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New Insights into Cosmic Growth from Type-Ia Supernovae

Research using ZTF data sheds light on the universe's expansion.

― 6 min read


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Scientists want to understand how the universe grows and changes over time. One way to do this is by studying type-Ia supernovae, which are explosions of stars that can be seen from very far away. These supernovae serve as "standard candles," meaning they have a consistent brightness that helps scientists measure distances in space. The Zwicky Transient Facility (ZTF) is a project designed to find and study these supernovae. This article discusses how researchers can measure the growth rate of structures in the universe using data from ZTF.

Background

The universe is made up of various components, including regular matter, dark matter, and dark energy. Dark energy is a mysterious force that seems to be causing the universe to expand at an accelerating pace. The relationship between these components is crucial for understanding cosmic evolution. Scientists use models to explain how gravity works, and the standard model suggests that general relativity governs gravity at all scales. This model includes cold dark matter and dark energy.

The growth of cosmic structures, like galaxies and galaxy clusters, can give clues about how gravity operates and the role of dark energy. Observations from supernovae and other cosmic events can help refine these models.

Measuring Growth Rates

To know how fast structures in the universe are growing, scientists measure something called the growth rate. This rate represents how quickly galaxies and other cosmic features are forming over time. Different methods are used to measure growth rates, including analyzing cosmic distances and Peculiar Velocities, which account for how objects in space are moving.

Peculiar velocities are different from regular motion because they include the effects of local gravitational influences. When measuring the distances to galaxies and supernovae, peculiar velocities affect the observed values. These velocities can cause some galaxies to appear closer or farther away than they are.

Using Type-Ia Supernovae

Type-Ia supernovae are chosen as a way to measure the expansion of the universe because they have a consistent peak brightness. When these explosions occur, astronomers can calculate their distance based on their brightness. However, to obtain accurate distances, scientists must consider the peculiar velocities of host galaxies.

The ZTF aims to conduct a survey that captures a significant number of type-Ia supernovae. The data collected will help researchers determine peculiar velocities and growth rates.

Simulation of ZTF Observations

Before applying real data, researchers create simulations to model what ZTF observations would look like. These simulations include various factors affecting Light Curves of supernovae, such as how the light from the explosion is affected by the motion of the galaxy.

The process starts by extracting information from N-body simulations that represent the universe. These simulations help researchers understand how galaxies and their velocities are distributed in space. After creating simulated observations, scientists can analyze the data to measure growth rates.

Modeling the Light Curves

The simulation pipeline involves generating light curves for type-Ia supernovae. Researchers consider multiple factors, including the time of the explosion and how bright it appears from Earth. They also take into account the effects of dust in our galaxy that might dim the observed brightness.

Once the light curves are generated, they undergo a fitting process to retrieve standard parameters. By adjusting these parameters, scientists can better understand the characteristics of each supernova and its host galaxy.

Spectroscopic Selection

After generating simulated light curves, researchers must confirm which events are indeed type-Ia supernovae. The ZTF conducts a Bright Transient Survey (BTS) to classify transients based on their brightness. A photometric detection phase selects candidates, followed by a spectroscopic follow-up for confirmation.

The photometric detection step involves measuring the light curves and ensuring they meet specific criteria. Events that do not meet these criteria are excluded from further analysis. This selection process helps create a cleaner and more accurate dataset.

Analyzing the Data

With a complete set of simulated and selected type-Ia supernovae, researchers can analyze them to measure peculiar velocities and growth rates. By applying the maximum likelihood method, scientists estimate how peculiar velocities affect the measured growth rate.

Through this analysis, researchers can identify biases introduced by selection effects. For example, if certain supernovae are more likely to be detected based on their brightness, this could skew the peculiar velocity measurements.

Growth Rate Results

Upon analyzing the data, researchers test various configurations to see how well they can measure the growth rate. By running simulations and fitting parameters, they can estimate potential biases in their results. The goal is to obtain an unbiased measurement of the growth rate despite the challenges posed by selection effects.

Initial results indicate that growth rates can be successfully measured using the ZTF dataset. By refining their methodology, researchers aim to improve the accuracy of these measurements.

Comparison with Previous Measurements

When evaluating the growth rate derived from ZTF data, researchers compare their findings with earlier measurements. Previous studies have used different methods and datasets, often with varying levels of precision. By understanding how their results compare, scientists can better assess the reliability of their measurements.

These comparisons indicate that the growth rate measured from ZTF data is consistent with previous findings, showcasing the potential of ZTF to contribute valuable insights into cosmic growth.

Future Prospects

As the ZTF project continues to gather data, researchers expect to enhance their measurements of cosmic growth rates. Upcoming data releases will provide further opportunities for analysis, and researchers are eager to explore how well ZTF can constrain models of gravity and dark energy.

In addition to refining their measurements, researchers hope to investigate how combinations of different datasets can lead to more robust conclusions about cosmic structure growth.

Conclusion

The Zwicky Transient Facility offers a unique opportunity to study type-Ia supernovae and measure the growth rates of cosmic structures. By employing simulations and precise data analysis techniques, researchers can gather insights into the underlying mechanisms of the universe.

The interplay between dark energy, gravity, and cosmic growth remains a significant area of research, and the work being conducted with ZTF data will undoubtedly advance our understanding of these critical topics in cosmology. As more data becomes available, the potential to unravel the mysteries of the universe becomes increasingly attainable.

Original Source

Title: Growth-rate measurement with type-Ia supernovae using ZTF survey simulations

Abstract: Measurements of the growth rate of structures at $z < 0.1$ with peculiar velocity surveys have the potential of testing the validity of general relativity on cosmic scales. In this work, we present growth-rate measurements from realistic simulated sets of type-Ia supernovae (SNe Ia) from the Zwicky Transient Facility (ZTF). We describe our simulation methodology, the light-curve fitting and peculiar velocity estimation. Using the maximum likelihood method, we derive constraints on $f\sigma_8$ using only ZTF SN Ia peculiar velocities. We carefully tested the method and we quantified biases due to selection effects (photometric detection, spectroscopic follow-up for typing) on several independent realizations. We simulated the equivalent of 6 years of ZTF data, and considering an unbiased spectroscopically typed sample at $z < 0.06$, we obtained unbiased estimates of $f\sigma_8$ with an average uncertainty of 19% precision. We also investigated the information gain in applying bias correction methods. Our results validate our framework which can be used on real ZTF data.

Authors: Bastien Carreres, Julian E. Bautista, Fabrice Feinstein, Dominique Fouchez, Benjamin Racine, Mathew Smith, Mellissa Amenouche, Marie Aubert, Suhail Dhawan, Madeleine Ginolin, Ariel Goobar, Philippe Gris, Leander Lacroix, Eric Nuss, Nicolas Regnault, Mickael Rigault, Estelle Robert, Philippe Rosnet, Kelian Sommer, Richard Dekany, Steven L. Groom, Niharika Sravan, Frank J. Masci, Josiah Purdum

Last Update: 2023-06-22 00:00:00

Language: English

Source URL: https://arxiv.org/abs/2303.01198

Source PDF: https://arxiv.org/pdf/2303.01198

Licence: https://creativecommons.org/licenses/by/4.0/

Changes: This summary was created with assistance from AI and may have inaccuracies. For accurate information, please refer to the original source documents linked here.

Thank you to arxiv for use of its open access interoperability.

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