Pith. sign in

REVIEW 4 major objections 4 minor 37 references

This paper claims that 22 years of GRAPES-3 muon data separate the atmospheric-temperature and interplanetary-magnetic-field effects on muon flux, making the detector a dual monitor accurate to 10% and 6%.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

T0 review · deepseek-v4-flash

2026-08-02 23:56 UTC pith:VGAHUQEO

load-bearing objection Solid incremental measurement with an over-sold monitoring claim; deserves review but needs out-of-sample validation. the 4 major comments →

arxiv 2602.11847 v2 pith:VGAHUQEO submitted 2026-02-12 astro-ph.HE astro-ph.SRphysics.space-ph

Monitoring the upper atmospheric temperature and interplanetary magnetic field with the GRAPES-3 muon telescope

S. Paul , K.P. Arunbabu , M. Chakraborty , S.K. Gupta , B. Hariharan , Y. Hayashi , P. Jagadeesan , A. Jain
show 12 more authors
M. Karthik H. Kojima S. Kawakami P.K. Mohanty Y. Muraki P.K. Nayak T. Nonaka A. Oshima M. Rameez K. Ramesh S. Shibata K. Tanaka
This is my paper
classification astro-ph.HE astro-ph.SRphysics.space-ph
keywords cosmic raysatmospheric muonsmuon telescopeupper atmospheric temperatureinterplanetary magnetic fieldsolar cyclefast Fourier transformtemperature coefficient
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

With 22 years (2001–2022) of GRAPES-3 muon rates, the paper tries to separate two overlapping influences on the atmospheric muon flux: upper-atmospheric temperature, which imprints an annual cycle, and the interplanetary magnetic field, which modulates the primary cosmic-ray flux over the solar cycle. The central result is a pair of coefficients obtained by iterative FFT-based filtering: a temperature coefficient of −0.2241 %/K and a magnetic-field coefficient of −0.574 %/nT for a hadronic attenuation length of 120 g/cm2. The paper argues these coefficients let the same ground-based detector act as a long-term monitor of both quantities, to within 10% for temperature and 6% for the magnetic field. A sympathetic reader would care because it turns one detector's continuous muon record into two simultaneous environmental monitors, and it shifts earlier temperature estimates by about 23% after removing solar-cycle effects.

Core claim

After correcting for atmospheric pressure and applying a 60-day running average, the authors isolate the annual component in both muon flux and effective temperature using a narrow band-pass filter centered on one cycle per year. They then iterate: correct the muon rate for the magnetic-field influence and refit the temperature coefficient, then correct for temperature and refit the magnetic-field coefficient. After three iterations the coefficients stabilize at αT = −0.2241 ± 0.0003 (stat.) ± 0.0220 (syst.) %/K and γM = −0.574 ± 0.027 (stat.) ± 0.011 (syst.) %/nT, for λ = 120 g/cm2. The paper states that the iteration removes the solar-cycle IMF modulation that otherwise leaks into the annu

What carries the argument

The key object is the effective temperature Teff: a weighted mean of the atmospheric temperature profile, with weights set by the hadronic attenuation length λ, which describes where cosmic-ray mesons interact and decay. The argument rides on an iterative frequency-domain fitting loop: a Fast Fourier Transform and a narrow band-pass filter isolate the one-year seasonal signal in the muon rate and in Teff; a linear fit gives the temperature coefficient; the muon rate is corrected by that coefficient; the residual is regressed against the scalar interplanetary magnetic-field magnitude measured at the L1 point; and the loop repeats until both coefficients converge. This machinery is what disent

Load-bearing premise

The load-bearing premise is that, after correcting for temperature, the remaining muon variation is a linear function of the scalar interplanetary magnetic-field magnitude measured at L1—and only that quantity—so the fitted γM would generalize outside the calibration period.

What would settle it

Hold out the most recent four years of the 22-year record, fit αT and γM on the earlier years, then use the muon rate to predict temperature and magnetic field in the held-out years; if the predictions do not match the reanalysis temperature and L1 magnetometer data within the claimed 10% and 6%, the monitor claim fails.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • The telescope can serve as a long-term, ground-based check on upper-atmospheric temperature and L1 magnetic-field measurements, with stated accuracies of 10% and 6%.
  • The final αT is about 23% larger in magnitude than the value obtained without the IMF iteration, so earlier estimates that omitted solar-cycle magnetic modulation are systematically low.
  • The systematic uncertainty in both coefficients is dominated by the assumed hadronic attenuation length λ, quantified as ±0.022 %/K and ±0.011 %/nT for a 50 g/cm2 uncertainty in λ.
  • If the coefficients are stable, the method can be extended to the telescope's directional bins and to future years, potentially giving a spatial picture of upper-atmosphere temperature changes.
  • A validated γM would provide a ground-based monitor of interplanetary conditions that can fill gaps in spacecraft data at L1.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the scalar-B linear model is incomplete, the fitted γM will partly absorb correlated solar-wind effects; a natural test is to fit with additional covariates such as solar-wind speed or field polarity and see whether γM shifts.
  • The claimed 10% and 6% monitoring accuracies are in-sample; an out-of-sample test on data after 2022 would be the real bar for that statement.
  • Applying the same iterative decomposition to muon telescopes at different latitudes could separate the common heliospheric signal from local atmospheric temperature, because the two components should scale differently between sites.
  • With an independent IMF record, the method could be inverted to constrain the effective hadronic attenuation length of the atmosphere, since Teff weights depend on λ.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

4 major / 4 minor

Summary. The paper analyzes 22 years (2001–2022) of GRAPES-3 muon-telescope data, together with MERRA-2 effective atmospheric temperatures and L1 IMF magnitude from ACE/WIND, to derive linear response coefficients for upper-atmospheric temperature (α_T) and interplanetary magnetic field (γ_M). The analysis applies a 60-day running average, isolates the annual component with an FFT band-pass filter, and iteratively alternates between temperature and IMF corrections to converge on α_T = −0.2241 ± 0.0003 (stat.) ± 0.0220 (syst.) % K⁻¹ and γ_M = −0.574 ± 0.027 (stat.) ± 0.011 (syst.) % nT⁻¹ for λ = 120 g cm⁻². The paper then claims that GRAPES-3 can monitor upper-atmospheric temperature and the IMF to within 10% and 6%, respectively.

Significance. If the monitoring claim is substantiated, the result would be valuable: the 22-year high-statistics record, the automated detector-stability correction, and the iterative FFT-based decomposition are genuine strengths, and the final α_T is consistent with earlier GRAPES-3 results and theoretical expectations. The method is clearly described and the dataset is unique. However, the central claim of a long-term monitor is currently an extrapolation from in-sample fits; the paper does not provide an out-of-sample validation or an end-to-end inversion test. The reported statistical uncertainties also appear to ignore the strong serial correlation introduced by the running average and narrow band-pass filter.

major comments (4)
  1. [§4.1–4.3, Eqs. (3), (6), Figs. 8, 17, 18] The statistical uncertainties (e.g., α_T = −0.2241 ± 0.0003 % K⁻¹, γ_M = −0.574 ± 0.027 % nT⁻¹) are derived from fits to a large number of 3-hour points after a 60-day running average and a narrow band-pass filter with Δf = 1/T. The effective independent information is only about 22 annual cycles, so the raw point count drastically understates the uncertainty. Please compute errors using the effective number of independent samples (e.g., from the autocorrelation function or by bootstrapping yearly segments) and report the result; this is essential before the claimed precision can be accepted.
  2. [§5, opening sentence and abstract] The statement that GRAPES-3 'can serve as a long term monitor of both the upper atmospheric temperature and the interplanetary magnetic field to within 10% and 6%' is not demonstrated. The coefficients and the means used in Eqs. (3) and (6) are estimated from the full 2001–2022 record; no holdout validation or real-time inversion test is shown. A real-time monitor would require a stable baseline and must predict unseen periods. I recommend a concrete end-to-end test: fit on one part of the data (e.g., 2001–2011) and predict the muon-rate residuals for the remaining decade, reporting the RMS of inferred versus measured T_eff and B_scalar.
  3. [§4.2, Eq. (6)] The IMF monitor is built on a single-driver linear model where the temperature-corrected muon residual is a function only of B_scalar. Solar-cycle modulation of GCRs is known to depend on other correlated heliospheric quantities (solar-wind speed, magnetic polarity/tilt, rigidity-dependent diffusion). The fitted γ_M may therefore absorb these drivers and may not transfer outside the calibration period. To support the physical interpretation and the monitoring claim, add residual checks against V_sw and polarity, or a multivariate regression; alternatively, state explicitly that γ_M is a purely empirical calibration coefficient valid only for interpolation within the fitted conditions.
  4. [§3, pressure correction] The pressure coefficient is fixed to β = −0.128 %/hPa from Ref. [24], but no uncertainty is quoted or propagated. Since pressure has a strong annual cycle at this site, an error in β directly mimics the annual temperature signal and can bias α_T, which is a central result. Please quote the uncertainty on β and propagate it into α_T and γ_M, or show that the results are insensitive to a plausible range of β.
minor comments (4)
  1. [Abstract vs. body] The abstract states α_T = −0.2241 ± 0.04 (stat.) % K⁻¹, while the body and tables report ±0.0003 (stat.). This discrepancy should be corrected.
  2. [§4.3 and Table 2] The text says α_T and γ_M 'converge for all values of λ', but Table 2 shows α_T varying by about 20% across λ = 80–180 g cm⁻². The intended meaning is that iteration to convergence is achieved for each λ; please rephrase to avoid implying λ-independence of α_T.
  3. [Figures] Several figure axis labels contain encoding artifacts such as 'ϕ/uni03BC' (e.g., Figs. 4, 5, 7, 8, 10, 11, 15–18). The figures should be recompiled with correct muon and ΔT_eff symbols.
  4. [§1 and §5] The introduction says the data span 'three solar cycles,' but §5 correctly says 'parts of three solar cycles.' Use consistent wording.

Circularity Check

1 steps flagged

Monitoring accuracy (10%/6%) is quoted from the in-sample fitted coefficients, not an out-of-sample validation; the core coefficient measurement itself is a legitimate empirical fit.

specific steps
  1. fitted input called prediction [Section 5 'Summary of results' (monitoring claim); Eqs. (3) and (6) in Sections 4.1/4.2]
    "These results demonstrate the potential of the GRAPES-3 muon telescope to serve as a long term monitor of both the upper atmospheric temperature and the interplanetary magnetic field to within 10% and 6% respectively."

    The claimed monitoring accuracy is not validated out-of-sample; it is simply the relative uncertainty of the same fitted coefficients. The quoted 10% matches the relative uncertainty of α_T (0.0220/0.2241 ≈ 9.8%) and the quoted 6% matches the combined relative uncertainty of γ_M (≈0.038/0.574 ≈ 6.6%). Equations (3) and (6) are inverted to infer T or B from the muon rate, so the monitor's stated accuracy is just the calibration's own parameter uncertainty. No holdout period, end-to-end inversion, or independent comparison is provided, making the monitoring capability an in-sample restatement of the fit rather than an independent prediction.

full rationale

The main coefficient measurements are not circular: α_T and γ_M are empirical fits of the GRAPES-3 muon rate against independent external data (MERRA-2 effective temperature and OMNI IMF magnitude), and the alternating iterative procedure is a standard backfitting/self-consistent estimation scheme rather than a definitional equivalence. The T_eff weighting follows standard effective-temperature formalism, and the pressure coefficient is taken from separate earlier work; the self-citation to the detector-efficiency correction algorithm [22] is a methodological dependence but is not used to define the target result. The genuine circular element is the monitoring claim: the demonstrated 'accuracy' reduces to the precision of the fitted calibration coefficients themselves, with no out-of-sample or end-to-end validation. This warrants a partial-circularity score of 6, while noting that the underlying coefficient determinations retain independent empirical content.

Axiom & Free-Parameter Ledger

6 free parameters · 5 axioms · 0 invented entities

The paper's central outputs are two empirical coefficients fitted to data under a two-driver linear model. Free parameters are the fitted coefficients themselves plus modeling choices (lambda, beta, filter window). No new physical entities are introduced.

free parameters (6)
  • Temperature coefficient alpha_T = -0.2241 %/K (lambda=120, iteration 3)
    Fitted slope of band-pass-filtered muon variation vs effective temperature deviation (Fig. 17, Eq. 3).
  • IMF coefficient gamma_M = -0.574 %/nT (lambda=120, iteration 3)
    Fitted slope of temperature-corrected muon variation vs B_scalar (Fig. 18, Eq. 6).
  • Hadronic attenuation length lambda = 120 g/cm2 central; varied 80-180
    Chosen central value for effective-temperature weighting; systematic uncertainty derived from linear variation over the 80-180 range.
  • Pressure coefficient beta = -0.128 %/hPa
    Adopted without propagated uncertainty from prior GRAPES-3 work [24]; enters the pressure correction applied before all fits.
  • 60-day running-average window = 60 days
    Chosen to suppress short-term variations; affects which frequencies remain in the data before FFT.
  • Band-pass filter half-width Delta f = 0.000125 CPD (=1/T)
    Set to the FFT frequency resolution; a methodological choice rather than a physical parameter.
axioms (5)
  • domain assumption Linear superposition: muon flux fractional variation = alpha_T * Delta_T_eff + gamma_M * Delta_B (Eqs. 3 and 6).
    All fits assume no cross-terms or nonlinear saturation between temperature and IMF effects.
  • ad hoc to paper The annual band-pass filtered muon signal is purely temperature-driven after IMF correction.
    The alpha_T extraction requires that no other annual process (pressure residual, detector cycle, water vapor) survives the 60-day and band-pass filtering.
  • domain assumption B_scalar at L1 is the single heliospheric driver of long-term cosmic-ray modulation relevant here.
    gamma_M is interpreted as the IMF response, but other solar-cycle drivers such as solar-wind speed, polarity, and tilt are ignored.
  • domain assumption Effective temperature with a single hadronic attenuation length lambda (Eq. 1) captures the atmospheric influence on muon flux.
    The Teff weighting assumes one lambda; the paper varies lambda only to estimate systematics, not the form of the weighting.
  • standard math FFT and band-pass filtering reconstruct the annual component without spectral leakage.
    The smooth-window filter (Eq. 4) is intended to avoid leakage, but the effective transfer function is not validated against synthetic signals.

pith-pipeline@v1.3.0-alltime-deepseek · 19658 in / 14650 out tokens · 133030 ms · 2026-08-02T23:56:49.990615+00:00 · methodology

0 comments
read the original abstract

We study the influence of variations in the upper atmospheric temperature and interplanetary magnetic field on the cosmic ray induced atmospheric muon flux measured by the GRAPES-3 experiment over 22 years (2001--2022) of data; spanning three solar cycles: the declining phase of Solar Cycle 23, the full Cycle 24, and the rising and maximum phases of Cycle 25. Located in Ooty- India, the GRAPES-3 large area (560\,$m^2$) muon telescope detects $\sim$4 billion muons daily above 1\,GeV, with an angular resolution of $\sim$4$^\circ$, enabling a statistical precision $<$0.01\% on the hourly muon rate. After accounting for the effect of atmospheric pressure variations, we compare this data with the upper atmospheric temperature inferred from NASA's MERRA-2 dataset as well as magnetic field data from the ACE and WIND spacecraft at Lagrange point L1. A simultaneous iterative fitting method employing Fast Fourier Transforms and a narrow band-pass filter reveals the temperature and magnetic field coefficients to be $\alpha_T=-\,0.2241\,\pm\,0.04\, (stat.)\,\pm\,0.0220\, (syst.)\,\%\,K^{-1}$ and $\gamma_{M}=-\,0.574\,\pm\,0.027\, (stat.)\,\pm\,0.011\, (syst.)\,\%\,\text{nT}^{-1}$, respectively, for an assumed hadronic attenuation length $\lambda$=120 g cm$^{-2}$, underscoring the potential of the GRAPES-3 muon telescope to serve as a real time monitor of the upper atmospheric temperature or interplanetary magnetic field.

Figures

Figures reproduced from arXiv: 2602.11847 by A. Jain, A. Oshima, B. Hariharan, H. Kojima, K.P. Arunbabu, K. Ramesh, K. Tanaka, M. Chakraborty, M. Karthik, M. Rameez, P. Jagadeesan, P.K. Mohanty, P.K. Nayak, S. Kawakami, S.K. Gupta, S. Paul, S. Shibata, T. Nonaka, Y. Hayashi, Y. Muraki.

Figure 1
Figure 1. Figure 1: A schematic of a 4-layer tracking muon telescope with 58 PRCs per layer. the upper atmospheric temperature or the magnetic field at L1 to within 10% and 6% respectively. This paper is organized as follows. In Section 2 we describe the GRAPES-3 observatory, with a focus on the muon tele￾scope, which collected the data forming the primary focus of this paper. Section 3 describes the datasets and the processi… view at source ↗
Figure 2
Figure 2. Figure 2: The 22-year average vertical temperature profile obtained from NASA’s MERRA-2 dataset at the location (θ = 11.5◦ , ϕ = 76.825◦ ) near the GRAPES-3 site, shown by the red line, as a function of pressure level. Temper￾atures are presented at 22 different pressure levels ranging from 775 hPa to 10 hPa above sea level. pressure p, with x (in g cm−2 ) ≃ 1.0195 p (in hPa) [25]. The hadronic attenuation length (λ… view at source ↗
Figure 3
Figure 3. Figure 3: Temporal variations of (a) the muon rate, (b) the effective temperature (Teff; assuming λ=120 g cm−2 ), and (c) the interplanetary magnetic field (Bscalar = √ B 2 x + B 2 y + B 2 z ) observed by the ACE and WIND spacecraft [27, 28, 29] at Lagrange point L1, during 22 years (2001–2022). The left panels show the 3-hourly averaged data, while the right panels present the same datasets after applying a 60-day … view at source ↗
Figure 4
Figure 4. Figure 4: Variation of (a) muon flux (in %), and (b) ∆Teff (in K; assuming λ=120 g cm−2 ), obtained using a 60-day low-pass filter (implemented via a running average) during 22 years (2001–2022). tified annual periodicity is then isolated using a narrow band￾pass filter to suppress all non-atmospheric (solar-induced) fre￾quencies, to enable a more accurate determination of αT. The filter, incorporating a smooth sine… view at source ↗
Figure 6
Figure 6. Figure 6: A band-pass filter was applied to the muon and temperature data to select frequencies centered at 0.002738 CPD. The two dashed vertical lines indicate the region of 100 % acceptance. −        # "      [PITH_FULL_IMAGE:figures/full_fig_p005_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Inverse fast Fourier transform (IFFT) data in the time domain for (a) muon flux variation (in %) and (b) ∆Teff (in K; assuming λ=120 g cm−2 ) over the 22 years (2001–2022). 5 [PITH_FULL_IMAGE:figures/full_fig_p005_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Variation of muon flux (in %) as a function of ∆Teff (in K; assum￾ing λ=120 g cm−2 ). The statistical uncertainties are smaller than the plotting symbols and are therefore not visible. The linear fit (solid red line) yields a temperature coefficient, αT = − 0.1817 ± 0.0002 % K−1 [PITH_FULL_IMAGE:figures/full_fig_p006_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Temporal variation of the muon rate before (blue) and after (red) correcting for the effect of atmospheric temperature using αT = −0.1817 %K−1 (assuming λ=120 g cm−2 ) over the 22 years (2001–2022). For visual clarity, the temperature-corrected muon rate has been shifted downward by 50 counts s −1 . For comparison, the muon rate corrected using the final value of αT = −0.2241 %K−1 is also depicted in green… view at source ↗
Figure 11
Figure 11. Figure 11: Variation of the temperature-corrected (using αT = − 0.1817 %K−1 ) muon flux as a function of Bscalar (nT). Each point represents the mean percent deviation of the temperature-corrected muon rate for a 0.01 nT change in Bscalar. Bscalar. The statistical uncertainties are smaller than the plotting symbols and are therefore not depicted. The linear fit (solid red line) yields a magnetic field coefficient, γ… view at source ↗
Figure 12
Figure 12. Figure 12: Inverse fast Fourier transform (IFFT) data (using the band-pass filter W(f), Equation 4) in the time domain for (a) muon flux variation (in %), (b) ∆Teff (in K; assuming λ=120 g cm−2 ), and (c) Bscalar (in nT) over the 22 years (2001–2022). IMF modulations with a similar periodicity to that of the at￾mospheric temperature to contaminate the temperature depen￾dence. The presence of concomitant modulations … view at source ↗
Figure 17
Figure 17. Figure 17: Variation of magnetic field-corrected muon flux (in %; using γM = −0.587 % nT−1 ) as a function of ∆Teff (in K; assuming λ=120 g cm−2 ). The linear fit (solid red line) yields a temperature coefficient, αT = − 0.2251 ± 0.0003 % K−1 . To further isolate the annual periodicity, the same band-pass filter W(f) (defined in Equation 4) was applied to both datasets. The resulting filtered spectra, shown as black… view at source ↗
Figure 15
Figure 15. Figure 15: Fast Fourier transform (FFT) spectra of (a) magnetic field-corrected muon flux variation (in %; using γM = −0.587 % nT−1 ) and (b) ∆Teff (in K; assuming λ=120 g cm−2 ) over the 22 years (2001–2022). −        # "      [PITH_FULL_IMAGE:figures/full_fig_p008_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Band pass filtered data for (a) magnetic field-corrected muon flux variation (in %; using γM = −0.587 % nT−1 ) and (b) ∆Teff (in K; assuming λ=120 g cm−2 ) over the 22 years (2001–2022). − −    Δ − −        μ   [PITH_FULL_IMAGE:figures/full_fig_p008_16.png] view at source ↗
Figure 18
Figure 18. Figure 18: Variation of temperature-corrected muon flux (in %; using αT = − 0.2251 %K−1 ) as a function of Bscalar (nT). The linear fit (solid red line) yields a magnetic field coefficient, γM = − 0.574 ± 0.027 %nT−1 . γM converge for all values of λ. To estimate the systematic uncertainty associated with the choice of λ, the variation of αT and γM with λ was examined at each iteration. For every iteration, the depe… view at source ↗
Figure 20
Figure 20. Figure 20: Progression of αT (blue dots) and γM (red dots) for an assumed hadronic attenuation length λ=120 g cm−2 through successive iterations. The vertical error bars indicate statistical uncertainties, while the surrounding blue and red shaded bands denote the corresponding systematic uncertainties orig￾inating from uncertainty in λ. The green dashed horizontal line and its sur￾rounding shaded band show αT and i… view at source ↗

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

37 extracted references · 13 canonical work pages · 1 internal anchor

  1. [1]

    S. E. Forbush, World-Wide Cosmic-Ray V ariations, 1937- 1952, Journal of Geophysical Research 59 (4) (1954) 525–542. doi:10.1029/JZ059i004p00525

    work page doi:10.1029/jz059i004p00525 1937

  2. [2]

    K. Badruddin, Anand, Study of the Forbush Decreases, Geomagnetic Storms, and Ground-Level Enhancements in Selected Intervals and Their Space Weather Implica- tions, Solar Physics 290 (4) (2015) 1271–1283. doi: 10.1007/s11207-015-0665-4

    work page doi:10.1007/s11207-015-0665-4 2015

  3. [3]

    K. P . Arunbabu, H. M. Antia, S. R. Dugad, S. K. Gupta, Y . Hayashi, S. Kawakami, P . K. Mohanty, A. Oshima, P . Subramanian, How are Forbush decreases related to interplanetary magnetic field enhancements?, Astronomy and Astrophysics 580 (2015) A41. arXiv:1504.06473, doi:10.1051/0004-6361/201425115

    Pith/arXiv arXiv 2015

  4. [4]

    P . K. Mohanty, D. Atri, S. R. Dugad, S. K. Gupta, B. Har- iharan, Y . Hayashi, A. Jain, S. Kawakami, S. D. Mor- ris, P . K. Nayak, A. Oshima, B. S. Rao, Solar diurnal anisotropy measured using muons in GRAPES-3 exper- iment in 2006, Pramana 81 (2) (2013) 343–357. doi: 10.1007/s12043-013-0561-0

    work page doi:10.1007/s12043-013-0561-0 2006

  5. [5]

    V enkatesan, Badruddin, Cosmic-Ray Intensity V aria- tions in the 3-DIMENSIONAL Heliosphere, Space Sci- ence Reviews 52 (1-2) (1990) 121–194

    D. V enkatesan, Badruddin, Cosmic-Ray Intensity V aria- tions in the 3-DIMENSIONAL Heliosphere, Space Sci- ence Reviews 52 (1-2) (1990) 121–194. doi:10.1007/ BF00704241

    1990

  6. [6]

    M. S. Potgieter, Solar Modulation of Cosmic Rays, Living Reviews in Solar Physics 10 (1) (2013) 3. arXiv:1306. 4421, doi:10.12942/lrsp-2013-3

    doi:10.12942/lrsp-2013-3 2013

  7. [7]

    T. K. Gaisser, R. Engel, E. Resconi, Cosmic Rays and Particle Physics, 2016

    2016

  8. [8]

    Greisen, Cosmic Ray Showers, Annual Review of Nuclear and Particle Science 10 (1960) 63–108

    K. Greisen, Cosmic Ray Showers, Annual Review of Nuclear and Particle Science 10 (1960) 63–108. doi: 10.1146/annurev.ns.10.120160.000431

    arXiv 1960

  9. [9]

    The L3 Collaboration, Measurement of the Atmospheric Muon Spectrum from 20 to 3000 GeV, arXiv e-prints (2004) hep–ex /0408114arXiv:hep-ex/0408114, doi: 10.48550/arXiv.hep-ex/0408114

    Pith/arXiv arXiv doi:10.48550/arxiv.hep-ex/0408114 2004

  10. [10]

    R. L. Workman, V . D. Burkert, V . Crede, E. Klempt, U. Thoma, L. Tiator, K. Agashe, G. Aielli, B. C. Allanach, C. Amsler, M. Antonelli, E. C. Aschenauer, D. M. As- ner, H. Baer, S. Banerjee, R. M. Barnett, L. Baudis, C. W. Bauer, J. J. Beatty, V . I. Belousov, J. Beringer, A. Bet- tini, O. Biebel, K. M. Black, E. Blucher, R. Bonventre, V . V . Bryzgalov,...

    2022

  11. [11]

    M. A. Shea, Book Review: Cosmic rays at Earth Re- searcher’s reference manual and data book. ISBN: 0-444- 59710-8; 1112 pages, hardbound; 190.59 Euro, 2001, Advances in Space Research 28 (12) (2001) 1773–1774. doi:10.1016/S0273-1177(01)00545-2

    work page doi:10.1016/s0273-1177(01)00545-2 2001

  12. [12]

    Hayashi, Y

    Y . Hayashi, Y . Aikawa, N. V . Gopalakrishnan, S. K. Gupta, N. Ikeda, N. Ito, A. Jain, A. V . John, S. Karthikeyan, S. Kawakami, H. Kojima, T. Matsuyama, D. K. Mohanty, P . K. Mohanty, S. D. Morris, T. Non- aka, A. Oshima, B. S. Rao, K. C. Ravindran, M. Sasano, K. Sivaprasad, B. V . Sreekantan, H. Tanaka, S. C. Ton- war, K. Viswanathan, T. Y oshikoshi, A...

    2005

  13. [13]

    P . H. Barrett, L. M. Bollinger, G. Cocconi, Y . Eisenberg, K. Greisen, Interpretation of Cosmic-Ray Measurements Far Underground, Reviews of Modern Physics 24 (3) (1952) 133–178. doi:10.1103/RevModPhys.24.133

    work page doi:10.1103/revmodphys.24.133 1952

  14. [14]

    Ambrosio, R

    MACRO Collaboration, M. Ambrosio, R. Antolini, G. Auriemma, R. Baker, A. Baldini, G. C. Barbarino, B. C. Barish, G. Battistoni, R. Bellotti, C. Bemporad, P . Bernardini, H. Bilokon, V . Bisi, C. Bloise, T. Bosio, C. Bower, S. Bussino, F. Cafagna, M. Calicchio, D. Cam- pana, M. Carboni, M. Castellano, S. Cecchini, F. Cei, V . Chiarella, A. Corona, S. Coutu...

    1997

  15. [15]

    E. W. Grashorn, Observation of Seasonal V ariations with the MINOS Far Detector, in: International Cosmic Ray Conference, V ol. 5 of International Cosmic Ray Confer- ence, 2008, pp. 1233–1236. arXiv:0710.1616, doi: 10.48550/arXiv.0710.1616

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.0710.1616 2008

  16. [16]

    Desiati, Seasonal V ariations of High Energy Cosmic Ray Muons Observed by the IceCube Observatory as a Probe of Kaon /Pion Ratio, in: International Cosmic Ray Conference, V ol

    P . Desiati, Seasonal V ariations of High Energy Cosmic Ray Muons Observed by the IceCube Observatory as a Probe of Kaon /Pion Ratio, in: International Cosmic Ray Conference, V ol. 1 of International Cosmic Ray Confer- ence, 2011, p. 78. doi:10.7529/ICRC2011/V01/0662

    work page doi:10.7529/icrc2011/v01/0662 2011

  17. [17]

    Adamson, I

    P . Adamson, I. Anghel, A. Aurisano, G. Barr, M. Bishai, A. Blake, G. J. Bock, D. Bogert, S. V . Cao, C. M. Castromonte, S. Childress, J. A. B. Coelho, L. Cor- win, D. Cronin-Hennessy, J. K. de Jong, A. V . De- van, N. E. Devenish, M. V . Diwan, C. O. Escobar, J. J. Evans, E. Falk, G. J. Feldman, T. H. Fields, M. V . Frohne, H. R. Gallagher, R. A. Gomes, ...

    Pith/arXiv arXiv 2014

  18. [18]

    K. P . Arunbabu, S. Ahmad, A. Chandra, S. R. Dugad, S. K. Gupta, B. Hariharan, Y . Hayashi, P . Jagadeesan, A. Jain, V . B. Jhansi, S. Kawakami, H. Kojima, P . K. Mohanty, S. D. Morris, P . K. Nayak, A. Oshima, B. S. Rao, L. V . Reddy, S. Shibata, K. Tanaka, M. Zuberi, De- pendence of the muon intensity on the atmospheric tem- perature measured by the GRA...

    doi:10.1016/j 2017

  19. [19]

    S. K. Gupta, Y . Aikawa, N. V . Gopalakrishnan, Y . Hayashi, N. Ikeda, N. Ito, A. Jain, A. V . John, S. Karthikeyan, S. Kawakami, T. Matsuyama, D. K. Mohanty, P . K. Mohanty, S. D. Morris, T. Nonaka, A. Oshima, B. S. Rao, K. C. Ravindran, M. Sasano, K. Sivaprasad, B. V . Sreekantan, H. Tanaka, S. C. Tonwar, K. Viswanathan, T. Y oshikoshi, GRAPES-3—A high-...

    2005

  20. [20]

    P . K. Mohanty, K. P . Arunbabu, T. Aziz, S. R. Dugad, S. K. Gupta, B. Hariharan, P . Jagadeesan, A. Jain, S. D. Morris, B. S. Rao, Y . Hayashi, S. Kawakami, A. Oshima, S. Shibata, S. Raha, P . Subramanian, H. Kojima, Tran- sient weakening of earth’s magnetic shield probed by a cosmic ray burst , Phys. Rev. Lett. 117 (2016) 171101. doi:10.1103/PhysRevLett...

    work page doi:10.1103/physrevlett.117.171101 2016

  21. [21]

    Hariharan, et al., Measurement of the Electrical Properties of a Thundercloud Through Muon Imaging by the GRAPES-3 Experiment, Phys

    B. Hariharan, et al., Measurement of the Electrical Properties of a Thundercloud Through Muon Imaging by the GRAPES-3 Experiment, Phys. Rev. Lett. 122 (2019) 105101. arXiv:1903.09801, doi:10.1103/ PhysRevLett.122.105101

    Pith/arXiv arXiv 2019

  22. [22]

    Paul, et al., An automated algorithmic method to mitigate long-term variations in the e fficiency of the GRAPES-3 muon telescope (5 2025)

    S. Paul, et al., An automated algorithmic method to mitigate long-term variations in the e fficiency of the GRAPES-3 muon telescope (5 2025). arXiv:2505. 16440

    2025

  23. [23]

    URL https://www.vaisala.com/en/measurement/ pressure

    V alsala barometers. URL https://www.vaisala.com/en/measurement/ pressure

  24. [24]

    P . K. Mohanty, S. Ahmad, H. M. Antia, K. P . Arunbabu, A. Chandra, S. R. Dugad, S. K. Gupta, B. Hariharan, Y . Hayashi, P . Jagadeesan, A. Jain, S. Kawakami, H. Ko- jima, S. D. Morris, P . K. Nayak, A. Oshima, B. S. Rao, L. V . Reddy, S. Shibata, Fast Fourier transform to mea- sure pressure coe fficient of muons in the GRAPES-3 ex- periment, Astroparticle ...

    work page doi:10.1016/j.astropartphys.2016.02.006 2016

  25. [25]

    I. G. Usoskin, G. A. Kovaltsov, I. A. Mironova, A. J. Tylka, W. F. Dietrich, Ionization e ffect of solar par- ticle GLE events in low and middle atmosphere, At- mospheric Chemistry & Physics 11 (5) (2011) 1979–

    2011

  26. [26]

    doi:10.5067/QBZ6MG944HW0

    Global Modeling and Assimilation O ffice (GMAO), inst3_3d_asm_np: Merra-2 3d, instantaneous, pressure-level, meteorology fields (3-hourly, 0.625ˇrx0.5ˇr, 72 levels), version 5.12.4 (2015). doi:10.5067/QBZ6MG944HW0. URL https://doi.org/10.5067/QBZ6MG944HW0

    work page doi:10.5067/qbz6mg944hw0 2015

  27. [27]

    C. W. Smith, J. L’Heureux, N. F. Ness, M. H. Acuña, L. F. Burlaga, J. Scheifele, The ACE Magnetic Fields Experiment, Space Science Reviews 86 (1998) 613–632. doi:10.1023/A:1005092216668

    doi:10.1023/a:1005092216668 1998

  28. [28]

    R. P . Lepping, M. H. Ac ˜una, L. F. Burlaga, W. M. Far- rell, J. A. Slavin, K. H. Schatten, F. Mariani, N. F. Ness, F. M. Neubauer, Y . C. Whang, J. B. Byrnes, R. S. Kennon, P . V . Panetta, J. Scheifele, E. M. Worley, The Wind Mag- netic Field Investigation, Space Science Reviews 71 (1-4) (1995) 207–229. doi:10.1007/BF00751330

    doi:10.1007/bf00751330 1995

  29. [29]

    J. H. King, N. E. Papitashvili, Solar wind spatial scales in and comparisons of hourly Wind and ACE plasma and magnetic field data, Journal of Geophysical Research (Space Physics) 110 (A2) (2005) A02104. doi:10. 1029/2004JA010649

    2005

  30. [30]

    A. N. Dmitrieva, R. P . Kokoulin, A. A. Petrukhin, D. A. Timashkov, Corrections for temperature effect for ground- based muon hodoscopes, Astroparticle Physics 34 (6) (2011) 401–411. doi:10.1016/j.astropartphys. 2010.10.013. 12

    doi:10.1016/j.astropartphys 2011

  31. [31]

    Riádigos, D

    I. Riádigos, D. García-Castro, D. González-Díaz, V . Pérez-Muñuzuri, Atmospheric temperature ef- fect in secondary cosmic rays observed with a 2m2 ground-based trpc detector , Earth and Space Science 7 (9) (2020) e2020EA001131, e2020EA001131 10.1029 /2020EA001131. arXiv: https://agupubs.onlinelibrary.wiley.com/ doi/pdf/10.1029/2020EA001131, doi:https: //d...

    work page doi:10.1029/2020ea001131 2020

  32. [32]

    A. H. Maghrabi, S. A. Alzahrani, A. S. Alruhaili, The Role of Atmospheric Pressure, Temperature, and Humid- ity on Cosmic Ray Muons at a Low Latitude Station, In- ternational Journal of Astronomy and Astrophysics 13 (3) (2023) 236–258. doi:10.4236/ijaa.2023.133014

    arXiv 2023

  33. [33]

    Philippov, V

    M. Philippov, V . Makhmutov, G. Bazilevskaya, F. Zagu- mennov, V . Fomenko, Y . Stozhkov, A. Orlov,Accounting for meteorological e ffects in the detector of the charged component of cosmic rays, Geoscientific Instrumentation, Methods and Data Systems 10 (2) (2021) 219–226. doi:10.5194/gi-10-219-2021 . URL https://gi.copernicus.org/articles/10/ 219/2021/

    work page doi:10.5194/gi-10-219-2021 2021

  34. [34]

    Savic, A

    M. Savic, A. Dragi, D. Maleti, N. V eselinovi, D. Jokovi, R. Banjanac, V . Udovii, D. Knezevi, New empirical meth- ods for correction of meteorological e ffects on cosmic ray muons, PoS ICRC2021 (2021) 1252. doi:10.22323/1. 395.1252

    doi:10.22323/1 2021

  35. [35]

    Seetha, S

    S. Seetha, S. Megala, Aditya-L1 mission, Current Sci- ence 113 (4) (2017) 610. doi:10.18520/cs/v113/i04/ 610-612

    work page doi:10.18520/cs/v113/i04/ 2017

  36. [36]

    T. Palmer, The ecmwf ensemble prediction system: Looking back (more than) 25years and projecting forward 25years , Quarterly Journal of the Royal Meteorological Society 145 (S1) (2019) 12–24. arXiv:https://rmets.onlinelibrary.wiley. com/doi/pdf/10.1002/qj.3383, doi:https: //doi.org/10.1002/qj.3383. URL https://rmets.onlinelibrary.wiley.com/ doi/abs/10.100...

    doi:10.1002/qj.3383 2019

  37. [1988]

    doi:10.5194/acp-11-1979-201110.5194/ acpd-10-30381-2010

    Pith/arXiv arXiv 1979