative computational tools and calibration techniques to enhance the precision of CMB polarization studies. Its core objectives are:
* Next-Generation Data Analysis:
- MICMAC: a non-parametric method for component separation, allowing for spatial variability in foregrounds.
- MegaTop: a Bayesian framework for B-mode signal extraction, reducing biases in parameter estimation.
* Improved Calibration and Systematics Control:
- HWP and Optical Modeling: refining telescope systematics for the Simons Observatory, CMB-S4 and LiteBIRD.
- Drone-Based Calibration: anovel artificial star method to characterize the instrumental response to unprecedented precision.
* High-Performance Computational Tools:
- FURAX: A GPU-accelerated differentiable programming framework for faster map-making and component separation.
- Bayesian Inference Techniques: advanced statistical tools for robust cosmological parameter estimation.
Expected Impact
SciPol’s methodologies are already being integrated into major CMB experiments, setting new standards for data analysis and calibration. The project’s key goals include:
* Advancing fundamental cosmology by improving constraints on inflation and primordial fluctuations.
* Enhancing CMB data quality, benefiting Simons Observatory, LiteBIRD, CMB-S4 and future missions.
* Introducing new calibration techniques, such as drone-based artificial stars for ground-based telescopes.
* Providing open-source softwares, with tools applicable to astrophysics, gravitational wave detection, and machine learning.
* Strengthening European leadership in CMB Science by fostering international collaborations and training the next generation of researchers.
* SciPol is actively engaged in outreach and education, collaborating with scientific illustrators and bridging the gap between cutting-edge research and the general public, fostering scientific curiosity and engagement.
By tackling key challenges in CMB research, SciPol is laying the foundation for next-generation cosmological discoveries, ensuring that future experiments extract the most precise insights into the early Universe and its fundamental physics.
1. Advanced Data Analysis Methods
SciPol enhances CMB polarization extraction with:
* Component Separation: MICMAC’s non-parametric, pixel-domain approach allows spatially varying spectral properties, improving over traditional models. [Leloup et al. 2023, Morshed et al. 2024, Kabalan et al., in prep.]
* Differentiable Programming: FURAX (JAX-based) accelerates map-making, component separation, and noise modeling. [https://github.com/CMBSciPol/furax]
* Maximum-Likelihood Map-Making: A GPU-powered solver, tested on Simons Observatory data, speeds up reconstructions while preserving accuracy. [Biquard et al., Sohn et al., in prep.]
2. Instrumental Systematics & Calibration
SciPol refines systematic mitigation through:
* HWP Modeling: Improved Simons Observatory SAT optical chain corrections. [Tsang King Sang et al., in prep.]
* Drone-Based Calibration: A drone-mounted artificial star offers real-time telescope calibration. [Coppi et al., 2025]
* Noise Covariance Modeling: Leveraging atmospheric fluctuations as a calibration tool. [Villarrubia-Aguilar et al., in prep.]
3. Statistical Methods for Cosmology
SciPol optimizes cosmological parameter estimation with:
* MegaTop Pipeline: A Bayesian framework for B-mode extraction, now part of Simons Observatory pipelines. [Jost, Beringue et al., in prep.]
* Bayesian Low-ℓ Analysis: Improved likelihood functions refine large-scale power spectrum estimates.
* Variational Inference & MCMC: Integrated into MICMAC for efficient parameter estimation.
4. Outcomes & Impact
SciPol’s tools are shaping CMB research:
* Adoption by Simons Observatory & LiteBIRD for robust data processing.
* Open-source release of FURAX & MICMAC benefits the scientific community.
* Drone calibration & atmospheric noise techniques are tested for standardization.
* Publications & influence extend beyond CMB into computational astrophysics and Bayesian statistics.
SciPol’s innovations set new standards in CMB analysis, paving the way for more precise cosmological measurements. More at scipol.in2p3.fr.
SciPol enhances CMB polarization extraction with:
* Component Separation: MICMAC’s non-parametric, pixel-domain approach allows spatially varying spectral properties, improving over traditional models. [Leloup et al. 2023, Morshed et al. 2024, Kabalan et al., in prep.]
* Differentiable Programming: FURAX (JAX-based) accelerates map-making, component separation, and noise modeling. [https://github.com/CMBSciPol/furax]
* Maximum-Likelihood Map-Making: A GPU-powered solver, tested on Simons Observatory data, speeds up reconstructions while preserving accuracy. [Biquard et al., Sohn et al., in prep.]
2. Instrumental Systematics & Calibration
SciPol refines systematic mitigation through:
* HWP Modeling: Improved Simons Observatory SAT optical chain corrections. [Tsang King Sang et al., in prep.]
* Drone-Based Calibration: A drone-mounted artificial star offers real-time telescope calibration. [Coppi et al., 2025]
* Noise Covariance Modeling: Leveraging atmospheric fluctuations as a calibration tool. [Villarrubia-Aguilar et al., in prep.]
3. Statistical Methods for Cosmology
SciPol optimizes cosmological parameter estimation with:
* MegaTop Pipeline: A Bayesian framework for B-mode extraction, now part of Simons Observatory pipelines. [Jost, Beringue et al., in prep.]
* Bayesian Low-ℓ Analysis: Improved likelihood functions refine large-scale power spectrum estimates.
* Variational Inference & MCMC: Integrated into MICMAC for efficient parameter estimation.
4. Outcomes & Impact
SciPol’s tools are shaping CMB research:
* Adoption by Simons Observatory & LiteBIRD for robust data processing.
* Open-source release of FURAX & MICMAC benefits the scientific community.
* Drone calibration & atmospheric noise techniques are tested for standardization.
* Publications & influence extend beyond CMB into computational astrophysics and Bayesian statistics.
SciPol’s innovations set new standards in CMB analysis, paving the way for more precise cosmological measurements. More at scipol.in2p3.fr.
1. Scientific & Technological Impact
SciPol has advanced CMB data analysis, improving precision in inflationary constraints. Its methodologies are integrated into Simons Observatory, CMB-S4, and LiteBIRD, ensuring lasting impact. Key contributions:
* CMB Signal Reconstruction: MICMAC & MegaTop reduce foreground and instrumental contamination.
* Efficient Data Processing: FURAX accelerates map-making and likelihood estimation via GPU-optimized differentiable programming.
* Innovative Calibration: A drone-based artificial star method offers a new telescope calibration standard.
* Broad Applications: Advances in Bayesian inference, HPC, and ML extend to astrophysics, geophysics, and medical imaging.
2. Key Needs for Further Uptake
To maximize SciPol’s impact, further efforts are needed:
* R&D & Validation: Real-data testing of MICMAC and map-making with Simons Observatory, plus Bayesian inference scaling for CMB-S4 & LiteBIRD.
* Adoption by Large Experiments: Integrating SciPol tools into official pipelines and expanding collaborations.
* Funding & Infrastructure: HPC access (e.g. IDRIS) and long-term funding for software and expert personnel.
* Open-Science & Standardization: Community-driven open-source support for FURAX, MICMAC & MegaTop, and global calibration standards.
3. Overview of Results
By SciPol’s conclusion, its key outcomes will include:
* Computational Tools: FURAX, MICMAC & MegaTop integrated into major CMB analyses.
* Calibration Advances: Drone-based artificial stars tested for observatories.
* Bayesian Innovations: Enhanced parameter estimation methods.
* Open-Access Software: Benefiting CMB and other sciences.
* European Leadership: Strengthening international collaborations.
Sustained funding, software maintenance, and integration into next-gen experiments are crucial for SciPol’s long-term impact on observational cosmology.
SciPol has advanced CMB data analysis, improving precision in inflationary constraints. Its methodologies are integrated into Simons Observatory, CMB-S4, and LiteBIRD, ensuring lasting impact. Key contributions:
* CMB Signal Reconstruction: MICMAC & MegaTop reduce foreground and instrumental contamination.
* Efficient Data Processing: FURAX accelerates map-making and likelihood estimation via GPU-optimized differentiable programming.
* Innovative Calibration: A drone-based artificial star method offers a new telescope calibration standard.
* Broad Applications: Advances in Bayesian inference, HPC, and ML extend to astrophysics, geophysics, and medical imaging.
2. Key Needs for Further Uptake
To maximize SciPol’s impact, further efforts are needed:
* R&D & Validation: Real-data testing of MICMAC and map-making with Simons Observatory, plus Bayesian inference scaling for CMB-S4 & LiteBIRD.
* Adoption by Large Experiments: Integrating SciPol tools into official pipelines and expanding collaborations.
* Funding & Infrastructure: HPC access (e.g. IDRIS) and long-term funding for software and expert personnel.
* Open-Science & Standardization: Community-driven open-source support for FURAX, MICMAC & MegaTop, and global calibration standards.
3. Overview of Results
By SciPol’s conclusion, its key outcomes will include:
* Computational Tools: FURAX, MICMAC & MegaTop integrated into major CMB analyses.
* Calibration Advances: Drone-based artificial stars tested for observatories.
* Bayesian Innovations: Enhanced parameter estimation methods.
* Open-Access Software: Benefiting CMB and other sciences.
* European Leadership: Strengthening international collaborations.
Sustained funding, software maintenance, and integration into next-gen experiments are crucial for SciPol’s long-term impact on observational cosmology.