Citizen / General public
Researcher
Student / Future student
Company
Public partner
Journalist
Teacher / Pupil

InSight regains energy after cleaning its solar panels

The SEIS experiment, part of NASA's Insight mission and operated and distributed by IPGP as part of the InSight National Observing Service (NOS), will run longer than expected when Mars is at its furthest from the Sun.

InSight regains energy after cleaning its solar panels

Publication date: 26/06/2021

General public, Observatories, Press, Research

Related observatories : InSight Observatory

The JPL team has succeeded in cleaning the solar panels, on which dust had accumulated since the landing 2 and a half years ago. By collecting Martian sand from the lander and sprinkling it near the solar panels during the windiest hour of the Martian day, the grains of sand carried away by the wind also washed away the fine dust that had accumulated in their path. The surplus energy recovered in this way will also be used to continue burying the SEIS seismometer cable, thereby improving the noise level of the experiment.

Latest news
Hera flies over Deimos on its way to Dimorphos : a major step forward for planetary protection
Hera flies over Deimos on its way to Dimorphos : a major step forward for planetary protection
On 12 March 2025, the European Space Agency's (ESA) Hera probe flew past Mars and its natural satellite Deimos. The aim of this crucial manoeuvre was ...
IPGP supports the Stand Up For Science movement
IPGP supports the Stand Up For Science movement
Stand Up For Science: Mobilising for science and academic freedom. 7 March 2025, a day to defend scientific research and education
IPGP and Terrensis sign partnership agreement for natural hydrogen research
IPGP and Terrensis sign partnership agreement for natural hydrogen research
On 4 March 2025, the Institut de Physique du Globe de Paris (IPGP) and Terrensis formalised a strategic partnership aimed at furthering research into ...
A new machine learning model for predicting magma viscosity
A new machine learning model for predicting magma viscosity
A team of researchers led by Charles Le Losq (IPGP, IUF) has developed an innovative machine learning model capable of predicting the viscosity of mag...