An independent assessment of significance of annual modulation in COSINE-100 data

Krishak, Aditi; Desai, S.

doi:10.21105/astro.1907.07199

Cited by 16 publications

(14 citation statements)

References 20 publications

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“…The only difference being that in D21, a separate a i was used for every station, whereas here we assume a i is the same between all the stations. Since this model contains a sinusoidal function, the lower bound on the period should be greater than the sampling time, in order to avoid the overfitting problem which may happen for sinusoidal functions [12,13]. The median sampling time is 12.4 days, while the mean sampling time is 23.5 days.…”

Section: Discussionmentioning

confidence: 99%

Search for variability in Newton's constant using local gravitational acceleration measurements

Bhagvati,

Desai

2021

Preprint

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We analyze the IGETS based gravitational acceleration measurements to search for a variability in Newton's constant G, as a complement to the analysis carried out in [1]. To understand the variation in gravitational acceleration, we fit the aforementioned data to a constant model, two sinusoidal models, and a linear model. We find that none of the four models provide a good fit to the data, showing that there is no evidence for periodicity or linear temporal variation in the acceleration measurements. Based on the sinusoidal models, we infer that the relative variance of G is 2 × 10 −9 , which is about four orders of magnitude more stringent than the amplitude of periodic variations inferred from previous G measurements. From the linear model, we obtain Ġ G < 2.14 × 10 −10 yr −1 .

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Section: Discussionmentioning

confidence: 99%

Search for variability in Newton's constant using local gravitational acceleration measurements

Bhagvati,

Desai

2021

Preprint

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“…We briefly summarize this technique and more details can be found in Refs. [56,58,59] or some of our previous works [60,61].…”

Section: Model Comparison Summarymentioning

confidence: 91%

Model comparison of $$\Lambda $$CDM vs $$R_h=ct$$ using cosmic chronometers

Singirikonda

Desai

2020

Eur. Phys. J. C

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In 2012, Bilicki and Seikel (Mon Not R Astron Soc 425:1664, 2012) showed that H(z) data reconstructed using Gaussian Process Regression from cosmic chronometers and baryon acoustic oscillations, conclusively rules out the $$R_h=ct$$Rh=ct model. These results were disputed by Melia and collaborators in two different works (Melia and Maier in Mon Not R Astron Soc 432:2669, 2013; Melia and Yennapureddy in JCAP 2018:034, 2018), who showed using both an unbinned analysis and Gaussian Process reconstructed H(z) data from chronometers, that $$R_h=ct$$Rh=ct is favored over $$\Lambda $$ΛCDM model. To resolve this imbroglio, we carry out model comparison of $$\Lambda $$ΛCDM versus $$R_h=ct$$Rh=ct by independently reproducing the above claims using the latest chronometer data. We perform model selection between these two models using Bayesian model comparison. We find that no one model between $$\Lambda $$ΛCDM and $$R_h=ct$$Rh=ct is decisively favored when uniform priors on $$\Lambda $$ΛCDM parameters are used. However, if we use priors centered around the Planck best-fit values, then $$\Lambda $$ΛCDM is very strongly preferred over $$R_h=ct$$Rh=ct.

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“…A comparison and contrast of these methods can be found in [43][44][45][46][47][48][49][50]. In our previous works, we have applied all these techniques to a large number of model selection problems in Astrophysics and Cosmology [40,[51][52][53][54][55][56][57][58][59]. In this work, we shall only use the Bayesian method for model comparison, as this is the most robust among the various techniques and does not involve any assumptions [46].…”

Section: Bayesian Model Comparisonmentioning

confidence: 99%

Search for Lorentz Invariance Violation from stacked Gamma-Ray Burst spectral lag data

Agrawal,

Singirikonda,

Desai

2021

Preprint

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A number of works have found evidence for a turn-over in the spectral lag data for individual Gamma-Ray Bursts (GRBs), caused by an energy-dependent speed of light, which could be a possible manifestation of Lorentz invariance violation (LIV). Here, we stack spectral lag data from a total of 37 GRBs (with a total of 91 measurements), to verify if the combined data is consistent with a unified model consisting of intrinsic astrophysical emission, along with another contribution due to LIV. We then carry out Bayesian model comparison to ascertain if this combined spectral lag data shows a preference for an energy-dependent speed of light, as compared to an intrinsic astrophysical emission mechanism. We do not find a decisive evidence for such an energy-dependent speed of light for two different models of LIV. Furthermore, none of the models considered provide a good fit to the stacked spectral lag data.

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An independent assessment of significance of annual modulation in COSINE-100 data

Cited by 16 publications

References 20 publications

Search for variability in Newton's constant using local gravitational acceleration measurements

Search for variability in Newton's constant using local gravitational acceleration measurements

Model comparison of $$\Lambda $$CDM vs $$R_h=ct$$ using cosmic chronometers

Search for Lorentz Invariance Violation from stacked Gamma-Ray Burst spectral lag data

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