scholarly journals Windowing artefacts likely account for recent claimed detection of oscillating cosmic scale factor

2020 ◽  
Vol 498 (4) ◽  
pp. 5512-5516
Author(s):  
Sasha R Brownsberger ◽  
Christopher W Stubbs ◽  
Daniel M Scolnic
Keyword(s):  
Type Ia Supernovae ◽  
Data Sets ◽  
Analysis Method ◽  
Data Set ◽  
Statistical Fluctuations ◽  
Data Analyses ◽  
Type Ia ◽  
History Of ◽  
Ia Supernovae ◽  
The Universe

ABSTRACT Using the Pantheon data set of Type Ia supernovae, a recent publication (R20 in this work) reports a  2σ detection of oscillations in the expansion history of the Universe. The study conducted by R20 is wholly worthwhile. However, we demonstrate that there is a $\gt 10{{\ \rm per\ cent}}$ chance of statistical fluctuations in the Pantheon data producing a false oscillatory signal larger than the oscillatory signal that R20 report. Their results are a less than 2σ detection. Applying the R20 methodology to simulated Pantheon data, we determine that these oscillations could arise due to analysis artefacts. The uneven spacing of Type Ia supernovae in redshift space and the complicated analysis method of R20 impose a structured throughput function. When analysed with the R20 prescription, about $11{{\ \rm per\ cent}}$ of artificial ΛCDM data sets produce a stronger oscillatory signal than the actual Pantheon data. Our results underscore the importance of understanding the false ‘signals’ that can be introduced by complicated data analyses.

scholarly journals CONSTRAINTS ON THE COSMOLOGICAL DENSITY PARAMETERS AND COSMIC TOPOLOGY

2007 ◽  
Vol 16 (02n03) ◽  
pp. 207-217 ◽  
Author(s):  
M. J. REBOUÇAS
Keyword(s):  
Mass Fraction ◽  
Type Ia Supernovae ◽  
Data Sets ◽  
X Ray ◽  
Set Constraints ◽  
Type Ia ◽  
Ia Supernovae ◽  
The Universe ◽  
Nontrivial Topology ◽  
Spatial Section

A nontrivial topology of the spatial section of the universe is an observable which can be probed for all homogeneous and isotropic universes, without any assumption on the cosmological density parameters. We discuss how one can use this observable to set constraints on the density parameters of the universe by using a specific spatial topology along with type Ia supernovae and X-ray gas mass fraction data sets.

scholarly journals SNe Ia as a cosmological probe

2015 ◽  
Vol 24 (14) ◽  
pp. 1530029 ◽  
Author(s):  
Xiangcun Meng ◽  
Yan Gao ◽  
Zhanwen Han
Keyword(s):  
Dark Energy ◽  
Equation Of State ◽  
High Redshift ◽  
Type Ia Supernovae ◽  
Analysis Techniques ◽  
Type Ia ◽  
History Of ◽  
Acceleration Of The Universe ◽  
Ia Supernovae ◽  
The Universe

Type Ia supernovae (SNe Ia) luminosities can be corrected in order to render them useful as standard candles that are able to probe the expansion history of the universe. This technique was successfully applied to discover the present acceleration of the universe. As the number of SNe Ia observed at high redshift increases and analysis techniques are perfected, people aim to use this technique to probe the equation-of-state of the dark energy (EOSDE). Nevertheless, the nature of SNe Ia progenitors remains controversial and concerns persist about possible evolution effects that may be larger and harder to characterize than the more obvious statistical uncertainties.

scholarly journals Defying the laws of gravity I: model-independent reconstruction of the Universe expansion from growth data

2020 ◽  
Vol 494 (1) ◽  
pp. 819-826 ◽  
Author(s):  
Benjamin L’Huillier ◽  
Arman Shafieloo ◽  
David Polarski ◽  
Alexei A Starobinsky
Keyword(s):  
Type Ia Supernovae ◽  
Growth Data ◽  
Growth History ◽  
Type Ia ◽  
History Of ◽  
Space Distortion ◽  
Model Independent ◽  
Ia Supernovae ◽  
The Universe

ABSTRACT Using redshift space distortion data, we perform model-independent reconstructions of the growth history of matter inhomogeneity in the expanding Universe using two methods: crossing statistics and Gaussian processes. We then reconstruct the corresponding history of the Universe background expansion and fit it to Type Ia supernovae data, putting constraints on (Ωm, 0, σ8, 0). The results obtained are consistent with the concordance flat-ΛCDM model and General Relativity as the gravity theory given the current quality of the inhomogeneity growth data.

Machine Learning Approaches for Supernovae Classification

2017 ◽  
pp. 207-219 ◽  
Author(s):  
Surbhi Agrawal ◽  
Kakoli Bora ◽  
Swati Routh
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Type Ia Supernovae ◽  
Learning Approaches ◽  
Data Set ◽  
Learning Techniques ◽  
Type Ia ◽  
Ia Supernovae ◽  
The Universe

In this chapter, authors have discussed few machine learning techniques and their application to perform the supernovae classification. Supernovae has various types, mainly categorized into two important types. Here, focus is given on the classification of Type-Ia supernova. Astronomers use Type-Ia supernovae as “standard candles” to measure distances in the Universe. Classification of supernovae is mainly a matter of concern for the astronomers in the absence of spectra. Through the application of different machine learning techniques on the data set authors have tried to check how well classification of supernovae can be performed using these techniques. Data set used is available at Riess et al. (2007) (astro-ph/0611572).

scholarly journals Imprint of modified Einstein’s gravity on white dwarfs: Unifying Type Ia supernovae

2015 ◽  
Vol 24 (12) ◽  
pp. 1544026 ◽  
Author(s):  
Upasana Das ◽  
Banibrata Mukhopadhyay
Keyword(s):  
White Dwarfs ◽  
Evolutionary History ◽  
Type Ia Supernovae ◽  
Single Model ◽  
Model Parameter ◽  
Type Ia ◽  
History Of ◽  
Ia Supernovae ◽  
First Time ◽  
The Universe

We establish the importance of modified Einstein’s gravity (MG) in white dwarfs (WDs) for the first time in the literature. We show that MG leads to significantly sub- and super-Chandrasekhar limiting mass WDs, depending on a single model parameter. However, conventional WDs on approaching Chandrasekhar’s limit are expected to trigger Type Ia supernovae (SNeIa), a key to unravel the evolutionary history of the universe. Nevertheless, observations of several peculiar, under- and over-luminous SNeIa argue for the limiting mass widely different from Chandrasekhar’s limit. Explosions of MG induced sub- and super-Chandrasekhar limiting mass WDs explain under- and over-luminous SNeIa respectively, thus unifying these two apparently disjoint sub-classes. Our discovery questions both the global validity of Einstein’s gravity and the uniqueness of Chandrasekhar’s limit.

scholarly journals Model-independent reconstruction of the expansion history of the Universe from Type Ia supernovae

2011 ◽  
Vol 419 (1) ◽  
pp. 513-521 ◽  
Author(s):  
S. Benitez-Herrera ◽  
F. Röpke ◽  
W. Hillebrandt ◽  
C. Mignone ◽  
M. Bartelmann ◽  
...  
Keyword(s):  
Type Ia Supernovae ◽  
Type Ia ◽  
History Of ◽  
Model Independent ◽  
Ia Supernovae ◽  
The Universe

scholarly journals ParSNIP: Generative Models of Transient Light Curves with Physics-enabled Deep Learning

2021 ◽  
Vol 162 (6) ◽  
pp. 275
Author(s):  
Kyle Boone
Keyword(s):  
Three Dimensional ◽  
Generative Models ◽  
Light Curves ◽  
Type Ia Supernovae ◽  
Data Sets ◽  
Data Set ◽  
Out Of Sample ◽  
Type Ia ◽  
Ia Supernovae

Abstract We present a novel method to produce empirical generative models of all kinds of astronomical transients from data sets of unlabeled light curves. Our hybrid model, which we call ParSNIP, uses a neural network to model the unknown intrinsic diversity of different transients and an explicit physics-based model of how light from the transient propagates through the universe and is observed. The ParSNIP model predicts the time-varying spectra of transients despite only being trained on photometric observations. With a three-dimensional intrinsic model, we are able to fit out-of-sample multiband light curves of many different kinds of transients with model uncertainties of 0.04–0.06 mag. The representation learned by the ParSNIP model is invariant to redshift, so it can be used to perform photometric classification of transients even with heavily biased training sets. Our classification techniques significantly outperform state-of-the-art methods on both simulated (PLAsTiCC) and real (PS1) data sets with 2.3× and 2× less contamination, respectively, for classification of Type Ia supernovae. We demonstrate how our model can identify previously unobserved kinds of transients and produce a sample that is 90% pure. The ParSNIP model can also estimate distances to Type Ia supernovae in the PS1 data set with an rms of 0.150 ± 0.007 mag compared to 0.155 ± 0.008 mag for the SALT2 model on the same sample. We discuss how our model could be used to produce distance estimates for supernova cosmology without the need for explicit classification.

Machine Learning Approaches for Supernovae Classification

2020 ◽  
pp. 294-306
Author(s):  
Surbhi Agrawal ◽  
Kakoli Bora ◽  
Swati Routh
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Type Ia Supernovae ◽  
Learning Approaches ◽  
Data Set ◽  
Learning Techniques ◽  
Type Ia ◽  
Ia Supernovae ◽  
The Universe

In this chapter, authors have discussed few machine learning techniques and their application to perform the supernovae classification. Supernovae has various types, mainly categorized into two important types. Here, focus is given on the classification of Type-Ia supernova. Astronomers use Type-Ia supernovae as “standard candles” to measure distances in the Universe. Classification of supernovae is mainly a matter of concern for the astronomers in the absence of spectra. Through the application of different machine learning techniques on the data set authors have tried to check how well classification of supernovae can be performed using these techniques. Data set used is available at Riess et al. (2007) (astro-ph/0611572).

scholarly journals Probing the isotropy of cosmic acceleration using different supernova samples

2019 ◽  
Vol 79 (9) ◽  
Author(s):  
Z. Q. Sun ◽  
F. Y. Wang
Keyword(s):  
Cosmic Acceleration ◽  
Type Ia Supernovae ◽  
Data Sets ◽  
Data Set ◽  
Cosmic Expansion ◽  
Fitting Method ◽  
Comparison Results ◽  
Type Ia ◽  
Ia Supernovae ◽  
Fitting Parameters

Abstract Recent studies indicated that an anisotropic cosmic expansion may exist. In this paper, we use three data sets of type Ia supernovae (SNe Ia) to probe the isotropy of cosmic acceleration. For the Union2.1 data set, the direction and magnitude of the dipole are $$(l=309.3^{\circ } {}^{+ 15.5^{\circ }}_{-15.7^{\circ }} ,\ b = -8.9^{\circ } {}^{ + 11.2^{\circ }}_{-9.8^{\circ }} )$$(l=309.3∘-15.7∘+15.5∘,b=-8.9∘-9.8∘+11.2∘), and $$\ A=(1.46 \pm 0.56) \times 10^{-3}$$A=(1.46±0.56)×10-3 from dipole fitting method. The hemisphere comparison results are $$\delta =0.20,l=352^{\circ },b=-9^{\circ }$$δ=0.20,l=352∘,b=-9∘. For the Constitution data set, the results are $$(l=67.0^{\circ }{}^{+ 66.5^{\circ }}_{-66.2^{\circ }},\ b=-0.6^{\circ }{}^{+ 25.2^{\circ }}_{-26.3^{\circ }})$$(l=67.0∘-66.2∘+66.5∘,b=-0.6∘-26.3∘+25.2∘), and $$\ A=(4.4 \pm 5.0) \times 10^{-4}$$A=(4.4±5.0)×10-4 for dipole fitting and $$\delta = 0.56,l=141^{\circ },b=-11^{\circ }$$δ=0.56,l=141∘,b=-11∘ for hemisphere comparison. For the JLA data set, no significant dipolar or quadrupolar deviation is found. We find previous works using (l, b, A) directly as fitting parameters may get improper results. We also explore the effects of anisotropic distributions of coordinates and redshifts on the results using Monte-Carlo simulations. We find that the anisotropic distribution of coordinates can cause dipole directions and make dipole magnitude larger. Anisotropic distribution of redshifts is found to have no significant effect on dipole fitting results.

scholarly journals Model-independent cosmic acceleration and redshift-dependent intrinsic luminosity in type-Ia supernovae

2019 ◽  
Vol 625 ◽  
pp. A15 ◽  
Author(s):  
I. Tutusaus ◽  
B. Lamine ◽  
A. Blanchard
Keyword(s):  
Spline Interpolation ◽  
High Redshift ◽  
Cosmic Acceleration ◽  
Expansion Rate ◽  
Type Ia Supernovae ◽  
Type Ia ◽  
Model Independent ◽  
Ia Supernovae ◽  
The Universe ◽  
Intrinsic Luminosity

Context. The cosmological concordance model (ΛCDM) is the current standard model in cosmology thanks to its ability to reproduce the observations. The first observational evidence for this model appeared roughly 20 years ago from the type-Ia supernovae (SNIa) Hubble diagram from two different groups. However, there has been some debate in the literature concerning the statistical treatment of SNIa, and their stature as proof of cosmic acceleration. Aims. In this paper we relax the standard assumption that SNIa intrinsic luminosity is independent of redshift, and examine whether it may have an impact on our cosmological knowledge and more precisely on the accelerated nature of the expansion of the universe. Methods. To maximise the scope of this study, we do not specify a given cosmological model, but we reconstruct the expansion rate of the universe through a cubic spline interpolation fitting the observations of the different cosmological probes: SNIa, baryon acoustic oscillations (BAO), and the high-redshift information from the cosmic microwave background (CMB). Results. We show that when SNIa intrinsic luminosity is not allowed to vary as a function of redshift, cosmic acceleration is definitely proven in a model-independent approach. However, allowing for redshift dependence, a nonaccelerated reconstruction of the expansion rate is able to fit, at the same level of ΛCDM, the combination of SNIa and BAO data, both treating the BAO standard ruler rd as a free parameter (not entering on the physics governing the BAO), and adding the recently published prior from CMB observations. We further extend the analysis by including the CMB data. In this case we also consider a third way to combine the different probes by explicitly computing rd from the physics of the early universe, and we show that a nonaccelerated reconstruction is able to nicely fit this combination of low- and high-redshift data. We also check that this reconstruction is compatible with the latest measurements of the growth rate of matter perturbations. We finally show that the value of the Hubble constant (H0) predicted by this reconstruction is in tension with model-independent measurements. Conclusions. We present a model-independent reconstruction of a nonaccelerated expansion rate of the universe that is able to fit all the main background cosmological probes nicely. However, the predicted value of H0 is in tension with recent direct measurements. Our analysis points out that a final reliable and consensual value for H0 is critical to definitively prove cosmic acceleration in a model-independent way.

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