LADDER: Revisiting the Cosmic Distance Ladder with Deep Learning Approaches and Exploring its Applications (2401.17029v2)

Abstract: We investigate the prospect of reconstructing the ''cosmic distance ladder'' of the Universe using a novel deep learning framework called LADDER - Learning Algorithm for Deep Distance Estimation and Reconstruction. LADDER is trained on the apparent magnitude data from the Pantheon Type Ia supernovae compilation, incorporating the full covariance information among data points, to produce predictions along with corresponding errors. After employing several validation tests with a number of deep learning models, we pick LADDER as the best performing one. We then demonstrate applications of our method in the cosmological context, including serving as a model-independent tool for consistency checks for other datasets like baryon acoustic oscillations, calibration of high-redshift datasets such as gamma ray bursts, and use as a model-independent mock catalog generator for future probes. Our analysis advocates for careful consideration of machine learning techniques applied to cosmological contexts.

• The paper introduces a novel deep learning framework (LADDER) that reconstructs the cosmic distance ladder using SNIa data.
• It employs robust MLP and LSTM architectures to capture data patterns and accurately extrapolate to high redshifts.
• The framework enables model-independent consistency checks, benchmarking cosmological datasets, and constraining dark energy parameters.

Overview of "LADDER: Revisiting the Cosmic Distance Ladder with Deep Learning Approaches and Exploring its Applications"

The paper presents a novel approach to reconstructing the cosmic distance ladder using deep learning techniques, encapsulated in a framework dubbed LADDER (Learning Algorithm for Deep Distance Estimation and Reconstruction). This work focuses on applying a machine learning model to the Pantheon Type Ia supernovae (SNIa) compilation, aiming to provide model-independent predictions of cosmic distances, crucial for understanding the Universe's expansion history.

Methodology

The authors propose the LADDER framework optimized for reconstructing cosmological distance measures from observational data. Key features of this methodology include incorporating the full covariance information of the Pantheon dataset and ensuring robustness to input noise and outliers. The deep learning model emphasizes accurate extrapolation to high redshifts, a significant challenge in cosmology due to limited observational data in those regions.

Training data involve apparent magnitude observations and associated uncertainties from a suite of SNIa, chosen for their reliability as standard candles. LADDER employs two neural network architectures—Multi-Layer Perceptron (MLP) and Long Short-Term Memory (LSTM) networks—with LSTMs demonstrating superior performance due to their ability to capture sequential data dependencies effectively.

Results

LADDER shows proficient reconstructions of cosmic distances, validated against traditional methods like Gaussian Processes (GP) and Support Vector Regression (SVR). Notably, it provides credible predictions even in gaps of the dataset, offering a model-independent approach to recalibrate high-redshift datasets, such as gamma-ray bursts (GRB), without succumbing to interpolation inaccuracies.

Key Applications

1. Model-Independent Consistency Checks: The framework successfully tests the consistency of different SNIa datasets (e.g., Pantheon and Pantheon+), proving its utility as a benchmarking tool for other astronomical datasets.
2. Pathology Tests: LADDER facilitates the examination of data consistency among different cosmological observations, particularly where standard models show discrepancies, such as the Hubble constant ($H_0$) tension.
3. Cosmological Model Parameter Constraints: The paper discusses its use in calibrating high-redshift datasets, applying it to GRBs for constraining parameters in dark energy models like $\Lambda$CDM and $w$CDM.
4. Synthetic Data Generation: By extrapolating beyond the trained data, LADDER can generate mock catalogs vital for future survey planning and cosmological forecasting.

Implications and Future Directions

The development of LADDER underscores the potential for deep learning techniques in cosmology, addressing the limitations of traditional methods constrained by predefined cosmological models. Its applications in model-independent analyses promise significant contributions to ongoing debates, such as the resolution of the $H_0$ tension and the exploration of non-standard cosmological scenarios.

Future work could explore enhancing LADDER's architecture to directly model derivatives of luminosity distances for reconstructing the Hubble parameter—critical for examining universe expansion dynamics. Moreover, further analyses and improvements on handling data sparsity with neural networks could refine predictions and support cosmological inferences with greater confidence.

Overall, LADDER represents a forward step toward incorporating sophisticated machine learning tools into cosmological research, enabling a more flexible understanding of the universe's expansion and finer integration of diverse observational datasets.