MARKUS ASCHWANDEN

• Nanoflares

• 2001-02-24 09:34-10:14 UT - TRACE and Yohkoh data
• 2001-02-24 11:08-11:45 UT - TRACE and Yohkoh data

• Temperature-Synthesized Nanoflare Statistics
  We perform automated detection of nanoflares in TRACE 171 A (0.8-1.3 MK), 195 A (1.1-1.6 MK), and Yohkoh Al.1 and AlMg (>2 MK). We use the same code as previously used in:
  • Aschwanden et al. 2000, ApJ 535, 1027
  • Aschwanden et al. 2000, ApJ 535, 1047
  
  There are two new goals of this analysis:
  1. Comparison of nanoflare statistics and statistical distributions of resulting physical parameters with code by Clare Parnell in order to assert systematic differences in independently developed numeric detection algorithms.
  2. Temperature synthesis of nanoflare statistics over a broad temperature range by combining the analysis from 2 EUV and 2 SXR bands. This is a new approach to obtain an unbiased distribution of nanoflare energies. A recent study has demonstrated that nanoflare statistics in a single temperature band is severly biased. Monte-Carlo simulations of this temperature bias and all truncation effects of threshold limited nanoflare detection result in a correction of the power-law slope of nanoflare energy frequency distributions from a=1.9 down to a=1.4. The corrected value seems also to be more consistent with energy distributions from hard X-rays and from theoretical avalanche models. On the other side, such flat slopes a<2 do not support Parker's hypothesis of coronal heating by nanoflares. A preprint of this new study can be downloaded from the web:
     Aschwanden,M.J. and Charbonneau,P. 2002, ApJL (subm. 2001 Nov 15)

• Aschwanden et al. 2000, ApJ 535, 1027
• Aschwanden et al. 2000, ApJ 535, 1047
• Aschwanden,M.J. and Charbonneau,P. 2002, ApJL (subm. 2001 Nov 15)
  "Effects of Temperature Bias on Nanoflare Statistics"

• I hope to gain the following from the meeting:
  • Data analysis comparison
  • Discussion of different biases involved in data analysis
  • Learn about new results from complementary studies in other wavelengths.
  • Draft a paper on joint analysis of same dataset with different codes.
• Specific areas of interest:
  • PHYSICAL DEFINITIONS of small-scale phenomena that bring order, systematize, clarify the morphological nomenclature we have (nanoflares, explosive events, blinkers, network flares ...)
  • DETECTION ALGORITHMS: comparisons and clear definitions of various detection algorithms, ideally compared at the same dataset.
  • PHYSICAL MODELING of small-scale events in terms of physical parameters (l, w, n_e, T_e, dt...), fractal scalings, which lead to a physically sound relation between the observed parameters (such as flare area, emission measure, temperature) and desired theoretical parameters (thermal energy), so that the distributions can be properly calculated and corrected for various measurement biases. Once we have done that reliably, the conclusions about the significance of small-scale phenomena for coronal heating follow by themselves.